






import copy
import os
from typing import Union

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


lowercase  :							int			  =    logging.get_logger(__name__)

lowercase  :							Optional[int]			  =    {
    '''google/pix2struct-textcaps-base''': (
        '''https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'''
    ),
}




class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						A     :   str						=					'pix2struct_text_model'
						A     :   str						=					['past_key_values']
						A     :   Optional[int]						=					{
						    'hidden_size': 'hidden_size',
						    'num_attention_heads': 'num_heads',
						    'num_hidden_layers': 'num_layers',
						}






						def __init__(	self		,				_SCREAMING_SNAKE_CASE=5_0244		,				_SCREAMING_SNAKE_CASE=768		,				_SCREAMING_SNAKE_CASE=64		,				_SCREAMING_SNAKE_CASE=2048		,				_SCREAMING_SNAKE_CASE=12		,				_SCREAMING_SNAKE_CASE=12		,				_SCREAMING_SNAKE_CASE=32		,				_SCREAMING_SNAKE_CASE=128		,				_SCREAMING_SNAKE_CASE=0.1		,				_SCREAMING_SNAKE_CASE=1e-6		,				_SCREAMING_SNAKE_CASE=1.0		,				_SCREAMING_SNAKE_CASE="gelu_new"		,				_SCREAMING_SNAKE_CASE=0		,				_SCREAMING_SNAKE_CASE=False		,				_SCREAMING_SNAKE_CASE=0		,				_SCREAMING_SNAKE_CASE=1		,				_SCREAMING_SNAKE_CASE=False		,				_SCREAMING_SNAKE_CASE=True		,				**_SCREAMING_SNAKE_CASE		,				) ->				Union[str, Any]:
								snake_case_				:							str						 =       vocab_size
								snake_case_				:							int						 =       hidden_size
								snake_case_				:							Optional[int]						 =       d_kv
								snake_case_				:							Optional[int]						 =       d_ff
								snake_case_				:							List[Any]						 =       num_layers
								snake_case_				:							Optional[int]						 =       num_heads
								snake_case_				:							str						 =       relative_attention_num_buckets
								snake_case_				:							int						 =       relative_attention_max_distance
								snake_case_				:							Any						 =       dropout_rate
								snake_case_				:							str						 =       layer_norm_epsilon
								snake_case_				:							int						 =       initializer_factor
								snake_case_				:							Union[str, Any]						 =       use_cache

								snake_case_				:							Tuple						 =       eos_token_id
								snake_case_				:							Union[str, Any]						 =       decoder_start_token_id

								# for backwards compatibility
								snake_case_				:							List[str]						 =       dense_act_fn

								super().__init__(
								    pad_token_id=_SCREAMING_SNAKE_CASE		,				eos_token_id=_SCREAMING_SNAKE_CASE		,				decoder_start_token_id=_SCREAMING_SNAKE_CASE		,				tie_word_embeddings=_SCREAMING_SNAKE_CASE		,				is_decoder=_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE		,				)






						@classmethod
						def       _lowerCAmelCase       (	cls		,				_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				) ->				"PretrainedConfig":
								cls._set_token_in_kwargs(_SCREAMING_SNAKE_CASE				)

								snake_case_				:							Optional[Any]						 =       cls.get_config_dict(_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				)

								# get the text config dict if we are loading from Pix2StructConfig
								if config_dict.get("model_type"				) == "pix2struct":
										snake_case_				:							List[Any]						 =       config_dict["text_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(_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				)




class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						A     :   Union[str, Any]						=					'pix2struct_vision_model'






						def __init__(	self		,				_SCREAMING_SNAKE_CASE=768		,				_SCREAMING_SNAKE_CASE=768		,				_SCREAMING_SNAKE_CASE=2048		,				_SCREAMING_SNAKE_CASE=64		,				_SCREAMING_SNAKE_CASE=12		,				_SCREAMING_SNAKE_CASE=12		,				_SCREAMING_SNAKE_CASE="gelu_new"		,				_SCREAMING_SNAKE_CASE=1e-6		,				_SCREAMING_SNAKE_CASE=0.0		,				_SCREAMING_SNAKE_CASE=0.0		,				_SCREAMING_SNAKE_CASE=1e-10		,				_SCREAMING_SNAKE_CASE=1.0		,				_SCREAMING_SNAKE_CASE=4096		,				_SCREAMING_SNAKE_CASE=32		,				_SCREAMING_SNAKE_CASE=128		,				**_SCREAMING_SNAKE_CASE		,				) ->				Any:
								super().__init__(**_SCREAMING_SNAKE_CASE				)

