





'''simple docstring'''




import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
  import jax

  from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel


class 					SCREAMING_SNAKE_CASE       (unittest.TestCase				):


     def __init__(		self		,      _UpperCAmelCase		,      _UpperCAmelCase=13		,      _UpperCAmelCase=30		,      _UpperCAmelCase=2		,      _UpperCAmelCase=3		,      _UpperCAmelCase=True		,      _UpperCAmelCase=True		,      _UpperCAmelCase=32		,      _UpperCAmelCase=5		,      _UpperCAmelCase=4		,      _UpperCAmelCase=37		,      _UpperCAmelCase="gelu"		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase=10		,      _UpperCAmelCase=0.02		,      ):

          '''simple docstring'''






          __A		:					Union[str, Any]						=			parent
          __A		:					str						=			batch_size
          __A		:					List[Any]						=			image_size
          __A		:					List[str]						=			patch_size
          __A		:					Tuple						=			num_channels
          __A		:					List[Any]						=			is_training
          __A		:					Union[str, Any]						=			use_labels
          __A		:					int						=			hidden_size
          __A		:					List[Any]						=			num_hidden_layers
          __A		:					List[Any]						=			num_attention_heads
          __A		:					List[str]						=			intermediate_size
          __A		:					Optional[int]						=			hidden_act
          __A		:					int						=			hidden_dropout_prob
          __A		:					Dict						=			attention_probs_dropout_prob
          __A		:					Union[str, Any]						=			type_sequence_label_size
          __A		:					Union[str, Any]						=			initializer_range

          # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
          __A		:					Optional[Any]						=			(image_size // patch_size) ** 2
          __A		:					List[str]						=			num_patches + 1


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Union[str, Any]						=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

          __A		:					Tuple						=			ViTConfig(
              image_size=self.image_size		,      patch_size=self.patch_size		,      num_channels=self.num_channels		,      hidden_size=self.hidden_size		,      num_hidden_layers=self.num_hidden_layers		,      num_attention_heads=self.num_attention_heads		,      intermediate_size=self.intermediate_size		,      hidden_act=self.hidden_act		,      hidden_dropout_prob=self.hidden_dropout_prob		,      attention_probs_dropout_prob=self.attention_probs_dropout_prob		,      is_decoder=__lowerCamelCase		,      initializer_range=self.initializer_range		,      )

          return config, pixel_values


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Union[str, Any]						=			FlaxViTModel(config=__lowerCamelCase)
          __A		:					List[Any]						=			model(__lowerCamelCase)
          # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
          __A		:					str						=			(self.image_size, self.image_size)
          __A		:					Union[str, Any]						=			(self.patch_size, self.patch_size)
          __A		:					Union[str, Any]						=			(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
          self.parent.assertEqual(result.last_hidden_state.shape		,      (self.batch_size, num_patches + 1, self.hidden_size))


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					int						=			self.type_sequence_label_size
          __A		:					Any						=			FlaxViTForImageClassification(config=__lowerCamelCase)
          __A		:					Union[str, Any]						=			model(__lowerCamelCase)
          self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.type_sequence_label_size))

          # test greyscale images
          __A		:					List[Any]						=			1
          __A		:					Dict						=			FlaxViTForImageClassification(__lowerCamelCase)

          __A		:					int						=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
          __A		:					Optional[int]						=			model(__lowerCamelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Optional[Any]						=			self.prepare_config_and_inputs()
          (
              __A							
          )		:					Union[str, Any]						=			config_and_inputs
          __A		:					Union[str, Any]						=			{'pixel_values': pixel_values}
          return config, inputs_dict




@require_flax
class 					SCREAMING_SNAKE_CASE       (lowerCAmelCase_   ,						unittest.TestCase				):
     lowerCAmelCase						     =			(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Tuple						=			FlaxViTModelTester(self)
          __A		:					Optional[Any]						=			ConfigTester(self		,      config_class=__lowerCamelCase		,      has_text_modality=__lowerCamelCase		,      hidden_size=37)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          self.config_tester.run_common_tests()


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					int						=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*__lowerCamelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Dict						=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Tuple						=			self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               __A		:					Any						=			model_class(__lowerCamelCase)
               __A		:					List[Any]						=			inspect.signature(model.__call__)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               __A		:					List[str]						=			[*signature.parameters.keys()]

               __A		:					Optional[int]						=			['pixel_values']
               self.assertListEqual(arg_names[:1]		,      __lowerCamelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Optional[int]						=			self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__):
                    __A		:					str						=			self._prepare_for_class(__lowerCamelCase		,      __lowerCamelCase)
                    __A		:					Optional[Any]						=			model_class(__lowerCamelCase)

                    @jax.jit
                    def model_jitted(_UpperCAmelCase		,      **_UpperCAmelCase):
                         return model(pixel_values=__lowerCamelCase		,      **__lowerCamelCase)

                    with self.subTest('JIT Enabled'):
                         __A		:					Optional[Any]						=			model_jitted(**__lowerCamelCase).to_tuple()

                    with self.subTest('JIT Disabled'):
                         with jax.disable_jit():
                              __A		:					int						=			model_jitted(**__lowerCamelCase).to_tuple()

                    self.assertEqual(len(__lowerCamelCase)		,      len(__lowerCamelCase))
                    for jitted_output, output in zip(__lowerCamelCase		,      __lowerCamelCase):
                         self.assertEqual(jitted_output.shape		,      output.shape)


     @slow
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          for model_class_name in self.all_model_classes:
               __A		:					Optional[Any]						=			model_class_name.from_pretrained('google/vit-base-patch16-224')
               __A		:					List[str]						=			model(np.ones((1, 3, 224, 224)))
               self.assertIsNotNone(__lowerCamelCase)





'''simple docstring'''






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_			      =		"."

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






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   Union[str, Any]:
      snake_case__       :   str	      =    SavedModel()
      snake_case__       :   Union[str, Any]	      =    []

      with open(os.path.join(__SCREAMING_SNAKE_CASE				,      'utils'				,      'tf_ops'				,      'onnx.json'		)		) as f:
            snake_case__       :   Optional[Any]	      =    json.load(__SCREAMING_SNAKE_CASE		)['opsets']

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

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

      snake_case__       :   List[str]	      =    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
      snake_case__       :   Optional[int]	      =    sorted(__SCREAMING_SNAKE_CASE		)
      snake_case__       :   Optional[Any]	      =    []

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

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


if __name__ == "__main__":
      A_			      =		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_			      =		parser.parse_args()

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