

import os
import re
import shutil
import sys
import tempfile
import unittest

import black


A_						 =    os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
A_						 =    '    def __init__(self, config):\n        super().__init__()\n        self.transform = BertPredictionHeadTransform(config)\n\n        # The output weights are the same as the input embeddings, but there is\n        # an output-only bias for each token.\n        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n        self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n        self.decoder.bias = self.bias\n\n    def forward(self, hidden_states):\n        hidden_states = self.transform(hidden_states)\n        hidden_states = self.decoder(hidden_states)\n        return hidden_states\n'



class 					_a		(unittest.TestCase       ):


					'''simple docstring'''






					def 						__A    (				self    ):
						A__							:					List[Any]       =	tempfile.mkdtemp()
						os.makedirs(os.path.join(self.transformer_dir					, """models/bert/"""    )    )
						A__							:					int       =	self.transformer_dir
						shutil.copy(
						    os.path.join(_snake_case					, """src/transformers/models/bert/modeling_bert.py"""    )					, os.path.join(self.transformer_dir					, """models/bert/modeling_bert.py"""    )					, )






					def 						__A    (				self    ):
						A__							:					Optional[Any]       =	"""src/transformers"""
						shutil.rmtree(self.transformer_dir    )






					def 						__A    (				self					, A__					, A__					, A__					, A__=None    ):
						A__							:					str       =	comment + F"""\nclass {class_name}(nn.Module):\n""" + class_code
						if overwrite_result is not None:
							A__							:					str       =	comment + F"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
						A__							:					Optional[int]       =	black.Mode(target_versions={black.TargetVersion.PYaa}					, line_length=119    )
						A__							:					Any       =	black.format_str(_snake_case					, mode=_snake_case    )
						A__							:					Union[str, Any]       =	os.path.join(self.transformer_dir					, """new_code.py"""    )
						with open(_snake_case					, """w"""					, newline="""\n"""    ) as f:
							f.write(_snake_case    )
						if overwrite_result is None:
							self.assertTrue(len(check_copies.is_copy_consistent(_snake_case    )    ) == 0    )
						else:
							check_copies.is_copy_consistent(f.name					, overwrite=_snake_case    )
							with open(_snake_case					, """r"""    ) as f:
								self.assertTrue(f.read()					, _snake_case    )






					def 						__A    (				self    ):
						A__							:					Tuple       =	check_copies.find_code_in_transformers("""models.bert.modeling_bert.BertLMPredictionHead"""    )
						self.assertEqual(_snake_case					, _snake_case    )






					def 						__A    (				self    ):
						self.check_copy_consistency(
						    """# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"""					, """BertLMPredictionHead"""					, REFERENCE_CODE + """\n"""					, )

						# With no empty line at the end
						self.check_copy_consistency(
						    """# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"""					, """BertLMPredictionHead"""					, _snake_case					, )

						# Copy consistency with rename
						self.check_copy_consistency(
						    """# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"""					, """TestModelLMPredictionHead"""					, re.sub("""Bert"""					, """TestModel"""					, _snake_case    )					, )

						# Copy consistency with a really long name
						A__							:					Dict       =	"""TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"""
						self.check_copy_consistency(
						    F"""# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}"""					, F"""{long_class_name}LMPredictionHead"""					, re.sub("""Bert"""					, _snake_case					, _snake_case    )					, )

						# Copy consistency with overwrite
						self.check_copy_consistency(
						    """# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"""					, """TestModelLMPredictionHead"""					, _snake_case					, overwrite_result=re.sub("""Bert"""					, """TestModel"""					, _snake_case    )					, )






					def 						__A    (				self    ):
						A__							:					Optional[int]       =	check_copies.LOCALIZED_READMES["""README_zh-hans.md"""]

