mirror of
https://github.com/huggingface/transformers.git
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223 lines
8.9 KiB
Python
223 lines
8.9 KiB
Python
# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import unittest
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import json
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import random
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import torch
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from pytorch_pretrained_bert import (OpenAIGPTConfig, OpenAIGPTModel,
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OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel)
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class OpenAIGPTModelTest(unittest.TestCase):
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class OpenAIGPTModelTester(object):
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def __init__(self,
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parent,
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batch_size=13,
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seq_length=7,
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is_training=True,
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use_position_ids=True,
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use_token_type_ids=True,
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use_labels=True,
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vocab_size=99,
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n_special=1,
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n_positions=33,
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n_embd=32,
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n_layer=5,
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n_head=4,
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n_choices=3,
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afn="gelu",
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resid_pdrop=0.1,
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attn_pdrop=0.1,
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embd_pdrop=0.1,
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type_sequence_label_size=2,
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initializer_range=0.02,
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num_labels=3,
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scope=None):
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self.parent = parent
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self.batch_size = batch_size
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self.seq_length = seq_length
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self.is_training = is_training
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self.use_position_ids = use_position_ids
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self.use_token_type_ids = use_token_type_ids
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self.use_labels = use_labels
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self.vocab_size = vocab_size
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self.n_special = n_special
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self.n_positions = n_positions
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self.n_embd = n_embd
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self.n_layer = n_layer
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self.n_head = n_head
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self.afn = afn
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self.n_choices = n_choices
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self.resid_pdrop = resid_pdrop
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self.attn_pdrop = attn_pdrop
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self.embd_pdrop = embd_pdrop
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self.type_sequence_label_size = type_sequence_label_size
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self.initializer_range = initializer_range
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self.num_labels = num_labels
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self.scope = scope
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def prepare_config_and_inputs(self):
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input_ids = OpenAIGPTModelTest.ids_tensor([self.batch_size, self.n_choices, self.seq_length], self.vocab_size)
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position_ids = None
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if self.use_position_ids:
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position_ids = OpenAIGPTModelTest.ids_tensor([self.batch_size, self.n_choices, self.seq_length], self.n_positions)
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token_type_ids = None
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if self.use_token_type_ids:
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total_voc = self.vocab_size + self.n_special
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token_type_ids = OpenAIGPTModelTest.ids_tensor([self.batch_size, self.n_choices, self.seq_length], total_voc)
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mc_labels = None
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lm_labels = None
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mc_token_ids = None
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if self.use_labels:
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mc_labels = OpenAIGPTModelTest.ids_tensor([self.batch_size], self.type_sequence_label_size)
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lm_labels = OpenAIGPTModelTest.ids_tensor([self.batch_size, self.n_choices, self.seq_length], self.num_labels)
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mc_token_ids = OpenAIGPTModelTest.ids_tensor([self.batch_size, self.n_choices], self.seq_length).float()
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config = OpenAIGPTConfig(
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vocab_size_or_config_json_file=self.vocab_size,
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n_positions=self.n_positions,
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n_special=self.n_special,
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n_embd=self.n_embd,
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n_layer=self.n_layer,
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n_head=self.n_head,
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afn=self.afn,
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resid_pdrop=self.resid_pdrop,
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attn_pdrop=self.attn_pdrop,
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embd_pdrop=self.embd_pdrop,
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initializer_range=self.initializer_range)
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return (config, input_ids, token_type_ids, position_ids,
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mc_labels, lm_labels, mc_token_ids)
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def create_openai_model(self, config, input_ids, token_type_ids, position_ids,
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mc_labels, lm_labels, mc_token_ids):
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model = OpenAIGPTModel(config)
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model.eval()
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hidden_states = model(input_ids, position_ids, token_type_ids)
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outputs = {
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"hidden_states": hidden_states,
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}
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return outputs
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def check_openai_model_output(self, result):
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self.parent.assertListEqual(
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list(result["hidden_states"].size()),
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[self.batch_size, self.n_choices, self.seq_length, self.n_embd])
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def create_openai_lm_head(self, config, input_ids, token_type_ids, position_ids,
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mc_labels, lm_labels, mc_token_ids):
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model = OpenAIGPTLMHeadModel(config)
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model.eval()
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loss = model(input_ids, position_ids, token_type_ids, lm_labels)
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lm_logits = model(input_ids, position_ids, token_type_ids)
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outputs = {
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"loss": loss,
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"lm_logits": lm_logits,
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}
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return outputs
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def check_openai_lm_head_output(self, result):
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total_voc = self.n_special + self.vocab_size
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self.parent.assertListEqual(
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list(result["lm_logits"].size()),
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[self.batch_size, self.n_choices, self.seq_length, total_voc])
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def check_openai_lm_head_loss_output(self, result):
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self.parent.assertListEqual(
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list(result["loss"].size()),
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[])
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def create_openai_double_heads(self, config, input_ids, token_type_ids, position_ids,
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mc_labels, lm_labels, mc_token_ids):
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model = OpenAIGPTDoubleHeadsModel(config)
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model.eval()
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loss = model(input_ids, mc_token_ids,
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lm_labels=lm_labels, mc_labels=mc_labels,
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token_type_ids=token_type_ids, position_ids=position_ids)
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lm_logits, mc_logits = model(input_ids, mc_token_ids, position_ids=position_ids, token_type_ids=token_type_ids)
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outputs = {
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"loss": loss,
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"lm_logits": lm_logits,
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"mc_logits": mc_logits,
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}
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return outputs
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def check_openai_double_heads_output(self, result):
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total_voc = self.n_special + self.vocab_size
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self.parent.assertListEqual(
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list(result["lm_logits"].size()),
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[self.batch_size, self.n_choices, self.seq_length, total_voc])
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self.parent.assertListEqual(
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list(result["mc_logits"].size()),
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[self.batch_size, self.n_choices])
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def check_openai_double_heads_loss_output(self, result):
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self.parent.assertListEqual(
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[list(l.size()) for l in result["loss"]],
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[[], []])
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def test_default(self):
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self.run_tester(OpenAIGPTModelTest.OpenAIGPTModelTester(self))
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def test_config_to_json_string(self):
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config = OpenAIGPTConfig(vocab_size_or_config_json_file=99, n_embd=37)
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obj = json.loads(config.to_json_string())
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self.assertEqual(obj["vocab_size"], 99)
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self.assertEqual(obj["n_embd"], 37)
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def run_tester(self, tester):
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config_and_inputs = tester.prepare_config_and_inputs()
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output_result = tester.create_openai_model(*config_and_inputs)
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tester.check_openai_model_output(output_result)
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output_result = tester.create_openai_lm_head(*config_and_inputs)
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tester.check_openai_lm_head_output(output_result)
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tester.check_openai_lm_head_loss_output(output_result)
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output_result = tester.create_openai_double_heads(*config_and_inputs)
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tester.check_openai_double_heads_output(output_result)
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tester.check_openai_double_heads_loss_output(output_result)
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@classmethod
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def ids_tensor(cls, shape, vocab_size, rng=None, name=None):
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"""Creates a random int32 tensor of the shape within the vocab size."""
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if rng is None:
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rng = random.Random()
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total_dims = 1
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for dim in shape:
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total_dims *= dim
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values = []
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for _ in range(total_dims):
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values.append(rng.randint(0, vocab_size - 1))
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return torch.tensor(data=values, dtype=torch.long).view(shape).contiguous()
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if __name__ == "__main__":
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unittest.main()
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