





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowercase													=			{
    """configuration_wav2vec2_conformer""": [
        """WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Wav2Vec2ConformerConfig""",
    ],
}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowercase													=			[
          """WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """Wav2Vec2ConformerForAudioFrameClassification""",
          """Wav2Vec2ConformerForCTC""",
          """Wav2Vec2ConformerForPreTraining""",
          """Wav2Vec2ConformerForSequenceClassification""",
          """Wav2Vec2ConformerForXVector""",
          """Wav2Vec2ConformerModel""",
          """Wav2Vec2ConformerPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_wavaveca_conformer import (
          WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
          WavaVecaConformerConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_wavaveca_conformer import (
                WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                WavaVecaConformerForAudioFrameClassification,
                WavaVecaConformerForCTC,
                WavaVecaConformerForPreTraining,
                WavaVecaConformerForSequenceClassification,
                WavaVecaConformerForXVector,
                WavaVecaConformerModel,
                WavaVecaConformerPreTrainedModel,
            )

else:
      import sys

      __lowercase													=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)











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							)
