'''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


SCREAMING_SNAKE_CASE_:	Optional[Any]    =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:	List[Any]    ={
    '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 (					UpperCamelCase__      ):
    a__			:		List[Any]			  =    """perceiver"""



    def __init__(self       :       Optional[int]  ,     __a       :       Tuple=256  ,     __a       :       Optional[Any]=1280  ,     __a       :       Optional[int]=768  ,     __a       :       Any=1  ,     __a       :       List[str]=26  ,     __a       :       Dict=8  ,     __a       :       List[Any]=8  ,     __a       :       Tuple=None  ,     __a       :       List[str]=None  ,     __a       :       Optional[int]="kv"  ,     __a       :       Union[str, Any]=1  ,     __a       :       List[str]=1  ,     __a       :       List[Any]="gelu"  ,     __a       :       List[str]=0.1  ,     __a       :       str=0.02  ,     __a       :       List[str]=1E-12  ,     __a       :       Optional[int]=True  ,     __a       :       Tuple=262  ,     __a       :       Dict=2048  ,     __a       :       int=56  ,     __a       :       Optional[int]=[368, 496]  ,     __a       :       Any=16  ,     __a       :       Optional[Any]=1920  ,     __a       :       Any=16  ,     __a       :       str=[1, 16, 224, 224]  ,     **__a       :       Any  ,     ):
         super().__init__(**__a					)

         UpperCAmelCase_			       =  num_latents
         UpperCAmelCase_			       =  d_latents
         UpperCAmelCase_			       =  d_model
         UpperCAmelCase_			       =  num_blocks
         UpperCAmelCase_			       =  num_self_attends_per_block
         UpperCAmelCase_			       =  num_self_attention_heads
         UpperCAmelCase_			       =  num_cross_attention_heads
         UpperCAmelCase_			       =  qk_channels
         UpperCAmelCase_			       =  v_channels
         UpperCAmelCase_			       =  cross_attention_shape_for_attention
         UpperCAmelCase_			       =  self_attention_widening_factor
         UpperCAmelCase_			       =  cross_attention_widening_factor
         UpperCAmelCase_			       =  hidden_act
         UpperCAmelCase_			       =  attention_probs_dropout_prob
         UpperCAmelCase_			       =  initializer_range
         UpperCAmelCase_			       =  layer_norm_eps
         UpperCAmelCase_			       =  use_query_residual
         # masked language modeling attributes
         UpperCAmelCase_			       =  vocab_size
         UpperCAmelCase_			       =  max_position_embeddings
         # image classification attributes
         UpperCAmelCase_			       =  image_size
         # flow attributes
         UpperCAmelCase_			       =  train_size
         # multimodal autoencoding attributes
         UpperCAmelCase_			       =  num_frames
         UpperCAmelCase_			       =  audio_samples_per_frame
         UpperCAmelCase_			       =  samples_per_patch
         UpperCAmelCase_			       =  output_shape




class 			__A (					UpperCamelCase__      ):



    @property
    def    _lowercase		(self       :       Dict					):
         if self.task == "multiple-choice":
              UpperCAmelCase_			       =  {0: "batch", 1: "choice", 2: "sequence"}
         else:
              UpperCAmelCase_			       =  {0: "batch", 1: "sequence"}
         return OrderedDict(
             [
                 ("inputs", dynamic_axis),
                 ("attention_mask", dynamic_axis),
             ]					)



    @property
    def    _lowercase		(self       :       Optional[Any]					):
         return 1E-4






    def    _lowercase		(self       :       Union[str, 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  ,     ):
         # copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

         if isinstance(__a  ,     __a					):
              # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
              UpperCAmelCase_			       =  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
              UpperCAmelCase_			       =  preprocessor.num_special_tokens_to_add(__a					)
              UpperCAmelCase_			       =  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
              UpperCAmelCase_			       =  [" ".join(["a"]					) * seq_length] * batch_size
              UpperCAmelCase_			       =  dict(preprocessor(__a  ,     return_tensors=__a					)					)
              UpperCAmelCase_			       =  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
              UpperCAmelCase_			       =  compute_effective_axis_dimension(__a  ,     fixed_dimension=OnnxConfig.default_fixed_batch					)
              UpperCAmelCase_			       =  self._generate_dummy_images(__a  ,     __a  ,     __a  ,     __a					)
              UpperCAmelCase_			       =  dict(preprocessor(images=__a  ,     return_tensors=__a					)					)
              UpperCAmelCase_			       =  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 inspect
import os
import sys
import unittest

import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu




class 							lowerCamelCase   (  unittest.TestCase ):


	def  A(    self):
							__UpperCAmelCase			:							List[Any]	    =	inspect.getfile(accelerate.test_utils)
							__UpperCAmelCase			:							Dict	    =	os.path.sep.join(mod_file.split(os.path.sep)[:-1] + ['''scripts''', '''test_script.py'''])
							__UpperCAmelCase			:							Union[str, Any]	    =	os.path.sep.join(inspect.getfile(self.__class__).split(os.path.sep)[:-1])


	@require_tpu
	def  A(    self):
							__UpperCAmelCase			:							Dict	    =	F"\n            {self.test_dir}/xla_spawn.py\n            --num_cores 8\n            {self.test_file_path}\n        ".split()
							__UpperCAmelCase			:							Union[str, Any]	    =	[sys.executable] + distributed_args
							execute_subprocess_async(lowercase__				,     env=os.environ.copy())

