





import argparse
import os
from functools import reduce

import fairseq
import torch
from datasets import load_dataset

from transformers import WavaVecaProcessor, logging
from transformers.models.dataavec.configuration_dataavec_audio import DataaVecAudioConfig

# Copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_audio.py
from transformers.models.dataavec.dataavec_audio import DataaVecAudioModel as Dummy  # noqa: F401
from transformers.models.dataavec.modeling_dataavec_audio import DataaVecAudioForCTC, DataaVecAudioModel


logging.set_verbosity_info()
__lowercase													=			logging.get_logger(__name__)

__lowercase													=			{
    """post_extract_proj""": """feature_projection.projection""",
    """models.0.layer_norm""": """feature_projection.layer_norm""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}
__lowercase													=			[
    """lm_head""",
]


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		Any     ,							__lowercase:		List[str]     ,							__lowercase:		List[Any]     ,							__lowercase:		Union[str, Any]     ,							__lowercase:		Optional[Any]):



  '''simple docstring'''






  for attribute in key.split('''.'''):
    __lowerCAmelCase			=				getattr(__lowercase     ,							__lowercase)

  if weight_type is not None:
    __lowerCAmelCase			=				getattr(__lowercase     ,							__lowercase).shape
  else:
    __lowerCAmelCase			=				hf_pointer.shape

  if hf_shape != value.shape:
    raise ValueError(
        F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
        F''' {value.shape} for {full_name}''')

  if weight_type == "weight":
    __lowerCAmelCase			=				value
  elif weight_type == "weight_g":
    __lowerCAmelCase			=				value
  elif weight_type == "weight_v":
    __lowerCAmelCase			=				value
  elif weight_type == "bias":
    __lowerCAmelCase			=				value
  else:
    __lowerCAmelCase			=				value

  logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.''')


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		str     ,							__lowercase:		str     ,							__lowercase:		List[str]):



  '''simple docstring'''






  __lowerCAmelCase			=				[]
  __lowerCAmelCase			=				fairseq_model.state_dict()

  if not is_headless:
    __lowerCAmelCase			=				hf_model.dataavec_audio.feature_extractor
    __lowerCAmelCase			=				hf_model.dataavec_audio.encoder.pos_conv_embed

  else:
    __lowerCAmelCase			=				hf_model.feature_extractor
    __lowerCAmelCase			=				hf_model.encoder.pos_conv_embed

  for name, value in fairseq_dict.items():
    __lowerCAmelCase			=				False
    if "conv_layers" in name:
      load_conv_layer(
          __lowercase     ,							__lowercase     ,							__lowercase     ,							__lowercase     ,							)
      __lowerCAmelCase			=				True
    elif "pos_conv" in name:
      load_pos_conv_layer(
          __lowercase     ,							__lowercase     ,							__lowercase     ,							__lowercase     ,							)
      __lowerCAmelCase			=				True
    else:
      for key, mapped_key in MAPPING.items():
        if not is_headless:
          __lowerCAmelCase			=				'''data2vec_audio.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
        if key in name or key.split('''w2v_model.''')[-1] == name.split('''.''')[0]:
          __lowerCAmelCase			=				True
          if "*" in mapped_key:
            __lowerCAmelCase			=				name.split(__lowercase)[0].split('''.''')[-2]
            __lowerCAmelCase			=				mapped_key.replace('''*'''     ,							__lowercase)
          if "weight_g" in name:
            __lowerCAmelCase			=				'''weight_g'''
          elif "weight_v" in name:
            __lowerCAmelCase			=				'''weight_v'''
          elif "bias" in name:
            __lowerCAmelCase			=				'''bias'''
          elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            __lowerCAmelCase			=				'''weight'''
          else:
            __lowerCAmelCase			=				None
          set_recursively(__lowercase     ,							__lowercase     ,							__lowercase     ,							__lowercase     ,							__lowercase)
        continue
    if not is_used:
      unused_weights.append(__lowercase)

  logger.warning(F'''Unused weights: {unused_weights}''')


