
import copy
import os
from typing import Union

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


lowerCamelCase__	=							logging.get_logger(__name__)

lowerCamelCase__	=							{
    "Salesforce/blip-vqa-base": "https://huggingface.co/Salesforce/blip-vqa-base/resolve/main/config.json",
    "Salesforce/blip-vqa-capfit-large": (
        "https://huggingface.co/Salesforce/blip-vqa-base-capfit/resolve/main/config.json"
    ),
    "Salesforce/blip-image-captioning-base": (
        "https://huggingface.co/Salesforce/blip-image-captioning-base/resolve/main/config.json"
    ),
    "Salesforce/blip-image-captioning-large": (
        "https://huggingface.co/Salesforce/blip-image-captioning-large/resolve/main/config.json"
    ),
    "Salesforce/blip-itm-base-coco": "https://huggingface.co/Salesforce/blip-itm-base-coco/resolve/main/config.json",
    "Salesforce/blip-itm-large-coco": "https://huggingface.co/Salesforce/blip-itm-large-coco/resolve/main/config.json",
    "Salesforce/blip-itm-base-flikr": "https://huggingface.co/Salesforce/blip-itm-base-flikr/resolve/main/config.json",
    "Salesforce/blip-itm-large-flikr": (
        "https://huggingface.co/Salesforce/blip-itm-large-flikr/resolve/main/config.json"
    ),
}







class        lowerCAmelCase__      (   __lowercase	):
						UpperCamelCase_							: Optional[Any]	=		"blip_text_model"

						def __init__(			self       ,							a=3_05_24       ,							a=7_68       ,							a=7_68       ,							a=30_72       ,							a=7_68       ,							a=12       ,							a=8       ,							a=5_12       ,							a="gelu"       ,							a=1e-12       ,							a=0.0       ,							a=0.0       ,							a=0.02       ,							a=3_05_22       ,							a=2       ,							a=0       ,							a=1_02       ,							a=True       ,							a=True       ,							**a       ,							)					->      Optional[Any]:


								'''simple docstring'''

								super().__init__(
								    pad_token_id=a       ,							bos_token_id=a       ,							eos_token_id=a       ,							sep_token_id=a       ,							**a       ,							)

								_UpperCamelCase						    =	vocab_size
								_UpperCamelCase						    =	hidden_size
								_UpperCamelCase						    =	encoder_hidden_size
								_UpperCamelCase						    =	intermediate_size
								_UpperCamelCase						    =	projection_dim
								_UpperCamelCase						    =	hidden_dropout_prob
								_UpperCamelCase						    =	num_hidden_layers
								_UpperCamelCase						    =	num_attention_heads
								_UpperCamelCase						    =	max_position_embeddings
								_UpperCamelCase						    =	layer_norm_eps
								_UpperCamelCase						    =	hidden_act
								_UpperCamelCase						    =	initializer_range
								_UpperCamelCase						    =	attention_probs_dropout_prob
								_UpperCamelCase						    =	is_decoder
								_UpperCamelCase						    =	use_cache







						@classmethod
						def  A_				(			cls       ,							a       ,							**a				)					->      "PretrainedConfig":


								'''simple docstring'''

								cls._set_token_in_kwargs(a				)

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

								# get the text config dict if we are loading from BlipConfig
								if config_dict.get("""model_type"""				) == "blip":
										_UpperCamelCase						    =	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(a       ,							**a				)







class        lowerCAmelCase__      (   __lowercase	):
						UpperCamelCase_							: Optional[int]	=		"blip_vision_model"

						def __init__(			self       ,							a=7_68       ,							a=30_72       ,							a=5_12       ,							a=12       ,							a=12       ,							a=3_84       ,							a=16       ,							a="gelu"       ,							a=1e-5       ,							a=0.0       ,							a=1e-10       ,							**a       ,							)					->      Optional[int]:


								'''simple docstring'''

								super().__init__(**a				)

