





from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_clip import CLIPTokenizer


__lowercase													=			logging.get_logger(__name__)

__lowercase													=			{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

__lowercase													=			{
    """vocab_file""": {
        """openai/clip-vit-base-patch32""": """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """openai/clip-vit-base-patch32""": """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """openai/clip-vit-base-patch32""": (
            """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json"""
        ),
    },
}

__lowercase													=			{
    """openai/clip-vit-base-patch32""": 77,
}

class   __snake_case				(			_a   ):






    '''simple docstring'''
    _lowerCAmelCase     =	VOCAB_FILES_NAMES
    _lowerCAmelCase     =	PRETRAINED_VOCAB_FILES_MAP
    _lowerCAmelCase     =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _lowerCAmelCase     =	["""input_ids""", """attention_mask"""]
    _lowerCAmelCase     =	CLIPTokenizer





    def __init__(   self						:		str			,						snake_case						:		int=None			,						snake_case						:		int=None			,						snake_case						:		Tuple=None			,						snake_case						:		int="<|endoftext|>"			,						snake_case						:		Optional[int]="<|startoftext|>"			,						snake_case						:		Dict="<|endoftext|>"			,						snake_case						:		Optional[int]="<|endoftext|>"			,						**snake_case						:		Optional[int]			,						):

      """simple docstring"""





      super().__init__(
          snake_case			,						snake_case			,						tokenizer_file=snake_case			,						unk_token=snake_case			,						bos_token=snake_case			,						eos_token=snake_case			,						pad_token=snake_case			,						**snake_case			,						)

      if not isinstance(self.backend_tokenizer.pre_tokenizer			,						pre_tokenizers.Sequence							):
        raise ValueError(
            '''The `backend_tokenizer` provided does not match the expected format. The CLIP tokenizer has been'''
            ''' heavily modified from transformers version 4.17.0. You need to convert the tokenizer you are using'''
            ''' to be compatible with this version.The easiest way to do so is'''
            ''' `CLIPTokenizerFast.from_pretrained("path_to_local_folder_or_hub_repo, from_slow=True)`. If you want'''
            ''' to use your existing tokenizer, you will have to revert to a version prior to 4.17.0 of'''
            ''' transformers.'''							)

      self._wrap_decode_method_backend_tokenizer()





    def 	A		(   self						:		Any							):

      """simple docstring"""





      __lowerCAmelCase			=				self.backend_tokenizer.decode

      def new_decode_method(*snake_case						:		Optional[Any]			,						**snake_case						:		Dict							):
        __lowerCAmelCase			=				orig_decode_method(*snake_case			,						**snake_case							)
        __lowerCAmelCase			=				text.replace(self.backend_tokenizer.model.end_of_word_suffix			,						''' '''							).strip()
        return text

      __lowerCAmelCase			=				new_decode_method





    def 	A		(   self						:		Dict			,						snake_case						:		List[int]			,						snake_case						:		Optional[List[int]] = None							):

      """simple docstring"""





      __lowerCAmelCase			=				[self.bos_token_id]
      __lowerCAmelCase			=				[self.eos_token_id]

      if token_ids_a is None:
        return bos_token + token_ids_a + eos_token
      return bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token





    def 	A		(   self						:		Optional[int]			,						snake_case						:		List[int]			,						snake_case						:		Optional[List[int]] = None							):

      """simple docstring"""





      __lowerCAmelCase			=				[self.bos_token_id]
      __lowerCAmelCase			=				[self.eos_token_id]

      if token_ids_a is None:
        return len(bos_token + token_ids_a + eos_token							) * [0]
      return len(bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token							) * [0]





    def 	A		(   self						:		List[str]			,						snake_case						:		str			,						snake_case						:		Optional[str] = None							):

      """simple docstring"""





      __lowerCAmelCase			=				self._tokenizer.model.save(snake_case			,						name=snake_case							)
      return tuple(snake_case							)







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							)
