'''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


lowercase_     		= logging.get_logger(__name__)

lowercase_     		= {
    '''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   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      List[str]       =							'perceiver'






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

								_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   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""






		@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		,						__A		,						__A = -1		,						__A = -1		,						__A = -1		,						__A = False		,						__A = None		,						__A = 3		,						__A = 40		,						__A = 40		,						) ->     Mapping[str, Any]:
								# 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
														_lowerCAmelCase										=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
														_lowerCAmelCase										=preprocessor.num_special_tokens_to_add(__A				)
														_lowerCAmelCase										=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
														_lowerCAmelCase										=[' '.join(['a']				) * seq_length] * batch_size
														_lowerCAmelCase										=dict(preprocessor(__A		,						return_tensors=__A				)				)
														_lowerCAmelCase										=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
														_lowerCAmelCase										=compute_effective_axis_dimension(__A		,						fixed_dimension=OnnxConfig.default_fixed_batch				)
														_lowerCAmelCase										=self._generate_dummy_images(__A		,						__A		,						__A		,						__A				)
														_lowerCAmelCase										=dict(preprocessor(images=__A		,						return_tensors=__A				)				)
														_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.'				)






'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase_     		= logging.get_logger(__name__)

lowercase_     		= {
    '''facebook/data2vec-text-base''': '''https://huggingface.co/data2vec/resolve/main/config.json''',
}
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      List[str]       =							'data2vec-text'






		def __init__(   self		,						__A=3_0522		,						__A=768		,						__A=12		,						__A=12		,						__A=3072		,						__A="gelu"		,						__A=0.1		,						__A=0.1		,						__A=512		,						__A=2		,						__A=0.02		,						__A=1E-12		,						__A=1		,						__A=0		,						__A=2		,						__A="absolute"		,						__A=True		,						__A=None		,						**__A		,						) ->     List[Any]:
								super().__init__(pad_token_id=__A		,						bos_token_id=__A		,						eos_token_id=__A		,						**__A				)

								_lowerCAmelCase										=vocab_size
								_lowerCAmelCase										=hidden_size
								_lowerCAmelCase										=num_hidden_layers
								_lowerCAmelCase										=num_attention_heads
								_lowerCAmelCase										=hidden_act
								_lowerCAmelCase										=intermediate_size
								_lowerCAmelCase										=hidden_dropout_prob
								_lowerCAmelCase										=attention_probs_dropout_prob
								_lowerCAmelCase										=max_position_embeddings
								_lowerCAmelCase										=type_vocab_size
								_lowerCAmelCase										=initializer_range
								_lowerCAmelCase										=layer_norm_eps
								_lowerCAmelCase										=position_embedding_type
								_lowerCAmelCase										=use_cache
								_lowerCAmelCase										=classifier_dropout

class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""






		@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(
								    [
								        ('input_ids', dynamic_axis),
								        ('attention_mask', dynamic_axis),
								    ]				)





