






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'''


      __snake_case 	=       "perceiver"




      def __init__(    self:	Any						,				a:	Tuple=256						,				a:	Optional[int]=1280						,				a:	List[Any]=768						,				a:	Any=1						,				a:	Dict=26						,				a:	Tuple=8						,				a:	Optional[int]=8						,				a:	List[Any]=None						,				a:	Optional[Any]=None						,				a:	Dict="kv"						,				a:	Optional[Any]=1						,				a:	int=1						,				a:	Union[str, Any]="gelu"						,				a:	Union[str, Any]=0.1						,				a:	Union[str, Any]=0.0_2						,				a:	Dict=1e-12						,				a:	List[Any]=True						,				a:	Optional[Any]=262						,				a:	List[str]=2048						,				a:	str=56						,				a:	Union[str, Any]=[368, 496]						,				a:	Optional[int]=16						,				a:	Optional[int]=1920						,				a:	Optional[int]=16						,				a:	str=[1, 16, 224, 224]						,				**a:	Any						,				):
       super().__init__(**_A    )

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



class 	A_ (	snake_case_   ):




      '''simple docstring'''


      @property
      def 					_snake_case     (    self:	Tuple    ):


       if self.task == "multiple-choice":
        __lowerCamelCase		:							Optional[int]		 =						{0: 'batch', 1: 'choice', 2: 'sequence'}
       else:
        __lowerCamelCase		:							Optional[int]		 =						{0: 'batch', 1: 'sequence'}
       return OrderedDict(
           [
               ('inputs', dynamic_axis),
               ('attention_mask', dynamic_axis),
           ]    )




      @property
      def 					_snake_case     (    self:	Optional[Any]    ):
       return 1e-4




      def 					_snake_case     (    self:	List[Any]						,				a:	Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]						,				a:	int = -1						,				a:	int = -1						,				a:	int = -1						,				a:	bool = False						,				a:	Optional[TensorType] = None						,				a:	int = 3						,				a:	int = 40						,				a:	int = 40						,				):


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





import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


lowercase_	   =     5_0_0_0_0_0

lowercase_   ,lowercase_	   =     os.path.split(__file__)
lowercase_	   =     os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
@get_duration
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Optional[int]		 =						dataset.map(**SCREAMING_SNAKE_CASE__						)
@get_duration
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Tuple		 =						dataset.filter(**SCREAMING_SNAKE_CASE__						)
def 				UpperCamelCase__					(  ):
 __lowerCamelCase		:							str		 =						{'num examples': SPEED_TEST_N_EXAMPLES}
 with tempfile.TemporaryDirectory() as tmp_dir:
  __lowerCamelCase		:							Any		 =						datasets.Features({'text': datasets.Value('string'						), 'numbers': datasets.Value('float32'						)}						)
  __lowerCamelCase		:							Any		 =						generate_example_dataset(
      os.path.join(SCREAMING_SNAKE_CASE__      ,	'dataset.arrow'						)      ,	SCREAMING_SNAKE_CASE__      ,	num_examples=SCREAMING_SNAKE_CASE__						)

  __lowerCamelCase		:							List[Any]		 =						transformers.AutoTokenizer.from_pretrained('bert-base-cased'      ,	use_fast=SCREAMING_SNAKE_CASE__						)

  def tokenize(SCREAMING_SNAKE_CASE__						):
   return tokenizer(examples['text']						)

  __lowerCamelCase		:							str		 =						map(SCREAMING_SNAKE_CASE__						)

  __lowerCamelCase		:							int		 =						map(SCREAMING_SNAKE_CASE__      ,	batched=SCREAMING_SNAKE_CASE__						)

  __lowerCamelCase		:							Optional[int]		 =						map(SCREAMING_SNAKE_CASE__      ,	function=lambda SCREAMING_SNAKE_CASE__						: None      ,	batched=SCREAMING_SNAKE_CASE__						)

  with dataset.formatted_as(type='numpy'						):
   __lowerCamelCase		:							Union[str, Any]		 =						map(SCREAMING_SNAKE_CASE__      ,	function=lambda SCREAMING_SNAKE_CASE__						: None      ,	batched=SCREAMING_SNAKE_CASE__						)

  with dataset.formatted_as(type='pandas'						):
   __lowerCamelCase		:							Any		 =						map(SCREAMING_SNAKE_CASE__      ,	function=lambda SCREAMING_SNAKE_CASE__						: None      ,	batched=SCREAMING_SNAKE_CASE__						)

  with dataset.formatted_as(type='torch'      ,	columns='numbers'						):
   __lowerCamelCase		:							List[str]		 =						map(SCREAMING_SNAKE_CASE__      ,	function=lambda SCREAMING_SNAKE_CASE__						: None      ,	batched=SCREAMING_SNAKE_CASE__						)

  with dataset.formatted_as(type='tensorflow'      ,	columns='numbers'						):
   __lowerCamelCase		:							List[Any]		 =						map(SCREAMING_SNAKE_CASE__      ,	function=lambda SCREAMING_SNAKE_CASE__						: None      ,	batched=SCREAMING_SNAKE_CASE__						)

  __lowerCamelCase		:							Optional[Any]		 =						map(SCREAMING_SNAKE_CASE__      ,	function=SCREAMING_SNAKE_CASE__      ,	batched=SCREAMING_SNAKE_CASE__						)

  __lowerCamelCase		:							Optional[int]		 =						filter(SCREAMING_SNAKE_CASE__						)

  # Activate later when tokenizer support batched inputs
  # with dataset.formatted_as(type='numpy'):
  #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

 with open(SCREAMING_SNAKE_CASE__      ,	'wb'						) as f:
  f.write(json.dumps(SCREAMING_SNAKE_CASE__						).encode('utf-8'						)						)


if __name__ == "__main__":  # useful to run the profiler
       benchmark_map_filter()

