




"""simple docstring"""





from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


A   =       logging.get_logger(__name__)


A   =       TypeVar("""DatasetType""", Dataset, IterableDataset)

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase = None				,       UpperCamelCase = None				,       UpperCamelCase = None				,       UpperCamelCase = None				,       UpperCamelCase = "first_exhausted"				,       )			->			DatasetType:


			"""simple docstring"""
			from .arrow_dataset import Dataset
			from .iterable_dataset import IterableDataset

			if not datasets:
						raise ValueError("Unable to interleave an empty list of datasets."							)
			for i, dataset in enumerate(UpperCamelCase							):
						if not isinstance(UpperCamelCase				,       (Dataset, IterableDataset)							):
									if isinstance(UpperCamelCase				,       (DatasetDict, IterableDatasetDict)							):
												if not dataset:
															raise ValueError(
															    f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
															    "is an empty dataset dictionary."							)
												raise ValueError(
												    f"Dataset at position {i} has at least one split: {list(UpperCamelCase							)}\n"
												    f"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(UpperCamelCase							)							)}']"							)
									raise ValueError(
									    f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(UpperCamelCase							).__name__}."							)
						if i == 0:
									__UpperCAmelCase	,     __UpperCAmelCase	: Any		=					(
									    (Dataset, IterableDataset) if isinstance(UpperCamelCase				,       UpperCamelCase							) else (IterableDataset, Dataset)
									)
						elif not isinstance(UpperCamelCase				,       UpperCamelCase							):
									raise ValueError(
									    f"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."							)
			if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
						raise ValueError(f"{stopping_strategy} is not supported. Please enter a valid stopping_strategy."							)
			if dataset_type is Dataset:
						return _interleave_map_style_datasets(
						    UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       info=UpperCamelCase				,       split=UpperCamelCase				,       stopping_strategy=UpperCamelCase							)
			else:
						return _interleave_iterable_datasets(
						    UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       info=UpperCamelCase				,       split=UpperCamelCase				,       stopping_strategy=UpperCamelCase							)





def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase = None				,       UpperCamelCase = None				,       UpperCamelCase = 0				,       )			->			DatasetType:


			"""simple docstring"""

			if not dsets:
						raise ValueError("Unable to concatenate an empty list of datasets."							)
			for i, dataset in enumerate(UpperCamelCase							):
						if not isinstance(UpperCamelCase				,       (Dataset, IterableDataset)							):
									if isinstance(UpperCamelCase				,       (DatasetDict, IterableDatasetDict)							):
												if not dataset:
															raise ValueError(
															    f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
															    "is an empty dataset dictionary."							)
												raise ValueError(
												    f"Dataset at position {i} has at least one split: {list(UpperCamelCase							)}\n"
												    f"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(UpperCamelCase							)							)}']"							)
									raise ValueError(
									    f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(UpperCamelCase							).__name__}."							)
						if i == 0:
									__UpperCAmelCase	,     __UpperCAmelCase	: int		=					(
									    (Dataset, IterableDataset) if isinstance(UpperCamelCase				,       UpperCamelCase							) else (IterableDataset, Dataset)
									)
						elif not isinstance(UpperCamelCase				,       UpperCamelCase							):
									raise ValueError(
									    f"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."							)
			if dataset_type is Dataset:
						return _concatenate_map_style_datasets(UpperCamelCase				,       info=UpperCamelCase				,       split=UpperCamelCase				,       axis=UpperCamelCase							)
			else:
						return _concatenate_iterable_datasets(UpperCamelCase				,       info=UpperCamelCase				,       split=UpperCamelCase				,       axis=UpperCamelCase							)








"""simple docstring"""





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):
        super().__init__()
        self.transform = BertPredictionHeadTransform(config)

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        self.bias = nn.Parameter(torch.zeros(config.vocab_size))

        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
        self.decoder.bias = self.bias

    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states
"""



class      a__				(	unittest.TestCase   ):


			def       a_     (      self      :    Optional[Any]):


						"""simple docstring"""




						__UpperCAmelCase	: List[Any]		=					tempfile.mkdtemp()
						os.makedirs(os.path.join(self.transformer_dir      ,  "models/bert/"))
						__UpperCAmelCase	: Tuple		=					self.transformer_dir
						shutil.copy(
						    os.path.join(UpperCamelCase_      ,  "src/transformers/models/bert/modeling_bert.py")      ,  os.path.join(self.transformer_dir      ,  "models/bert/modeling_bert.py")      ,  )


			def       a_     (      self      :    int):


						"""simple docstring"""




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


			def       a_     (      self      :    str      ,  UpperCamelCase_      :    str      ,  UpperCamelCase_      :    int      ,  UpperCamelCase_      :    Optional[int]      ,  UpperCamelCase_      :    List[str]=None):


						"""simple docstring"""




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


			def       a_     (      self      :    Tuple):


						"""simple docstring"""




						__UpperCAmelCase	: Any		=					check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead")
						self.assertEqual(UpperCamelCase_      ,  UpperCamelCase_)


			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




						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"      ,  UpperCamelCase_      ,  )

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

						# Copy consistency with a really long name
						__UpperCAmelCase	: Optional[Any]		=					"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"      ,  UpperCamelCase_      ,  UpperCamelCase_)      ,  )

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


			def       a_     (      self      :    Dict):


						"""simple docstring"""




						__UpperCAmelCase	: List[str]		=					check_copies.LOCALIZED_READMES["README_zh-hans.md"]

						__UpperCAmelCase	: str		=					(
						    "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."
						)
						__UpperCAmelCase	: List[str]		=					(
						    "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"
						)
						__UpperCAmelCase	: str		=					(
						    "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"
						)

						__UpperCAmelCase	,     __UpperCAmelCase	: Any		=					check_copies.convert_to_localized_md(
						    UpperCamelCase_      ,  UpperCamelCase_      ,  localized_readme["format_model_list"])

						self.assertFalse(UpperCamelCase_)
						self.assertEqual(UpperCamelCase_      ,  UpperCamelCase_)

						__UpperCAmelCase	,     __UpperCAmelCase	: int		=					check_copies.convert_to_localized_md(
						    UpperCamelCase_      ,  UpperCamelCase_      ,  localized_readme["format_model_list"])

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

						__UpperCAmelCase	: str		=					(
						    "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."
						)
						__UpperCAmelCase	: Optional[int]		=					(
						    "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"
						)
						__UpperCAmelCase	: 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"
						)

						__UpperCAmelCase	,     __UpperCAmelCase	: List[str]		=					check_copies.convert_to_localized_md(
						    UpperCamelCase_      ,  UpperCamelCase_      ,  localized_readme["format_model_list"])

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


