


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


SCREAMING_SNAKE_CASE       :      Tuple												= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE       :      Union[str, Any]												= {
    "salesforce/blip2-opt-2.7b": "https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json",
}






class _lowerCamelCase(						_a   ):
       lowercase_ :	int     =			"""blip_2_vision_model"""
       def __init__(					self,      lowerCamelCase=14_08,      lowerCamelCase=61_44,      lowerCamelCase=39,      lowerCamelCase=16,      lowerCamelCase=2_24,      lowerCamelCase=14,      lowerCamelCase="gelu",      lowerCamelCase=0.0_0_0_0_1,      lowerCamelCase=0.0,      lowerCamelCase=1E-10,      lowerCamelCase=True,      **lowerCamelCase,      )       ->   str:




           """simple docstring"""

           super().__init__(**lowerCamelCase)

           _lowercase       :							Tuple      		=		hidden_size
           _lowercase       :							Tuple      		=		intermediate_size
           _lowercase       :							Optional[int]      		=		num_hidden_layers
           _lowercase       :							Tuple      		=		num_attention_heads
           _lowercase       :							List[Any]      		=		patch_size
           _lowercase       :							List[str]      		=		image_size
           _lowercase       :							Dict      		=		initializer_range
           _lowercase       :							str      		=		attention_dropout
           _lowercase       :							Tuple      		=		layer_norm_eps
           _lowercase       :							List[Any]      		=		hidden_act
           _lowercase       :							Optional[Any]      		=		qkv_bias




       @classmethod
       def 			UpperCamelCase     (					cls,      lowerCamelCase,      **lowerCamelCase)       ->   "PretrainedConfig":




           """simple docstring"""

           cls._set_token_in_kwargs(lowerCamelCase)

           _lowercase		,				_lowercase       :							Dict      		=		cls.get_config_dict(lowerCamelCase,      **lowerCamelCase)

           # get the vision config dict if we are loading from Blip2Config
           if config_dict.get('model_type') == "blip-2":
               _lowercase       :							Tuple      		=		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 _lowerCamelCase(						_a   ):
       lowercase_ :	List[Any]     =			"""blip_2_qformer"""
       def __init__(					self,      lowerCamelCase=3_05_22,      lowerCamelCase=7_68,      lowerCamelCase=12,      lowerCamelCase=12,      lowerCamelCase=30_72,      lowerCamelCase="gelu",      lowerCamelCase=0.1,      lowerCamelCase=0.1,      lowerCamelCase=5_12,      lowerCamelCase=0.0_2,      lowerCamelCase=1E-12,      lowerCamelCase=0,      lowerCamelCase="absolute",      lowerCamelCase=2,      lowerCamelCase=14_08,      **lowerCamelCase,      )       ->   Tuple:




           """simple docstring"""

           super().__init__(pad_token_id=lowerCamelCase,      **lowerCamelCase)

           _lowercase       :							Any      		=		vocab_size
           _lowercase       :							int      		=		hidden_size
           _lowercase       :							List[str]      		=		num_hidden_layers
           _lowercase       :							List[str]      		=		num_attention_heads
           _lowercase       :							Dict      		=		hidden_act
           _lowercase       :							str      		=		intermediate_size
           _lowercase       :							Optional[int]      		=		hidden_dropout_prob
           _lowercase       :							str      		=		attention_probs_dropout_prob
           _lowercase       :							int      		=		max_position_embeddings
           _lowercase       :							Dict      		=		initializer_range
           _lowercase       :							Optional[int]      		=		layer_norm_eps
           _lowercase       :							Tuple      		=		position_embedding_type
           _lowercase       :							Tuple      		=		cross_attention_frequency
           _lowercase       :							List[Any]      		=		encoder_hidden_size




       @classmethod
       def 			UpperCamelCase     (					cls,      lowerCamelCase,      **lowerCamelCase)       ->   "PretrainedConfig":




           """simple docstring"""

           cls._set_token_in_kwargs(lowerCamelCase)

           _lowercase		,				_lowercase       :							int      		=		cls.get_config_dict(lowerCamelCase,      **lowerCamelCase)