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		int     ,							__lowercase:		Optional[int]):



  '''simple docstring'''






  __lowerCAmelCase			=				path.split('''.''')
  return reduce(__lowercase     ,							__lowercase     ,							__lowercase)


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		Union[str, Any]     ,							__lowercase:		List[str]     ,							__lowercase:		Optional[Any]     ,							__lowercase:		Optional[int]):



  '''simple docstring'''






  __lowerCAmelCase			=				access_by_string(__lowercase     ,							__lowercase)
  __lowerCAmelCase			=				hf_weight.data

  if fsq_value.shape != hf_value.shape:
    raise ValueError(F'''{full_name} has size {fsq_value.shape}, but {hf_value.shape} was found.''')
  __lowerCAmelCase			=				fsq_value
  logger.info(F'''{full_name} was correctly initialized from {hf_weight_path}.''')


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		Union[str, Any]     ,							__lowercase:		Optional[Any]     ,							__lowercase:		Dict     ,							__lowercase:		Union[str, Any]):



  '''simple docstring'''






  __lowerCAmelCase			=				full_name.split('''conv_layers.''')[-1]
  __lowerCAmelCase			=				name.split('''.''')
  __lowerCAmelCase			=				int(items[0])
  __lowerCAmelCase			=				int(items[1])

  __lowerCAmelCase			=				name.split('''.''')[-1]
  if type_id == 0:
    __lowerCAmelCase			=				'''conv'''
  elif type_id == 2:
    __lowerCAmelCase			=				'''layer_norm'''
  else:
    unused_weights.append(__lowercase)
    return

  set_weights(__lowercase     ,							__lowercase     ,							__lowercase     ,							F'''conv_layers.{layer_id}.{layer_type}.{weight_type}''')


def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		int     ,							__lowercase:		List[Any]     ,							__lowercase:		List[Any]     ,							__lowercase:		Union[str, Any]):



  '''simple docstring'''






  __lowerCAmelCase			=				full_name.split('''pos_conv.''')[-1]
  __lowerCAmelCase			=				name.split('''.''')
  __lowerCAmelCase			=				int(items[0])
  __lowerCAmelCase			=				int(items[1])

  __lowerCAmelCase			=				name.split('''.''')[-1]
  if type_id != 0:
    unused_weights.append(__lowercase)
    return
  else:
    __lowerCAmelCase			=				'''conv'''

  set_weights(__lowercase     ,							__lowercase     ,							__lowercase     ,							F'''layers.{layer_id}.{layer_type}.{weight_type}''')


@torch.no_grad()
def 					__SCREAMING_SNAKE_CASE  (							__lowercase:		Optional[Any]     ,							__lowercase:		Optional[Any]     ,							__lowercase:		List[Any]=None     ,							__lowercase:		List[Any]=None     ,							__lowercase:		List[Any]=True):



  '''simple docstring'''






  if config_path is not None:
    __lowerCAmelCase			=				DataaVecAudioConfig.from_pretrained(__lowercase)
  else:
    __lowerCAmelCase			=				DataaVecAudioConfig()

  if not is_finetuned:
    # Modify final_proj layer name
    __lowerCAmelCase			=				DataaVecAudioModel(__lowercase)
    __lowerCAmelCase			=				os.path.dirname(__lowercase)

    __lowerCAmelCase			=				torch.load(__lowercase)
    __lowerCAmelCase			=				state_dict['''model'''].pop('''final_proj.0.weight''')
    __lowerCAmelCase			=				state_dict['''model'''].pop('''final_proj.0.bias''')
    __lowerCAmelCase			=				os.path.join(__lowercase     ,							'''converted.pt''')
    torch.save(__lowercase     ,							__lowercase)
  else:
    __lowerCAmelCase			=				DataaVecAudioForCTC(__lowercase)
    __lowerCAmelCase			=				checkpoint_path

  def load_dataavec(__lowercase:		Optional[Any]):
    __lowerCAmelCase	,						__lowerCAmelCase	,						__lowerCAmelCase			=				fairseq.checkpoint_utils.load_model_ensemble_and_task([path])
    return model[0].eval()

