


"""simple docstring"""





import datasets

from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py


snake_case__						:   List[Any]			 = '''\
@INPROCEEDINGS{Papineni02bleu:a,
    author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
    title = {BLEU: a Method for Automatic Evaluation of Machine Translation},
    booktitle = {},
    year = {2002},
    pages = {311--318}
}
@inproceedings{lin-och-2004-orange,
    title = "{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation",
    author = "Lin, Chin-Yew  and
      Och, Franz Josef",
    booktitle = "{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics",
    month = "aug 23{--}aug 27",
    year = "2004",
    address = "Geneva, Switzerland",
    publisher = "COLING",
    url = "https://www.aclweb.org/anthology/C04-1072",
    pages = "501--507",
}
'''

snake_case__						:   List[str]			 = '''\
BLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.
Quality is considered to be the correspondence between a machine\'s output and that of a human: "the closer a machine translation is to a professional human translation,
the better it is" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and
remains one of the most popular automated and inexpensive metrics.

Scores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.
Those scores are then averaged over the whole corpus to reach an estimate of the translation\'s overall quality. Intelligibility or grammatical correctness
are not taken into account[citation needed].

BLEU\'s output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1
representing more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the
reference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional
reference translations will increase the BLEU score.
'''

snake_case__						:   Any			 = '''
Computes BLEU score of translated segments against one or more references.
Args:
    predictions: list of translations to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    max_order: Maximum n-gram order to use when computing BLEU score.
    smooth: Whether or not to apply Lin et al. 2004 smoothing.
Returns:
    \'bleu\': bleu score,
    \'precisions\': geometric mean of n-gram precisions,
    \'brevity_penalty\': brevity penalty,
    \'length_ratio\': ratio of lengths,
    \'translation_length\': translation_length,
    \'reference_length\': reference_length
Examples:

    >>> predictions = [
    ...     ["hello", "there", "general", "kenobi"],                             # tokenized prediction of the first sample
    ...     ["foo", "bar", "foobar"]                                             # tokenized prediction of the second sample
    ... ]
    >>> references = [
    ...     [["hello", "there", "general", "kenobi"], ["hello", "there", "!"]],  # tokenized references for the first sample (2 references)
    ...     [["foo", "bar", "foobar"]]                                           # tokenized references for the second sample (1 reference)
    ... ]
    >>> bleu = datasets.load_metric("bleu")
    >>> results = bleu.compute(predictions=predictions, references=references)
    >>> print(results["bleu"])
    1.0
'''



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,    _KWARGS_DESCRIPTION	)
class 						snake_case_(				datasets.Metric	):






     def    lowerCamelCase__   (		self     :    Dict ):
           return datasets.MetricInfo(
               description=_DESCRIPTION    , citation=_CITATION    , inputs_description=_KWARGS_DESCRIPTION    , features=datasets.Features(
                   {
                       '''predictions''': datasets.Sequence(datasets.Value('''string'''    , id='''token''' )    , id='''sequence''' ),
                       '''references''': datasets.Sequence(
                           datasets.Sequence(datasets.Value('''string'''    , id='''token''' )    , id='''sequence''' )    , id='''references''' ),
                   } )    , codebase_urls=['''https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py''']    , reference_urls=[
                   '''https://en.wikipedia.org/wiki/BLEU''',
                   '''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
               ]    , )






     def    lowerCamelCase__   (		self     :    List[Any]    , UpperCamelCase_     :    Tuple    , UpperCamelCase_     :    List[Any]    , UpperCamelCase_     :    List[str]=4    , UpperCamelCase_     :    Tuple=False ):
           lowerCAmelCase    :		Optional[Any]				=      compute_bleu(
               reference_corpus=UpperCamelCase_    , translation_corpus=UpperCamelCase_    , max_order=UpperCamelCase_    , smooth=UpperCamelCase_ )
           ((lowerCAmelCase),					(lowerCAmelCase),					(lowerCAmelCase),					(lowerCAmelCase),					(lowerCAmelCase),					(lowerCAmelCase))    :		Dict				=      score
           return {
               "bleu": bleu,
               "precisions": precisions,
               "brevity_penalty": bp,
               "length_ratio": ratio,
               "translation_length": translation_length,
               "reference_length": reference_length,
           }







