from typing import List, Optional, Union

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


__UpperCAmelCase     							=						logging.get_logger(__name__)

__UpperCAmelCase     							=						{
    'huggingface/time-series-transformer-tourism-monthly': (
        'https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}



class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   str							=  '''time_series_transformer'''
		_snake_case   :   Union[str, Any]							=  {
		    '''hidden_size''': '''d_model''',
		    '''num_attention_heads''': '''encoder_attention_heads''',
		    '''num_hidden_layers''': '''encoder_layers''',
		}



		def __init__(							self    ,		_UpperCamelCase = None    ,		_UpperCamelCase = None    ,		_UpperCamelCase = "student_t"    ,		_UpperCamelCase = "nll"    ,		_UpperCamelCase = 1    ,		_UpperCamelCase = [1, 2, 3, 4, 5, 6, 7]    ,		_UpperCamelCase = "mean"    ,		_UpperCamelCase = 0    ,		_UpperCamelCase = 0    ,		_UpperCamelCase = 0    ,		_UpperCamelCase = 0    ,		_UpperCamelCase = None    ,		_UpperCamelCase = None    ,		_UpperCamelCase = 3_2    ,		_UpperCamelCase = 3_2    ,		_UpperCamelCase = 2    ,		_UpperCamelCase = 2    ,		_UpperCamelCase = 2    ,		_UpperCamelCase = 2    ,		_UpperCamelCase = True    ,		_UpperCamelCase = "gelu"    ,		_UpperCamelCase = 6_4    ,		_UpperCamelCase = 0.1    ,		_UpperCamelCase = 0.1    ,		_UpperCamelCase = 0.1    ,		_UpperCamelCase = 0.1    ,		_UpperCamelCase = 0.1    ,		_UpperCamelCase = 1_0_0    ,		_UpperCamelCase = 0.02    ,		_UpperCamelCase=True    ,		**_UpperCamelCase    ,		)     ->							Union[str, Any]:
							# time series specific configuration
							UpperCAmelCase_   :				int						=						prediction_length
							UpperCAmelCase_   :				List[str]						=						context_length or prediction_length
							UpperCAmelCase_   :				Union[str, Any]						=						distribution_output
							UpperCAmelCase_   :				Optional[int]						=						loss
							UpperCAmelCase_   :				Dict						=						input_size
							UpperCAmelCase_   :				Tuple						=						num_time_features
							UpperCAmelCase_   :				Dict						=						lags_sequence
							UpperCAmelCase_   :				Dict						=						scaling
							UpperCAmelCase_   :				Dict						=						num_dynamic_real_features
							UpperCAmelCase_   :				List[Any]						=						num_static_real_features
							UpperCAmelCase_   :				Tuple						=						num_static_categorical_features
							if cardinality and num_static_categorical_features > 0:
												if len(_UpperCamelCase       ) != num_static_categorical_features:
																	raise ValueError(
																	    'The cardinality should be a list of the same length as `num_static_categorical_features`'       )
												UpperCAmelCase_   :				List[str]						=						cardinality
							else:
												UpperCAmelCase_   :				str						=						[0]
							if embedding_dimension and num_static_categorical_features > 0:
												if len(_UpperCamelCase       ) != num_static_categorical_features:
																	raise ValueError(
																	    'The embedding dimension should be a list of the same length as `num_static_categorical_features`'       )
												UpperCAmelCase_   :				List[Any]						=						embedding_dimension
							else:
												UpperCAmelCase_   :				Any						=						[min(5_0    ,		(cat + 1) // 2       ) for cat in self.cardinality]
							UpperCAmelCase_   :				Union[str, Any]						=						num_parallel_samples

							# Transformer architecture configuration
							UpperCAmelCase_   :				str						=						input_size * len(_UpperCamelCase       ) + self._number_of_features
							UpperCAmelCase_   :				Optional[Any]						=						d_model
							UpperCAmelCase_   :				str						=						encoder_attention_heads
							UpperCAmelCase_   :				Dict						=						decoder_attention_heads
							UpperCAmelCase_   :				List[Any]						=						encoder_ffn_dim
							UpperCAmelCase_   :				Any						=						decoder_ffn_dim
							UpperCAmelCase_   :				List[Any]						=						encoder_layers
							UpperCAmelCase_   :				int						=						decoder_layers

							UpperCAmelCase_   :				Any						=						dropout
							UpperCAmelCase_   :				List[str]						=						attention_dropout
							UpperCAmelCase_   :				str						=						activation_dropout
							UpperCAmelCase_   :				Union[str, Any]						=						encoder_layerdrop
							UpperCAmelCase_   :				List[str]						=						decoder_layerdrop

							UpperCAmelCase_   :				List[Any]						=						activation_function
							UpperCAmelCase_   :				int						=						init_std

							UpperCAmelCase_   :				Union[str, Any]						=						use_cache

							super().__init__(is_encoder_decoder=_UpperCamelCase    ,		**_UpperCamelCase       )



		@property
		def  __UpperCAmelCase      (							self       )     ->							int:
							return (
							    sum(self.embedding_dimension       )
							    + self.num_dynamic_real_features
							    + self.num_time_features
							    + self.num_static_real_features
							    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
							)














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     							=						'.'

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




def 					lowercase__				(					__snake_case   :							Union[str, Any]					,	__snake_case   :							Optional[int]					,	__snake_case   :							Union[str, Any]  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Optional[Any]						=						SavedModel()
					UpperCAmelCase_   :				Optional[Any]						=						[]

					with open(os.path.join(__snake_case					,	'utils'					,	'tf_ops'					,	'onnx.json'  )  ) as f:
										UpperCAmelCase_   :				Optional[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()  )

					UpperCAmelCase_   :				List[Any]						=						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_   :				Optional[int]						=						sorted(__snake_case  )
					UpperCAmelCase_   :				int						=						[]

					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     							=						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)'
		)
		__UpperCAmelCase     							=						parser.parse_args()

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






