



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
snake_case__					:     int    		=   """."""

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



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			SavedModel()
	__lowercase		       =			[]

	with open(os.path.join(_SCREAMING_SNAKE_CASE  ,	"utils"  ,	"tf_ops"  ,	"onnx.json"   )   ) as f:
		__lowercase		       =			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()   )

	__lowercase		       =			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
	__lowercase		       =			sorted(_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			[]

	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__":
	snake_case__					:     Optional[Any]    		=   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)"""
	)
	snake_case__					:     Dict    		=   parser.parse_args()

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








from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


snake_case__					:     Any    		=   logging.get_logger(__name__)

class   _A   ( _lowercase  ,						_lowercase	):


						'''simple docstring'''





						_snake_case			:     Dict      							=      """maskformer-swin"""

						_snake_case			:     List[str]      							=      {
						    """num_attention_heads""": """num_heads""",
						    """num_hidden_layers""": """num_layers""",
						}



						def __init__(	self	:							List[str]							,      lowerCamelCase	:							Any=224							,      lowerCamelCase	:							Optional[Any]=4							,      lowerCamelCase	:							Dict=3							,      lowerCamelCase	:							Tuple=96							,      lowerCamelCase	:							str=[2, 2, 6, 2]							,      lowerCamelCase	:							Dict=[3, 6, 12, 24]							,      lowerCamelCase	:							Optional[Any]=7							,      lowerCamelCase	:							Any=4.0							,      lowerCamelCase	:							Union[str, Any]=True							,      lowerCamelCase	:							List[str]=0.0							,      lowerCamelCase	:							Optional[int]=0.0							,      lowerCamelCase	:							List[str]=0.1							,      lowerCamelCase	:							int="gelu"							,      lowerCamelCase	:							Optional[int]=False							,      lowerCamelCase	:							List[Any]=0.02							,      lowerCamelCase	:							Tuple=1e-5							,      lowerCamelCase	:							Dict=None							,      lowerCamelCase	:							Dict=None							,      **lowerCamelCase	:							int							,      ):






							'''simple docstring'''



							super().__init__(**lowerCamelCase	)

							__lowercase		       =			image_size
							__lowercase		       =			patch_size
							__lowercase		       =			num_channels
							__lowercase		       =			embed_dim
							__lowercase		       =			depths
							__lowercase		       =			len(lowerCamelCase	)
							__lowercase		       =			num_heads
							__lowercase		       =			window_size
							__lowercase		       =			mlp_ratio
							__lowercase		       =			qkv_bias
							__lowercase		       =			hidden_dropout_prob
							__lowercase		       =			attention_probs_dropout_prob
							__lowercase		       =			drop_path_rate
							__lowercase		       =			hidden_act
							__lowercase		       =			use_absolute_embeddings
							__lowercase		       =			layer_norm_eps
							__lowercase		       =			initializer_range
							# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
							# this indicates the channel dimension after the last stage of the model
							__lowercase		       =			int(embed_dim * 2 ** (len(lowerCamelCase	) - 1)	)
							__lowercase		       =			["stem"] + [f"""stage{idx}""" for idx in range(1							,      len(lowerCamelCase	) + 1	)]
							__lowercase       ,    __lowercase		       =			get_aligned_output_features_output_indices(
							    out_features=lowerCamelCase							,      out_indices=lowerCamelCase							,      stage_names=self.stage_names	)



