

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,
)


__lowerCAmelCase			     =       {"""configuration_xglm""": ["""XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XGLMConfig"""]}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCAmelCase			     =       ["""XGLMTokenizer"""]

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCAmelCase			     =       ["""XGLMTokenizerFast"""]

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


try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCAmelCase			     =       [
	    """FlaxXGLMForCausalLM""",
	    """FlaxXGLMModel""",
	    """FlaxXGLMPreTrainedModel""",
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCAmelCase			     =       [
	    """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

	__lowerCAmelCase			     =       _LazyModule(__name__, globals()["""__file__"""], _import_structure)







'''simple docstring'''







import copy
import os
from typing import Union

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


__lowerCamelCase									=     logging.get_logger(__name__)

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


class        A__   (   _snake_case      ):
    lowercase						       =		"pix2struct_text_model"
    lowercase						       =		["past_key_values"]
    lowercase						       =		{
        "hidden_size": "hidden_size",
        "num_attention_heads": "num_heads",
        "num_hidden_layers": "num_layers",
    }



    def __init__(       self		,					UpperCamelCase__=50244		,					UpperCamelCase__=768		,					UpperCamelCase__=64		,					UpperCamelCase__=2048		,					UpperCamelCase__=12		,					UpperCamelCase__=12		,					UpperCamelCase__=32		,					UpperCamelCase__=128		,					UpperCamelCase__=0.1		,					UpperCamelCase__=1e-6		,					UpperCamelCase__=1.0		,					UpperCamelCase__="gelu_new"		,					UpperCamelCase__=0		,					UpperCamelCase__=False		,					UpperCamelCase__=0		,					UpperCamelCase__=1		,					UpperCamelCase__=False		,					UpperCamelCase__=True		,					**UpperCamelCase__		,					)    ->   List[str]:





           '''simple docstring'''
           A_						       =   vocab_size
           A_						       =   hidden_size
           A_						       =   d_kv
           A_						       =   d_ff
           A_						       =   num_layers
           A_						       =   num_heads
           A_						       =   relative_attention_num_buckets
           A_						       =   relative_attention_max_distance
           A_						       =   dropout_rate
           A_						       =   layer_norm_epsilon
           A_						       =   initializer_factor
           A_						       =   use_cache

           A_						       =   eos_token_id
           A_						       =   decoder_start_token_id

           # for backwards compatibility
           A_						       =   dense_act_fn

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







    @classmethod
    def        snake_case_      (       cls		,					UpperCamelCase__		,					**UpperCamelCase__							)    ->   "PretrainedConfig":





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

           A_       ,      A_						       =   cls.get_config_dict(UpperCamelCase__		,					**UpperCamelCase__							)

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


class        A__   (   _snake_case      ):
    lowercase						       =		"pix2struct_vision_model"



    def __init__(       self		,					UpperCamelCase__=768		,					UpperCamelCase__=768		,					UpperCamelCase__=2048		,					UpperCamelCase__=64		,					UpperCamelCase__=12		,					UpperCamelCase__=12		,					UpperCamelCase__="gelu_new"		,					UpperCamelCase__=1e-6		,					UpperCamelCase__=0.0		,					UpperCamelCase__=0.0		,					UpperCamelCase__=1e-1_0		,					UpperCamelCase__=1.0		,					UpperCamelCase__=4096		,					UpperCamelCase__=32		,					UpperCamelCase__=128		,					**UpperCamelCase__		,					)    ->   int:





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

           A_						       =   hidden_size
           A_						       =   patch_embed_hidden_size
           A_						       =   d_ff
           A_						       =   dropout_rate
           A_						       =   num_hidden_layers
           A_						       =   num_attention_heads
           A_						       =   initializer_range
           A_						       =   initializer_factor
           A_						       =   attention_dropout
           A_						       =   layer_norm_eps
           A_						       =   dense_act_fn
           A_						       =   seq_len
           A_						       =   relative_attention_num_buckets
           A_						       =   relative_attention_max_distance
           A_						       =   d_kv







    @classmethod
    def        snake_case_      (       cls		,					UpperCamelCase__		,					**UpperCamelCase__							)    ->   "PretrainedConfig":





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

           A_       ,      A_						       =   cls.get_config_dict(UpperCamelCase__		,					**UpperCamelCase__							)

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


class        A__   (   _snake_case      ):
    lowercase						       =		"pix2struct"
    lowercase						       =		True



    def __init__(       self		,					UpperCamelCase__=None		,					UpperCamelCase__=None		,					UpperCamelCase__=1.0		,					UpperCamelCase__=0.02		,					UpperCamelCase__=False		,					UpperCamelCase__=False		,					UpperCamelCase__=True		,					**UpperCamelCase__		,					)    ->   str:





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

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

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

           A_						       =   PixaStructTextConfig(**UpperCamelCase__							)
           A_						       =   PixaStructVisionConfig(**UpperCamelCase__							)

           A_						       =   self.text_config.decoder_start_token_id
           A_						       =   self.text_config.pad_token_id
           A_						       =   self.text_config.eos_token_id

           A_						       =   initializer_factor
           A_						       =   initializer_range

           A_						       =   self.initializer_range
           A_						       =   self.initializer_range

           A_						       =   is_vqa



    @classmethod
    def        snake_case_      (       cls		,					UpperCamelCase__		,					UpperCamelCase__		,					**UpperCamelCase__							)    ->   Optional[int]:





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







    def        snake_case_      (       self							)    ->   List[Any]:





           '''simple docstring'''
           A_						       =   copy.deepcopy(self.__dict__							)
           A_						       =   self.text_config.to_dict()
           A_						       =   self.vision_config.to_dict()
           A_						       =   self.__class__.model_type
           return output
