


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

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


UpperCAmelCase_      : str  					=       logging.get_logger(__name__)

UpperCAmelCase_      : List[Any]  					=       {
    'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json',
    # See all Marian models at https://huggingface.co/models?filter=marian
}
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     Union[str, Any]        =						'''marian'''
      snake_case__				:     Dict        =						['''past_key_values''']
      snake_case__				:     Union[str, Any]        =						{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
      def __init__(       self				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       List[str]=5_8_1_0_1     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=None     ,							SCREAMING_SNAKE_CASE__				:       Any=1_0_2_4     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=1_2     ,							SCREAMING_SNAKE_CASE__				:       Tuple=4_0_9_6     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=1_6     ,							SCREAMING_SNAKE_CASE__				:       List[str]=1_2     ,							SCREAMING_SNAKE_CASE__				:       Tuple=4_0_9_6     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=1_6     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=0.0     ,							SCREAMING_SNAKE_CASE__				:       Tuple=0.0     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=True     ,							SCREAMING_SNAKE_CASE__				:       str=True     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]="gelu"     ,							SCREAMING_SNAKE_CASE__				:       Dict=1_0_2_4     ,							SCREAMING_SNAKE_CASE__				:       Tuple=0.1     ,							SCREAMING_SNAKE_CASE__				:       Any=0.0     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=0.0     ,							SCREAMING_SNAKE_CASE__				:       Tuple=0.02     ,							SCREAMING_SNAKE_CASE__				:       str=5_8_1_0_0     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=False     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=5_8_1_0_0     ,							SCREAMING_SNAKE_CASE__				:       Dict=0     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=0     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=True     ,							**SCREAMING_SNAKE_CASE__				:       List[str]     ,							)		->			Union[str, Any]:
             a_					:		Any							=  vocab_size
             a_					:		int							=  decoder_vocab_size or vocab_size
             a_					:		List[str]							=  max_position_embeddings
             a_					:		List[Any]							=  d_model
             a_					:		int							=  encoder_ffn_dim
             a_					:		List[str]							=  encoder_layers
             a_					:		Optional[Any]							=  encoder_attention_heads
             a_					:		List[str]							=  decoder_ffn_dim
             a_					:		Any							=  decoder_layers
             a_					:		List[Any]							=  decoder_attention_heads
             a_					:		List[Any]							=  dropout
             a_					:		Union[str, Any]							=  attention_dropout
             a_					:		Any							=  activation_dropout
             a_					:		List[str]							=  activation_function
             a_					:		str							=  init_std
             a_					:		int							=  encoder_layerdrop
             a_					:		Union[str, Any]							=  decoder_layerdrop
             a_					:		Any							=  use_cache
             a_					:		str							=  encoder_layers
             a_					:		Optional[int]							=  scale_embedding  # scale factor will be sqrt(d_model) if True
             a_					:		Tuple							=  share_encoder_decoder_embeddings
             super().__init__(
                 pad_token_id=SCREAMING_SNAKE_CASE__     ,							eos_token_id=SCREAMING_SNAKE_CASE__     ,							is_encoder_decoder=SCREAMING_SNAKE_CASE__     ,							decoder_start_token_id=SCREAMING_SNAKE_CASE__     ,							forced_eos_token_id=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     ,							)
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      @property
      # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			Mapping[str, Mapping[int, str]]:
             if self.task in ["default", "seq2seq-lm"]:
                    a_					:		str							=  OrderedDict(
                        [
                            ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                            ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                        ]     )

                    if self.use_past:
                           a_					:		Tuple							=  {0: 'batch'}
                           a_					:		List[str]							=  {0: 'batch', 1: 'past_decoder_sequence + sequence'}
                    else:
                           a_					:		Optional[Any]							=  {0: 'batch', 1: 'decoder_sequence'}
                           a_					:		List[str]							=  {0: 'batch', 1: 'decoder_sequence'}

                    if self.use_past:
                           self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE__     ,							direction='inputs'     )
             elif self.task == "causal-lm":
                    # TODO: figure this case out.
                    a_					:		List[Any]							=  OrderedDict(
                        [
                            ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                            ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                        ]     )
                    if self.use_past:
                           a_							,		a_					:		Any							=  self.num_layers
                           for i in range(SCREAMING_SNAKE_CASE__     ):
                                  a_					:		Dict							=  {0: 'batch', 2: 'past_sequence + sequence'}
                                  a_					:		Union[str, Any]							=  {0: 'batch', 2: 'past_sequence + sequence'}
             else:
                    a_					:		Union[str, Any]							=  OrderedDict(
                        [
                            ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                            ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                            ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
                            ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
                        ]     )

