




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__					:  List[Any]      							=				TaTokenizer

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

lowerCamelCase__					:  Union[str, Any]      							=				TaTokenizerFast

lowerCamelCase__					:  Dict      							=				{'configuration_mt5': ['MT5Config', 'MT5OnnxConfig']}

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

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

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__					:  Tuple      							=				['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__					:  int      							=				_LazyModule(
       __name__,
       globals()['__file__'],
       _import_structure,
       extra_objects={'MT5Tokenizer': MTaTokenizer, 'MT5TokenizerFast': MTaTokenizerFast},
       module_spec=__spec__,
   )





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


lowerCamelCase__					:  Optional[int]      							=				logging.get_logger(__name__)

lowerCamelCase__					:  Tuple      							=				{
    'microsoft/swinv2-tiny-patch4-window8-256': (
        'https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json'
    ),
}





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       "swinv2"

 lowercase_							       =       {
     "num_attention_heads": "num_heads",
     "num_hidden_layers": "num_layers",
 }



 def __init__(				self    :						Dict							,							_lowerCAmelCase    :						Optional[Any]=224							,							_lowerCAmelCase    :						Optional[int]=4							,							_lowerCAmelCase    :						Tuple=3							,							_lowerCAmelCase    :						Tuple=96							,							_lowerCAmelCase    :						Dict=[2, 2, 6, 2]							,							_lowerCAmelCase    :						Optional[Any]=[3, 6, 12, 24]							,							_lowerCAmelCase    :						str=7							,							_lowerCAmelCase    :						List[Any]=4.0							,							_lowerCAmelCase    :						List[str]=True							,							_lowerCAmelCase    :						List[Any]=0.0							,							_lowerCAmelCase    :						List[Any]=0.0							,							_lowerCAmelCase    :						Any=0.1							,							_lowerCAmelCase    :						List[Any]="gelu"							,							_lowerCAmelCase    :						str=False							,							_lowerCAmelCase    :						str=0.02							,							_lowerCAmelCase    :						List[Any]=1E-5							,							_lowerCAmelCase    :						str=32							,							**_lowerCAmelCase    :						List[Any]							,							):
       super().__init__(**_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     image_size
       SCREAMING_SNAKE_CASE_      		=     patch_size
       SCREAMING_SNAKE_CASE_      		=     num_channels
       SCREAMING_SNAKE_CASE_      		=     embed_dim
       SCREAMING_SNAKE_CASE_      		=     depths
       SCREAMING_SNAKE_CASE_      		=     len(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     num_heads
       SCREAMING_SNAKE_CASE_      		=     window_size
       SCREAMING_SNAKE_CASE_      		=     mlp_ratio
       SCREAMING_SNAKE_CASE_      		=     qkv_bias
       SCREAMING_SNAKE_CASE_      		=     hidden_dropout_prob
       SCREAMING_SNAKE_CASE_      		=     attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE_      		=     drop_path_rate
       SCREAMING_SNAKE_CASE_      		=     hidden_act
       SCREAMING_SNAKE_CASE_      		=     use_absolute_embeddings
       SCREAMING_SNAKE_CASE_      		=     layer_norm_eps
       SCREAMING_SNAKE_CASE_      		=     initializer_range
       SCREAMING_SNAKE_CASE_      		=     encoder_stride
       # we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
       # this indicates the channel dimension after the last stage of the model
       SCREAMING_SNAKE_CASE_      		=     int(embed_dim * 2 ** (len(_lowerCAmelCase		) - 1)		)
       SCREAMING_SNAKE_CASE_      		=     (0, 0, 0, 0)