
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							,							)							-> Tuple:
							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	)							-> Tuple:
							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							,							)							-> List[str]:
							return cls(
							    vision_config=vision_config.to_dict()							,							qformer_config=qformer_config.to_dict()							,							text_config=text_config.to_dict()							,							**_UpperCamelCase							,							)




				def 	__a		(   self	)							-> Optional[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





# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper

import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname

import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary

from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

_A  =	2

# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
_A  =	{
    # fairseq:
    "wmt19-ru-en": {"length_penalty": 1.1},
    "wmt19-en-ru": {"length_penalty": 1.15},
    "wmt19-en-de": {"length_penalty": 1.0},
    "wmt19-de-en": {"length_penalty": 1.1},
    # allenai:
    "wmt16-en-de-dist-12-1": {"length_penalty": 0.6},
    "wmt16-en-de-dist-6-1": {"length_penalty": 0.6},
    "wmt16-en-de-12-1": {"length_penalty": 0.8},
    "wmt19-de-en-6-6-base": {"length_penalty": 0.6},
    "wmt19-de-en-6-6-big": {"length_penalty": 0.6},
}

# this remaps the different models to their organization names
_A  =	{}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
			_A  =	"facebook"
for m in [
    "wmt16-en-de-dist-12-1",
    "wmt16-en-de-dist-6-1",
    "wmt16-en-de-12-1",
    "wmt19-de-en-6-6-base",
    "wmt19-de-en-6-6-big",
]:
			_A  =	"allenai"






def   lowerCamelCase__							(							__lowerCAmelCase    :    Dict							):




			"""simple docstring"""


			lowerCAmelCase_         =      dict((re.sub(r"@@$"       ,				""       ,				__lowerCAmelCase							), v) if k.endswith("@@"							) else (re.sub(r"$"       ,				"</w>"       ,				__lowerCAmelCase							), v) for k, v in d.items()							)
			lowerCAmelCase_         =      "<s> <pad> </s> <unk>".split()
			# restore the special tokens
			for k in keep_keys:
						del da[F"""{k}</w>"""]
						lowerCAmelCase_         =      d[k]  # restore
			return da






def   lowerCamelCase__							(							__lowerCAmelCase    :    List[str]       ,				__lowerCAmelCase    :    Optional[Any]							):




			"""simple docstring"""


			assert os.path.exists(__lowerCAmelCase							)
			os.makedirs(__lowerCAmelCase       ,				exist_ok=__lowerCAmelCase							)
			print(F"""Writing results to {pytorch_dump_folder_path}"""							)

			# handle various types of models

			lowerCAmelCase_         =      basename(__lowerCAmelCase							)
			lowerCAmelCase_         =      dirname(__lowerCAmelCase							)

			lowerCAmelCase_         =      fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
			lowerCAmelCase_         =      cls.hub_models()
			lowerCAmelCase_         =      {"bpe": "fastbpe", "tokenizer": "moses"}
			lowerCAmelCase_         =      "."
			# note: since the model dump is old, fairseq has upgraded its model some
			# time later, and it does a whole lot of rewrites and splits on the saved
			# weights, therefore we can't use torch.load() directly on the model file.
			# see: upgrade_state_dict(state_dict) in fairseq_model.py
			print(F"""using checkpoint {checkpoint_file}"""							)
			lowerCAmelCase_         =      hub_utils.from_pretrained(
			    __lowerCAmelCase       ,				__lowerCAmelCase       ,				__lowerCAmelCase       ,				archive_map=__lowerCAmelCase       ,				**__lowerCAmelCase							)

			lowerCAmelCase_         =      vars(chkpt["args"]["model"]							)

			lowerCAmelCase_         =      args["source_lang"]
			lowerCAmelCase_         =      args["target_lang"]

			lowerCAmelCase_         =      dirname(__lowerCAmelCase							)
			lowerCAmelCase_         =      basename(__lowerCAmelCase							)