  __lowerCAmelCase			=				load_dataavec(__lowercase)

  recursively_load_weights(__lowercase     ,							__lowercase     ,							not is_finetuned)

  __lowerCAmelCase			=				WavaVecaProcessor.from_pretrained('''facebook/wav2vec2-large-lv60''')

  __lowerCAmelCase			=				load_dataset('''patrickvonplaten/librispeech_asr_dummy'''     ,							'''clean'''     ,							split='''validation''')
  __lowerCAmelCase			=				[x['''array'''] for x in ds[:4]['''audio''']]

  __lowerCAmelCase			=				processor(__lowercase     ,							return_tensors='''pt'''     ,							padding=__lowercase)

  __lowerCAmelCase			=				inputs.input_values
  __lowerCAmelCase			=				inputs.attention_mask
  #    input_values = inputs.input_values[:, :-1]
  #    attention_mask = inputs.attention_mask[:, :-1]

  hf_wavavec.eval()
  model.eval()
  if is_finetuned:
    __lowerCAmelCase			=				model(source=__lowercase     ,							padding_mask=(1 - attention_mask)     ,							mask=__lowercase     ,							features_only=__lowercase)[
        '''encoder_out'''
    ].transpose(0     ,							1)
    __lowerCAmelCase			=				hf_wavavec(__lowercase     ,							attention_mask=__lowercase)['''logits''']

    __lowerCAmelCase			=				torch.argmax(__lowercase     ,							dim=-1)
    __lowerCAmelCase			=				processor.batch_decode(__lowercase)

    print(F'''Expected Output: {ds[:4]["text"]}, Pred: {output_string}''')
  else:
    __lowerCAmelCase			=				model(source=__lowercase     ,							padding_mask=(1 - attention_mask)     ,							mask=__lowercase     ,							features_only=__lowercase)[
        '''layer_results'''
    ][-1][0].transpose(0     ,							1)
    __lowerCAmelCase			=				hf_wavavec(__lowercase     ,							attention_mask=__lowercase)['''last_hidden_state''']

  print(our_output.shape     ,							their_output.shape)
  __lowerCAmelCase			=				torch.max(torch.abs(our_output - their_output)).item()
  print(F'''max_absolute_diff = {max_absolute_diff}''')  # ~ 1e-7
  __lowerCAmelCase			=				torch.allclose(__lowercase     ,							__lowercase     ,							atol=1E-3)
  print('''Do both models output the same tensors?'''     ,							'''🔥''' if success else '''💩''')
  if not success:
    raise Exception('''Something went wRoNg''')

  hf_wavavec.save_pretrained(__lowercase)

  if is_finetuned:
    processor.save_pretrained(__lowercase)
  else:
    processor.feature_extractor.save_pretrained(__lowercase)


if __name__ == "__main__":
      __lowercase													=			argparse.ArgumentParser()
      parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
      parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
      parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
      parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
      parser.add_argument(
          """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
      )
      __lowercase													=			parser.parse_args()
      convert_wavaveca_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
      )











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


__lowercase													=			logging.get_logger(__name__)

__lowercase													=			{
    """google/bigbird-pegasus-large-arxiv""": (
        """https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json"""
    ),
    """google/bigbird-pegasus-large-pubmed""": (
        """https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json"""
    ),
    """google/bigbird-pegasus-large-bigpatent""": (
        """https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json"""
    ),
    # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
}

class   __snake_case				(			_a   ):