             return common_inputs
      @property
      # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			Mapping[str, Mapping[int, str]]:
             if self.task in ["default", "seq2seq-lm"]:
                    a_					:		Optional[Any]							=  super().outputs
             else:
                    a_					:		Optional[int]							=  super(SCREAMING_SNAKE_CASE__     ,							self     ).outputs
                    if self.use_past:
                           a_							,		a_					:		Optional[int]							=  self.num_layers
                           for i in range(SCREAMING_SNAKE_CASE__     ):
                                  a_					:		Optional[Any]							=  {0: 'batch', 2: 'past_sequence + sequence'}
                                  a_					:		List[Any]							=  {0: 'batch', 2: 'past_sequence + sequence'}
             return common_outputs
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       PreTrainedTokenizer     ,							SCREAMING_SNAKE_CASE__				:       int = -1     ,							SCREAMING_SNAKE_CASE__				:       int = -1     ,							SCREAMING_SNAKE_CASE__				:       bool = False     ,							SCREAMING_SNAKE_CASE__				:       Optional[TensorType] = None     ,							)		->			Mapping[str, Any]:
             a_					:		Optional[Any]							=  self._generate_dummy_inputs_for_encoder_and_decoder(
                 SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )

             # Generate decoder inputs
             a_					:		Tuple							=  seq_length if not self.use_past else 1
             a_					:		Union[str, Any]							=  self._generate_dummy_inputs_for_encoder_and_decoder(
                 SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
             a_					:		Optional[int]							=  {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
             a_					:		Tuple							=  dict(**SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )

             if self.use_past:
                    if not is_torch_available():
                           raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'     )
                    else:
                           import torch
                    a_							,		a_					:		int							=  common_inputs['input_ids'].shape
                    a_					:		Any							=  common_inputs['decoder_input_ids'].shape[1]
                    a_							,		a_					:		List[str]							=  self.num_attention_heads
                    a_					:		List[Any]							=  (
                        batch,
                        num_encoder_attention_heads,
                        encoder_seq_length,
                        self._config.hidden_size // num_encoder_attention_heads,
                    )
                    a_					:		Tuple							=  decoder_seq_length + 3
                    a_					:		Tuple							=  (
                        batch,
                        num_decoder_attention_heads,
                        decoder_past_length,
                        self._config.hidden_size // num_decoder_attention_heads,
                    )

                    a_					:		Optional[Any]							=  torch.cat(
                        [common_inputs['decoder_attention_mask'], torch.ones(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )]     ,							dim=1     )

                    a_					:		str							=  []
                    # If the number of encoder and decoder layers are present in the model configuration, both are considered
                    a_							,		a_					:		Tuple							=  self.num_layers
                    a_					:		List[Any]							=  min(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
                    a_					:		Optional[int]							=  max(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ) - min_num_layers
                    a_					:		List[Any]							=  'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

                    for _ in range(SCREAMING_SNAKE_CASE__     ):
                           common_inputs["past_key_values"].append(
                               (
                                   torch.zeros(SCREAMING_SNAKE_CASE__     ),
                                   torch.zeros(SCREAMING_SNAKE_CASE__     ),
                                   torch.zeros(SCREAMING_SNAKE_CASE__     ),
                                   torch.zeros(SCREAMING_SNAKE_CASE__     ),
                               )     )
                    # TODO: test this.
                    a_					:		List[Any]							=  encoder_shape if remaining_side_name == 'encoder' else decoder_shape
                    for _ in range(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ):
                           common_inputs["past_key_values"].append((torch.zeros(SCREAMING_SNAKE_CASE__     ), torch.zeros(SCREAMING_SNAKE_CASE__     ))     )
             return common_inputs
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       PreTrainedTokenizer     ,							SCREAMING_SNAKE_CASE__				:       int = -1     ,							SCREAMING_SNAKE_CASE__				:       int = -1     ,							SCREAMING_SNAKE_CASE__				:       bool = False     ,							SCREAMING_SNAKE_CASE__				:       Optional[TensorType] = None     ,							)		->			Mapping[str, Any]:
             a_					:		int							=  self._generate_dummy_inputs_for_encoder_and_decoder(
                 SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )

             if self.use_past:
                    if not is_torch_available():
                           raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'     )
                    else:
                           import torch
                    a_							,		a_					:		Tuple							=  common_inputs['input_ids'].shape
                    # Not using the same length for past_key_values
                    a_					:		List[Any]							=  seqlen + 2
                    a_							,		a_					:		Optional[int]							=  self.num_layers
                    a_							,		a_					:		str							=  self.num_attention_heads
                    a_					:		List[Any]							=  (
                        batch,
                        num_encoder_attention_heads,
                        past_key_values_length,
                        self._config.hidden_size // num_encoder_attention_heads,
                    )

