






import copy
import os
from typing import Union

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


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

_a     : Dict   	=      {
    '''google/pix2struct-textcaps-base''': (
        '''https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'''
    ),
}







class 						_UpperCAmelCase	(	lowerCAmelCase__		):




      """simple docstring"""



      A    =     '''pix2struct_text_model'''
      A    =     ['''past_key_values''']
      A    =     {
          '''hidden_size''': '''hidden_size''',
          '''num_attention_heads''': '''num_heads''',
          '''num_hidden_layers''': '''num_layers''',
      }
      def __init__( self     ,						_lowerCAmelCase=50_244     ,						_lowerCAmelCase=768     ,						_lowerCAmelCase=64     ,						_lowerCAmelCase=2_048     ,						_lowerCAmelCase=12     ,						_lowerCAmelCase=12     ,						_lowerCAmelCase=32     ,						_lowerCAmelCase=128     ,						_lowerCAmelCase=0.1     ,						_lowerCAmelCase=1e-6     ,						_lowerCAmelCase=1.0     ,						_lowerCAmelCase="gelu_new"     ,						_lowerCAmelCase=0     ,						_lowerCAmelCase=False     ,						_lowerCAmelCase=0     ,						_lowerCAmelCase=1     ,						_lowerCAmelCase=False     ,						_lowerCAmelCase=True     ,						**_lowerCAmelCase     ,						):



          '''simple docstring'''
          lowerCAmelCase__ :Union[str, Any]   				=					vocab_size
          lowerCAmelCase__ :str   				=					hidden_size
          lowerCAmelCase__ :List[Any]   				=					d_kv
          lowerCAmelCase__ :Any   				=					d_ff
          lowerCAmelCase__ :Optional[int]   				=					num_layers
          lowerCAmelCase__ :List[Any]   				=					num_heads
          lowerCAmelCase__ :Dict   				=					relative_attention_num_buckets
          lowerCAmelCase__ :Any   				=					relative_attention_max_distance
          lowerCAmelCase__ :List[Any]   				=					dropout_rate
          lowerCAmelCase__ :List[str]   				=					layer_norm_epsilon
          lowerCAmelCase__ :Dict   				=					initializer_factor
          lowerCAmelCase__ :Tuple   				=					use_cache

          lowerCAmelCase__ :Tuple   				=					eos_token_id
          lowerCAmelCase__ :List[str]   				=					decoder_start_token_id

          # for backwards compatibility
          lowerCAmelCase__ :str   				=					dense_act_fn

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



      @classmethod
      def       snake_case_   ( cls     ,						_lowerCAmelCase     ,						**_lowerCAmelCase		):



          '''simple docstring'''
          cls._set_token_in_kwargs(snake_case_		)

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

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







class 						_UpperCAmelCase	(	lowerCAmelCase__		):




      """simple docstring"""



      A    =     '''pix2struct_vision_model'''
      def __init__( self     ,						_lowerCAmelCase=768     ,						_lowerCAmelCase=768     ,						_lowerCAmelCase=2_048     ,						_lowerCAmelCase=64     ,						_lowerCAmelCase=12     ,						_lowerCAmelCase=12     ,						_lowerCAmelCase="gelu_new"     ,						_lowerCAmelCase=1e-6     ,						_lowerCAmelCase=0.0     ,						_lowerCAmelCase=0.0     ,						_lowerCAmelCase=1e-10     ,						_lowerCAmelCase=1.0     ,						_lowerCAmelCase=4_096     ,						_lowerCAmelCase=32     ,						_lowerCAmelCase=128     ,						**_lowerCAmelCase     ,						):



          '''simple docstring'''
          super().__init__(**snake_case_		)

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



      @classmethod
      def       snake_case_   ( cls     ,						_lowerCAmelCase     ,						**_lowerCAmelCase		):



          '''simple docstring'''
          cls._set_token_in_kwargs(snake_case_		)

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

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







class 						_UpperCAmelCase	(	lowerCAmelCase__		):




      """simple docstring"""



      A    =     '''pix2struct'''
      A    =     True
      def __init__( self     ,						_lowerCAmelCase=None     ,						_lowerCAmelCase=None     ,						_lowerCAmelCase=1.0     ,						_lowerCAmelCase=0.02     ,						_lowerCAmelCase=False     ,						_lowerCAmelCase=False     ,						_lowerCAmelCase=True     ,						**_lowerCAmelCase     ,						):



          '''simple docstring'''
          super().__init__(tie_word_embeddings=snake_case_     ,						is_encoder_decoder=snake_case_     ,						**snake_case_		)

          if text_config is None:
              lowerCAmelCase__ :List[Any]   				=					{}
              logger.info("text_config is None. Initializing the Pix2StructTextConfig with default values."		)

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

          lowerCAmelCase__ :List[str]   				=					PixaStructTextConfig(**snake_case_		)
          lowerCAmelCase__ :Optional[int]   				=					PixaStructVisionConfig(**snake_case_		)

          lowerCAmelCase__ :int   				=					self.text_config.decoder_start_token_id
          lowerCAmelCase__ :Tuple   				=					self.text_config.pad_token_id
          lowerCAmelCase__ :Union[str, Any]   				=					self.text_config.eos_token_id

          lowerCAmelCase__ :Optional[Any]   				=					initializer_factor
          lowerCAmelCase__ :List[Any]   				=					initializer_range

          lowerCAmelCase__ :str   				=					self.initializer_range
          lowerCAmelCase__ :List[str]   				=					self.initializer_range

          lowerCAmelCase__ :List[Any]   				=					is_vqa
      @classmethod
      def       snake_case_   ( cls     ,						_lowerCAmelCase     ,						_lowerCAmelCase     ,						**_lowerCAmelCase		):



          '''simple docstring'''
          return cls(text_config=text_config.to_dict()     ,						vision_config=vision_config.to_dict()     ,						**snake_case_		)



      def       snake_case_   ( self		):



          '''simple docstring'''
          lowerCAmelCase__ :Dict   				=					copy.deepcopy(self.__dict__		)
          lowerCAmelCase__ :Union[str, Any]   				=					self.text_config.to_dict()
          lowerCAmelCase__ :Dict   				=					self.vision_config.to_dict()
          lowerCAmelCase__ :Union[str, Any]   				=					self.__class__.model_type
          return output







from typing import TYPE_CHECKING

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


a			:			Optional[Any]												=					{
    '''configuration_llama''': ['''LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LlamaConfig'''],
}

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

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a			:			Optional[int]												=					['''LlamaTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a			:			Any												=					[
	    '''LlamaForCausalLM''',
	    '''LlamaModel''',
	    '''LlamaPreTrainedModel''',
	    '''LlamaForSequenceClassification''',
	]


if TYPE_CHECKING:
	from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_llama import LlamaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_llama_fast import LlamaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
	import sys

	a			:			Dict												=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