    '''simple docstring'''
    _lowerCAmelCase     =	"""bigbird_pegasus"""
    _lowerCAmelCase     =	["""past_key_values"""]
    _lowerCAmelCase     =	{
        """num_attention_heads""": """encoder_attention_heads""",
        """hidden_size""": """d_model""",
        """attention_probs_dropout_prob""": """attention_dropout""",
    }





    def __init__(   self						:		Any			,						snake_case						:		Optional[int]=96_103			,						snake_case						:		Tuple=4_096			,						snake_case						:		str=16			,						snake_case						:		Any=4_096			,						snake_case						:		Any=16			,						snake_case						:		Dict=16			,						snake_case						:		str=4_096			,						snake_case						:		Tuple=16			,						snake_case						:		Dict=0.0			,						snake_case						:		str=0.0			,						snake_case						:		Union[str, Any]=True			,						snake_case						:		List[Any]=True			,						snake_case						:		Optional[Any]="gelu_new"			,						snake_case						:		Union[str, Any]=1_024			,						snake_case						:		Union[str, Any]=0.1			,						snake_case						:		int=0.0			,						snake_case						:		Optional[Any]=0.0			,						snake_case						:		str=0.02			,						snake_case						:		int=2			,						snake_case						:		Any=0.0			,						snake_case						:		str=True			,						snake_case						:		Tuple=0			,						snake_case						:		Any=2			,						snake_case						:		int=1			,						snake_case						:		Any="block_sparse"			,						snake_case						:		List[Any]=64			,						snake_case						:		Dict=3			,						snake_case						:		Optional[Any]=False			,						**snake_case						:		Dict			,						):

      """simple docstring"""





      __lowerCAmelCase			=				vocab_size
      __lowerCAmelCase			=				max_position_embeddings
      __lowerCAmelCase			=				d_model
      __lowerCAmelCase			=				encoder_ffn_dim
      __lowerCAmelCase			=				encoder_layers
      __lowerCAmelCase			=				encoder_attention_heads
      __lowerCAmelCase			=				decoder_ffn_dim
      __lowerCAmelCase			=				decoder_layers
      __lowerCAmelCase			=				decoder_attention_heads
      __lowerCAmelCase			=				dropout
      __lowerCAmelCase			=				attention_dropout
      __lowerCAmelCase			=				activation_dropout
      __lowerCAmelCase			=				activation_function
      __lowerCAmelCase			=				init_std
      __lowerCAmelCase			=				encoder_layerdrop
      __lowerCAmelCase			=				decoder_layerdrop
      __lowerCAmelCase			=				classifier_dropout
      __lowerCAmelCase			=				use_cache
      __lowerCAmelCase			=				encoder_layers
      __lowerCAmelCase			=				scale_embedding  # scale factor will be sqrt(d_model) if True

      # extra config
      __lowerCAmelCase			=				attention_type
      __lowerCAmelCase			=				block_size
      __lowerCAmelCase			=				num_random_blocks
      __lowerCAmelCase			=				use_bias

      super().__init__(
          pad_token_id=snake_case			,						bos_token_id=snake_case			,						eos_token_id=snake_case			,						is_encoder_decoder=snake_case			,						decoder_start_token_id=snake_case			,						**snake_case			,						)


class   __snake_case				(			_a   ):






    '''simple docstring'''





    @property
    def 	A		(   self						:		List[str]							):

      """simple docstring"""





      if self.task in ["default", "seq2seq-lm"]:
        __lowerCAmelCase			=				OrderedDict(
            [
                ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
            ]							)

        if self.use_past:
          __lowerCAmelCase			=				{0: '''batch'''}
          __lowerCAmelCase			=				{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
        else:
          __lowerCAmelCase			=				{0: '''batch''', 1: '''decoder_sequence'''}
          __lowerCAmelCase			=				{0: '''batch''', 1: '''decoder_sequence'''}

        if self.use_past:
          self.fill_with_past_key_values_(snake_case			,						direction='''inputs'''							)
      elif self.task == "causal-lm":
        # TODO: figure this case out.
        __lowerCAmelCase			=				OrderedDict(
            [
                ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
            ]							)
        if self.use_past:
          __lowerCAmelCase	,						__lowerCAmelCase			=				self.num_layers
          for i in range(snake_case							):
            __lowerCAmelCase			=				{0: '''batch''', 2: '''past_sequence + sequence'''}
            __lowerCAmelCase			=				{0: '''batch''', 2: '''past_sequence + sequence'''}
      else:
        __lowerCAmelCase			=				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
    def 	A		(   self						:		Optional[int]							):

