
"""simple docstring"""




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_							:			List[Any]											= logging.get_logger(__name__)

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





class    lowerCamelCase						(A__			):
			lowerCamelCase__   :		Tuple							=							'blip_2_vision_model'

			def __init__(					self				:						List[Any]    ,				__UpperCAmelCase				:						Dict=1_4_0_8    ,				__UpperCAmelCase				:						int=6_1_4_4    ,				__UpperCAmelCase				:						Optional[Any]=3_9    ,				__UpperCAmelCase				:						List[str]=1_6    ,				__UpperCAmelCase				:						Any=2_2_4    ,				__UpperCAmelCase				:						Any=1_4    ,				__UpperCAmelCase				:						Optional[Any]="gelu"    ,				__UpperCAmelCase				:						int=0.00_001    ,				__UpperCAmelCase				:						int=0.0    ,				__UpperCAmelCase				:						List[str]=1e-10    ,				__UpperCAmelCase				:						List[str]=True    ,				**__UpperCAmelCase				:						Dict    ,				)   ->    Union[str, Any]:
						super().__init__(**__UpperCAmelCase       )

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





			@classmethod
			def 					SCREAMING_SNAKE_CASE				(					cls				:						Optional[Any]    ,				__UpperCAmelCase				:						Union[str, os.PathLike]    ,				**__UpperCAmelCase				:						Tuple       )   ->    "PretrainedConfig":
						cls._set_token_in_kwargs(__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      cls.get_config_dict(__UpperCAmelCase    ,				**__UpperCAmelCase       )

						# get the vision config dict if we are loading from Blip2Config
						if config_dict.get("""model_type"""       ) == "blip-2":
									SCREAMING_SNAKE_CASE__								=      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__			):
			lowerCamelCase__   :		Dict							=							'blip_2_qformer'

			def __init__(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						int=3_0_5_2_2    ,				__UpperCAmelCase				:						Any=7_6_8    ,				__UpperCAmelCase				:						List[Any]=1_2    ,				__UpperCAmelCase				:						List[Any]=1_2    ,				__UpperCAmelCase				:						Optional[Any]=3_0_7_2    ,				__UpperCAmelCase				:						Any="gelu"    ,				__UpperCAmelCase				:						Optional[int]=0.1    ,				__UpperCAmelCase				:						Dict=0.1    ,				__UpperCAmelCase				:						Any=5_1_2    ,				__UpperCAmelCase				:						Dict=0.02    ,				__UpperCAmelCase				:						Optional[Any]=1e-12    ,				__UpperCAmelCase				:						str=0    ,				__UpperCAmelCase				:						int="absolute"    ,				__UpperCAmelCase				:						Dict=2    ,				__UpperCAmelCase				:						Dict=1_4_0_8    ,				**__UpperCAmelCase				:						List[str]    ,				)   ->    int:
						super().__init__(pad_token_id=__UpperCAmelCase    ,				**__UpperCAmelCase       )

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





			@classmethod
			def 					SCREAMING_SNAKE_CASE				(					cls				:						Optional[Any]    ,				__UpperCAmelCase				:						Union[str, os.PathLike]    ,				**__UpperCAmelCase				:						int       )   ->    "PretrainedConfig":
						cls._set_token_in_kwargs(__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      cls.get_config_dict(__UpperCAmelCase    ,				**__UpperCAmelCase       )

						# get the qformer config dict if we are loading from Blip2Config
						if config_dict.get("""model_type"""       ) == "blip-2":
									SCREAMING_SNAKE_CASE__								=      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__			):
			lowerCamelCase__   :		int							=							'blip-2'
			lowerCamelCase__   :		Optional[Any]							=							True

			def __init__(					self				:						Union[str, Any]    ,				__UpperCAmelCase				:						Tuple=None    ,				__UpperCAmelCase				:						Optional[Any]=None    ,				__UpperCAmelCase				:						Any=None    ,				__UpperCAmelCase				:						Dict=3_2    ,				**__UpperCAmelCase				:						List[Any]       )   ->    str:
						super().__init__(**__UpperCAmelCase       )

						if vision_config is None:
									SCREAMING_SNAKE_CASE__								=      {}
									logger.info("""vision_config is None. initializing the Blip2VisionConfig with default values."""       )

						if qformer_config is None:
									SCREAMING_SNAKE_CASE__								=      {}
									logger.info("""qformer_config is None. Initializing the Blip2QFormerConfig with default values."""       )