								snake_case_				:							Optional[Any]						 =       hidden_size
								snake_case_				:							Any						 =       patch_embed_hidden_size
								snake_case_				:							Tuple						 =       d_ff
								snake_case_				:							Optional[int]						 =       dropout_rate
								snake_case_				:							List[Any]						 =       num_hidden_layers
								snake_case_				:							int						 =       num_attention_heads
								snake_case_				:							Optional[Any]						 =       initializer_range
								snake_case_				:							Optional[Any]						 =       initializer_factor
								snake_case_				:							str						 =       attention_dropout
								snake_case_				:							Optional[Any]						 =       layer_norm_eps
								snake_case_				:							int						 =       dense_act_fn
								snake_case_				:							Optional[Any]						 =       seq_len
								snake_case_				:							List[str]						 =       relative_attention_num_buckets
								snake_case_				:							Optional[int]						 =       relative_attention_max_distance
								snake_case_				:							Any						 =       d_kv






						@classmethod
						def       _lowerCAmelCase       (	cls		,				_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				) ->				"PretrainedConfig":
								cls._set_token_in_kwargs(_SCREAMING_SNAKE_CASE				)

								snake_case_				:							List[Any]						 =       cls.get_config_dict(_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				)

								# get the vision config dict if we are loading from Pix2StructConfig
								if config_dict.get("model_type"				) == "pix2struct":
										snake_case_				:							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(_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				)




class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						A     :   Optional[Any]						=					'pix2struct'
						A     :   Any						=					True






						def __init__(	self		,				_SCREAMING_SNAKE_CASE=None		,				_SCREAMING_SNAKE_CASE=None		,				_SCREAMING_SNAKE_CASE=1.0		,				_SCREAMING_SNAKE_CASE=0.02		,				_SCREAMING_SNAKE_CASE=False		,				_SCREAMING_SNAKE_CASE=False		,				_SCREAMING_SNAKE_CASE=True		,				**_SCREAMING_SNAKE_CASE		,				) ->				Dict:
								super().__init__(tie_word_embeddings=_SCREAMING_SNAKE_CASE		,				is_encoder_decoder=_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				)

								if text_config is None:
										snake_case_				:							Dict						 =       {}
										logger.info("text_config is None. Initializing the Pix2StructTextConfig with default values."				)

								if vision_config is None:
										snake_case_				:							List[str]						 =       {}
										logger.info("vision_config is None. Initializing the Pix2StructVisionConfig with default values."				)

								snake_case_				:							Dict						 =       PixaStructTextConfig(**_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Dict						 =       PixaStructVisionConfig(**_SCREAMING_SNAKE_CASE				)

								snake_case_				:							List[Any]						 =       self.text_config.decoder_start_token_id
								snake_case_				:							List[str]						 =       self.text_config.pad_token_id
								snake_case_				:							Optional[int]						 =       self.text_config.eos_token_id

								snake_case_				:							List[Any]						 =       initializer_factor
								snake_case_				:							Optional[int]						 =       initializer_range

								snake_case_				:							Tuple						 =       self.initializer_range
								snake_case_				:							Tuple						 =       self.initializer_range

								snake_case_				:							int						 =       is_vqa






						@classmethod
						def       _lowerCAmelCase       (	cls		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				) ->				List[str]:
								return cls(text_config=text_config.to_dict()		,				vision_config=vision_config.to_dict()		,				**_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self				) ->				Any:
								snake_case_				:							int						 =       copy.deepcopy(self.__dict__				)
								snake_case_				:							Dict						 =       self.text_config.to_dict()
								snake_case_				:							List[str]						 =       self.vision_config.to_dict()
								snake_case_				:							Any						 =       self.__class__.model_type
								return output








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,
)


lowercase  :							Union[str, Any]			  =    {'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							Optional[int]			  =    ['''XGLMTokenizer''']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							Tuple			  =    ['''XGLMTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							str			  =    [
							    '''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''XGLMForCausalLM''',
							    '''XGLMModel''',
							    '''XGLMPreTrainedModel''',
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							str			  =    [
							    '''FlaxXGLMForCausalLM''',
							    '''FlaxXGLMModel''',
							    '''FlaxXGLMPreTrainedModel''',
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							Tuple			  =    [
							    '''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFXGLMForCausalLM''',
							    '''TFXGLMModel''',
							    '''TFXGLMPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm import XGLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm_fast import XGLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_xglm import (
														    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFXGLMForCausalLM,
														    TFXGLMModel,
														    TFXGLMPreTrainedModel,
														)


else:
							import sys

							lowercase  :							Dict			  =    _LazyModule(__name__, globals()['''__file__'''], _import_structure)
