





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


UpperCAmelCase_					=					logging.get_logger(__name__)

UpperCAmelCase_					=					{
    "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__			(      _UpperCamelCase							):

   '''simple docstring'''


   a		:				str  						=						'perceiver'




   def __init__(       self,						__magic_name__=256,						__magic_name__=1280,						__magic_name__=768,						__magic_name__=1,						__magic_name__=26,						__magic_name__=8,						__magic_name__=8,						__magic_name__=None,						__magic_name__=None,						__magic_name__="kv",						__magic_name__=1,						__magic_name__=1,						__magic_name__="gelu",						__magic_name__=0.1,						__magic_name__=0.02,						__magic_name__=1E-12,						__magic_name__=True,						__magic_name__=262,						__magic_name__=2048,						__magic_name__=56,						__magic_name__=[368, 496],						__magic_name__=16,						__magic_name__=1920,						__magic_name__=16,						__magic_name__=[1, 16, 224, 224],						**__magic_name__,						)					-> Union[str, Any]:






       """simple docstring"""
       super().__init__(**__magic_name__				)

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






class        lowercase__			(      _UpperCamelCase							):

   '''simple docstring'''


   @property
   def  UpperCamelCase__    (       self				)					-> int:






       """simple docstring"""
       if self.task == "multiple-choice":
           UpperCamelCase__				:							str			  = {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				)					-> Union[str, Any]:






       """simple docstring"""
       return 1E-4




   def  UpperCamelCase__    (       self,						__magic_name__,						__magic_name__ = -1,						__magic_name__ = -1,						__magic_name__ = -1,						__magic_name__ = False,						__magic_name__ = None,						__magic_name__ = 3,						__magic_name__ = 40,						__magic_name__ = 40,						)					-> List[Any]:






       """simple docstring"""
       # copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

       if isinstance(__magic_name__,						__magic_name__				):
           # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
           UpperCamelCase__				:							Dict			  = compute_effective_axis_dimension(
               __magic_name__,						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__				:							str			  = preprocessor.num_special_tokens_to_add(__magic_name__				)
           UpperCamelCase__				:							List[str]			  = compute_effective_axis_dimension(
               __magic_name__,						fixed_dimension=OnnxConfig.default_fixed_sequence,						num_token_to_add=__magic_name__				)
           # Generate dummy inputs according to compute batch and sequence
           UpperCamelCase__				:							Union[str, Any]			  = [" ".join(['''a''']				) * seq_length] * batch_size
           UpperCamelCase__				:							Tuple			  = dict(preprocessor(__magic_name__,						return_tensors=__magic_name__				)				)
           UpperCamelCase__				:							Tuple			  = inputs.pop('''input_ids'''				)
           return inputs
       elif isinstance(__magic_name__,						__magic_name__				) 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__				:							int			  = compute_effective_axis_dimension(__magic_name__,						fixed_dimension=OnnxConfig.default_fixed_batch				)
           UpperCamelCase__				:							List[Any]			  = self._generate_dummy_images(__magic_name__,						__magic_name__,						__magic_name__,						__magic_name__				)
           UpperCamelCase__				:							Tuple			  = dict(preprocessor(images=__magic_name__,						return_tensors=__magic_name__				)				)
           UpperCamelCase__				:							Optional[int]			  = 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 importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append(".")

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Optional[int]							)    ->  List[str]:





     '''simple docstring'''

     _UpperCAmelCase     :      Any														=       test_file.split(os.path.sep							)
     if components[0:2] != ["tests", "models"]:
          raise ValueError(
              "`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got "
              f'{test_file} instead.'							)
     _UpperCAmelCase     :      int														=       components[-1]
     if not test_fn.endswith("py"							):
          raise ValueError(f'`test_file` should be a python file. Got {test_fn} instead.'							)
     if not test_fn.startswith("test_modeling_"							):
          raise ValueError(
              f'`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead.'							)

     _UpperCAmelCase     :      Any														=       components[:-1] + [test_fn.replace(".py"		,    ""							)]
     _UpperCAmelCase     :      List[str]														=       ".".join(SCREAMING_SNAKE_CASE__							)

     return test_module_path

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		List[Any]							)    ->  Dict:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[int]														=       get_module_path(SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      List[Any]														=       importlib.import_module(SCREAMING_SNAKE_CASE__							)

     return test_module

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Union[str, Any]							)    ->  List[str]:





     '''simple docstring'''

     _UpperCAmelCase     :      Union[str, Any]														=       []
     _UpperCAmelCase     :      str														=       get_test_module(SCREAMING_SNAKE_CASE__							)
     for attr in dir(SCREAMING_SNAKE_CASE__							):
          if attr.endswith("ModelTester"							):
               tester_classes.append(getattr(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)							)