						A__							:					Tuple       =	(
						    """1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"""
						    """ Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"""
						    """ Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"""
						    """ Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1."""
						    """ **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),"""
						    """ released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"""
						    """ lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same"""
						    """ method has been applied to compress GPT2 into"""
						    """ [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"""
						    """ [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"""
						    """ Multilingual BERT into"""
						    """ [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"""
						    """ version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**"""
						    """ (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders"""
						    """ as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang"""
						    """ Luong, Quoc V. Le, Christopher D. Manning."""
						)
						A__							:					int       =	(
						    """1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"""
						    """ Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"""
						    """ Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"""
						    """ Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"""
						)
						A__							:					Union[str, Any]       =	(
						    """1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"""
						    """ Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"""
						    """ Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"""
						    """ Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1."""
						    """ **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文"""
						    """ [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"""
						    """ lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same"""
						    """ method has been applied to compress GPT2 into"""
						    """ [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"""
						    """ [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"""
						    """ Multilingual BERT into"""
						    """ [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"""
						    """ version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自"""
						    """ Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather"""
						    """ than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,"""
						    """ Christopher D. Manning 发布。\n"""
						)

						A__     ,     A__							:					List[str]       =	check_copies.convert_to_localized_md(
						    _snake_case					, _snake_case					, localized_readme["""format_model_list"""]    )

						self.assertFalse(_snake_case    )
						self.assertEqual(_snake_case					, _snake_case    )

						A__     ,     A__							:					str       =	check_copies.convert_to_localized_md(
						    _snake_case					, _snake_case					, localized_readme["""format_model_list"""]    )

						# Check whether the number of models is equal to README.md after conversion.
						self.assertTrue(_snake_case    )

						A__							:					Optional[int]       =	(
						    """1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"""
						    """ Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"""
						    """ Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"""
						    """ Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut."""
						)
						A__							:					List[str]       =	(
						    """1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and"""
						    """ the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"""
						    """ Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"""
						    """ Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"""
						)
						A__							:					Union[str, Any]       =	(
						    """1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"""
						    """ Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"""
						    """ Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"""
						    """ Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"""
						)

						A__     ,     A__							:					Dict       =	check_copies.convert_to_localized_md(
						    _snake_case					, _snake_case					, localized_readme["""format_model_list"""]    )

						# Check if the model link is synchronized.
						self.assertEqual(_snake_case					, _snake_case    )






import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
	from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments



@require_torch
class 					_a		(unittest.TestCase       ):


					'''simple docstring'''






					def 						__A    (				self					, A__    ):
						for model_result in results.values():
							for batch_size, sequence_length in zip(model_result["""bs"""]					, model_result["""ss"""]    ):
								A__							:					str       =	model_result["""result"""][batch_size][sequence_length]
								self.assertIsNotNone(A__    )






					def 						__A    (				self    ):
						A__							:					Dict       =	"""sshleifer/tiny-gpt2"""
						A__							:					Tuple       =	PyTorchBenchmarkArguments(
						    models=[MODEL_ID]					, training=A__					, inference=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=A__					, )
						A__							:					int       =	PyTorchBenchmark(A__    )
						A__							:					List[Any]       =	benchmark.run()
						self.check_results_dict_not_empty(results.time_inference_result    )
						self.check_results_dict_not_empty(results.memory_inference_result    )






					def 						__A    (				self    ):
						A__							:					Dict       =	"""sgugger/tiny-distilbert-classification"""
						A__							:					Dict       =	PyTorchBenchmarkArguments(
						    models=[MODEL_ID]					, training=A__					, inference=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=A__					, only_pretrain_model=A__					, )
						A__							:					str       =	PyTorchBenchmark(A__    )
						A__							:					List[str]       =	benchmark.run()
						self.check_results_dict_not_empty(results.time_inference_result    )
						self.check_results_dict_not_empty(results.memory_inference_result    )






					def 						__A    (				self    ):
						A__							:					Any       =	"""sshleifer/tiny-gpt2"""
						A__							:					List[Any]       =	PyTorchBenchmarkArguments(
						    models=[MODEL_ID]					, training=A__					, inference=A__					, torchscript=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=A__					, )
						A__							:					Tuple       =	PyTorchBenchmark(A__    )
						A__							:					str       =	benchmark.run()
						self.check_results_dict_not_empty(results.time_inference_result    )
						self.check_results_dict_not_empty(results.memory_inference_result    )