								_UpperCamelCase						    =	hidden_size
								_UpperCamelCase						    =	intermediate_size
								_UpperCamelCase						    =	projection_dim
								_UpperCamelCase						    =	num_hidden_layers
								_UpperCamelCase						    =	num_attention_heads
								_UpperCamelCase						    =	patch_size
								_UpperCamelCase						    =	image_size
								_UpperCamelCase						    =	initializer_range
								_UpperCamelCase						    =	attention_dropout
								_UpperCamelCase						    =	layer_norm_eps
								_UpperCamelCase						    =	hidden_act







						@classmethod
						def  A_				(			cls       ,							a       ,							**a				)					->      "PretrainedConfig":


								'''simple docstring'''

								cls._set_token_in_kwargs(a				)

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

								# get the vision config dict if we are loading from BlipConfig
								if config_dict.get("""model_type"""				) == "blip":
										_UpperCamelCase						    =	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(a       ,							**a				)







class        lowerCAmelCase__      (   __lowercase	):
						UpperCamelCase_							: Union[str, Any]	=		"blip"
						UpperCamelCase_							: int	=		True

						def __init__(			self       ,							a=None       ,							a=None       ,							a=5_12       ,							a=2.6592       ,							a=2_56       ,							**a       ,							)					->      Dict:


								'''simple docstring'''

								super().__init__(**a				)

								if text_config is None:
										_UpperCamelCase						    =	{}
										logger.info("""`text_config` is `None`. Initializing the `BlipTextConfig` with default values."""				)

								if vision_config is None:
										_UpperCamelCase						    =	{}
										logger.info("""`vision_config` is `None`. Initializing the `BlipVisionConfig` with default values."""				)

								_UpperCamelCase						    =	BlipTextConfig(**a				)
								_UpperCamelCase						    =	BlipVisionConfig(**a				)

								_UpperCamelCase						    =	self.vision_config.hidden_size

								_UpperCamelCase						    =	projection_dim
								_UpperCamelCase						    =	logit_scale_init_value
								_UpperCamelCase						    =	1.0
								_UpperCamelCase						    =	0.02
								_UpperCamelCase						    =	image_text_hidden_size

						@classmethod
						def  A_				(			cls       ,							a       ,							a       ,							**a				)					->      Dict:


								'''simple docstring'''

								return cls(text_config=text_config.to_dict()       ,							vision_config=vision_config.to_dict()       ,							**a				)







						def  A_				(			self				)					->      str:


								'''simple docstring'''

								_UpperCamelCase						    =	copy.deepcopy(self.__dict__				)
								_UpperCamelCase						    =	self.text_config.to_dict()
								_UpperCamelCase						    =	self.vision_config.to_dict()
								_UpperCamelCase						    =	self.__class__.model_type
								return output





from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand


def 							__A(lowerCAmelCase )		->				List[str]:

		"""simple docstring"""






		return ConvertCommand(
		    args.model_type						,  args.tf_checkpoint						,  args.pytorch_dump_output						,  args.config						,  args.finetuning_task_name )


lowerCamelCase__	=							"\ntransformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires\nTensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.\n"







class        lowerCAmelCase__      (   __lowercase	):

						@staticmethod
						def  A_				(			a				)					->      Optional[int]:


								'''simple docstring'''

								_UpperCamelCase						    =	parser.add_parser(
								    """convert"""       ,							help="""CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints."""       ,							)
								train_parser.add_argument("""--model_type"""       ,							type=a       ,							required=a       ,							help="""Model's type."""				)
								train_parser.add_argument(
								    """--tf_checkpoint"""       ,							type=a       ,							required=a       ,							help="""TensorFlow checkpoint path or folder."""				)
								train_parser.add_argument(
								    """--pytorch_dump_output"""       ,							type=a       ,							required=a       ,							help="""Path to the PyTorch saved model output."""				)
								train_parser.add_argument("""--config"""       ,							type=a       ,							default=""""""       ,							help="""Configuration file path or folder."""				)
								train_parser.add_argument(
								    """--finetuning_task_name"""       ,							type=a       ,							default=a       ,							help="""Optional fine-tuning task name if the TF model was a finetuned model."""       ,							)
								train_parser.set_defaults(func=a				)