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

						SCREAMING_SNAKE_CASE__								=      BlipaVisionConfig(**__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      BlipaQFormerConfig(**__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      text_config["""model_type"""] if """model_type""" in text_config else """opt"""
						SCREAMING_SNAKE_CASE__								=      CONFIG_MAPPING[text_model_type](**__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      self.text_config.tie_word_embeddings
						SCREAMING_SNAKE_CASE__								=      self.text_config.is_encoder_decoder

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

			@classmethod
			def 					SCREAMING_SNAKE_CASE				(					cls				:						Dict    ,				__UpperCAmelCase				:						BlipaVisionConfig    ,				__UpperCAmelCase				:						BlipaQFormerConfig    ,				__UpperCAmelCase				:						PretrainedConfig    ,				**__UpperCAmelCase				:						Tuple    ,				)   ->    List[Any]:
						return cls(
						    vision_config=vision_config.to_dict()    ,				qformer_config=qformer_config.to_dict()    ,				text_config=text_config.to_dict()    ,				**__UpperCAmelCase    ,				)





			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    Optional[Any]:
						SCREAMING_SNAKE_CASE__								=      copy.deepcopy(self.__dict__       )
						SCREAMING_SNAKE_CASE__								=      self.vision_config.to_dict()
						SCREAMING_SNAKE_CASE__								=      self.qformer_config.to_dict()
						SCREAMING_SNAKE_CASE__								=      self.text_config.to_dict()
						SCREAMING_SNAKE_CASE__								=      self.__class__.model_type
						return output





"""simple docstring"""




import argparse
import logging
from collections import namedtuple

import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer  # The authors' implementation

from transformers import BertTokenizer


logging.basicConfig(level=logging.INFO)
A_							:			Tuple											= logging.getLogger(__name__)


A_							:			Tuple											= "Hello world! cécé herlolip"


A_							:			Dict											= namedtuple(
    "BertAbsConfig",
    [
        "temp_dir",
        "large",
        "use_bert_emb",
        "finetune_bert",
        "encoder",
        "share_emb",
        "max_pos",
        "enc_layers",
        "enc_hidden_size",
        "enc_heads",
        "enc_ff_size",
        "enc_dropout",
        "dec_layers",
        "dec_hidden_size",
        "dec_heads",
        "dec_ff_size",
        "dec_dropout",
    ],
)

def 	A       (     snake_case__		,				snake_case__    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      BertAbsConfig(
			    temp_dir="""."""		,				finetune_bert=snake_case__		,				large=snake_case__		,				share_emb=snake_case__		,				use_bert_emb=snake_case__		,				encoder="""bert"""		,				max_pos=5_12		,				enc_layers=6		,				enc_hidden_size=5_12		,				enc_heads=8		,				enc_ff_size=5_12		,				enc_dropout=0.2		,				dec_layers=6		,				dec_hidden_size=7_68		,				dec_heads=8		,				dec_ff_size=20_48		,				dec_dropout=0.2		,				)
			SCREAMING_SNAKE_CASE__								=      torch.load(snake_case__		,				lambda snake_case__		,				snake_case__    : storage    )
			SCREAMING_SNAKE_CASE__								=      AbsSummarizer(snake_case__		,				torch.device("""cpu"""    )		,				snake_case__    )
			original.eval()

			SCREAMING_SNAKE_CASE__								=      BertAbsSummarizer(snake_case__		,				torch.device("""cpu"""    )    )
			new_model.eval()

