




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

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


lowerCamelCase       :       Dict        =    logging.get_logger(__name__)

lowerCamelCase       :       int        =    {
    '''deepmind/language-perceiver''': '''https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json''',
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}




class 						__lowercase		(lowerCAmelCase_							):




      """simple docstring"""





      _snake_case							     =						"""perceiver"""

      def __init__(      self				,      A=2_5_6				,      A=1_2_8_0				,      A=7_6_8				,      A=1				,      A=2_6				,      A=8				,      A=8				,      A=None				,      A=None				,      A="kv"				,      A=1				,      A=1				,      A="gelu"				,      A=0.1				,      A=0.02				,      A=1e-1_2				,      A=True				,      A=2_6_2				,      A=2_0_4_8				,      A=5_6				,      A=[3_6_8, 4_9_6]				,      A=1_6				,      A=1_9_2_0				,      A=1_6				,      A=[1, 1_6, 2_2_4, 2_2_4]				,      **A				,      )		->				str:
         super().__init__(**snake_case_	)

         snake_case      :				Dict				=       num_latents
         snake_case      :				Tuple				=       d_latents
         snake_case      :				Any				=       d_model
         snake_case      :				List[str]				=       num_blocks
         snake_case      :				str				=       num_self_attends_per_block
         snake_case      :				int				=       num_self_attention_heads
         snake_case      :				Any				=       num_cross_attention_heads
         snake_case      :				Any				=       qk_channels
         snake_case      :				List[Any]				=       v_channels
         snake_case      :				int				=       cross_attention_shape_for_attention
         snake_case      :				Any				=       self_attention_widening_factor
         snake_case      :				Union[str, Any]				=       cross_attention_widening_factor
         snake_case      :				List[str]				=       hidden_act
         snake_case      :				Optional[Any]				=       attention_probs_dropout_prob
         snake_case      :				List[Any]				=       initializer_range
         snake_case      :				Union[str, Any]				=       layer_norm_eps
         snake_case      :				List[str]				=       use_query_residual
         # masked language modeling attributes
         snake_case      :				Optional[int]				=       vocab_size
         snake_case      :				Dict				=       max_position_embeddings
         # image classification attributes
         snake_case      :				List[Any]				=       image_size
         # flow attributes
         snake_case      :				int				=       train_size
         # multimodal autoencoding attributes
         snake_case      :				Union[str, Any]				=       num_frames
         snake_case      :				List[Any]				=       audio_samples_per_frame
         snake_case      :				Optional[int]				=       samples_per_patch
         snake_case      :				Dict				=       output_shape




class 						__lowercase		(lowerCAmelCase_							):




      """simple docstring"""





      @property
      def 	UpperCAmelCase      (      self	)		->				List[str]:
         if self.task == "multiple-choice":
            snake_case      :				str				=       {0: """batch""", 1: """choice""", 2: """sequence"""}
         else:
            snake_case      :				List[str]				=       {0: """batch""", 1: """sequence"""}
         return OrderedDict(
             [
                 ("""inputs""", dynamic_axis),
                 ("""attention_mask""", dynamic_axis),
             ]	)

      @property
      def 	UpperCAmelCase      (      self	)		->				List[str]:
         return 1e-4

      def 	UpperCAmelCase      (      self				,      A				,      A = -1				,      A = -1				,      A = -1				,      A = False				,      A = None				,      A = 3				,      A = 4_0				,      A = 4_0				,      )		->				int:
         # copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

         if isinstance(snake_case_				,      snake_case_	):
            # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
            snake_case      :				List[str]				=       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
            snake_case      :				Any				=       preprocessor.num_special_tokens_to_add(snake_case_	)
            snake_case      :				int				=       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
            snake_case      :				List[str]				=       [""" """.join(["""a"""]	) * seq_length] * batch_size
            snake_case      :				Tuple				=       dict(preprocessor(snake_case_				,      return_tensors=snake_case_	)	)
            snake_case      :				int				=       inputs.pop("""input_ids"""	)
            return inputs
         elif isinstance(snake_case_				,      snake_case_	) and preprocessor.model_input_names[0] == "pixel_values":
            # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
            snake_case      :				Any				=       compute_effective_axis_dimension(snake_case_				,      fixed_dimension=OnnxConfig.default_fixed_batch	)
            snake_case      :				Dict				=       self._generate_dummy_images(snake_case_				,      snake_case_				,      snake_case_				,      snake_case_	)
            snake_case      :				List[Any]				=       dict(preprocessor(images=snake_case_				,      return_tensors=snake_case_	)	)
            snake_case      :				str				=       inputs.pop("""pixel_values"""	)
            return inputs
         else:
            raise ValueError(
                """Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor."""	)












'''simple docstring'''




import re

from filelock import FileLock


try:
  import nltk

  __SCREAMING_SNAKE_CASE     :Optional[int]       =				True
except (ImportError, ModuleNotFoundError):
  __SCREAMING_SNAKE_CASE     :str       =				False

if NLTK_AVAILABLE:
  with FileLock('''.lock''') as lock:
    nltk.download('''punkt''', quiet=True)
def 	UpperCAmelCase_							(					__lowercase							:      str       )		->       str:


     '''simple docstring'''



     re.sub("<n>"			,     ""			,     __lowercase       )  # remove pegasus newline char
     assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
     return "\n".join(nltk.sent_tokenize(__lowercase       )       )




