
'''simple docstring'''

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

import black


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.
snake_case									=    """    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    lowerCAmelCase       (     unittest.TestCase  ):


 def    _A	(    self  : Optional[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   str     		=	tempfile.mkdtemp()
  os.makedirs(os.path.join(self.transformer_dir			,							"models/bert/"    )    )
  lowerCAmelCase__     :   int     		=	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    _A	(    self  : Optional[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   int     		=	"src/transformers"
  shutil.rmtree(self.transformer_dir    )


 def    _A	(    self  : List[Any]			,							a__  : Any			,							a__  : Optional[int]			,							a__  : Optional[Any]			,							a__  : Dict=None    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Any     		=	comment + F'''\nclass {class_name}(nn.Module):\n''' + class_code
  if overwrite_result is not None:
   lowerCAmelCase__     :   Any     		=	comment + F'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
  lowerCAmelCase__     :   Union[str, Any]     		=	black.Mode(target_versions={black.TargetVersion.PYaa}			,							line_length=119    )
  lowerCAmelCase__     :   Optional[int]     		=	black.format_str(a__			,							mode=a__    )
  lowerCAmelCase__     :   Tuple     		=	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    _A	(    self  : str    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Dict     		=	check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead"    )
  self.assertEqual(a__			,							a__    )


 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"			,							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__     :   int     		=	"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    _A	(    self  : List[Any]    ):
  '''simple docstring'''

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

  lowerCAmelCase__     :   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."
  )
  lowerCAmelCase__     :   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"
  )
  lowerCAmelCase__     :   Tuple     		=	(
      "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__     :   List[Any]     		=	check_copies.convert_to_localized_md(
      a__			,							a__			,							localized_readme["format_model_list"]    )

  self.assertFalse(a__    )
  self.assertEqual(a__			,							a__    )

  lowerCAmelCase__		,     lowerCAmelCase__     :   int     		=	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__     :   List[Any]     		=	(
      "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__     :   Union[str, Any]     		=	(
      "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__     :   Optional[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"
  )

  lowerCAmelCase__		,     lowerCAmelCase__     :   str     		=	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'''

import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image
else:




     class    lowerCAmelCase       :


      @staticmethod
      def    _A	(    *a__  : str			,							**a__  : List[str]    ):
       '''simple docstring'''

       pass


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   List[str]     		=	hashlib.mda(image.tobytes()			)
 return m.hexdigest()







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class    lowerCAmelCase       (     unittest.TestCase  ):
 A_				:				List[Any]	=			MODEL_FOR_DEPTH_ESTIMATION_MAPPING


 def    _A	(    self  : List[Any]			,							a__  : int			,							a__  : Dict			,							a__  : Optional[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[str]     		=	DepthEstimationPipeline(model=a__			,							image_processor=a__    )
  return depth_estimator, [
      "./tests/fixtures/tests_samples/COCO/000000039769.png",
      "./tests/fixtures/tests_samples/COCO/000000039769.png",
  ]


 def    _A	(    self  : int			,							a__  : Optional[int]			,							a__  : List[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Any     		=	depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
  self.assertEqual({"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )}			,							a__    )
  import datasets

  lowerCAmelCase__     :   List[str]     		=	datasets.load_dataset("hf-internal-testing/fixtures_image_utils"			,							"image"			,							split="test"    )
  lowerCAmelCase__     :   List[str]     		=	depth_estimator(
      [
          Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    ),
          "http://images.cocodataset.org/val2017/000000039769.jpg",
          # RGBA
          dataset[0]["file"],
          # LA
          dataset[1]["file"],
          # L
          dataset[2]["file"],
      ]    )
  self.assertEqual(
      [
          {"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )},
          {"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )},
          {"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )},
          {"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )},
          {"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )},
      ]			,							a__			,							)


 @require_tf
 @unittest.skip("Depth estimation is not implemented in TF"    )
 def    _A	(    self  : Optional[Any]    ):
  '''simple docstring'''

  pass


 @slow
 @require_torch
 def    _A	(    self  : Any    ):
  '''simple docstring'''

  lowerCAmelCase__     :   str     		=	"Intel/dpt-large"
  lowerCAmelCase__     :   Dict     		=	pipeline("depth-estimation"			,							model=a__    )
  lowerCAmelCase__     :   Any     		=	depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg"    )
  lowerCAmelCase__     :   Any     		=	hashimage(outputs["depth"]    )

  # This seems flaky.
  # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
  self.assertEqual(nested_simplify(outputs["predicted_depth"].max().item()    )			,							29.304    )
  self.assertEqual(nested_simplify(outputs["predicted_depth"].min().item()    )			,							2.662    )


 @require_torch
 def    _A	(    self  : Any    ):
  '''simple docstring'''

  self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT"    )





