


'''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


UpperCAmelCase_      :		Dict          =		logging.get_logger(__name__)

UpperCAmelCase_      :		Dict          =		{
    "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'''




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




 def __init__(    self		,      lowerCamelCase_=2_5_6		,      lowerCamelCase_=1_2_8_0		,      lowerCamelCase_=7_6_8		,      lowerCamelCase_=1		,      lowerCamelCase_=2_6		,      lowerCamelCase_=8		,      lowerCamelCase_=8		,      lowerCamelCase_=None		,      lowerCamelCase_=None		,      lowerCamelCase_="kv"		,      lowerCamelCase_=1		,      lowerCamelCase_=1		,      lowerCamelCase_="gelu"		,      lowerCamelCase_=0.1		,      lowerCamelCase_=0.02		,      lowerCamelCase_=1e-12		,      lowerCamelCase_=True		,      lowerCamelCase_=2_6_2		,      lowerCamelCase_=2_0_4_8		,      lowerCamelCase_=5_6		,      lowerCamelCase_=[3_6_8, 4_9_6]		,      lowerCamelCase_=1_6		,      lowerCamelCase_=1_9_2_0		,      lowerCamelCase_=1_6		,      lowerCamelCase_=[1, 1_6, 2_2_4, 2_2_4]		,      **lowerCamelCase_		,      )      ->    Optional[int]:
    super().__init__(**lowerCamelCase_      )

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







class 					a			(				snake_case__  ):
 '''simple docstring'''




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




 @property
 def   __UpperCamelCase   (    self      )      ->    float:
    return 1e-4




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_ = -1		,      lowerCamelCase_ = -1		,      lowerCamelCase_ = -1		,      lowerCamelCase_ = False		,      lowerCamelCase_ = None		,      lowerCamelCase_ = 3		,      lowerCamelCase_ = 4_0		,      lowerCamelCase_ = 4_0		,      )      ->    Mapping[str, Any]:
    # copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

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






'''simple docstring'''




import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler







@dataclass
class 					a			(				snake_case__  ):
 '''simple docstring'''




 __lowerCAmelCase						: int							  =		0
 __lowerCAmelCase						: bool							  =		False
 __lowerCAmelCase						: float							  =		3.0







class 					a			(				unittest.TestCase  ):
 '''simple docstring'''




 def   __UpperCamelCase   (    self      )      ->    Dict:
    # If no defaults are changed, `to_kwargs` returns an empty dict.
    self.assertDictEqual(MockClass().to_kwargs()		,      {}      )
    self.assertDictEqual(MockClass(a=2      ).to_kwargs()		,      {'a': 2}      )
    self.assertDictEqual(MockClass(a=2		,      b=lowerCamelCase_      ).to_kwargs()		,      {'a': 2, 'b': True}      )
    self.assertDictEqual(MockClass(a=2		,      c=2.25      ).to_kwargs()		,      {'a': 2, 'c': 2.25}      )




 @require_cuda
 def   __UpperCamelCase   (    self      )      ->    int:
    # If no defaults are changed, `to_kwargs` returns an empty dict.
    _a					:  List[str]						=  GradScalerKwargs(init_scale=1_0_2_4		,      growth_factor=2      )
    AcceleratorState._reset_state()
    _a					:  Optional[int]						=  Accelerator(mixed_precision='fp16'		,      kwargs_handlers=[scaler_handler]      )
    print(accelerator.use_fpaa      )
    _a					:  Tuple						=  accelerator.scaler

    # Check the kwargs have been applied
    self.assertEqual(scaler._init_scale		,      1024.0      )
    self.assertEqual(scaler._growth_factor		,      2.0      )

    # Check the other values are at the default
    self.assertEqual(scaler._backoff_factor		,      0.5      )
    self.assertEqual(scaler._growth_interval		,      2_0_0_0      )
    self.assertEqual(scaler._enabled		,      lowerCamelCase_      )




 @require_multi_gpu
 def   __UpperCamelCase   (    self      )      ->    Optional[Any]:
    _a					:  List[str]						=  ['torchrun', F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__      )]
    execute_subprocess_async(lowerCamelCase_		,      env=os.environ.copy()      )


if __name__ == "__main__":
 UpperCAmelCase_      :		Tuple          =		DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
 UpperCAmelCase_      :		str          =		Accelerator(kwargs_handlers=[ddp_scaler])
 UpperCAmelCase_      :		List[Any]          =		torch.nn.Linear(100, 200)
 UpperCAmelCase_      :		Tuple          =		accelerator.prepare(model)

 # Check the values changed in kwargs
 UpperCAmelCase_      :		List[str]          =		""
 UpperCAmelCase_      :		Any          =		model.bucket_bytes_cap // (1024 * 1024)
 if observed_bucket_cap_map != 15:
  error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
 if model.find_unused_parameters is not True:
  error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

 # Check the values of the defaults
 if model.dim != 0:
  error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
 if model.broadcast_buffers is not True:
  error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
 if model.gradient_as_bucket_view is not False:
  error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

 # Raise error at the end to make sure we don't stop at the first failure.
 if len(error_msg) > 0:
  raise ValueError(error_msg)


