

"""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  :      Optional[Any]							      =		logging.get_logger(__name__)

__UpperCamelCase  :      str							      =		{
    '''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					(		a_       ):






					"""simple docstring"""



					lowercase__				 =       "perceiver"







					def __init__(       self			:				Dict   ,lowercase_			:				int=2_5_6   ,lowercase_			:				Optional[Any]=1_2_8_0   ,lowercase_			:				int=7_6_8   ,lowercase_			:				Dict=1   ,lowercase_			:				List[str]=2_6   ,lowercase_			:				Dict=8   ,lowercase_			:				Any=8   ,lowercase_			:				Optional[int]=None   ,lowercase_			:				List[str]=None   ,lowercase_			:				List[str]="kv"   ,lowercase_			:				int=1   ,lowercase_			:				Union[str, Any]=1   ,lowercase_			:				Optional[Any]="gelu"   ,lowercase_			:				Any=0.1   ,lowercase_			:				Optional[int]=0.02   ,lowercase_			:				Union[str, Any]=1E-12   ,lowercase_			:				Optional[int]=True   ,lowercase_			:				Tuple=2_6_2   ,lowercase_			:				List[str]=2_0_4_8   ,lowercase_			:				str=5_6   ,lowercase_			:				Any=[3_6_8, 4_9_6]   ,lowercase_			:				Union[str, Any]=1_6   ,lowercase_			:				Optional[int]=1_9_2_0   ,lowercase_			:				str=1_6   ,lowercase_			:				Optional[Any]=[1, 1_6, 2_2_4, 2_2_4]   ,**lowercase_			:				Any   ,):
						super().__init__(**lowercase_		)

						lowerCAmelCase__	:						List[Any]  				=	num_latents
						lowerCAmelCase__	:						Optional[Any]  				=	d_latents
						lowerCAmelCase__	:						Tuple  				=	d_model
						lowerCAmelCase__	:						Any  				=	num_blocks
						lowerCAmelCase__	:						Optional[Any]  				=	num_self_attends_per_block
						lowerCAmelCase__	:						List[str]  				=	num_self_attention_heads
						lowerCAmelCase__	:						str  				=	num_cross_attention_heads
						lowerCAmelCase__	:						List[Any]  				=	qk_channels
						lowerCAmelCase__	:						List[Any]  				=	v_channels
						lowerCAmelCase__	:						List[str]  				=	cross_attention_shape_for_attention
						lowerCAmelCase__	:						Any  				=	self_attention_widening_factor
						lowerCAmelCase__	:						List[str]  				=	cross_attention_widening_factor
						lowerCAmelCase__	:						str  				=	hidden_act
						lowerCAmelCase__	:						Dict  				=	attention_probs_dropout_prob
						lowerCAmelCase__	:						Union[str, Any]  				=	initializer_range
						lowerCAmelCase__	:						Optional[int]  				=	layer_norm_eps
						lowerCAmelCase__	:						Any  				=	use_query_residual
						# masked language modeling attributes
						lowerCAmelCase__	:						Union[str, Any]  				=	vocab_size
						lowerCAmelCase__	:						List[Any]  				=	max_position_embeddings
						# image classification attributes
						lowerCAmelCase__	:						List[str]  				=	image_size
						# flow attributes
						lowerCAmelCase__	:						List[Any]  				=	train_size
						# multimodal autoencoding attributes
						lowerCAmelCase__	:						Optional[int]  				=	num_frames
						lowerCAmelCase__	:						str  				=	audio_samples_per_frame
						lowerCAmelCase__	:						int  				=	samples_per_patch
						lowerCAmelCase__	:						Any  				=	output_shape







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					@property
					def        __lowerCAmelCase (       self			:				Optional[Any]		):
						if self.task == "multiple-choice":
							lowerCAmelCase__	:						Tuple  				=	{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
						else:
							lowerCAmelCase__	:						Any  				=	{0: '''batch''', 1: '''sequence'''}
						return OrderedDict(
						    [
						        ('''inputs''', dynamic_axis),
						        ('''attention_mask''', dynamic_axis),
						    ]		)







					@property
					def        __lowerCAmelCase (       self			:				List[str]		):
						return 1E-4







					def        __lowerCAmelCase (       self			:				Tuple   ,lowercase_			:				Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]   ,lowercase_			:				int = -1   ,lowercase_			:				int = -1   ,lowercase_			:				int = -1   ,lowercase_			:				bool = False   ,lowercase_			:				Optional[TensorType] = None   ,lowercase_			:				int = 3   ,lowercase_			:				int = 4_0   ,lowercase_			:				int = 4_0   ,):
						# copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

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








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


if is_sentencepiece_available():
 from ..ta.tokenization_ta import TaTokenizer
else:
 from ...utils.dummy_sentencepiece_objects import TaTokenizer

lowerCamelCase											=       TaTokenizer

if is_tokenizers_available():
 from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
 from ...utils.dummy_tokenizers_objects import TaTokenizerFast

lowerCamelCase											=       TaTokenizerFast

lowerCamelCase											=       {'configuration_mt5': ['MT5Config', 'MT5OnnxConfig']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase											=       [
     'MT5EncoderModel',
     'MT5ForConditionalGeneration',
     'MT5ForQuestionAnswering',
     'MT5Model',
     'MT5PreTrainedModel',
     'MT5Stack',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase											=       ['TFMT5EncoderModel', 'TFMT5ForConditionalGeneration', 'TFMT5Model']

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase											=       ['FlaxMT5EncoderModel', 'FlaxMT5ForConditionalGeneration', 'FlaxMT5Model']


if TYPE_CHECKING:
 from .configuration_mta import MTaConfig, MTaOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mta import (
      MTaEncoderModel,
      MTaForConditionalGeneration,
      MTaForQuestionAnswering,
      MTaModel,
      MTaPreTrainedModel,
      MTaStack,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
 import sys

 lowerCamelCase											=       _LazyModule(
     __name__,
     globals()['__file__'],
     _import_structure,
     extra_objects={'MT5Tokenizer': MTaTokenizer, 'MT5TokenizerFast': MTaTokenizerFast},
     module_spec=__spec__,
 )


