






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

import black


_SCREAMING_SNAKE_CASE   =  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.
_SCREAMING_SNAKE_CASE   =  "    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 					SCREAMING_SNAKE_CASE_  (       unittest.TestCase ):
       """simple docstring"""


       def 				UpperCamelCase__						(				self	:Any):



          """simple docstring"""

          _lowercase              =tempfile.mkdtemp()
          os.makedirs(os.path.join(self.transformer_dir,      'models/bert/'))
          _lowercase              =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 				UpperCamelCase__						(				self	:Tuple):



          """simple docstring"""

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





       def 				UpperCamelCase__						(				self	:List[str],      snake_case	:str,      snake_case	:Optional[int],      snake_case	:Optional[Any],      snake_case	:Union[str, Any]=None):



          """simple docstring"""

          _lowercase              =comment + f'''\nclass {class_name}(nn.Module):\n''' + class_code
          if overwrite_result is not None:
             _lowercase              =comment + f'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
          _lowercase              =black.Mode(target_versions={black.TargetVersion.PYaa},      line_length=119)
          _lowercase              =black.format_str(snake_case,      mode=snake_case)
          _lowercase              =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 				UpperCamelCase__						(				self	:int):



          """simple docstring"""

          _lowercase              =check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead')
          self.assertEqual(snake_case,      snake_case)





       def 				UpperCamelCase__						(				self	:Tuple):



          """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',      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
          _lowercase              ='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 				UpperCamelCase__						(				self	:Optional[Any]):



          """simple docstring"""

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

          _lowercase              =(
              '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.'
          )
          _lowercase              =(
              '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'
          )
          _lowercase              =(
              '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'
          )

          _lowercase       ,       _lowercase              =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)

          _lowercase       ,       _lowercase              =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)

          _lowercase              =(
              '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.'
          )
          _lowercase              =(
              '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'
          )
          _lowercase              =(
              '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'
          )

          _lowercase       ,       _lowercase              =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 unittest

import numpy as np
import requests

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

    from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
    _SCREAMING_SNAKE_CASE   =  False

if is_vision_available():
    from PIL import Image

    from transformers import PixaStructImageProcessor






class 					SCREAMING_SNAKE_CASE_  (       unittest.TestCase ):
       """simple docstring"""


       def __init__(				self	:str,      snake_case	:int,      snake_case	:Dict=7,      snake_case	:List[Any]=3,      snake_case	:int=18,      snake_case	:List[str]=30,      snake_case	:str=400,      snake_case	:int=None,      snake_case	:List[Any]=True,      snake_case	:Optional[int]=True,      snake_case	:Union[str, Any]=None,      ):



          """simple docstring"""

          _lowercase              =size if size is not None else {'height': 20, 'width': 20}
          _lowercase              =parent
          _lowercase              =batch_size
          _lowercase              =num_channels
          _lowercase              =image_size
          _lowercase              =min_resolution
          _lowercase              =max_resolution
          _lowercase              =size
          _lowercase              =do_normalize
          _lowercase              =do_convert_rgb
          _lowercase              =[512, 1024, 2048, 4096]
          _lowercase              =patch_size if patch_size is not None else {'height': 16, 'width': 16}





       def 				UpperCamelCase__						(				self	:Any):



          """simple docstring"""

          return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}





       def 				UpperCamelCase__						(				self	:int):



          """simple docstring"""

          _lowercase              ='https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
          _lowercase              =Image.open(requests.get(snake_case,      stream=snake_case).raw).convert('RGB')
          return raw_image






@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11  ,						reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''  ,						)
@require_torch
@require_vision
class 					SCREAMING_SNAKE_CASE_  (       _a  ,						unittest.TestCase ):
       """simple docstring"""


       __lowerCAmelCase    :		Tuple					=PixaStructImageProcessor if is_vision_available() else None





       def 				UpperCamelCase__						(				self	:str):



          """simple docstring"""

          _lowercase              =PixaStructImageProcessingTester(self)





       @property
       def 				UpperCamelCase__						(				self	:Optional[int]):



          """simple docstring"""

          return self.image_processor_tester.prepare_image_processor_dict()





       def 				UpperCamelCase__						(				self	:str):



          """simple docstring"""

          _lowercase              =self.image_processing_class(**self.image_processor_dict)
          self.assertTrue(hasattr(snake_case,      'do_normalize'))
          self.assertTrue(hasattr(snake_case,      'do_convert_rgb'))





       def 				UpperCamelCase__						(				self	:Any):



          """simple docstring"""

          _lowercase              =self.image_processor_tester.prepare_dummy_image()

          _lowercase              =self.image_processing_class(**self.image_processor_dict)
          _lowercase              =2048

          _lowercase              =image_processor(snake_case,      return_tensors='pt',      max_patches=snake_case)
          self.assertTrue(torch.allclose(inputs.flattened_patches.mean(),      torch.tensor(0.0_6_0_6),      atol=1e-3,      rtol=1e-3))





       def 				UpperCamelCase__						(				self	:int):



          """simple docstring"""

          _lowercase              =self.image_processing_class(**self.image_processor_dict)
          # create random PIL images
          _lowercase              =prepare_image_inputs(self.image_processor_tester,      equal_resolution=snake_case)
          for image in image_inputs:
             self.assertIsInstance(snake_case,      Image.Image)

