






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


lowerCamelCase      :       Dict    =						logging.get_logger(__name__)

lowerCamelCase      :       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 				_UpperCamelCase      (a_      ):
      snake_case_	=				"""perceiver"""


      def __init__(						self       ,    __UpperCamelCase=2_5_6       ,    __UpperCamelCase=1_2_8_0       ,    __UpperCamelCase=7_6_8       ,    __UpperCamelCase=1       ,    __UpperCamelCase=2_6       ,    __UpperCamelCase=8       ,    __UpperCamelCase=8       ,    __UpperCamelCase=None       ,    __UpperCamelCase=None       ,    __UpperCamelCase="kv"       ,    __UpperCamelCase=1       ,    __UpperCamelCase=1       ,    __UpperCamelCase="gelu"       ,    __UpperCamelCase=0.1       ,    __UpperCamelCase=0.0_2       ,    __UpperCamelCase=1e-12       ,    __UpperCamelCase=True       ,    __UpperCamelCase=2_6_2       ,    __UpperCamelCase=2_0_4_8       ,    __UpperCamelCase=5_6       ,    __UpperCamelCase=[3_6_8, 4_9_6]       ,    __UpperCamelCase=1_6       ,    __UpperCamelCase=1_9_2_0       ,    __UpperCamelCase=1_6       ,    __UpperCamelCase=[1, 1_6, 2_2_4, 2_2_4]       ,    **__UpperCamelCase       ,    )->						int:
           super().__init__(**__UpperCamelCase						)

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



class 				_UpperCamelCase      (a_      ):


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


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


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

           if isinstance(__UpperCamelCase       ,    __UpperCamelCase						):
                # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
                __lowerCAmelCase             =       compute_effective_axis_dimension(
                    __UpperCamelCase       ,    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             =       preprocessor.num_special_tokens_to_add(__UpperCamelCase						)
                __lowerCAmelCase             =       compute_effective_axis_dimension(
                    __UpperCamelCase       ,    fixed_dimension=OnnxConfig.default_fixed_sequence       ,    num_token_to_add=__UpperCamelCase						)
                # Generate dummy inputs according to compute batch and sequence
                __lowerCAmelCase             =       [" ".join(["a"]						) * seq_length] * batch_size
                __lowerCAmelCase             =       dict(preprocessor(__UpperCamelCase       ,    return_tensors=__UpperCamelCase						)						)
                __lowerCAmelCase             =       inputs.pop("input_ids"						)
                return inputs
           elif isinstance(__UpperCamelCase       ,    __UpperCamelCase						) 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             =       compute_effective_axis_dimension(__UpperCamelCase       ,    fixed_dimension=OnnxConfig.default_fixed_batch						)
                __lowerCAmelCase             =       self._generate_dummy_images(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						)
                __lowerCAmelCase             =       dict(preprocessor(images=__UpperCamelCase       ,    return_tensors=__UpperCamelCase						)						)
                __lowerCAmelCase             =       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 argparse
import importlib
from pathlib import Path


# Test all the extensions added in the setup
lowerCamelCase      :       str    =						[
    '''kernels/rwkv/wkv_cuda.cu''',
    '''kernels/rwkv/wkv_op.cpp''',
    '''kernels/deformable_detr/ms_deform_attn.h''',
    '''kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh''',
    '''models/graphormer/algos_graphormer.pyx''',
]





def    __lowerCAmelCase   (    __snake_case							):
     # Test all the extensions added in the setup
     for file in FILES_TO_FIND:
          if not (transformers_path / file).exists():
               return False
     return True


if __name__ == "__main__":
 lowerCamelCase      :       str    =						argparse.ArgumentParser()
 parser.add_argument('''--check_lib''', action='''store_true''', help='''Whether to check the build or the actual package.''')
 lowerCamelCase      :       Dict    =						parser.parse_args()
 if args.check_lib:
  lowerCamelCase      :       Optional[int]    =						importlib.import_module('''transformers''')
  lowerCamelCase      :       str    =						Path(transformers_module.__file__).parent
 else:
  lowerCamelCase      :       List[Any]    =						Path.cwd() / '''build/lib/transformers'''
 if not test_custom_files_are_present(transformers_path):
  raise ValueError('''The built release does not contain the custom files. Fix this before going further!''')