           # get the qformer config dict if we are loading from Blip2Config
           if config_dict.get('model_type') == "blip-2":
               _lowercase       :							str      		=		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(lowerCamelCase,      **lowerCamelCase)






class _lowerCamelCase(						_a   ):
       lowercase_ :	Dict     =			"""blip-2"""
       lowercase_ :	Union[str, Any]     =			True
       def __init__(					self,      lowerCamelCase=None,      lowerCamelCase=None,      lowerCamelCase=None,      lowerCamelCase=32,      **lowerCamelCase)       ->   Union[str, Any]:




           """simple docstring"""

           super().__init__(**lowerCamelCase)

           if vision_config is None:
               _lowercase       :							List[Any]      		=		{}
               logger.info('vision_config is None. initializing the Blip2VisionConfig with default values.')

           if qformer_config is None:
               _lowercase       :							str      		=		{}
               logger.info('qformer_config is None. Initializing the Blip2QFormerConfig with default values.')

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

           _lowercase       :							Tuple      		=		BlipaVisionConfig(**lowerCamelCase)
           _lowercase       :							List[Any]      		=		BlipaQFormerConfig(**lowerCamelCase)
           _lowercase       :							Tuple      		=		text_config['model_type'] if 'model_type' in text_config else 'opt'
           _lowercase       :							Optional[Any]      		=		CONFIG_MAPPING[text_model_type](**lowerCamelCase)

           _lowercase       :							Optional[int]      		=		self.text_config.tie_word_embeddings
           _lowercase       :							int      		=		self.text_config.is_encoder_decoder

           _lowercase       :							List[Any]      		=		num_query_tokens
           _lowercase       :							Dict      		=		self.vision_config.hidden_size
           _lowercase       :							Any      		=		self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
           _lowercase       :							Union[str, Any]      		=		1.0
           _lowercase       :							List[str]      		=		0.0_2
       @classmethod
       def 			UpperCamelCase     (					cls,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      **lowerCamelCase,      )       ->   Union[str, Any]:




           """simple docstring"""

           return cls(
               vision_config=vision_config.to_dict(),      qformer_config=qformer_config.to_dict(),      text_config=text_config.to_dict(),      **lowerCamelCase,      )




       def 			UpperCamelCase     (					self)       ->   Union[str, Any]:




           """simple docstring"""

           _lowercase       :							Tuple      		=		copy.deepcopy(self.__dict__)
           _lowercase       :							Union[str, Any]      		=		self.vision_config.to_dict()
           _lowercase       :							Optional[Any]      		=		self.qformer_config.to_dict()
           _lowercase       :							Dict      		=		self.text_config.to_dict()
           _lowercase       :							int      		=		self.__class__.model_type
           return output






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_tf_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE       :      List[str]												= {
    "configuration_speech_to_text": ["SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "Speech2TextConfig"],
    "processing_speech_to_text": ["Speech2TextProcessor"],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE       :      Union[str, Any]												= ["Speech2TextTokenizer"]

try:
 if not is_speech_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE       :      int												= ["Speech2TextFeatureExtractor"]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE       :      Optional[Any]												= [
     "TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFSpeech2TextForConditionalGeneration",
     "TFSpeech2TextModel",
     "TFSpeech2TextPreTrainedModel",
 ]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE       :      str												= [
     "SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "Speech2TextForConditionalGeneration",
     "Speech2TextModel",
     "Speech2TextPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
 from .processing_speech_to_text import SpeechaTextProcessor

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_speech_to_text import SpeechaTextTokenizer

 try:
  if not is_speech_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_speech_to_text import (
      TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFSpeechaTextForConditionalGeneration,
      TFSpeechaTextModel,
      TFSpeechaTextPreTrainedModel,
  )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_speech_to_text import (
      SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
      SpeechaTextForConditionalGeneration,
      SpeechaTextModel,
      SpeechaTextPreTrainedModel,
  )

else:
 import sys

 SCREAMING_SNAKE_CASE       :      Optional[Any]												= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