			# dicts
			lowerCAmelCase_         =      os.path.join(__lowerCAmelCase       ,				F"""dict.{src_lang}.txt"""							)
			lowerCAmelCase_         =      os.path.join(__lowerCAmelCase       ,				F"""dict.{tgt_lang}.txt"""							)

			lowerCAmelCase_         =      Dictionary.load(__lowerCAmelCase							)
			lowerCAmelCase_         =      rewrite_dict_keys(src_dict.indices							)
			lowerCAmelCase_         =      len(__lowerCAmelCase							)
			lowerCAmelCase_         =      os.path.join(__lowerCAmelCase       ,				"vocab-src.json"							)
			print(F"""Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records"""							)
			with open(__lowerCAmelCase       ,				"w"       ,				encoding="utf-8"							) as f:
						f.write(json.dumps(__lowerCAmelCase       ,				ensure_ascii=__lowerCAmelCase       ,				indent=__lowerCAmelCase							)							)

			# detect whether this is a do_lower_case situation, which can be derived by checking whether we
			# have at least one uppercase letter in the source vocab
			lowerCAmelCase_         =      True
			for k in src_vocab.keys():
						if not k.islower():
									lowerCAmelCase_         =      False
									break

			lowerCAmelCase_         =      Dictionary.load(__lowerCAmelCase							)
			lowerCAmelCase_         =      rewrite_dict_keys(tgt_dict.indices							)
			lowerCAmelCase_         =      len(__lowerCAmelCase							)
			lowerCAmelCase_         =      os.path.join(__lowerCAmelCase       ,				"vocab-tgt.json"							)
			print(F"""Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records"""							)
			with open(__lowerCAmelCase       ,				"w"       ,				encoding="utf-8"							) as f:
						f.write(json.dumps(__lowerCAmelCase       ,				ensure_ascii=__lowerCAmelCase       ,				indent=__lowerCAmelCase							)							)

			# merges_file (bpecodes)
			lowerCAmelCase_         =      os.path.join(__lowerCAmelCase       ,				VOCAB_FILES_NAMES["merges_file"]							)
			for fn in ["bpecodes", "code"]:  # older fairseq called the merges file "code"
						lowerCAmelCase_         =      os.path.join(__lowerCAmelCase       ,				__lowerCAmelCase							)
						if os.path.exists(__lowerCAmelCase							):
									break
			with open(__lowerCAmelCase       ,				encoding="utf-8"							) as fin:
						lowerCAmelCase_         =      fin.read()
			lowerCAmelCase_         =      re.sub(r" \d+$"       ,				""       ,				__lowerCAmelCase       ,				0       ,				re.M							)  # remove frequency number
			print(F"""Generating {merges_file}"""							)
			with open(__lowerCAmelCase       ,				"w"       ,				encoding="utf-8"							) as fout:
						fout.write(__lowerCAmelCase							)

			# model config
			lowerCAmelCase_         =      os.path.join(__lowerCAmelCase       ,				"config.json"							)

			# validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
			# may have to modify the tokenizer if a different type is used by a future model
			assert args["bpe"] == "fastbpe", F"""need to extend tokenizer to support bpe={args['bpe']}"""
			assert args["tokenizer"] == "moses", F"""need to extend tokenizer to support bpe={args['tokenizer']}"""

