



"""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


A    =			logging.get_logger(__name__)

A    =			{
    '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__		(      UpperCamelCase     ):
		lowerCAmelCase_       :	Dict 							=     """perceiver"""



		def __init__( self  :    Optional[Any]      ,							snake_case  :    str=256      ,							snake_case  :    Optional[int]=1_280      ,							snake_case  :    Optional[int]=768      ,							snake_case  :    Union[str, Any]=1      ,							snake_case  :    int=26      ,							snake_case  :    Any=8      ,							snake_case  :    Union[str, Any]=8      ,							snake_case  :    Any=None      ,							snake_case  :    List[Any]=None      ,							snake_case  :    List[str]="kv"      ,							snake_case  :    str=1      ,							snake_case  :    List[str]=1      ,							snake_case  :    Dict="gelu"      ,							snake_case  :    int=0.1      ,							snake_case  :    List[Any]=0.02      ,							snake_case  :    Optional[Any]=1E-1_2      ,							snake_case  :    Dict=True      ,							snake_case  :    Any=262      ,							snake_case  :    List[str]=2_048      ,							snake_case  :    Dict=56      ,							snake_case  :    Tuple=[368, 496]      ,							snake_case  :    int=16      ,							snake_case  :    Any=1_920      ,							snake_case  :    List[Any]=16      ,							snake_case  :    Dict=[1, 16, 224, 224]      ,							**snake_case  :    str      ,							)			->						Optional[Any]:






									'''simple docstring'''

									super().__init__(**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__		(      UpperCamelCase     ):



		@property
		def 					A_		( self  :    Any				)			->						Mapping[str, Mapping[int, str]]:






									'''simple docstring'''

									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 					A_		( self  :    List[str]				)			->						float:






									'''simple docstring'''

									return 1E-4



		def 					A_		( self  :    Optional[Any]      ,							snake_case  :    Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]      ,							snake_case  :    int = -1      ,							snake_case  :    int = -1      ,							snake_case  :    int = -1      ,							snake_case  :    bool = False      ,							snake_case  :    Optional[TensorType] = None      ,							snake_case  :    int = 3      ,							snake_case  :    int = 40      ,							snake_case  :    int = 40      ,							)			->						Mapping[str, Any]:






									'''simple docstring'''

									if isinstance(snake_case      ,							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(
																    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(snake_case				)
																A         =					compute_effective_axis_dimension(
																    snake_case      ,							fixed_dimension=OnnxConfig.default_fixed_sequence      ,							num_token_to_add=snake_case				)
																# Generate dummy inputs according to compute batch and sequence
																A         =					[' '.join(['a']				) * seq_length] * batch_size
																A         =					dict(preprocessor(snake_case      ,							return_tensors=snake_case				)				)
																A         =					inputs.pop('input_ids'				)
																return inputs
									elif isinstance(snake_case      ,							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(snake_case      ,							fixed_dimension=OnnxConfig.default_fixed_batch				)
																A         =					self._generate_dummy_images(snake_case      ,							snake_case      ,							snake_case      ,							snake_case				)
																A         =					dict(preprocessor(images=snake_case      ,							return_tensors=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.'				)








"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


A    =			{
    'configuration_gpt_neo': ['GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoConfig', 'GPTNeoOnnxConfig'],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A    =			[
							    'GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'GPTNeoForCausalLM',
							    'GPTNeoForQuestionAnswering',
							    'GPTNeoForSequenceClassification',
							    'GPTNeoForTokenClassification',
							    'GPTNeoModel',
							    'GPTNeoPreTrainedModel',
							    'load_tf_weights_in_gpt_neo',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A    =			[
							    'FlaxGPTNeoForCausalLM',
							    'FlaxGPTNeoModel',
							    'FlaxGPTNeoPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_gpt_neo import (
														    GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GPTNeoForCausalLM,
														    GPTNeoForQuestionAnswering,
														    GPTNeoForSequenceClassification,
														    GPTNeoForTokenClassification,
														    GPTNeoModel,
														    GPTNeoPreTrainedModel,
														    load_tf_weights_in_gpt_neo,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
							import sys

							A    =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