      """simple docstring"""





      if self.task in ["default", "seq2seq-lm"]:
        __lowerCAmelCase			=				super().outputs
      else:
        __lowerCAmelCase			=				super(snake_case			,						self							).outputs
        if self.use_past:
          __lowerCAmelCase	,						__lowerCAmelCase			=				self.num_layers
          for i in range(snake_case							):
            __lowerCAmelCase			=				{0: '''batch''', 2: '''past_sequence + sequence'''}
            __lowerCAmelCase			=				{0: '''batch''', 2: '''past_sequence + sequence'''}
      return common_outputs





    def 	A		(   self						:		List[Any]			,						snake_case						:		PreTrainedTokenizer			,						snake_case						:		int = -1			,						snake_case						:		int = -1			,						snake_case						:		bool = False			,						snake_case						:		Optional[TensorType] = None			,						):

      """simple docstring"""





      __lowerCAmelCase			=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          snake_case			,						snake_case			,						snake_case			,						snake_case			,						snake_case							)

      # Generate decoder inputs
      __lowerCAmelCase			=				seq_length if not self.use_past else 1
      __lowerCAmelCase			=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          snake_case			,						snake_case			,						snake_case			,						snake_case			,						snake_case							)
      __lowerCAmelCase			=				{f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
      __lowerCAmelCase			=				dict(**snake_case			,						**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
        __lowerCAmelCase	,						__lowerCAmelCase			=				common_inputs['''input_ids'''].shape
        __lowerCAmelCase			=				common_inputs['''decoder_input_ids'''].shape[1]
        __lowerCAmelCase	,						__lowerCAmelCase			=				self.num_attention_heads
        __lowerCAmelCase			=				(
            batch,
            num_encoder_attention_heads,
            encoder_seq_length,
            self._config.hidden_size // num_encoder_attention_heads,
        )
        __lowerCAmelCase			=				decoder_seq_length + 3
        __lowerCAmelCase			=				(
            batch,
            num_decoder_attention_heads,
            decoder_past_length,
            self._config.hidden_size // num_decoder_attention_heads,
        )

        __lowerCAmelCase			=				torch.cat(
            [common_inputs['''decoder_attention_mask'''], torch.ones(snake_case			,						snake_case							)]			,						dim=1							)

        __lowerCAmelCase			=				[]
        # If the number of encoder and decoder layers are present in the model configuration, both are considered
        __lowerCAmelCase	,						__lowerCAmelCase			=				self.num_layers
        __lowerCAmelCase			=				min(snake_case			,						snake_case							)
        __lowerCAmelCase			=				max(snake_case			,						snake_case							) - min_num_layers
        __lowerCAmelCase			=				'''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

        for _ in range(snake_case							):
          common_inputs["past_key_values"].append(
              (
                  torch.zeros(snake_case							),
                  torch.zeros(snake_case							),
                  torch.zeros(snake_case							),
                  torch.zeros(snake_case							),
              )							)
        # TODO: test this.
        __lowerCAmelCase			=				encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
        for _ in range(snake_case			,						snake_case							):
          common_inputs["past_key_values"].append((torch.zeros(snake_case							), torch.zeros(snake_case							))							)
      return common_inputs





    def 	A		(   self						:		Tuple			,						snake_case						:		PreTrainedTokenizer			,						snake_case						:		int = -1			,						snake_case						:		int = -1			,						snake_case						:		bool = False			,						snake_case						:		Optional[TensorType] = None			,						):

