'''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_:					List[Any]  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:					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				(			UpperCamelCase__     ):
    a__	:						Union[str, Any]	       =  """perceiver"""



    def __init__(self   :   int       ,      __a   :   Optional[Any]=256       ,      __a   :   List[str]=1280       ,      __a   :   List[Any]=768       ,      __a   :   Optional[Any]=1       ,      __a   :   Dict=26       ,      __a   :   Optional[Any]=8       ,      __a   :   str=8       ,      __a   :   List[Any]=None       ,      __a   :   Union[str, Any]=None       ,      __a   :   Optional[Any]="kv"       ,      __a   :   List[str]=1       ,      __a   :   Optional[Any]=1       ,      __a   :   List[str]="gelu"       ,      __a   :   str=0.1       ,      __a   :   List[str]=0.02       ,      __a   :   str=1E-12       ,      __a   :   Optional[Any]=True       ,      __a   :   Optional[int]=262       ,      __a   :   Optional[Any]=2048       ,      __a   :   List[Any]=56       ,      __a   :   Any=[368, 496]       ,      __a   :   Tuple=16       ,      __a   :   Optional[int]=1920       ,      __a   :   Dict=16       ,      __a   :   Union[str, Any]=[1, 16, 224, 224]       ,      **__a   :   Union[str, 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   :   str			):
         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   :   Dict			):
         return 1E-4






    def  _lowercase						(self   :   int       ,      __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."			)





'''simple docstring'''





import os
import time

import pytest

from datasets.utils.filelock import FileLock, Timeout





def      lowerCAmelCase_    ( snake_case_	:			Union[str, Any]      )   ->				Optional[int]:


     '''simple docstring'''




     UpperCAmelCase_	   =       FileLock(str(tmpdir / "foo.lock"      )      )
     UpperCAmelCase_	   =       FileLock(str(tmpdir / "foo.lock"      )      )
     UpperCAmelCase_	   =       0.01
     with locka.acquire():
          with pytest.raises(snake_case_      ):
               UpperCAmelCase_	   =       time.time()
               locka.acquire(snake_case_      )
               assert time.time() - _start > timeout






def      lowerCAmelCase_    ( snake_case_	:			int      )   ->				Optional[int]:


     '''simple docstring'''




     UpperCAmelCase_	   =       "a" * 10_00 + ".lock"
     UpperCAmelCase_	   =       FileLock(str(tmpdir / filename      )      )
     assert locka._lock_file.endswith(".lock"      )
     assert not locka._lock_file.endswith(snake_case_      )
     assert len(os.path.basename(locka._lock_file      )      ) <= 2_55
     UpperCAmelCase_	   =       FileLock(tmpdir / filename      )
     with locka.acquire():
          with pytest.raises(snake_case_      ):
               locka.acquire(0      )




