





"""simple docstring"""




import copy
import os
from typing import Union

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


a		:				str  			=    logging.get_logger(__name__)

a		:				Dict  			=    {
    '''microsoft/git-base''': '''https://huggingface.co/microsoft/git-base/resolve/main/config.json''',
}


class  __UpperCamelCase       (		a__		):
							lowerCamelCase     :      int         ="""git_vision_model"""






							def __init__(     self   ,      lowerCAmelCase__=768   ,      lowerCAmelCase__=3072   ,      lowerCAmelCase__=12   ,      lowerCAmelCase__=12   ,      lowerCAmelCase__=3   ,      lowerCAmelCase__=224   ,      lowerCAmelCase__=16   ,      lowerCAmelCase__="quick_gelu"   ,      lowerCAmelCase__=1E-5   ,      lowerCAmelCase__=0.0   ,      lowerCAmelCase__=0.02   ,      **lowerCAmelCase__   ,      )			->				str:
								super().__init__(**lowerCAmelCase__   )

								a   :	Tuple			=       hidden_size
								a   :	List[Any]			=       intermediate_size
								a   :	List[Any]			=       num_hidden_layers
								a   :	Tuple			=       num_attention_heads
								a   :	str			=       num_channels
								a   :	Union[str, Any]			=       patch_size
								a   :	List[Any]			=       image_size
								a   :	Optional[int]			=       initializer_range
								a   :	Tuple			=       attention_dropout
								a   :	Union[str, Any]			=       layer_norm_eps
								a   :	List[Any]			=       hidden_act






							@classmethod
							def 					__a     (     cls   ,      lowerCAmelCase__   ,      **lowerCAmelCase__   )			->				"PretrainedConfig":
								cls._set_token_in_kwargs(lowerCAmelCase__   )

								a, a   :	List[str]			=       cls.get_config_dict(lowerCAmelCase__   ,      **lowerCAmelCase__   )

								# get the vision config dict if we are loading from GITConfig
								if config_dict.get("model_type"   ) == "git":
									a   :	Dict			=       config_dict["vision_config"]

								if "model_type" in config_dict and hasattr(cls   ,      "model_type"   ) and config_dict["model_type"] != cls.model_type:
									logger.warning(
									    f"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
									    f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""   )

								return cls.from_dict(lowerCAmelCase__   ,      **lowerCAmelCase__   )


class  __UpperCamelCase       (		a__		):
							lowerCamelCase     :      List[Any]         ="""git"""






							def __init__(     self   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=3_0522   ,      lowerCAmelCase__=768   ,      lowerCAmelCase__=6   ,      lowerCAmelCase__=12   ,      lowerCAmelCase__=3072   ,      lowerCAmelCase__="gelu"   ,      lowerCAmelCase__=0.1   ,      lowerCAmelCase__=0.1   ,      lowerCAmelCase__=1024   ,      lowerCAmelCase__=0.02   ,      lowerCAmelCase__=1E-12   ,      lowerCAmelCase__=0   ,      lowerCAmelCase__="absolute"   ,      lowerCAmelCase__=True   ,      lowerCAmelCase__=False   ,      lowerCAmelCase__=101   ,      lowerCAmelCase__=102   ,      lowerCAmelCase__=None   ,      **lowerCAmelCase__   ,      )			->				List[Any]:
								super().__init__(bos_token_id=lowerCAmelCase__   ,      eos_token_id=lowerCAmelCase__   ,      pad_token_id=lowerCAmelCase__   ,      **lowerCAmelCase__   )

								if vision_config is None:
									a   :	List[str]			=       {}
									logger.info("vision_config is None. initializing the GitVisionConfig with default values."   )

								a   :	Optional[int]			=       GitVisionConfig(**lowerCAmelCase__   )
								a   :	str			=       vocab_size
								a   :	Union[str, Any]			=       hidden_size
								a   :	str			=       num_hidden_layers
								a   :	List[str]			=       num_attention_heads
								a   :	Any			=       hidden_act
								a   :	Tuple			=       intermediate_size
								a   :	List[Any]			=       hidden_dropout_prob
								a   :	Tuple			=       attention_probs_dropout_prob
								a   :	str			=       max_position_embeddings
								a   :	int			=       initializer_range
								a   :	Optional[int]			=       layer_norm_eps
								a   :	int			=       position_embedding_type
								a   :	Optional[Any]			=       use_cache
								a   :	Any			=       tie_word_embeddings
								a   :	List[Any]			=       num_image_with_embedding

								a   :	int			=       bos_token_id
								a   :	str			=       eos_token_id






							def 					__a     (     self   )			->				str:
								a   :	int			=       copy.deepcopy(self.__dict__   )
								a   :	Optional[int]			=       self.vision_config.to_dict()
								a   :	Optional[Any]			=       self.__class__.model_type
								return output









"""simple docstring"""




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

a		:				List[Any]  			=    TaTokenizer

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

a		:				Union[str, Any]  			=    TaTokenizerFast

a		:				Union[str, Any]  			=    {'''configuration_mt5''': ['''MT5Config''', '''MT5OnnxConfig''']}

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

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

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a		:				Optional[Any]  			=    ['''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

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