					@unittest.skipIf(torch_device == """cpu"""					, """Cant do half precision"""    )
					def 						__A    (				self    ):
						A__							:					Optional[Any]       =	"""sshleifer/tiny-gpt2"""
						A__							:					Optional[Any]       =	PyTorchBenchmarkArguments(
						    models=[MODEL_ID]					, training=A__					, inference=A__					, fpaa=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=A__					, )
						A__							:					str       =	PyTorchBenchmark(A__    )
						A__							:					Any       =	benchmark.run()
						self.check_results_dict_not_empty(results.time_inference_result    )
						self.check_results_dict_not_empty(results.memory_inference_result    )






					def 						__A    (				self    ):
						A__							:					Optional[Any]       =	"""sshleifer/tiny-gpt2"""
						A__							:					Tuple       =	AutoConfig.from_pretrained(A__    )
						# set architectures equal to `None`
						A__							:					List[Any]       =	None
						A__							:					str       =	PyTorchBenchmarkArguments(
						    models=[MODEL_ID]					, training=A__					, inference=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=A__					, )
						A__							:					List[str]       =	PyTorchBenchmark(A__					, configs=[config]    )
						A__							:					Any       =	benchmark.run()
						self.check_results_dict_not_empty(results.time_inference_result    )
						self.check_results_dict_not_empty(results.memory_inference_result    )






					def 						__A    (				self    ):
						A__							:					Optional[int]       =	"""sshleifer/tiny-gpt2"""
						A__							:					Optional[int]       =	PyTorchBenchmarkArguments(
						    models=[MODEL_ID]					, training=A__					, inference=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=A__					, )
						A__							:					Any       =	PyTorchBenchmark(A__    )
						A__							:					Dict       =	benchmark.run()
						self.check_results_dict_not_empty(results.time_train_result    )
						self.check_results_dict_not_empty(results.memory_train_result    )






					@unittest.skipIf(torch_device == """cpu"""					, """Can't do half precision"""    )
					def 						__A    (				self    ):
						A__							:					Optional[int]       =	"""sshleifer/tiny-gpt2"""
						A__							:					List[str]       =	PyTorchBenchmarkArguments(
						    models=[MODEL_ID]					, training=A__					, inference=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, fpaa=A__					, multi_process=A__					, )
						A__							:					Dict       =	PyTorchBenchmark(A__    )
						A__							:					Optional[int]       =	benchmark.run()
						self.check_results_dict_not_empty(results.time_train_result    )
						self.check_results_dict_not_empty(results.memory_train_result    )






					def 						__A    (				self    ):
						A__							:					int       =	"""sshleifer/tiny-gpt2"""
						A__							:					Optional[int]       =	AutoConfig.from_pretrained(A__    )
						A__							:					str       =	PyTorchBenchmarkArguments(
						    models=[MODEL_ID]					, training=A__					, inference=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=A__					, )
						A__							:					int       =	PyTorchBenchmark(A__					, configs=[config]    )
						A__							:					Tuple       =	benchmark.run()
						self.check_results_dict_not_empty(results.time_inference_result    )
						self.check_results_dict_not_empty(results.memory_inference_result    )






					def 						__A    (				self    ):
						A__							:					List[str]       =	"""sshleifer/tinier_bart"""
						A__							:					List[str]       =	AutoConfig.from_pretrained(A__    )
						A__							:					List[str]       =	PyTorchBenchmarkArguments(
						    models=[MODEL_ID]					, training=A__					, inference=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=A__					, )
						A__							:					Union[str, Any]       =	PyTorchBenchmark(A__					, configs=[config]    )
						A__							:					str       =	benchmark.run()
						self.check_results_dict_not_empty(results.time_inference_result    )
						self.check_results_dict_not_empty(results.memory_inference_result    )






