



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

import black


__snake_case     :List[str]			       =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.
__snake_case     :List[str]			       ='    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        lowerCAmelCase__					(  unittest.TestCase       ):


				def __UpperCamelCase						(							self      :  int       )		->     List[str]:
											A      						=    tempfile.mkdtemp()
											os.makedirs(os.path.join(self.transformer_dir						, 'models/bert/'       )       )
											A      						=    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 __UpperCamelCase						(							self      :  Any       )		->     str:
											A      						=    '''src/transformers'''
											shutil.rmtree(self.transformer_dir       )


				def __UpperCamelCase						(							self      :  str						, __UpperCamelCase      :  str						, __UpperCamelCase      :  Any						, __UpperCamelCase      :  List[Any]						, __UpperCamelCase      :  Tuple=None       )		->     str:
											A      						=    comment + f'''\nclass {class_name}(nn.Module):\n''' + class_code
											if overwrite_result is not None:
																		A      						=    comment + f'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
											A      						=    black.Mode(target_versions={black.TargetVersion.PYaa}						, line_length=119       )
											A      						=    black.format_str(__UpperCamelCase						, mode=__UpperCamelCase       )
											A      						=    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 __UpperCamelCase						(							self      :  Dict       )		->     List[Any]:
											A      						=    check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead'       )
											self.assertEqual(__UpperCamelCase						, __UpperCamelCase       )


				def __UpperCamelCase						(							self      :  Optional[int]       )		->     str:
											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
											A      						=    '''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 __UpperCamelCase						(							self      :  Union[str, Any]       )		->     Any:
											A      						=    check_copies.LOCALIZED_READMES['''README_zh-hans.md''']

											A      						=    (
											    '''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      						=    (
											    '''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      						=    (
											    '''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      						=    check_copies.convert_to_localized_md(
											    __UpperCamelCase						, __UpperCamelCase						, localized_readme['format_model_list']       )

											self.assertFalse(__UpperCamelCase       )
											self.assertEqual(__UpperCamelCase						, __UpperCamelCase       )

											A      						=    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       )

											A      						=    (
											    '''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      						=    (
											    '''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      						=    (
											    '''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      						=    check_copies.convert_to_localized_md(
											    __UpperCamelCase						, __UpperCamelCase						, localized_readme['format_model_list']       )

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







'''simple docstring'''



# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
      import torch
class UpperCAmelCase_       (     TensorFormatter[Mapping, '''torch.Tensor''', Mapping]					):




      """simple docstring"""




      def __init__(			self      :		Dict  ,   UpperCAmelCase      :		str=None  ,   **UpperCAmelCase      :		List[Any]		)	->	int:


       '''simple docstring'''

       super().__init__(features=UpperCAmelCase		)
       lowercase       : List[Any]						=torch_tensor_kwargs
       import torch  # noqa import torch at initialization






      def 					A__   (			self      :		Union[str, Any]  ,   UpperCAmelCase      :		Dict		)	->	List[str]:


       '''simple docstring'''

       import torch

       if isinstance(UpperCAmelCase  ,   UpperCAmelCase		) and column:
        if all(
            isinstance(UpperCAmelCase  ,   torch.Tensor		) and x.shape == column[0].shape and x.dtype == column[0].dtype
            for x in column		):
         return torch.stack(UpperCAmelCase		)
       return column






      def 					A__   (			self      :		List[str]  ,   UpperCAmelCase      :		Optional[int]		)	->	Optional[Any]:


       '''simple docstring'''

       import torch

       if isinstance(UpperCAmelCase  ,   (str, bytes, type(UpperCAmelCase		))		):
        return value
       elif isinstance(UpperCAmelCase  ,   (np.character, np.ndarray)		) and np.issubdtype(value.dtype  ,   np.character		):
        return value.tolist()

       lowercase       : Any						={}

       if isinstance(UpperCAmelCase  ,   (np.number, np.ndarray)		) and np.issubdtype(value.dtype  ,   np.integer		):
        lowercase       : Tuple						={'''dtype''': torch.intaa}
       elif isinstance(UpperCAmelCase  ,   (np.number, np.ndarray)		) and np.issubdtype(value.dtype  ,   np.floating		):
        lowercase       : int						={'''dtype''': torch.floataa}
       elif config.PIL_AVAILABLE and "PIL" in sys.modules:
        import PIL.Image

        if isinstance(UpperCAmelCase  ,   PIL.Image.Image		):
         lowercase       : List[Any]						=np.asarray(UpperCAmelCase		)
       return torch.tensor(UpperCAmelCase  ,   **{**default_dtype, **self.torch_tensor_kwargs}		)






      def 					A__   (			self      :		List[Any]  ,   UpperCAmelCase      :		Dict		)	->	Tuple:


       '''simple docstring'''

       import torch

       # support for torch, tf, jax etc.
       if hasattr(UpperCAmelCase  ,   '''__array__'''		) and not isinstance(UpperCAmelCase  ,   torch.Tensor		):
        lowercase       : List[Any]						=data_struct.__array__()
       # support for nested types like struct of list of struct
       if isinstance(UpperCAmelCase  ,   np.ndarray		):
        if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
         return self._consolidate([self.recursive_tensorize(UpperCAmelCase		) for substruct in data_struct]		)
       elif isinstance(UpperCAmelCase  ,   (list, tuple)		):
        return self._consolidate([self.recursive_tensorize(UpperCAmelCase		) for substruct in data_struct]		)
       return self._tensorize(UpperCAmelCase		)






      def 					A__   (			self      :		str  ,   UpperCAmelCase      :		dict		)	->	Any:


       '''simple docstring'''

       return map_nested(self._recursive_tensorize  ,   UpperCAmelCase  ,   map_list=UpperCAmelCase		)






      def 					A__   (			self      :		Union[str, Any]  ,   UpperCAmelCase      :		pa.Table		)	->	Mapping:


       '''simple docstring'''

       lowercase       : List[str]						=self.numpy_arrow_extractor().extract_row(UpperCAmelCase		)
       lowercase       : Any						=self.python_features_decoder.decode_row(UpperCAmelCase		)
       return self.recursive_tensorize(UpperCAmelCase		)






      def 					A__   (			self      :		Any  ,   UpperCAmelCase      :		pa.Table		)	->	"torch.Tensor":


       '''simple docstring'''

       lowercase       : int						=self.numpy_arrow_extractor().extract_column(UpperCAmelCase		)
       lowercase       : Tuple						=self.python_features_decoder.decode_column(UpperCAmelCase  ,   pa_table.column_names[0]		)
       lowercase       : Optional[Any]						=self.recursive_tensorize(UpperCAmelCase		)
       lowercase       : Any						=self._consolidate(UpperCAmelCase		)
       return column






      def 					A__   (			self      :		str  ,   UpperCAmelCase      :		pa.Table		)	->	Mapping:


       '''simple docstring'''

       lowercase       : Tuple						=self.numpy_arrow_extractor().extract_batch(UpperCAmelCase		)
       lowercase       : List[str]						=self.python_features_decoder.decode_batch(UpperCAmelCase		)
       lowercase       : Dict						=self.recursive_tensorize(UpperCAmelCase		)
       for column_name in batch:
        lowercase       : str						=self._consolidate(batch[column_name]		)
       return batch



