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_     :






        '''simple docstring'''






        @staticmethod
        def 			UpperCamelCase__   (	*__lowercase , **__lowercase)       ->	Union[str, Any]:
            pass



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







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


@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 	lowerCamelCase_     (   unittest.TestCase ):






    '''simple docstring'''






    a__	:						Tuple						=      MODEL_FOR_DEPTH_ESTIMATION_MAPPING







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase)       ->	Tuple:
        __UpperCamelCase     :Optional[int]			  =		DepthEstimationPipeline(model=A_ , image_processor=A_)
        return depth_estimator, [
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
        ]







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase)       ->	Tuple:
        __UpperCamelCase     :Optional[int]			  =		depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
        self.assertEqual({'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)} , A_)
        import datasets

        __UpperCamelCase     :Dict			  =		datasets.load_dataset('''hf-internal-testing/fixtures_image_utils''' , '''image''' , split='''test''')
        __UpperCamelCase     :List[Any]			  =		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 			UpperCamelCase__   (	self)       ->	Optional[Any]:
        pass







    @slow
    @require_torch
    def 			UpperCamelCase__   (	self)       ->	Tuple:
        __UpperCamelCase     :List[str]			  =		'''Intel/dpt-large'''
        __UpperCamelCase     :int			  =		pipeline('''depth-estimation''' , model=A_)
        __UpperCamelCase     :Tuple			  =		depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg''')
        __UpperCamelCase     :List[Any]			  =		hashimage(outputs['''depth'''])

        # This seems flaky.
        # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
        self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item()) , 29.3_04)
        self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item()) , 2.6_62)







    @require_torch
    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        # This is highly irregular to have no small tests.
        self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT''')









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_A		       =						{
    'configuration_convbert': ['CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvBertConfig', 'ConvBertOnnxConfig'],
    'tokenization_convbert': ['ConvBertTokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						['ConvBertTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						[
       'CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ConvBertForMaskedLM',
       'ConvBertForMultipleChoice',
       'ConvBertForQuestionAnswering',
       'ConvBertForSequenceClassification',
       'ConvBertForTokenClassification',
       'ConvBertLayer',
       'ConvBertModel',
       'ConvBertPreTrainedModel',
       'load_tf_weights_in_convbert',
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						[
       'TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFConvBertForMaskedLM',
       'TFConvBertForMultipleChoice',
       'TFConvBertForQuestionAnswering',
       'TFConvBertForSequenceClassification',
       'TFConvBertForTokenClassification',
       'TFConvBertLayer',
       'TFConvBertModel',
       'TFConvBertPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
   from .tokenization_convbert import ConvBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_convbert_fast import ConvBertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_convbert import (
          CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          ConvBertForMaskedLM,
          ConvBertForMultipleChoice,
          ConvBertForQuestionAnswering,
          ConvBertForSequenceClassification,
          ConvBertForTokenClassification,
          ConvBertLayer,
          ConvBertModel,
          ConvBertPreTrainedModel,
          load_tf_weights_in_convbert,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_convbert import (
          TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFConvBertForMaskedLM,
          TFConvBertForMultipleChoice,
          TFConvBertForQuestionAnswering,
          TFConvBertForSequenceClassification,
          TFConvBertForTokenClassification,
          TFConvBertLayer,
          TFConvBertModel,
          TFConvBertPreTrainedModel,
      )


else:
   import sys

   _A		       =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