					def 						__A    (				self    ):
						A__							:					Optional[int]       =	"""sshleifer/tiny-gpt2"""
						A__							:					Union[str, Any]       =	AutoConfig.from_pretrained(A__    )
						A__							:					Tuple       =	PyTorchBenchmarkArguments(
						    models=[MODEL_ID]					, training=A__					, inference=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=A__					, )
						A__							:					int       =	PyTorchBenchmark(A__					, configs=[config]    )
						A__							:					Tuple       =	benchmark.run()
						self.check_results_dict_not_empty(results.time_train_result    )
						self.check_results_dict_not_empty(results.memory_train_result    )






					def 						__A    (				self    ):
						A__							:					Dict       =	"""sshleifer/tinier_bart"""
						A__							:					int       =	AutoConfig.from_pretrained(A__    )
						A__							:					Union[str, Any]       =	PyTorchBenchmarkArguments(
						    models=[MODEL_ID]					, training=A__					, inference=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, multi_process=A__					, )
						A__							:					List[Any]       =	PyTorchBenchmark(A__					, configs=[config]    )
						A__							:					Optional[Any]       =	benchmark.run()
						self.check_results_dict_not_empty(results.time_train_result    )
						self.check_results_dict_not_empty(results.memory_train_result    )






					def 						__A    (				self    ):
						A__							:					int       =	"""sshleifer/tiny-gpt2"""
						with tempfile.TemporaryDirectory() as tmp_dir:
							A__							:					Dict       =	PyTorchBenchmarkArguments(
							    models=[MODEL_ID]					, training=A__					, inference=A__					, save_to_csv=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, inference_time_csv_file=os.path.join(A__					, """inf_time.csv"""    )					, train_memory_csv_file=os.path.join(A__					, """train_mem.csv"""    )					, inference_memory_csv_file=os.path.join(A__					, """inf_mem.csv"""    )					, train_time_csv_file=os.path.join(A__					, """train_time.csv"""    )					, env_info_csv_file=os.path.join(A__					, """env.csv"""    )					, multi_process=A__					, )
							A__							:					Optional[Any]       =	PyTorchBenchmark(A__    )
							benchmark.run()
							self.assertTrue(Path(os.path.join(A__					, """inf_time.csv"""    )    ).exists()    )
							self.assertTrue(Path(os.path.join(A__					, """train_time.csv"""    )    ).exists()    )
							self.assertTrue(Path(os.path.join(A__					, """inf_mem.csv"""    )    ).exists()    )
							self.assertTrue(Path(os.path.join(A__					, """train_mem.csv"""    )    ).exists()    )
							self.assertTrue(Path(os.path.join(A__					, """env.csv"""    )    ).exists()    )






					def 						__A    (				self    ):
						A__							:					Optional[int]       =	"""sshleifer/tiny-gpt2"""

						def _check_summary_is_not_empty(A__    ):
							self.assertTrue(hasattr(A__					, """sequential"""    )    )
							self.assertTrue(hasattr(A__					, """cumulative"""    )    )
							self.assertTrue(hasattr(A__					, """current"""    )    )
							self.assertTrue(hasattr(A__					, """total"""    )    )

						with tempfile.TemporaryDirectory() as tmp_dir:
							A__							:					Dict       =	PyTorchBenchmarkArguments(
							    models=[MODEL_ID]					, training=A__					, inference=A__					, sequence_lengths=[8]					, batch_sizes=[1]					, log_filename=os.path.join(A__					, """log.txt"""    )					, log_print=A__					, trace_memory_line_by_line=A__					, multi_process=A__					, )
							A__							:					Dict       =	PyTorchBenchmark(A__    )
							A__							:					str       =	benchmark.run()
							_check_summary_is_not_empty(result.inference_summary    )
							_check_summary_is_not_empty(result.train_summary    )
							self.assertTrue(Path(os.path.join(A__					, """log.txt"""    )    ).exists()    )