    # sort with class names
     return sorted(SCREAMING_SNAKE_CASE__		,    key=lambda SCREAMING_SNAKE_CASE__							: x.__name__							)

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		int							)    ->  Optional[int]:





     '''simple docstring'''

     _UpperCAmelCase     :      Union[str, Any]														=       []
     _UpperCAmelCase     :      Tuple														=       get_test_module(SCREAMING_SNAKE_CASE__							)
     for attr in dir(SCREAMING_SNAKE_CASE__							):
          _UpperCAmelCase     :      List[Any]														=       getattr(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)
          # (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
          # `all_model_classes` is not empty (which also excludes other special classes).
          _UpperCAmelCase     :      Optional[Any]														=       getattr(SCREAMING_SNAKE_CASE__		,    "all_model_classes"		,    []							)
          if len(SCREAMING_SNAKE_CASE__							) > 0:
               test_classes.append(SCREAMING_SNAKE_CASE__							)

    # sort with class names
     return sorted(SCREAMING_SNAKE_CASE__		,    key=lambda SCREAMING_SNAKE_CASE__							: x.__name__							)

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		int							)    ->  int:





     '''simple docstring'''

     _UpperCAmelCase     :      Union[str, Any]														=       get_test_classes(SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      Tuple														=       set()
     for test_class in test_classes:
          model_classes.update(test_class.all_model_classes							)

     # sort with class names
     return sorted(SCREAMING_SNAKE_CASE__		,    key=lambda SCREAMING_SNAKE_CASE__							: x.__name__							)

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Tuple							)    ->  Any:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[Any]														=       test_class()
     if hasattr(SCREAMING_SNAKE_CASE__		,    "setUp"							):
          test.setUp()

     _UpperCAmelCase     :      int														=       None
     if hasattr(SCREAMING_SNAKE_CASE__		,    "model_tester"							):
          # `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
          if test.model_tester is not None:
               _UpperCAmelCase     :      List[str]														=       test.model_tester.__class__

     return model_tester

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Optional[int]		,    SCREAMING_SNAKE_CASE__      :		int							)    ->  Union[str, Any]:





     '''simple docstring'''

     _UpperCAmelCase     :      str														=       get_test_classes(SCREAMING_SNAKE_CASE__							)

     _UpperCAmelCase     :      Optional[int]														=       []
     for test_class in test_classes:
          if model_class in test_class.all_model_classes:
               target_test_classes.append(SCREAMING_SNAKE_CASE__							)

    # sort with class names
     return sorted(SCREAMING_SNAKE_CASE__		,    key=lambda SCREAMING_SNAKE_CASE__							: x.__name__							)

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Optional[int]		,    SCREAMING_SNAKE_CASE__      :		List[Any]							)    ->  Optional[Any]:





     '''simple docstring'''

     _UpperCAmelCase     :      str														=       get_test_classes_for_model(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)

     _UpperCAmelCase     :      int														=       []
     for test_class in test_classes:
          _UpperCAmelCase     :      Optional[Any]														=       get_model_tester_from_test_class(SCREAMING_SNAKE_CASE__							)
          if tester_class is not None:
               tester_classes.append(SCREAMING_SNAKE_CASE__							)

    # sort with class names
     return sorted(SCREAMING_SNAKE_CASE__		,    key=lambda SCREAMING_SNAKE_CASE__							: x.__name__							)

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Any							)    ->  int:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[Any]														=       get_test_classes(SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      List[Any]														=       {test_class: get_model_tester_from_test_class(SCREAMING_SNAKE_CASE__							) for test_class in test_classes}
     return test_tester_mapping

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Optional[int]							)    ->  Dict:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[Any]														=       get_model_classes(SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      int														=       {
         model_class: get_test_classes_for_model(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							) for model_class in model_classes
     }
     return model_test_mapping

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Optional[int]							)    ->  Tuple:





     '''simple docstring'''

     _UpperCAmelCase     :      Tuple														=       get_model_classes(SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      Union[str, Any]														=       {
         model_class: get_tester_classes_for_model(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							) for model_class in model_classes
     }
     return model_to_tester_mapping

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		str							)    ->  List[str]:





     '''simple docstring'''

     if isinstance(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							):
          return o
     elif isinstance(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							):
          return o.__name__
     elif isinstance(SCREAMING_SNAKE_CASE__		,    (list, tuple)							):
          return [to_json(SCREAMING_SNAKE_CASE__							) for x in o]
     elif isinstance(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							):
          return {to_json(SCREAMING_SNAKE_CASE__							): to_json(SCREAMING_SNAKE_CASE__							) for k, v in o.items()}
     else:
          return o