      """simple docstring"""





      __lowerCAmelCase			=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          snake_case			,						snake_case			,						snake_case			,						snake_case			,						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
        __lowerCAmelCase	,						__lowerCAmelCase			=				common_inputs['''input_ids'''].shape
        # Not using the same length for past_key_values
        __lowerCAmelCase			=				seqlen + 2
        __lowerCAmelCase	,						__lowerCAmelCase			=				self.num_layers
        __lowerCAmelCase	,						__lowerCAmelCase			=				self.num_attention_heads
        __lowerCAmelCase			=				(
            batch,
            num_encoder_attention_heads,
            past_key_values_length,
            self._config.hidden_size // num_encoder_attention_heads,
        )

        __lowerCAmelCase			=				common_inputs['''attention_mask'''].dtype
        __lowerCAmelCase			=				torch.cat(
            [common_inputs['''attention_mask'''], torch.ones(snake_case			,						snake_case			,						dtype=snake_case							)]			,						dim=1							)
        __lowerCAmelCase			=				[
            (torch.zeros(snake_case							), torch.zeros(snake_case							)) for _ in range(snake_case							)
        ]
      return common_inputs





    def 	A		(   self						:		Optional[Any]			,						snake_case						:		PreTrainedTokenizer			,						snake_case						:		int = -1			,						snake_case						:		int = -1			,						snake_case						:		bool = False			,						snake_case						:		Optional[TensorType] = None			,						):

      """simple docstring"""





      __lowerCAmelCase			=				compute_effective_axis_dimension(
          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
      __lowerCAmelCase			=				tokenizer.num_special_tokens_to_add(snake_case							)
      __lowerCAmelCase			=				compute_effective_axis_dimension(
          snake_case			,						fixed_dimension=OnnxConfig.default_fixed_sequence			,						num_token_to_add=snake_case							)

      # Generate dummy inputs according to compute batch and sequence
      __lowerCAmelCase			=				[''' '''.join([tokenizer.unk_token]							) * seq_length] * batch_size
      __lowerCAmelCase			=				dict(tokenizer(snake_case			,						return_tensors=snake_case							)							)
      return common_inputs





    def 	A		(   self						:		List[str]			,						snake_case						:		PreTrainedTokenizer			,						snake_case						:		int = -1			,						snake_case						:		int = -1			,						snake_case						:		bool = False			,						snake_case						:		Optional[TensorType] = None			,						):

      """simple docstring"""





      if self.task in ["default", "seq2seq-lm"]:
        __lowerCAmelCase			=				self._generate_dummy_inputs_for_default_and_seqaseq_lm(
            snake_case			,						batch_size=snake_case			,						seq_length=snake_case			,						is_pair=snake_case			,						framework=snake_case							)

      elif self.task == "causal-lm":
        __lowerCAmelCase			=				self._generate_dummy_inputs_for_causal_lm(
            snake_case			,						batch_size=snake_case			,						seq_length=snake_case			,						is_pair=snake_case			,						framework=snake_case							)
      else:
        __lowerCAmelCase			=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            snake_case			,						batch_size=snake_case			,						seq_length=snake_case			,						is_pair=snake_case			,						framework=snake_case							)

      return common_inputs





    def 	A		(   self						:		int			,						snake_case						:		Optional[int]			,						snake_case						:		List[Any]			,						snake_case						:		str			,						snake_case						:		Union[str, Any]							):

      """simple docstring"""





      if self.task in ["default", "seq2seq-lm"]:
        __lowerCAmelCase			=				super()._flatten_past_key_values_(snake_case			,						snake_case			,						snake_case			,						snake_case							)
      else:
        __lowerCAmelCase			=				super(snake_case			,						self							)._flatten_past_key_values_(
            snake_case			,						snake_case			,						snake_case			,						snake_case							)
