

'''simple docstring'''

import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader




def 				UpperCamelCase(					UpperCAmelCase_     ):
 UpperCAmelCase	:     Tuple						 =			np.inf

 def set_batch_size(UpperCAmelCase_     ) -> None:
  nonlocal batch_size
  if isinstance(UpperCAmelCase_  ,	UpperCAmelCase_     ):
   UpperCAmelCase	:     Union[str, Any]						 =			min(UpperCAmelCase_  ,	config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS     )
  elif isinstance(UpperCAmelCase_  ,	UpperCAmelCase_     ):
   UpperCAmelCase	:     Tuple						 =			min(UpperCAmelCase_  ,	config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS     )
  elif isinstance(UpperCAmelCase_  ,	UpperCAmelCase_     ) and feature.dtype == "binary":
   UpperCAmelCase	:     Optional[int]						 =			min(UpperCAmelCase_  ,	config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS     )

 _visit(UpperCAmelCase_  ,	UpperCAmelCase_     )

 return None if batch_size is np.inf else batch_size




class 	A_			(    _snake_case							):






     '''simple docstring'''



     def __init__(		self			:					List[Any]					,      lowercase_			:					NestedDataStructureLike[PathLike]					,      lowercase_			:					Optional[NamedSplit] = None					,      lowercase_			:					Optional[Features] = None					,      lowercase_			:					str = None					,      lowercase_			:					bool = False					,      lowercase_			:					bool = False					,      lowercase_			:					Optional[int] = None					,      **lowercase_			:					Dict					,      )						->     List[str]:
      super().__init__(
          lowercase_					,      split=lowercase_					,      features=lowercase_					,      cache_dir=lowercase_					,      keep_in_memory=lowercase_					,      streaming=lowercase_					,      num_proc=lowercase_					,      **lowercase_					,      )
      UpperCAmelCase	:     Optional[Any]						 =			path_or_paths if isinstance(lowercase_					,      lowercase_	) else {self.split: path_or_paths}
      UpperCAmelCase	:     Tuple						 =			_PACKAGED_DATASETS_MODULES['parquet'][1]
      UpperCAmelCase	:     str						 =			Parquet(
          cache_dir=lowercase_					,      data_files=lowercase_					,      features=lowercase_					,      hash=lowercase_					,      **lowercase_					,      )




     def        UpperCAmelCase_	(		self			:					Any	)						->     Optional[int]:
      # Build iterable dataset
      if self.streaming:
       UpperCAmelCase	:     int						 =			self.builder.as_streaming_dataset(split=self.split	)
      # Build regular (map-style) dataset
      else:
       UpperCAmelCase	:     List[str]						 =			None
       UpperCAmelCase	:     List[Any]						 =			None
       UpperCAmelCase	:     Union[str, Any]						 =			None
       UpperCAmelCase	:     Optional[int]						 =			None

       self.builder.download_and_prepare(
           download_config=lowercase_					,      download_mode=lowercase_					,      verification_mode=lowercase_					,      base_path=lowercase_					,      num_proc=self.num_proc					,      )
       UpperCAmelCase	:     Optional[Any]						 =			self.builder.as_dataset(
           split=self.split					,      verification_mode=lowercase_					,      in_memory=self.keep_in_memory	)
      return dataset




class 	A_			:






     '''simple docstring'''



     def __init__(		self			:					Dict					,      lowercase_			:					Dataset					,      lowercase_			:					Union[PathLike, BinaryIO]					,      lowercase_			:					Optional[int] = None					,      **lowercase_			:					List[Any]					,      )						->     Dict:
      UpperCAmelCase	:     List[str]						 =			dataset
      UpperCAmelCase	:     Tuple						 =			path_or_buf
      UpperCAmelCase	:     List[Any]						 =			batch_size or get_writer_batch_size(dataset.features	)
      UpperCAmelCase	:     Tuple						 =			parquet_writer_kwargs


     def        UpperCAmelCase_	(		self			:					Any	)						->     int:
      UpperCAmelCase	:     int						 =			self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

