'''simple docstring'''





from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_:					str  ={
    'configuration_clip': [
        'CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'CLIPConfig',
        'CLIPOnnxConfig',
        'CLIPTextConfig',
        'CLIPVisionConfig',
    ],
    'processing_clip': ['CLIPProcessor'],
    'tokenization_clip': ['CLIPTokenizer'],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_:					Any  =['CLIPTokenizerFast']

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_:					Optional[Any]  =['CLIPFeatureExtractor']
    SCREAMING_SNAKE_CASE_:					str  =['CLIPImageProcessor']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_:					Optional[int]  =[
        'CLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
        'CLIPModel',
        'CLIPPreTrainedModel',
        'CLIPTextModel',
        'CLIPTextModelWithProjection',
        'CLIPVisionModel',
        'CLIPVisionModelWithProjection',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_:					Tuple  =[
        'TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFCLIPModel',
        'TFCLIPPreTrainedModel',
        'TFCLIPTextModel',
        'TFCLIPVisionModel',
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_:					Optional[int]  =[
        'FlaxCLIPModel',
        'FlaxCLIPPreTrainedModel',
        'FlaxCLIPTextModel',
        'FlaxCLIPTextPreTrainedModel',
        'FlaxCLIPVisionModel',
        'FlaxCLIPVisionPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_clip import (
        CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
        CLIPConfig,
        CLIPOnnxConfig,
        CLIPTextConfig,
        CLIPVisionConfig,
    )
    from .processing_clip import CLIPProcessor
    from .tokenization_clip import CLIPTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_clip_fast import CLIPTokenizerFast

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_clip import CLIPFeatureExtractor
        from .image_processing_clip import CLIPImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_clip import (
            CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
            CLIPModel,
            CLIPPreTrainedModel,
            CLIPTextModel,
            CLIPTextModelWithProjection,
            CLIPVisionModel,
            CLIPVisionModelWithProjection,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_clip import (
            TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFCLIPModel,
            TFCLIPPreTrainedModel,
            TFCLIPTextModel,
            TFCLIPVisionModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_clip import (
            FlaxCLIPModel,
            FlaxCLIPPreTrainedModel,
            FlaxCLIPTextModel,
            FlaxCLIPTextPreTrainedModel,
            FlaxCLIPVisionModel,
            FlaxCLIPVisionPreTrainedModel,
        )


else:
    import sys

    SCREAMING_SNAKE_CASE_:					List[Any]  =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





'''simple docstring'''





from typing import Mapping

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


SCREAMING_SNAKE_CASE_:					List[str]  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:					List[Any]  ={
    't5-small': 'https://huggingface.co/t5-small/resolve/main/config.json',
    't5-base': 'https://huggingface.co/t5-base/resolve/main/config.json',
    't5-large': 'https://huggingface.co/t5-large/resolve/main/config.json',
    't5-3b': 'https://huggingface.co/t5-3b/resolve/main/config.json',
    't5-11b': 'https://huggingface.co/t5-11b/resolve/main/config.json',
}




class 	__A				(			UpperCamelCase__     ):
    a__	:						Dict	       =  """t5"""
    a__	:						List[str]	       =  ["""past_key_values"""]
    a__	:						Union[str, Any]	       =  {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}



    def __init__(self   :   List[str]       ,      __a   :   Any=32128       ,      __a   :   Optional[int]=512       ,      __a   :   Union[str, Any]=64       ,      __a   :   List[str]=2048       ,      __a   :   Optional[int]=6       ,      __a   :   Union[str, Any]=None       ,      __a   :   Union[str, Any]=8       ,      __a   :   Dict=32       ,      __a   :   List[Any]=128       ,      __a   :   Optional[Any]=0.1       ,      __a   :   List[Any]=1E-6       ,      __a   :   str=1.0       ,      __a   :   Dict="relu"       ,      __a   :   int=True       ,      __a   :   int=True       ,      __a   :   Optional[Any]=0       ,      __a   :   Dict=1       ,      **__a   :   List[Any]       ,      ):
         UpperCAmelCase_	   =       vocab_size
         UpperCAmelCase_	   =       d_model
         UpperCAmelCase_	   =       d_kv
         UpperCAmelCase_	   =       d_ff
         UpperCAmelCase_	   =       num_layers
         UpperCAmelCase_	   =       (
             num_decoder_layers if num_decoder_layers is not None else self.num_layers
         )  # default = symmetry
         UpperCAmelCase_	   =       num_heads
         UpperCAmelCase_	   =       relative_attention_num_buckets
         UpperCAmelCase_	   =       relative_attention_max_distance
         UpperCAmelCase_	   =       dropout_rate
         UpperCAmelCase_	   =       layer_norm_epsilon
         UpperCAmelCase_	   =       initializer_factor
         UpperCAmelCase_	   =       feed_forward_proj
         UpperCAmelCase_	   =       use_cache

         UpperCAmelCase_	   =       self.feed_forward_proj.split("-"			)
         UpperCAmelCase_	   =       act_info[-1]
         UpperCAmelCase_	   =       act_info[0] == "gated"

         if len(__a			) > 1 and act_info[0] != "gated" or len(__a			) > 2:
              raise ValueError(
                  f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
                  "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
                  "'gated-gelu' or 'relu'"			)

         # for backwards compatibility
         if feed_forward_proj == "gated-gelu":
              UpperCAmelCase_	   =       "gelu_new"

         super().__init__(
             pad_token_id=__a       ,      eos_token_id=__a       ,      is_encoder_decoder=__a       ,      **__a       ,      )




class 	__A				(			UpperCamelCase__     ):



    @property
    def  _lowercase						(self   :   str			):
         UpperCAmelCase_	   =       {
             "input_ids": {0: "batch", 1: "encoder_sequence"},
             "attention_mask": {0: "batch", 1: "encoder_sequence"},
         }
         if self.use_past:
              UpperCAmelCase_	   =       "past_encoder_sequence + sequence"
              UpperCAmelCase_	   =       {0: "batch"}
              UpperCAmelCase_	   =       {0: "batch", 1: "past_decoder_sequence + sequence"}
         else:
              UpperCAmelCase_	   =       {0: "batch", 1: "decoder_sequence"}
              UpperCAmelCase_	   =       {0: "batch", 1: "decoder_sequence"}

         if self.use_past:
              self.fill_with_past_key_values_(__a       ,      direction="inputs"			)

         return common_inputs






    @property
    def  _lowercase						(self   :   Optional[int]			):
         return 13




