




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 							UpperCamelCase_       :

      '''simple docstring'''






      @staticmethod
      def 			SCREAMING_SNAKE_CASE				(     *UpperCAmelCase__ :      Union[str, Any]   ,					**UpperCAmelCase__ :      Any)		->Dict:





             '''simple docstring'''

             pass







def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			str:



       """simple docstring"""

       A__ 					=       hashlib.mda(image.tobytes()		)
       return m.hexdigest()


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

    '''simple docstring'''






    UpperCAmelCase__				    = MODEL_FOR_DEPTH_ESTIMATION_MAPPING


    def 			SCREAMING_SNAKE_CASE				(     self :      str   ,					UpperCAmelCase__ :      Union[str, Any]   ,					UpperCAmelCase__ :      Dict   ,					UpperCAmelCase__ :      Optional[Any])		->Optional[int]:





           '''simple docstring'''

           A__ 					=       DepthEstimationPipeline(model=UpperCAmelCase__   ,					image_processor=UpperCAmelCase__)
           return depth_estimator, [
               "./tests/fixtures/tests_samples/COCO/000000039769.png",
               "./tests/fixtures/tests_samples/COCO/000000039769.png",
           ]


    def 			SCREAMING_SNAKE_CASE				(     self :      List[str]   ,					UpperCAmelCase__ :      Any   ,					UpperCAmelCase__ :      List[Any])		->Tuple:





           '''simple docstring'''

           A__ 					=       depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
           self.assertEqual({'''predicted_depth''': ANY(torch.Tensor), '''depth''': ANY(Image.Image)}   ,					UpperCAmelCase__)
           import datasets

           A__ 					=       datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''   ,					'''image'''   ,					split='''test''')
           A__ 					=       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)},
               ]   ,					UpperCAmelCase__   ,					)


    @require_tf
    @unittest.skip('''Depth estimation is not implemented in TF''')
    def 			SCREAMING_SNAKE_CASE				(     self :      Union[str, Any])		->List[str]:





           '''simple docstring'''

           pass


    @slow
    @require_torch
    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[Any])		->Tuple:





           '''simple docstring'''

           A__ 					=       '''Intel/dpt-large'''
           A__ 					=       pipeline('''depth-estimation'''   ,					model=UpperCAmelCase__)
           A__ 					=       depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg''')
           A__ 					=       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 			SCREAMING_SNAKE_CASE				(     self :      Dict)		->Optional[int]:





           '''simple docstring'''

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







import argparse
import json
import os

from tensorflow.core.protobuf.saved_model_pba import SavedModel


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
a_      :      Optional[int]    =     '.'

# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
a_      :      List[str]    =     [
    'Assert',
    'AssignVariableOp',
    'EmptyTensorList',
    'MergeV2Checkpoints',
    'ReadVariableOp',
    'ResourceGather',
    'RestoreV2',
    'SaveV2',
    'ShardedFilename',
    'StatefulPartitionedCall',
    'StaticRegexFullMatch',
    'VarHandleOp',
]






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= SavedModel()
 a__												= []

 with open(os.path.join(__UpperCAmelCase							,  '''utils'''							,  '''tf_ops'''							,  '''onnx.json'''       )       ) as f:
  a__												= json.load(__UpperCAmelCase       )['''opsets''']

 for i in range(1							,  opset + 1       ):
  onnx_ops.extend(onnx_opsets[str(__UpperCAmelCase       )]       )

 with open(__UpperCAmelCase							,  '''rb'''       ) as f:
  saved_model.ParseFromString(f.read()       )

 a__												= set()

 # Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
 for meta_graph in saved_model.meta_graphs:
  # Add operations in the graph definition
  model_op_names.update(node.op for node in meta_graph.graph_def.node       )

  # Go through the functions in the graph definition
  for func in meta_graph.graph_def.library.function:
   # Add operations in each function
   model_op_names.update(node.op for node in func.node_def       )

    # Convert to list, sorted if you want
 a__												= sorted(__UpperCAmelCase       )
 a__												= []

 for op in model_op_names:
  if op not in onnx_ops and op not in INTERNAL_OPS:
   incompatible_ops.append(__UpperCAmelCase       )

 if strict and len(__UpperCAmelCase       ) > 0:
  raise Exception(f"Found the following incompatible ops for the opset {opset}:\n" + incompatible_ops       )
 elif len(__UpperCAmelCase       ) > 0:
  print(f"Found the following incompatible ops for the opset {opset}:"       )
  print(*__UpperCAmelCase							,  sep='''\n'''       )
 else:
  print(f"The saved model {saved_model_path} can properly be converted with ONNX."       )


if __name__ == "__main__":
     a_      :      Dict    =     argparse.ArgumentParser()
     parser.add_argument('--saved_model_path', help='Path of the saved model to check (the .pb file).')
     parser.add_argument(
         '--opset', default=12, type=int, help='The ONNX opset against which the model has to be tested.'
     )
     parser.add_argument(
         '--framework', choices=['onnx'], default='onnx', help='Frameworks against which to test the saved model.'
     )
     parser.add_argument(
         '--strict', action='store_true', help='Whether make the checking strict (raise errors) or not (raise warnings)'
     )
     a_      :      str    =     parser.parse_args()

     if args.framework == "onnx":
          onnx_compliancy(args.saved_model_path, args.strict, args.opset)