			# -------------------
			# Convert the weights
			# -------------------

			logging.info("""convert the model"""    )
			new_model.bert.load_state_dict(original.bert.state_dict()    )
			new_model.decoder.load_state_dict(original.decoder.state_dict()    )
			new_model.generator.load_state_dict(original.generator.state_dict()    )

			# ----------------------------------
			# Make sure the outpus are identical
			# ----------------------------------

			logging.info("""Make sure that the models' outputs are identical"""    )
			SCREAMING_SNAKE_CASE__								=      BertTokenizer.from_pretrained("""bert-base-uncased"""    )

			# prepare the model inputs
			SCREAMING_SNAKE_CASE__								=      tokenizer.encode("""This is sample éàalj'-."""    )
			encoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(snake_case__    ))    )
			SCREAMING_SNAKE_CASE__								=      torch.tensor(snake_case__    ).unsqueeze(0    )
			SCREAMING_SNAKE_CASE__								=      tokenizer.encode("""This is sample 3 éàalj'-."""    )
			decoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(snake_case__    ))    )
			SCREAMING_SNAKE_CASE__								=      torch.tensor(snake_case__    ).unsqueeze(0    )

			# failsafe to make sure the weights reset does not affect the
			# loaded weights.
			assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight    )    ) == 0

			# forward pass
			SCREAMING_SNAKE_CASE__								=      encoder_input_ids
			SCREAMING_SNAKE_CASE__								=      decoder_input_ids
			SCREAMING_SNAKE_CASE__								=      SCREAMING_SNAKE_CASE__								=      None
			SCREAMING_SNAKE_CASE__								=      None
			SCREAMING_SNAKE_CASE__								=      SCREAMING_SNAKE_CASE__								=      None
			SCREAMING_SNAKE_CASE__								=      SCREAMING_SNAKE_CASE__								=      None
			SCREAMING_SNAKE_CASE__								=      None

			# The original model does not apply the geneator layer immediatly but rather in
			# the beam search (where it combines softmax + linear layer). Since we already
			# apply the softmax in our generation process we only apply the linear layer here.
			# We make sure that the outputs of the full stack are identical
			SCREAMING_SNAKE_CASE__								=      original(snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__    )[0]
			SCREAMING_SNAKE_CASE__								=      original.generator(snake_case__    )

			SCREAMING_SNAKE_CASE__								=      new_model(
			    snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__    )[0]
			SCREAMING_SNAKE_CASE__								=      new_model.generator(snake_case__    )

			SCREAMING_SNAKE_CASE__								=      torch.max(torch.abs(output_converted_model - output_original_model    )    ).item()
			print("""Maximum absolute difference beween weights: {:.2f}""".format(snake_case__    )    )
			SCREAMING_SNAKE_CASE__								=      torch.max(torch.abs(output_converted_generator - output_original_generator    )    ).item()
			print("""Maximum absolute difference beween weights: {:.2f}""".format(snake_case__    )    )

			SCREAMING_SNAKE_CASE__								=      torch.allclose(snake_case__		,				snake_case__		,				atol=1e-3    )
			if are_identical:
						logging.info("""all weights are equal up to 1e-3"""    )
			else:
						raise ValueError("""the weights are different. The new model is likely different from the original one."""    )

			# The model has been saved with torch.save(model) and this is bound to the exact
			# directory structure. We save the state_dict instead.
			logging.info("""saving the model's state dictionary"""    )
			torch.save(
			    new_model.state_dict()		,				"""./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin"""    )


if __name__ == "__main__":
						A_							:			Dict											= argparse.ArgumentParser()
						parser.add_argument(
						    "--bertabs_checkpoint_path",
						    default=None,
						    type=str,
						    required=True,
						    help="Path the official PyTorch dump.",
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path",
						    default=None,
						    type=str,
						    required=True,
						    help="Path to the output PyTorch model.",
						)
						A_							:			Tuple											= parser.parse_args()

						convert_bertabs_checkpoints(
						    args.bertabs_checkpoint_path,
						    args.pytorch_dump_folder_path,
						)