			lowerCAmelCase_         =      {
			    "architectures": ["FSMTForConditionalGeneration"],
			    "model_type": "fsmt",
			    "activation_dropout": args["activation_dropout"],
			    "activation_function": "relu",
			    "attention_dropout": args["attention_dropout"],
			    "d_model": args["decoder_embed_dim"],
			    "dropout": args["dropout"],
			    "init_std": 0.02,
			    "max_position_embeddings": args["max_source_positions"],
			    "num_hidden_layers": args["encoder_layers"],
			    "src_vocab_size": src_vocab_size,
			    "tgt_vocab_size": tgt_vocab_size,
			    "langs": [src_lang, tgt_lang],
			    "encoder_attention_heads": args["encoder_attention_heads"],
			    "encoder_ffn_dim": args["encoder_ffn_embed_dim"],
			    "encoder_layerdrop": args["encoder_layerdrop"],
			    "encoder_layers": args["encoder_layers"],
			    "decoder_attention_heads": args["decoder_attention_heads"],
			    "decoder_ffn_dim": args["decoder_ffn_embed_dim"],
			    "decoder_layerdrop": args["decoder_layerdrop"],
			    "decoder_layers": args["decoder_layers"],
			    "bos_token_id": 0,
			    "pad_token_id": 1,
			    "eos_token_id": 2,
			    "is_encoder_decoder": True,
			    "scale_embedding": not args["no_scale_embedding"],
			    "tie_word_embeddings": args["share_all_embeddings"],
			}

			# good hparam defaults to start with
			lowerCAmelCase_         =      5
			lowerCAmelCase_         =      False
			if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
						lowerCAmelCase_         =      best_score_hparams[model_dir]["length_penalty"]
			else:
						lowerCAmelCase_         =      1.0

			print(F"""Generating {fsmt_model_config_file}"""							)
			with open(__lowerCAmelCase       ,				"w"       ,				encoding="utf-8"							) as f:
						f.write(json.dumps(__lowerCAmelCase       ,				ensure_ascii=__lowerCAmelCase       ,				indent=__lowerCAmelCase							)							)

			# tokenizer config
			lowerCAmelCase_         =      os.path.join(__lowerCAmelCase       ,				__lowerCAmelCase							)

			lowerCAmelCase_         =      {
			    "langs": [src_lang, tgt_lang],
			    "model_max_length": 1024,
			    "do_lower_case": do_lower_case,
			}

			print(F"""Generating {fsmt_tokenizer_config_file}"""							)
			with open(__lowerCAmelCase       ,				"w"       ,				encoding="utf-8"							) as f:
						f.write(json.dumps(__lowerCAmelCase       ,				ensure_ascii=__lowerCAmelCase       ,				indent=__lowerCAmelCase							)							)

			# model
			lowerCAmelCase_         =      chkpt["models"][0]
			lowerCAmelCase_         =      model.state_dict()

			# rename keys to start with 'model.'
			lowerCAmelCase_         =      OrderedDict(("model." + k, v) for k, v in model_state_dict.items()							)

			# remove unneeded keys
			lowerCAmelCase_         =      [
			    "model.model",
			    "model.encoder.version",
			    "model.decoder.version",
			    "model.encoder_embed_tokens.weight",
			    "model.decoder_embed_tokens.weight",
			    "model.encoder.embed_positions._float_tensor",
			    "model.decoder.embed_positions._float_tensor",
			]
			for k in ignore_keys:
						model_state_dict.pop(__lowerCAmelCase       ,				__lowerCAmelCase							)

			lowerCAmelCase_         =      FSMTConfig.from_pretrained(__lowerCAmelCase							)
			lowerCAmelCase_         =      FSMTForConditionalGeneration(__lowerCAmelCase							)

			# check that it loads ok
			model_new.load_state_dict(__lowerCAmelCase       ,				strict=__lowerCAmelCase							)

			# save
			lowerCAmelCase_         =      os.path.join(__lowerCAmelCase       ,				__lowerCAmelCase							)
			print(F"""Generating {pytorch_weights_dump_path}"""							)
			torch.save(__lowerCAmelCase       ,				__lowerCAmelCase							)

			print("Conversion is done!"							)
			print("\nLast step is to upload the files to s3"							)
			print(F"""cd {data_root}"""							)
			print(F"""transformers-cli upload {model_dir}"""							)


if __name__ == "__main__":
			_A  =	argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--fsmt_checkpoint_path",
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        "Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"
			        " bpecodes, etc."
			    ),
			)
			parser.add_argument(
			    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
			)
			_A  =	parser.parse_args()
			convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)



