"""simple docstring"""



import math

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__magic_name__     					=       logging.get_logger(__name__)

__magic_name__     					=       {
    "facebook/data2vec-base-960h": "https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json",
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}

class SCREAMING_SNAKE_CASE  (     _lowerCAmelCase						):







 """simple docstring"""


 a_							:						List[str]	      ="data2vec-audio"


 def __init__( self		:   List[Any]       , _snake_case		:   int=32       , _snake_case		:   Union[str, Any]=768       , _snake_case		:   Any=12       , _snake_case		:   Tuple=12       , _snake_case		:   Any=3072       , _snake_case		:   Any="gelu"       , _snake_case		:   Tuple=0.1       , _snake_case		:   Any=0.1       , _snake_case		:   int=0.1       , _snake_case		:   int=0.0       , _snake_case		:   Union[str, Any]=0.1       , _snake_case		:   List[str]=0.1       , _snake_case		:   str=0.02       , _snake_case		:   List[Any]=1E-5       , _snake_case		:   Tuple="gelu"       , _snake_case		:   Any=(512, 512, 512, 512, 512, 512, 512)       , _snake_case		:   Optional[int]=(5, 2, 2, 2, 2, 2, 2)       , _snake_case		:   Optional[int]=(10, 3, 3, 3, 3, 2, 2)       , _snake_case		:   str=False       , _snake_case		:   Any=16       , _snake_case		:   int=19       , _snake_case		:   Tuple=5       , _snake_case		:   Dict=0.05       , _snake_case		:   int=10       , _snake_case		:   List[str]=2       , _snake_case		:   int=0.0       , _snake_case		:   Union[str, Any]=10       , _snake_case		:   str=0       , _snake_case		:   Tuple="sum"       , _snake_case		:   Optional[Any]=False       , _snake_case		:   int=False       , _snake_case		:   Dict=256       , _snake_case		:   str=(512, 512, 512, 512, 1500)       , _snake_case		:   Optional[Any]=(5, 3, 3, 1, 1)       , _snake_case		:   Union[str, Any]=(1, 2, 3, 1, 1)       , _snake_case		:   str=512       , _snake_case		:   Union[str, Any]=0       , _snake_case		:   Union[str, Any]=1       , _snake_case		:   Union[str, Any]=2       , _snake_case		:   Any=False       , _snake_case		:   Union[str, Any]=3       , _snake_case		:   Any=2       , _snake_case		:   str=3       , _snake_case		:   List[Any]=None       , **_snake_case		:   List[str]       , )					->      Dict:




    '''simple docstring'''






    super().__init__(**SCREAMING_SNAKE_CASE__       , pad_token_id=SCREAMING_SNAKE_CASE__       , bos_token_id=SCREAMING_SNAKE_CASE__       , eos_token_id=SCREAMING_SNAKE_CASE__					)
    a__  					=		hidden_size
    a__  					=		feat_extract_activation
    a__  					=		list(SCREAMING_SNAKE_CASE__					)
    a__  					=		list(SCREAMING_SNAKE_CASE__					)
    a__  					=		list(SCREAMING_SNAKE_CASE__					)
    a__  					=		conv_bias
    a__  					=		num_conv_pos_embeddings
    a__  					=		num_conv_pos_embedding_groups
    a__  					=		conv_pos_kernel_size
    a__  					=		len(self.conv_dim					)
    a__  					=		num_hidden_layers
    a__  					=		intermediate_size
    a__  					=		hidden_act
    a__  					=		num_attention_heads
    a__  					=		hidden_dropout
    a__  					=		attention_dropout
    a__  					=		activation_dropout
    a__  					=		feat_proj_dropout
    a__  					=		final_dropout
    a__  					=		layerdrop
    a__  					=		layer_norm_eps
    a__  					=		initializer_range
    a__  					=		vocab_size
    a__  					=		use_weighted_layer_sum

    if (
        (len(self.conv_stride					) != self.num_feat_extract_layers)
        or (len(self.conv_kernel					) != self.num_feat_extract_layers)
        or (len(self.conv_dim					) != self.num_feat_extract_layers)
    ):
       raise ValueError(
           'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
           ' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
           F''' {len(self.conv_dim					)}`, `len(config.conv_stride) = {len(self.conv_stride					)}`,'''
           F''' `len(config.conv_kernel) = {len(self.conv_kernel					)}`.'''					)

    # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
    a__  					=		mask_time_prob
    a__  					=		mask_time_length
    a__  					=		mask_time_min_masks
    a__  					=		mask_feature_prob
    a__  					=		mask_feature_length
    a__  					=		mask_feature_min_masks

    # ctc loss
    a__  					=		ctc_loss_reduction
    a__  					=		ctc_zero_infinity

    # adapter
    a__  					=		add_adapter
    a__  					=		adapter_kernel_size
    a__  					=		adapter_stride
    a__  					=		num_adapter_layers
    a__  					=		output_hidden_size or hidden_size

    # SequenceClassification-specific parameter. Feel free to ignore for other classes.
    a__  					=		classifier_proj_size

    # XVector-specific parameters. Feel free to ignore for other classes.
    a__  					=		list(SCREAMING_SNAKE_CASE__					)
    a__  					=		list(SCREAMING_SNAKE_CASE__					)
    a__  					=		list(SCREAMING_SNAKE_CASE__					)
    a__  					=		xvector_output_dim



 @property
 def 							_lowerCAmelCase					( self		:   List[Any]					)					->      List[str]:




    '''simple docstring'''






    return math.prod(self.conv_stride					)







"""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     =							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       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					Optional[int]							  =     "perceiver"


			def __init__(      self						,	SCREAMING_SNAKE_CASE__=256						,	SCREAMING_SNAKE_CASE__=1280						,	SCREAMING_SNAKE_CASE__=768						,	SCREAMING_SNAKE_CASE__=1						,	SCREAMING_SNAKE_CASE__=26						,	SCREAMING_SNAKE_CASE__=8						,	SCREAMING_SNAKE_CASE__=8						,	SCREAMING_SNAKE_CASE__=None						,	SCREAMING_SNAKE_CASE__=None						,	SCREAMING_SNAKE_CASE__="kv"						,	SCREAMING_SNAKE_CASE__=1						,	SCREAMING_SNAKE_CASE__=1						,	SCREAMING_SNAKE_CASE__="gelu"						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=0.0_2						,	SCREAMING_SNAKE_CASE__=1e-12						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=262						,	SCREAMING_SNAKE_CASE__=2048						,	SCREAMING_SNAKE_CASE__=56						,	SCREAMING_SNAKE_CASE__=[368, 496]						,	SCREAMING_SNAKE_CASE__=16						,	SCREAMING_SNAKE_CASE__=1920						,	SCREAMING_SNAKE_CASE__=16						,	SCREAMING_SNAKE_CASE__=[1, 16, 224, 224]						,	**SCREAMING_SNAKE_CASE__						,	)      ->	Optional[int]:
				super().__init__(**SCREAMING_SNAKE_CASE__		)

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






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


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


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







			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = -1						,	SCREAMING_SNAKE_CASE__ = -1						,	SCREAMING_SNAKE_CASE__ = -1						,	SCREAMING_SNAKE_CASE__ = False						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = 3						,	SCREAMING_SNAKE_CASE__ = 40						,	SCREAMING_SNAKE_CASE__ = 40						,	)      ->	Mapping[str, Any]:
				# copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

				if isinstance(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		):
					# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
					A__											=					compute_effective_axis_dimension(
					    SCREAMING_SNAKE_CASE__						,	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__											=					preprocessor.num_special_tokens_to_add(SCREAMING_SNAKE_CASE__		)
					A__											=					compute_effective_axis_dimension(
					    SCREAMING_SNAKE_CASE__						,	fixed_dimension=OnnxConfig.default_fixed_sequence						,	num_token_to_add=SCREAMING_SNAKE_CASE__		)
					# Generate dummy inputs according to compute batch and sequence
					A__											=					[" ".join(["a"]		) * seq_length] * batch_size
					A__											=					dict(preprocessor(SCREAMING_SNAKE_CASE__						,	return_tensors=SCREAMING_SNAKE_CASE__		)		)
					A__											=					inputs.pop("input_ids"		)
					return inputs
				elif isinstance(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		) 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__											=					compute_effective_axis_dimension(SCREAMING_SNAKE_CASE__						,	fixed_dimension=OnnxConfig.default_fixed_batch		)
					A__											=					self._generate_dummy_images(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
					A__											=					dict(preprocessor(images=SCREAMING_SNAKE_CASE__						,	return_tensors=SCREAMING_SNAKE_CASE__		)		)
					A__											=					inputs.pop("pixel_values"		)
					return inputs
				else:
					raise ValueError(
					    "Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor."		)




