'''simple docstring'''




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

import black


__UpperCAmelCase												=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.
__UpperCAmelCase												="    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			):






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[Any]   ):





         """simple docstring"""



         __lowerCamelCase          =  tempfile.mkdtemp()
         os.makedirs(os.path.join(self.transformer_dir  ,    '''models/bert/'''   )   )
         __lowerCamelCase          =  self.transformer_dir
         shutil.copy(
             os.path.join(a  ,    '''src/transformers/models/bert/modeling_bert.py'''   )  ,    os.path.join(self.transformer_dir  ,    '''models/bert/modeling_bert.py'''   )  ,    )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Dict   ):





         """simple docstring"""



         __lowerCamelCase          =  '''src/transformers'''
         shutil.rmtree(self.transformer_dir   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Any  ,    a		:		List[Any]  ,    a		:		Optional[Any]  ,    a		:		List[str]  ,    a		:		Tuple=None   ):





         """simple docstring"""



         __lowerCamelCase          =  comment + f"""\nclass {class_name}(nn.Module):\n""" + class_code
         if overwrite_result is not None:
             __lowerCamelCase          =  comment + f"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
         __lowerCamelCase          =  black.Mode(target_versions={black.TargetVersion.PYaa}  ,    line_length=1_19   )
         __lowerCamelCase          =  black.format_str(a  ,    mode=a   )
         __lowerCamelCase          =  os.path.join(self.transformer_dir  ,    '''new_code.py'''   )
         with open(a  ,    '''w'''  ,    newline='''\n'''   ) as f:
             f.write(a   )
         if overwrite_result is None:
             self.assertTrue(len(check_copies.is_copy_consistent(a   )   ) == 0   )
         else:
             check_copies.is_copy_consistent(f.name  ,    overwrite=a   )
             with open(a  ,    '''r'''   ) as f:
                 self.assertTrue(f.read()  ,    a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[int]   ):





         """simple docstring"""



         __lowerCamelCase          =  check_copies.find_code_in_transformers('''models.bert.modeling_bert.BertLMPredictionHead'''   )
         self.assertEqual(a  ,    a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Union[str, Any]   ):





         """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'''  ,    a  ,    )

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

         # Copy consistency with a really long name
         __lowerCamelCase          =  '''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'''  ,    a  ,    a   )  ,    )

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






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[str]   ):





         """simple docstring"""



         __lowerCamelCase          =  check_copies.LOCALIZED_READMES['''README_zh-hans.md''']

         __lowerCamelCase          =  (
             '''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.'''
         )
         __lowerCamelCase          =  (
             '''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'''
         )
         __lowerCamelCase          =  (
             '''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'''
         )

         __lowerCamelCase     ,  __lowerCamelCase          =  check_copies.convert_to_localized_md(
             a  ,    a  ,    localized_readme['''format_model_list''']   )

         self.assertFalse(a   )
         self.assertEqual(a  ,    a   )

         __lowerCamelCase     ,  __lowerCamelCase          =  check_copies.convert_to_localized_md(
             a  ,    a  ,    localized_readme['''format_model_list''']   )

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

         __lowerCamelCase          =  (
             '''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.'''
         )
         __lowerCamelCase          =  (
             '''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'''
         )
         __lowerCamelCase          =  (
             '''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'''
         )

         __lowerCamelCase     ,  __lowerCamelCase          =  check_copies.convert_to_localized_md(
             a  ,    a  ,    localized_readme['''format_model_list''']   )

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

'''simple docstring'''




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
  import PIL


__UpperCAmelCase												=logging.get_logger(__name__)




class    a__				(       UpperCAmelCase__			):
     lowerCamelCase  :					Dict							=["pixel_values"]






     def __init__(			self		:		List[str]  ,    a		:		bool = True  ,    a		:		Dict[str, int] = None  ,    a		:		int = 0.9  ,    a		:		PILImageResampling = PILImageResampling.BICUBIC  ,    a		:		bool = True  ,    a		:		Dict[str, int] = None  ,    a		:		Union[int, float] = 1 / 2_55  ,    a		:		bool = True  ,    a		:		bool = True  ,    a		:		Optional[Union[float, List[float]]] = None  ,    a		:		Optional[Union[float, List[float]]] = None  ,    **a		:		Dict  ,    ):





