
import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


_A   =     logging.get_logger(__name__)

_A   =     {
    "Salesforce/instruct-blip-flan-t5": "https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json",
}





class 	_lowerCAmelCase					(	__a       ):
				_lowercase					='''instructblip_vision_model'''




				def __init__(					self				,				_UpperCamelCase=1_408				,				_UpperCamelCase=6_144				,				_UpperCamelCase=39				,				_UpperCamelCase=16				,				_UpperCamelCase=224				,				_UpperCamelCase=14				,				_UpperCamelCase="gelu"				,				_UpperCamelCase=1e-6				,				_UpperCamelCase=0.0				,				_UpperCamelCase=1e-1_0				,				_UpperCamelCase=True				,				**_UpperCamelCase				,				)				->      str:
							super().__init__(**_UpperCamelCase     )

							lowerCAmelCase_						=	hidden_size
							lowerCAmelCase_						=	intermediate_size
							lowerCAmelCase_						=	num_hidden_layers
							lowerCAmelCase_						=	num_attention_heads
							lowerCAmelCase_						=	patch_size
							lowerCAmelCase_						=	image_size
							lowerCAmelCase_						=	initializer_range
							lowerCAmelCase_						=	attention_dropout
							lowerCAmelCase_						=	layer_norm_eps
							lowerCAmelCase_						=	hidden_act
							lowerCAmelCase_						=	qkv_bias




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

							lowerCAmelCase_	,  lowerCAmelCase_						=	cls.get_config_dict(_UpperCamelCase				,				**_UpperCamelCase     )

							# get the vision config dict if we are loading from InstructBlipConfig
							if config_dict.get("model_type"     ) == "instructblip":
										lowerCAmelCase_						=	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 	_lowerCAmelCase					(	__a       ):
				_lowercase					='''instructblip_qformer'''




				def __init__(					self				,				_UpperCamelCase=30_522				,				_UpperCamelCase=768				,				_UpperCamelCase=12				,				_UpperCamelCase=12				,				_UpperCamelCase=3_072				,				_UpperCamelCase="gelu"				,				_UpperCamelCase=0.1				,				_UpperCamelCase=0.1				,				_UpperCamelCase=512				,				_UpperCamelCase=0.02				,				_UpperCamelCase=1e-1_2				,				_UpperCamelCase=0				,				_UpperCamelCase="absolute"				,				_UpperCamelCase=2				,				_UpperCamelCase=1_408				,				**_UpperCamelCase				,				)				->      Dict:
							super().__init__(pad_token_id=_UpperCamelCase				,				**_UpperCamelCase     )

							lowerCAmelCase_						=	vocab_size
							lowerCAmelCase_						=	hidden_size
							lowerCAmelCase_						=	num_hidden_layers
							lowerCAmelCase_						=	num_attention_heads
							lowerCAmelCase_						=	hidden_act
							lowerCAmelCase_						=	intermediate_size
							lowerCAmelCase_						=	hidden_dropout_prob
							lowerCAmelCase_						=	attention_probs_dropout_prob
							lowerCAmelCase_						=	max_position_embeddings
							lowerCAmelCase_						=	initializer_range
							lowerCAmelCase_						=	layer_norm_eps
							lowerCAmelCase_						=	position_embedding_type
							lowerCAmelCase_						=	cross_attention_frequency
							lowerCAmelCase_						=	encoder_hidden_size




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

							lowerCAmelCase_	,  lowerCAmelCase_						=	cls.get_config_dict(_UpperCamelCase				,				**_UpperCamelCase     )

							# get the qformer config dict if we are loading from InstructBlipConfig
							if config_dict.get("model_type"     ) == "instructblip":
										lowerCAmelCase_						=	config_dict["qformer_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 	_lowerCAmelCase					(	__a       ):
				_lowercase					='''instructblip'''
				_lowercase					=True




				def __init__(					self				,				_UpperCamelCase=None				,				_UpperCamelCase=None				,				_UpperCamelCase=None				,				_UpperCamelCase=32				,				**_UpperCamelCase     )				->      Optional[Any]:
							super().__init__(**_UpperCamelCase     )

							if vision_config is None:
										lowerCAmelCase_						=	{}
										logger.info("vision_config is None. initializing the InstructBlipVisionConfig with default values."     )

							if qformer_config is None:
										lowerCAmelCase_						=	{}
										logger.info("qformer_config is None. Initializing the InstructBlipQFormerConfig with default values."     )

							if text_config is None:
										lowerCAmelCase_						=	{}
										logger.info("text_config is None. Initializing the text config with default values (`OPTConfig`)."     )

							lowerCAmelCase_						=	InstructBlipVisionConfig(**_UpperCamelCase     )
							lowerCAmelCase_						=	InstructBlipQFormerConfig(**_UpperCamelCase     )
							lowerCAmelCase_						=	text_config["model_type"] if "model_type" in text_config else "opt"
							lowerCAmelCase_						=	CONFIG_MAPPING[text_model_type](**_UpperCamelCase     )

							lowerCAmelCase_						=	self.text_config.tie_word_embeddings
							lowerCAmelCase_						=	self.text_config.is_encoder_decoder

							lowerCAmelCase_						=	num_query_tokens
							lowerCAmelCase_						=	self.vision_config.hidden_size
							lowerCAmelCase_						=	self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
							lowerCAmelCase_						=	1.0
							lowerCAmelCase_						=	0.02




				@classmethod
				def      __a						(					cls				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				**_UpperCamelCase				,				)				->      Optional[int]:
							return cls(
							    vision_config=vision_config.to_dict()				,				qformer_config=qformer_config.to_dict()				,				text_config=text_config.to_dict()				,				**_UpperCamelCase				,				)




				def      __a						(					self     )				->      int:
							lowerCAmelCase_						=	copy.deepcopy(self.__dict__     )
							lowerCAmelCase_						=	self.vision_config.to_dict()
							lowerCAmelCase_						=	self.qformer_config.to_dict()
							lowerCAmelCase_						=	self.text_config.to_dict()
							lowerCAmelCase_						=	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,
)


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

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

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

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


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


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

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