                    a_					:		Tuple							=  common_inputs['attention_mask'].dtype
                    a_					:		Dict							=  torch.cat(
                        [common_inputs['attention_mask'], torch.ones(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							dtype=SCREAMING_SNAKE_CASE__     )]     ,							dim=1     )
                    a_					:		Dict							=  [
                        (torch.zeros(SCREAMING_SNAKE_CASE__     ), torch.zeros(SCREAMING_SNAKE_CASE__     )) for _ in range(SCREAMING_SNAKE_CASE__     )
                    ]
             return common_inputs
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       PreTrainedTokenizer     ,							SCREAMING_SNAKE_CASE__				:       int = -1     ,							SCREAMING_SNAKE_CASE__				:       int = -1     ,							SCREAMING_SNAKE_CASE__				:       bool = False     ,							SCREAMING_SNAKE_CASE__				:       Optional[TensorType] = None     ,							)		->			Mapping[str, Any]:
             # Copied from OnnxConfig.generate_dummy_inputs
             # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
             # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
             a_					:		Dict							=  compute_effective_axis_dimension(
                 SCREAMING_SNAKE_CASE__     ,							fixed_dimension=OnnxConfig.default_fixed_batch     ,							num_token_to_add=0     )

             # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
             a_					:		str							=  tokenizer.num_special_tokens_to_add(SCREAMING_SNAKE_CASE__     )
             a_					:		Union[str, Any]							=  compute_effective_axis_dimension(
                 SCREAMING_SNAKE_CASE__     ,							fixed_dimension=OnnxConfig.default_fixed_sequence     ,							num_token_to_add=SCREAMING_SNAKE_CASE__     )

             # Generate dummy inputs according to compute batch and sequence
             a_					:		Optional[Any]							=  [' '.join([tokenizer.unk_token]     ) * seq_length] * batch_size
             a_					:		Dict							=  dict(tokenizer(SCREAMING_SNAKE_CASE__     ,							return_tensors=SCREAMING_SNAKE_CASE__     )     )
             return common_inputs
      def        SCREAMING_SNAKE_CASE							(       self				:       str     ,							SCREAMING_SNAKE_CASE__				:       PreTrainedTokenizer     ,							SCREAMING_SNAKE_CASE__				:       int = -1     ,							SCREAMING_SNAKE_CASE__				:       int = -1     ,							SCREAMING_SNAKE_CASE__				:       bool = False     ,							SCREAMING_SNAKE_CASE__				:       Optional[TensorType] = None     ,							)		->			Mapping[str, Any]:
             if self.task in ["default", "seq2seq-lm"]:
                    a_					:		Tuple							=  self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                        SCREAMING_SNAKE_CASE__     ,							batch_size=SCREAMING_SNAKE_CASE__     ,							seq_length=SCREAMING_SNAKE_CASE__     ,							is_pair=SCREAMING_SNAKE_CASE__     ,							framework=SCREAMING_SNAKE_CASE__     )

             else:
                    a_					:		Dict							=  self._generate_dummy_inputs_for_causal_lm(
                        SCREAMING_SNAKE_CASE__     ,							batch_size=SCREAMING_SNAKE_CASE__     ,							seq_length=SCREAMING_SNAKE_CASE__     ,							is_pair=SCREAMING_SNAKE_CASE__     ,							framework=SCREAMING_SNAKE_CASE__     )

             return common_inputs
      def        SCREAMING_SNAKE_CASE							(       self				:       int     ,							SCREAMING_SNAKE_CASE__				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       Dict     )		->			Tuple:
             if self.task in ["default", "seq2seq-lm"]:
                    a_					:		Dict							=  super()._flatten_past_key_values_(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
             else:
                    a_					:		Optional[int]							=  super(SCREAMING_SNAKE_CASE__     ,							self     )._flatten_past_key_values_(
                        SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )

      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       int     )		->			float:
             return 1E-4






from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments







def        SCREAMING_SNAKE_CASE_						(			)	->		Any:

       """simple docstring"""

       a_					:		Optional[Any]							=  HfArgumentParser(__A				)
       a_					:		Optional[int]							=  parser.parse_args_into_dataclasses()[0]
       a_					:		List[Any]							=  TensorFlowBenchmark(args=__A				)
       try:
              a_					:		List[str]							=  parser.parse_args_into_dataclasses()[0]
       except ValueError as e:
              a_					:		Dict							=  'Arg --no_{0} is no longer used, please use --no-{0} instead.'
              a_					:		Dict							=  ' '.join(str(__A				).split(' '				)[:-1]				)
              a_					:		int							=  ''
              a_					:		int							=  eval(str(__A				).split(' '				)[-1]				)
              a_					:		Any							=  []
              for arg in depreciated_args:
                     # arg[2:] removes '--'
                     if arg[2:] in TensorFlowBenchmark.deprecated_args:
                            # arg[5:] removes '--no_'
                            full_error_msg += arg_error_msg.format(arg[5:]				)
                     else:
                            wrong_args.append(__A				)
              if len(__A				) > 0:
                     a_					:		str							=  full_error_msg + begin_error_msg + str(__A				)
              raise ValueError(__A				)
       benchmark.run()


if __name__ == "__main__":
   main()