						def __init__(			self       ,							a       ,							a       ,							a       ,							a       ,							a       ,							*a       ,							)					->      Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	logging.get_logger("""transformers-cli/converting"""				)

								self._logger.info(F'Loading model {model_type}'				)
								_UpperCamelCase						    =	model_type
								_UpperCamelCase						    =	tf_checkpoint
								_UpperCamelCase						    =	pytorch_dump_output
								_UpperCamelCase						    =	config
								_UpperCamelCase						    =	finetuning_task_name







						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

								if self._model_type == "albert":
										try:
												from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
												    convert_tf_checkpoint_to_pytorch,
												)
										except ImportError:
												raise ImportError(a				)

										convert_tf_checkpoint_to_pytorch(self._tf_checkpoint       ,							self._config       ,							self._pytorch_dump_output				)
								elif self._model_type == "bert":
										try:
												from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
												    convert_tf_checkpoint_to_pytorch,
												)
										except ImportError:
												raise ImportError(a				)

										convert_tf_checkpoint_to_pytorch(self._tf_checkpoint       ,							self._config       ,							self._pytorch_dump_output				)
								elif self._model_type == "funnel":
										try:
												from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
												    convert_tf_checkpoint_to_pytorch,
												)
										except ImportError:
												raise ImportError(a				)

										convert_tf_checkpoint_to_pytorch(self._tf_checkpoint       ,							self._config       ,							self._pytorch_dump_output				)
								elif self._model_type == "t5":
										try:
												from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
										except ImportError:
												raise ImportError(a				)

										convert_tf_checkpoint_to_pytorch(self._tf_checkpoint       ,							self._config       ,							self._pytorch_dump_output				)
								elif self._model_type == "gpt":
										from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
										    convert_openai_checkpoint_to_pytorch,
										)

										convert_openai_checkpoint_to_pytorch(self._tf_checkpoint       ,							self._config       ,							self._pytorch_dump_output				)
								elif self._model_type == "transfo_xl":
										try:
												from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
												    convert_transfo_xl_checkpoint_to_pytorch,
												)
										except ImportError:
												raise ImportError(a				)

										if "ckpt" in self._tf_checkpoint.lower():
												_UpperCamelCase						    =	self._tf_checkpoint
												_UpperCamelCase						    =	""""""
										else:
												_UpperCamelCase						    =	self._tf_checkpoint
												_UpperCamelCase						    =	""""""
										convert_transfo_xl_checkpoint_to_pytorch(
										    a       ,							self._config       ,							self._pytorch_dump_output       ,							a				)
								elif self._model_type == "gpt2":
										try:
												from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
												    convert_gpta_checkpoint_to_pytorch,
												)
										except ImportError:
												raise ImportError(a				)

										convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint       ,							self._config       ,							self._pytorch_dump_output				)
								elif self._model_type == "xlnet":
										try:
												from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
												    convert_xlnet_checkpoint_to_pytorch,
												)
										except ImportError:
												raise ImportError(a				)

										convert_xlnet_checkpoint_to_pytorch(
										    self._tf_checkpoint       ,							self._config       ,							self._pytorch_dump_output       ,							self._finetuning_task_name				)
								elif self._model_type == "xlm":
										from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
										    convert_xlm_checkpoint_to_pytorch,
										)

										convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint       ,							self._pytorch_dump_output				)
								elif self._model_type == "lxmert":
										from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
										    convert_lxmert_checkpoint_to_pytorch,
										)

										convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint       ,							self._pytorch_dump_output				)
								elif self._model_type == "rembert":
										from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
										    convert_rembert_tf_checkpoint_to_pytorch,
										)

										convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint       ,							self._config       ,							self._pytorch_dump_output				)
								else:
										raise ValueError(
										    """--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]"""				)