          # Test not batched input
          _lowercase              =(
              (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
              * self.image_processor_tester.num_channels
          ) + 2

          for max_patch in self.image_processor_tester.max_patches:
             # Test not batched input
             _lowercase              =image_processor(
                 image_inputs[0],      return_tensors='pt',      max_patches=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape,      (1, max_patch, expected_hidden_dim),      )

             # Test batched
             _lowercase              =image_processor(
                 snake_case,      return_tensors='pt',      max_patches=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),      )





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



          """simple docstring"""

          _lowercase              =self.image_processing_class(**self.image_processor_dict)
          # create random PIL images
          _lowercase              =prepare_image_inputs(self.image_processor_tester,      equal_resolution=snake_case)
          for image in image_inputs:
             self.assertIsInstance(snake_case,      Image.Image)

          # Test not batched input
          _lowercase              =(
              (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
              * self.image_processor_tester.num_channels
          ) + 2

          _lowercase              =True

          for max_patch in self.image_processor_tester.max_patches:
             # Test not batched input
             with self.assertRaises(snake_case):
                _lowercase              =image_processor(
                    image_inputs[0],      return_tensors='pt',      max_patches=snake_case).flattened_patches

             _lowercase              ='Hello'

             _lowercase              =image_processor(
                 image_inputs[0],      return_tensors='pt',      max_patches=snake_case,      header_text=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape,      (1, max_patch, expected_hidden_dim),      )

             # Test batched
             _lowercase              =image_processor(
                 snake_case,      return_tensors='pt',      max_patches=snake_case,      header_text=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),      )





       def 				UpperCamelCase__						(				self	:int):



          """simple docstring"""

          _lowercase              =self.image_processing_class(**self.image_processor_dict)
          # create random numpy tensors
          _lowercase              =prepare_image_inputs(self.image_processor_tester,      equal_resolution=snake_case,      numpify=snake_case)
          for image in image_inputs:
             self.assertIsInstance(snake_case,      np.ndarray)

          _lowercase              =(
              (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
              * self.image_processor_tester.num_channels
          ) + 2

          for max_patch in self.image_processor_tester.max_patches:
             # Test not batched input
             _lowercase              =image_processor(
                 image_inputs[0],      return_tensors='pt',      max_patches=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape,      (1, max_patch, expected_hidden_dim),      )

             # Test batched
             _lowercase              =image_processor(
                 snake_case,      return_tensors='pt',      max_patches=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),      )





       def 				UpperCamelCase__						(				self	:Any):



          """simple docstring"""

          _lowercase              =self.image_processing_class(**self.image_processor_dict)
          # create random PyTorch tensors
          _lowercase              =prepare_image_inputs(self.image_processor_tester,      equal_resolution=snake_case,      torchify=snake_case)
          for image in image_inputs:
             self.assertIsInstance(snake_case,      torch.Tensor)

          # Test not batched input
          _lowercase              =(
              (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
              * self.image_processor_tester.num_channels
          ) + 2

          for max_patch in self.image_processor_tester.max_patches:
             # Test not batched input
             _lowercase              =image_processor(
                 image_inputs[0],      return_tensors='pt',      max_patches=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape,      (1, max_patch, expected_hidden_dim),      )

             # Test batched
             _lowercase              =image_processor(
                 snake_case,      return_tensors='pt',      max_patches=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),      )






@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11  ,						reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''  ,						)
@require_torch
@require_vision
class 					SCREAMING_SNAKE_CASE_  (       _a  ,						unittest.TestCase ):
       """simple docstring"""


       __lowerCAmelCase    :		Optional[Any]					=PixaStructImageProcessor if is_vision_available() else None





       def 				UpperCamelCase__						(				self	:Optional[int]):



          """simple docstring"""

          _lowercase              =PixaStructImageProcessingTester(self,      num_channels=4)
          _lowercase              =3





       @property
       def 				UpperCamelCase__						(				self	:str):



          """simple docstring"""

          return self.image_processor_tester.prepare_image_processor_dict()





       def 				UpperCamelCase__						(				self	:Optional[int]):



          """simple docstring"""

          _lowercase              =self.image_processing_class(**self.image_processor_dict)
          self.assertTrue(hasattr(snake_case,      'do_normalize'))
          self.assertTrue(hasattr(snake_case,      'do_convert_rgb'))





       def 				UpperCamelCase__						(				self	:Optional[Any]):



          """simple docstring"""

          _lowercase              =self.image_processing_class(**self.image_processor_dict)
          # create random PIL images
          _lowercase              =prepare_image_inputs(self.image_processor_tester,      equal_resolution=snake_case)
          for image in image_inputs:
             self.assertIsInstance(snake_case,      Image.Image)

          # Test not batched input
          _lowercase              =(
              (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
              * (self.image_processor_tester.num_channels - 1)
          ) + 2

          for max_patch in self.image_processor_tester.max_patches:
             # Test not batched input
             _lowercase              =image_processor(
                 image_inputs[0],      return_tensors='pt',      max_patches=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape,      (1, max_patch, expected_hidden_dim),      )

             # Test batched
             _lowercase              =image_processor(
                 snake_case,      return_tensors='pt',      max_patches=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),      )


