
"""simple docstring"""

from typing import List

import numpy as np






def 							A		(       snake_case   :dict	)   ->			int:
 __UpperCamelCase       						= {key: len(snake_case	) for key, value in gen_kwargs.items() if isinstance(snake_case       ,      snake_case	)}
 if len(set(lists_lengths.values()	)	) > 1:
  raise RuntimeError(
      (
          'Sharding is ambiguous for this dataset: '
          + 'we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n'
          + '\n'.join(f'\t- key {key} has length {length}' for key, length in lists_lengths.items()	)
          + '\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, '
          + 'and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.'
      )	)
 __UpperCamelCase       						= max(lists_lengths.values()       ,      default=0	)
 return max(1       ,      snake_case	)






def 							A		(       snake_case   :int       ,      snake_case   :int	)   ->			List[range]:
 __UpperCamelCase       						= []
 for group_idx in range(snake_case	):
  __UpperCamelCase       						= num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
  if num_shards_to_add == 0:
   break
  __UpperCamelCase       						= shards_indices_per_group[-1].stop if shards_indices_per_group else 0
  __UpperCamelCase       						= range(snake_case       ,      start + num_shards_to_add	)
  shards_indices_per_group.append(snake_case	)
 return shards_indices_per_group






def 							A		(       snake_case   :dict       ,      snake_case   :int	)   ->			List[dict]:
 __UpperCamelCase       						= _number_of_shards_in_gen_kwargs(snake_case	)
 if num_shards == 1:
  return [dict(snake_case	)]
 else:
  __UpperCamelCase       						= _distribute_shards(num_shards=snake_case       ,      max_num_jobs=snake_case	)
  return [
      {
          key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
          if isinstance(snake_case       ,      snake_case	)
          else value
          for key, value in gen_kwargs.items()
      }
      for group_idx in range(len(snake_case	)	)
  ]






def 							A		(       snake_case   :List[dict]	)   ->			dict:
 return {
     key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
     if isinstance(gen_kwargs_list[0][key]       ,      snake_case	)
     else gen_kwargs_list[0][key]
     for key in gen_kwargs_list[0]
 }






def 							A		(       snake_case   :np.random.Generator       ,      snake_case   :dict	)   ->			dict:
 __UpperCamelCase       						= {len(snake_case	) for value in gen_kwargs.values() if isinstance(snake_case       ,      snake_case	)}
 __UpperCamelCase       						= {}
 for size in list_sizes:
  __UpperCamelCase       						= list(range(snake_case	)	)
  rng.shuffle(indices_per_size[size]	)
 # Now let's copy the gen_kwargs and shuffle the lists based on their sizes
 __UpperCamelCase       						= dict(snake_case	)
 for key, value in shuffled_kwargs.items():
  if isinstance(snake_case       ,      snake_case	):
   __UpperCamelCase       						= [value[i] for i in indices_per_size[len(snake_case	)]]
 return shuffled_kwargs





"""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
UpperCamelCase			:       Tuple									=				"."

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






def 							A		(       snake_case   :List[str]       ,      snake_case   :Optional[int]       ,      snake_case   :Optional[Any]	)   ->			Any:
 __UpperCamelCase       						= SavedModel()
 __UpperCamelCase       						= []

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

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

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

 __UpperCamelCase       						= 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
 __UpperCamelCase       						= sorted(snake_case	)
 __UpperCamelCase       						= []

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

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


if __name__ == "__main__":
      UpperCamelCase			:       List[str]									=				argparse.ArgumentParser()
      parser.add_argument("--saved_model_path", help="Path of the saved model to check (the .pb file).")
      parser.add_argument(
          "--opset", default=1_2, 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)"
      )
      UpperCamelCase			:       str									=				parser.parse_args()

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



