


from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


A      :    Optional[Any]		=					logging.get_logger(__name__)

A      :    Union[str, Any]		=					{
    'EleutherAI/gpt-j-6B': 'https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}







class 			_lowercase (							a__):


       """simple docstring"""




       A__											=			'''gptj'''
       A__											=			{
           '''max_position_embeddings''': '''n_positions''',
           '''hidden_size''': '''n_embd''',
           '''num_attention_heads''': '''n_head''',
           '''num_hidden_layers''': '''n_layer''',
       }





       def __init__(   self							:			Optional[Any]					,      __lowerCamelCase							:			Union[str, Any]=50400					,      __lowerCamelCase							:			Optional[Any]=2048					,      __lowerCamelCase							:			Dict=4096					,      __lowerCamelCase							:			List[str]=28					,      __lowerCamelCase							:			List[str]=16					,      __lowerCamelCase							:			Optional[Any]=64					,      __lowerCamelCase							:			str=None					,      __lowerCamelCase							:			Union[str, Any]="gelu_new"					,      __lowerCamelCase							:			List[Any]=0.0					,      __lowerCamelCase							:			List[str]=0.0					,      __lowerCamelCase							:			str=0.0					,      __lowerCamelCase							:			Optional[Any]=1E-5					,      __lowerCamelCase							:			Dict=0.0_2					,      __lowerCamelCase							:			Dict=True					,      __lowerCamelCase							:			Optional[int]=50256					,      __lowerCamelCase							:			Any=50256					,      __lowerCamelCase							:			Any=False					,      **__lowerCamelCase							:			Any					,      ):


             '''simple docstring'''



             lowerCamelCase__		:				Union[str, Any]    =  vocab_size
             lowerCamelCase__		:				Optional[int]    =  n_positions
             lowerCamelCase__		:				Optional[Any]    =  n_embd
             lowerCamelCase__		:				Union[str, Any]    =  n_layer
             lowerCamelCase__		:				Optional[Any]    =  n_head
             lowerCamelCase__		:				Any    =  n_inner
             lowerCamelCase__		:				str    =  rotary_dim
             lowerCamelCase__		:				Optional[int]    =  activation_function
             lowerCamelCase__		:				Tuple    =  resid_pdrop
             lowerCamelCase__		:				Optional[Any]    =  embd_pdrop
             lowerCamelCase__		:				Optional[Any]    =  attn_pdrop
             lowerCamelCase__		:				Optional[Any]    =  layer_norm_epsilon
             lowerCamelCase__		:				Any    =  initializer_range
             lowerCamelCase__		:				Tuple    =  use_cache

             lowerCamelCase__		:				Dict    =  bos_token_id
             lowerCamelCase__		:				str    =  eos_token_id

             super().__init__(
                 bos_token_id=SCREAMING_SNAKE_CASE__					,      eos_token_id=SCREAMING_SNAKE_CASE__					,      tie_word_embeddings=SCREAMING_SNAKE_CASE__					,      **SCREAMING_SNAKE_CASE__       )







class 			_lowercase (							a__):


       """simple docstring"""




       def __init__(   self							:			Tuple					,      __lowerCamelCase							:			List[Any]					,      __lowerCamelCase							:			str = "default"					,      __lowerCamelCase							:			Union[str, Any] = None					,      __lowerCamelCase							:			List[Any] = False					,      ):


             '''simple docstring'''



             super().__init__(SCREAMING_SNAKE_CASE__					,      task=SCREAMING_SNAKE_CASE__					,      patching_specs=SCREAMING_SNAKE_CASE__					,      use_past=SCREAMING_SNAKE_CASE__       )
             if not getattr(self._config					,      "pad_token_id"					,      SCREAMING_SNAKE_CASE__       ):
                   # TODO: how to do that better?
                   lowerCamelCase__		:				List[str]    =  0





       @property
       def 						lowerCAmelCase     (   self							:			int       ):


             '''simple docstring'''



             lowerCamelCase__		:				Tuple    =  OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}       )
             if self.use_past:
                   self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE__					,      direction="inputs"       )
                   lowerCamelCase__		:				Optional[int]    =  {0: """batch""", 1: """past_sequence + sequence"""}
             else:
                   lowerCamelCase__		:				Union[str, Any]    =  {0: """batch""", 1: """sequence"""}

             return common_inputs





       @property
       def 						lowerCAmelCase     (   self							:			Any       ):


             '''simple docstring'''



             return self._config.n_layer





       @property
       def 						lowerCAmelCase     (   self							:			Optional[int]       ):


             '''simple docstring'''



             return self._config.n_head





       def 						lowerCAmelCase     (   self							:			List[str]					,      __lowerCamelCase							:			List[str]					,      __lowerCamelCase							:			Optional[int] = -1					,      __lowerCamelCase							:			Tuple = -1					,      __lowerCamelCase							:			Optional[Any] = False					,      __lowerCamelCase							:			List[Any] = None					,      ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[Any]    =  super(SCREAMING_SNAKE_CASE__					,      self       ).generate_dummy_inputs(
                 SCREAMING_SNAKE_CASE__					,      batch_size=SCREAMING_SNAKE_CASE__					,      seq_length=SCREAMING_SNAKE_CASE__					,      is_pair=SCREAMING_SNAKE_CASE__					,      framework=SCREAMING_SNAKE_CASE__       )

             # We need to order the input in the way they appears in the forward()
             lowerCamelCase__		:				Optional[Any]    =  OrderedDict({"input_ids": common_inputs["input_ids"]}       )

             # Need to add the past_keys
             if self.use_past:
                   if not is_torch_available():
                         raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."       )
                   else:
                         import torch

                         lowerCamelCase__		:				Union[str, Any]    =  common_inputs["""input_ids"""].shape
                         # Not using the same length for past_key_values
                         lowerCamelCase__		:				Optional[Any]    =  seqlen + 2
                         lowerCamelCase__		:				int    =  (
                             batch,
                             self.num_attention_heads,
                             past_key_values_length,
                             self._config.hidden_size // self.num_attention_heads,
                         )
                         lowerCamelCase__		:				str    =  [
                             (torch.zeros(SCREAMING_SNAKE_CASE__       ), torch.zeros(SCREAMING_SNAKE_CASE__       )) for _ in range(self.num_layers       )
                         ]

             lowerCamelCase__		:				List[Any]    =  common_inputs["""attention_mask"""]
             if self.use_past:
                   lowerCamelCase__		:				Union[str, Any]    =  ordered_inputs["""attention_mask"""].dtype
                   lowerCamelCase__		:				List[Any]    =  torch.cat(
                       [ordered_inputs["attention_mask"], torch.ones(SCREAMING_SNAKE_CASE__					,      SCREAMING_SNAKE_CASE__					,      dtype=SCREAMING_SNAKE_CASE__       )]					,      dim=1       )

             return ordered_inputs





       @property
       def 						lowerCAmelCase     (   self							:			Optional[Any]       ):


             '''simple docstring'''



             return 13




"""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__      :    List[str]    							= '.'

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

def lowercase_  (   _snake_case   ,_snake_case   ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:       List[str]           =   SavedModel()
							SCREAMING_SNAKE_CASE__						:       Dict           =   []

							with open(os.path.join(_snake_case   ,"""utils"""   ,"""tf_ops"""   ,"""onnx.json"""						)						) as f:
														SCREAMING_SNAKE_CASE__						:       Any           =   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()						)

							SCREAMING_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
							SCREAMING_SNAKE_CASE__						:       int           =   sorted(_snake_case						)
							SCREAMING_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(_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__      :    Optional[int]    							= 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__      :    Dict    							= parser.parse_args()

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