      if isinstance(self.path_or_buf					,      (str, bytes, os.PathLike)	):
       with open(self.path_or_buf					,      'wb+'	) as buffer:
        UpperCAmelCase	:     List[str]						 =			self._write(file_obj=lowercase_					,      batch_size=lowercase_					,      **self.parquet_writer_kwargs	)
      else:
       UpperCAmelCase	:     Tuple						 =			self._write(file_obj=self.path_or_buf					,      batch_size=lowercase_					,      **self.parquet_writer_kwargs	)
      return written




     def        UpperCAmelCase_	(		self			:					List[Any]					,      lowercase_			:					BinaryIO					,      lowercase_			:					int					,      **lowercase_			:					List[Any]	)						->     int:
      UpperCAmelCase	:     Tuple						 =			0
      UpperCAmelCase	:     Tuple						 =			parquet_writer_kwargs.pop('path_or_buf'					,      lowercase_	)
      UpperCAmelCase	:     int						 =			self.dataset.features.arrow_schema

      UpperCAmelCase	:     int						 =			pq.ParquetWriter(lowercase_					,      schema=lowercase_					,      **lowercase_	)

      for offset in logging.tqdm(
          range(0					,      len(self.dataset	)					,      lowercase_	)					,      unit='ba'					,      disable=not logging.is_progress_bar_enabled()					,      desc='Creating parquet from Arrow format'					,      ):
       UpperCAmelCase	:     Any						 =			query_table(
           table=self.dataset._data					,      key=slice(lowercase_					,      offset + batch_size	)					,      indices=self.dataset._indices if self.dataset._indices is not None else None					,      )
       writer.write_table(lowercase_	)
       written += batch.nbytes
      writer.close()
      return written







'''simple docstring'''

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


lowercase__         =      logging.get_logger(__name__)

lowercase__         =      {
    "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 	A_			(    _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:     Optional[int]  		=  """perceiver"""


     def __init__(		self			:					str					,      lowercase_			:					List[str]=256					,      lowercase_			:					List[str]=1_280					,      lowercase_			:					str=768					,      lowercase_			:					Tuple=1					,      lowercase_			:					str=26					,      lowercase_			:					List[Any]=8					,      lowercase_			:					int=8					,      lowercase_			:					List[str]=None					,      lowercase_			:					Dict=None					,      lowercase_			:					int="kv"					,      lowercase_			:					Union[str, Any]=1					,      lowercase_			:					List[str]=1					,      lowercase_			:					Any="gelu"					,      lowercase_			:					Optional[Any]=0.1					,      lowercase_			:					str=0.02					,      lowercase_			:					Optional[Any]=1E-12					,      lowercase_			:					Any=True					,      lowercase_			:					Union[str, Any]=262					,      lowercase_			:					Union[str, Any]=2_048					,      lowercase_			:					Optional[int]=56					,      lowercase_			:					int=[368, 496]					,      lowercase_			:					str=16					,      lowercase_			:					Optional[int]=1_920					,      lowercase_			:					Tuple=16					,      lowercase_			:					int=[1, 16, 224, 224]					,      **lowercase_			:					Union[str, Any]					,      )						->     List[str]:
      super().__init__(**lowercase_	)

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




class 	A_			(    _snake_case							):






     '''simple docstring'''



     @property
     def        UpperCAmelCase_	(		self			:					Union[str, Any]	)						->     Mapping[str, Mapping[int, str]]:
      if self.task == "multiple-choice":
       UpperCAmelCase	:     Tuple						 =			{0: 'batch', 1: 'choice', 2: 'sequence'}
      else:
       UpperCAmelCase	:     Tuple						 =			{0: 'batch', 1: 'sequence'}
      return OrderedDict(
          [
              ('inputs', dynamic_axis),
              ('attention_mask', dynamic_axis),
          ]	)


     @property
     def        UpperCAmelCase_	(		self			:					str	)						->     float:
      return 1E-4




     def        UpperCAmelCase_	(		self			:					str					,      lowercase_			:					Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]					,      lowercase_			:					int = -1					,      lowercase_			:					int = -1					,      lowercase_			:					int = -1					,      lowercase_			:					bool = False					,      lowercase_			:					Optional[TensorType] = None					,      lowercase_			:					int = 3					,      lowercase_			:					int = 40					,      lowercase_			:					int = 40					,      )						->     Mapping[str, Any]:
      # copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

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




