

'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_	= logging.get_logger(__name__)

A_	= {
    "vinvino02/glpn-kitti": "https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json",
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}


class  UpperCAmelCase (							UpperCAmelCase__    ):

		'''simple docstring'''






		SCREAMING_SNAKE_CASE_           =					'glpn'
		def __init__(					self   ,			SCREAMING_SNAKE_CASE_=3   ,			SCREAMING_SNAKE_CASE_=4   ,			SCREAMING_SNAKE_CASE_=[2, 2, 2, 2]   ,			SCREAMING_SNAKE_CASE_=[8, 4, 2, 1]   ,			SCREAMING_SNAKE_CASE_=[32, 64, 160, 256]   ,			SCREAMING_SNAKE_CASE_=[7, 3, 3, 3]   ,			SCREAMING_SNAKE_CASE_=[4, 2, 2, 2]   ,			SCREAMING_SNAKE_CASE_=[1, 2, 5, 8]   ,			SCREAMING_SNAKE_CASE_=[4, 4, 4, 4]   ,			SCREAMING_SNAKE_CASE_="gelu"   ,			SCREAMING_SNAKE_CASE_=0.0   ,			SCREAMING_SNAKE_CASE_=0.0   ,			SCREAMING_SNAKE_CASE_=0.02   ,			SCREAMING_SNAKE_CASE_=0.1   ,			SCREAMING_SNAKE_CASE_=1E-6   ,			SCREAMING_SNAKE_CASE_=64   ,			SCREAMING_SNAKE_CASE_=10   ,			SCREAMING_SNAKE_CASE_=-1   ,			**SCREAMING_SNAKE_CASE_   ,			)  ->     Union[str, Any]:






							'''simple docstring'''



							super().__init__(**SCREAMING_SNAKE_CASE_		)

							lowerCamelCase_  				=      num_channels
							lowerCamelCase_  				=      num_encoder_blocks
							lowerCamelCase_  				=      depths
							lowerCamelCase_  				=      sr_ratios
							lowerCamelCase_  				=      hidden_sizes
							lowerCamelCase_  				=      patch_sizes
							lowerCamelCase_  				=      strides
							lowerCamelCase_  				=      mlp_ratios
							lowerCamelCase_  				=      num_attention_heads
							lowerCamelCase_  				=      hidden_act
							lowerCamelCase_  				=      hidden_dropout_prob
							lowerCamelCase_  				=      attention_probs_dropout_prob
							lowerCamelCase_  				=      initializer_range
							lowerCamelCase_  				=      drop_path_rate
							lowerCamelCase_  				=      layer_norm_eps
							lowerCamelCase_  				=      decoder_hidden_size
							lowerCamelCase_  				=      max_depth
							lowerCamelCase_  				=      head_in_index









'''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)
