





from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


SCREAMING_SNAKE_CASE_   :      Any			=   logging.get_logger(__name__)


SCREAMING_SNAKE_CASE_   :      str			=   TypeVar('''DatasetType''', Dataset, IterableDataset)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case = None						,   snake_case = None						,   snake_case = None						,   snake_case = None						,   snake_case = "first_exhausted"						,   )		->    int:
		from .arrow_dataset import Dataset
		from .iterable_dataset import IterableDataset

		if not datasets:
				raise ValueError('Unable to interleave an empty list of datasets.'							)
		for i, dataset in enumerate(__UpperCAmelCase							):
				if not isinstance(__UpperCAmelCase						,   (Dataset, IterableDataset)							):
						if isinstance(__UpperCAmelCase						,   (DatasetDict, IterableDatasetDict)							):
								if not dataset:
										raise ValueError(
										    F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
										    'is an empty dataset dictionary.'							)
								raise ValueError(
								    F"Dataset at position {i} has at least one split: {list(__UpperCAmelCase							)}\n"
								    F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(__UpperCAmelCase							)							)}']"							)
						raise ValueError(
						    F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__UpperCAmelCase							).__name__}."							)
				if i == 0:
						__lowercase						,      __lowercase											=     (
						    (Dataset, IterableDataset) if isinstance(__UpperCAmelCase						,   __UpperCAmelCase							) else (IterableDataset, Dataset)
						)
				elif not isinstance(__UpperCAmelCase						,   __UpperCAmelCase							):
						raise ValueError(
						    F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."							)
		if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
				raise ValueError(F"{stopping_strategy} is not supported. Please enter a valid stopping_strategy."							)
		if dataset_type is Dataset:
				return _interleave_map_style_datasets(
				    __UpperCAmelCase						,   __UpperCAmelCase						,   __UpperCAmelCase						,   info=__UpperCAmelCase						,   split=__UpperCAmelCase						,   stopping_strategy=__UpperCAmelCase							)
		else:
				return _interleave_iterable_datasets(
				    __UpperCAmelCase						,   __UpperCAmelCase						,   __UpperCAmelCase						,   info=__UpperCAmelCase						,   split=__UpperCAmelCase						,   stopping_strategy=__UpperCAmelCase							)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case = None						,   snake_case = None						,   snake_case = 0						,   )		->    Union[str, Any]:

		if not dsets:
				raise ValueError('Unable to concatenate an empty list of datasets.'							)
		for i, dataset in enumerate(__UpperCAmelCase							):
				if not isinstance(__UpperCAmelCase						,   (Dataset, IterableDataset)							):
						if isinstance(__UpperCAmelCase						,   (DatasetDict, IterableDatasetDict)							):
								if not dataset:
										raise ValueError(
										    F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
										    'is an empty dataset dictionary.'							)
								raise ValueError(
								    F"Dataset at position {i} has at least one split: {list(__UpperCAmelCase							)}\n"
								    F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(__UpperCAmelCase							)							)}']"							)
						raise ValueError(
						    F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__UpperCAmelCase							).__name__}."							)
				if i == 0:
						__lowercase						,      __lowercase											=     (
						    (Dataset, IterableDataset) if isinstance(__UpperCAmelCase						,   __UpperCAmelCase							) else (IterableDataset, Dataset)
						)
				elif not isinstance(__UpperCAmelCase						,   __UpperCAmelCase							):
						raise ValueError(
						    F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."							)
		if dataset_type is Dataset:
				return _concatenate_map_style_datasets(__UpperCAmelCase						,   info=__UpperCAmelCase						,   split=__UpperCAmelCase						,   axis=__UpperCAmelCase							)
		else:
				return _concatenate_iterable_datasets(__UpperCAmelCase						,   info=__UpperCAmelCase						,   split=__UpperCAmelCase						,   axis=__UpperCAmelCase							)









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)