         """simple docstring"""



         super().__init__(**a   )
         __lowerCamelCase          =  size if size is not None else {'''shortest_edge''': 2_24}
         __lowerCamelCase          =  get_size_dict(a  ,    default_to_square=a   )
         __lowerCamelCase          =  crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
         __lowerCamelCase          =  get_size_dict(a  ,    param_name='''crop_size'''   )

         __lowerCamelCase          =  do_resize
         __lowerCamelCase          =  size
         __lowerCamelCase          =  crop_pct
         __lowerCamelCase          =  resample
         __lowerCamelCase          =  do_center_crop
         __lowerCamelCase          =  crop_size
         __lowerCamelCase          =  do_rescale
         __lowerCamelCase          =  rescale_factor
         __lowerCamelCase          =  do_normalize
         __lowerCamelCase          =  image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
         __lowerCamelCase          =  image_std if image_std is not None else IMAGENET_DEFAULT_STD






     def        SCREAMING_SNAKE_CASE__       (			self		:		Any  ,    a		:		np.ndarray  ,    a		:		Dict[str, int]  ,    a		:		Optional[float] = None  ,    a		:		PILImageResampling = PILImageResampling.BICUBIC  ,    a		:		Optional[Union[str, ChannelDimension]] = None  ,    **a		:		List[str]  ,    ):





         """simple docstring"""



         __lowerCamelCase          =  get_size_dict(a  ,    default_to_square=a   )
         if "shortest_edge" not in size and ("height" not in size or "width" not in size):
             raise ValueError(f"""size must contain 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""   )
         if crop_pct is not None:
             if "shortest_edge" in size:
                 __lowerCamelCase          =  int(size['''shortest_edge'''] / crop_pct   )
             elif "height" in size and "width" in size:
                 if size["height"] == size["width"]:
                     __lowerCamelCase          =  int(size['''height'''] / crop_pct   )
                 else:
                     __lowerCamelCase          =  (int(size['''height'''] / crop_pct   ), int(size['''width'''] / crop_pct   ))
             else:
                 raise ValueError('''Invalid size for resize: {}'''.format(a   )   )

             __lowerCamelCase          =  get_resize_output_image_size(a  ,    size=a  ,    default_to_square=a   )
         else:
             if "shortest_edge" in size:
                 __lowerCamelCase          =  get_resize_output_image_size(a  ,    size=size['''shortest_edge''']  ,    default_to_square=a   )
             elif "height" in size and "width" in size:
                 __lowerCamelCase          =  (size['''height'''], size['''width'''])
             else:
                 raise ValueError('''Invalid size for resize: {}'''.format(a   )   )

         return resize(a  ,    size=a  ,    resample=a  ,    data_format=a  ,    **a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Dict  ,    a		:		np.ndarray  ,    a		:		Dict[str, int]  ,    a		:		Optional[Union[str, ChannelDimension]] = None  ,    **a		:		Dict  ,    ):





         """simple docstring"""



         __lowerCamelCase          =  get_size_dict(a   )
         if "height" not in size or "width" not in size:
             raise ValueError(f"""size must contain 'height' and 'width' as keys. Got {size.keys()}"""   )
         return center_crop(a  ,    size=(size['''height'''], size['''width'''])  ,    data_format=a  ,    **a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[Any]  ,    a		:		np.ndarray  ,    a		:		Union[int, float]  ,    a		:		Optional[Union[str, ChannelDimension]] = None  ,    **a		:		Union[str, Any]  ,    ):





         """simple docstring"""



         return rescale(a  ,    scale=a  ,    data_format=a  ,    **a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[str]  ,    a		:		np.ndarray  ,    a		:		Union[float, List[float]]  ,    a		:		Union[float, List[float]]  ,    a		:		Optional[Union[str, ChannelDimension]] = None  ,    **a		:		Any  ,    ):





         """simple docstring"""



         return normalize(a  ,    mean=a  ,    std=a  ,    data_format=a  ,    **a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[int]  ,    a		:		ImageInput  ,    a		:		bool = None  ,    a		:		Dict[str, int] = None  ,    a		:		int = None  ,    a		:		PILImageResampling = None  ,    a		:		bool = None  ,    a		:		Dict[str, int] = None  ,    a		:		bool = None  ,    a		:		float = None  ,    a		:		bool = None  ,    a		:		Optional[Union[float, List[float]]] = None  ,    a		:		Optional[Union[float, List[float]]] = None  ,    a		:		Optional[Union[str, TensorType]] = None  ,    a		:		ChannelDimension = ChannelDimension.FIRST  ,    **a		:		Tuple  ,    ):





         """simple docstring"""



         __lowerCamelCase          =  do_resize if do_resize is not None else self.do_resize
         __lowerCamelCase          =  crop_pct if crop_pct is not None else self.crop_pct
         __lowerCamelCase          =  resample if resample is not None else self.resample
         __lowerCamelCase          =  do_center_crop if do_center_crop is not None else self.do_center_crop
         __lowerCamelCase          =  do_rescale if do_rescale is not None else self.do_rescale
         __lowerCamelCase          =  rescale_factor if rescale_factor is not None else self.rescale_factor
         __lowerCamelCase          =  do_normalize if do_normalize is not None else self.do_normalize
         __lowerCamelCase          =  image_mean if image_mean is not None else self.image_mean
         __lowerCamelCase          =  image_std if image_std is not None else self.image_std

         __lowerCamelCase          =  size if size is not None else self.size
         __lowerCamelCase          =  get_size_dict(a  ,    default_to_square=a   )
         __lowerCamelCase          =  crop_size if crop_size is not None else self.crop_size
         __lowerCamelCase          =  get_size_dict(a  ,    param_name='''crop_size'''   )

         __lowerCamelCase          =  make_list_of_images(a   )

         if not valid_images(a   ):
             raise ValueError(
                 '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                 '''torch.Tensor, tf.Tensor or jax.ndarray.'''   )

         if do_resize and size is None or resample is None:
             raise ValueError('''Size and resample must be specified if do_resize is True.'''   )

         if do_center_crop and crop_pct is None:
             raise ValueError('''Crop_pct must be specified if do_center_crop is True.'''   )

         if do_rescale and rescale_factor is None:
             raise ValueError('''Rescale factor must be specified if do_rescale is True.'''   )

         if do_normalize and (image_mean is None or image_std is None):
             raise ValueError('''Image mean and std must be specified if do_normalize is True.'''   )

         # All transformations expect numpy arrays.
         __lowerCamelCase          =  [to_numpy_array(a   ) for image in images]

         if do_resize:
             __lowerCamelCase          =  [self.resize(image=a  ,    size=a  ,    crop_pct=a  ,    resample=a   ) for image in images]

         if do_center_crop:
             __lowerCamelCase          =  [self.center_crop(image=a  ,    size=a   ) for image in images]

         if do_rescale:
             __lowerCamelCase          =  [self.rescale(image=a  ,    scale=a   ) for image in images]

         if do_normalize:
             __lowerCamelCase          =  [self.normalize(image=a  ,    mean=a  ,    std=a   ) for image in images]

         __lowerCamelCase          =  [to_channel_dimension_format(a  ,    a   ) for image in images]

         __lowerCamelCase          =  {'''pixel_values''': images}
         return BatchFeature(data=a  ,    tensor_type=a   )