import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipaConfig,
    BlipaForConditionalGeneration,
    BlipaProcessor,
    BlipaVisionConfig,
    BlipImageProcessor,
    OPTConfig,
    TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD






def     a(      )		->       Optional[int]:
					"""simple docstring"""





					a					     =   "https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png"
					a					     =   Image.open(requests.get(A ,     stream=A       ).raw       ).convert("RGB"       )

					return image






def     a(      A       :    Tuple       )		->       List[str]:
					"""simple docstring"""





					a					     =   []
					# fmt: off

					# vision encoder
					rename_keys.append(("visual_encoder.cls_token", "vision_model.embeddings.class_embedding")       )
					rename_keys.append(("visual_encoder.pos_embed", "vision_model.embeddings.position_embedding")       )
					rename_keys.append(("visual_encoder.patch_embed.proj.weight", "vision_model.embeddings.patch_embedding.weight")       )
					rename_keys.append(("visual_encoder.patch_embed.proj.bias", "vision_model.embeddings.patch_embedding.bias")       )
					rename_keys.append(("ln_vision.weight", "vision_model.post_layernorm.weight")       )
					rename_keys.append(("ln_vision.bias", "vision_model.post_layernorm.bias")       )

					for i in range(config.vision_config.num_hidden_layers       ):
										rename_keys.append((f'''visual_encoder.blocks.{i}.norm1.weight''', f'''vision_model.encoder.layers.{i}.layer_norm1.weight''')       )
										rename_keys.append((f'''visual_encoder.blocks.{i}.norm1.bias''', f'''vision_model.encoder.layers.{i}.layer_norm1.bias''')       )
										rename_keys.append((f'''visual_encoder.blocks.{i}.norm2.weight''', f'''vision_model.encoder.layers.{i}.layer_norm2.weight''')       )
										rename_keys.append((f'''visual_encoder.blocks.{i}.norm2.bias''', f'''vision_model.encoder.layers.{i}.layer_norm2.bias''')       )
										rename_keys.append((f'''visual_encoder.blocks.{i}.attn.qkv.weight''', f'''vision_model.encoder.layers.{i}.self_attn.qkv.weight''')       )
										rename_keys.append((f'''visual_encoder.blocks.{i}.attn.proj.weight''', f'''vision_model.encoder.layers.{i}.self_attn.projection.weight''',)       )
										rename_keys.append((f'''visual_encoder.blocks.{i}.attn.proj.bias''', f'''vision_model.encoder.layers.{i}.self_attn.projection.bias''')       )
										rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc1.weight''', f'''vision_model.encoder.layers.{i}.mlp.fc1.weight''')       )
										rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc1.bias''', f'''vision_model.encoder.layers.{i}.mlp.fc1.bias''')       )
										rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc2.weight''', f'''vision_model.encoder.layers.{i}.mlp.fc2.weight''')       )
										rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc2.bias''', f'''vision_model.encoder.layers.{i}.mlp.fc2.bias''')       )

					# QFormer
					rename_keys.append(("Qformer.bert.embeddings.LayerNorm.weight", "qformer.layernorm.weight")       )
					rename_keys.append(("Qformer.bert.embeddings.LayerNorm.bias", "qformer.layernorm.bias")       )

					# fmt: on
					return rename_keys






def     a(      A       :    str ,     A       :    Tuple ,     A       :    Optional[int]       )		->       int:
					"""simple docstring"""





					a					     =   dct.pop(A       )
					a					     =   val






def     a(      A       :    Union[str, Any] ,     A       :    str       )		->       List[Any]:
					"""simple docstring"""





					for i in range(config.vision_config.num_hidden_layers       ):
										# read in original q and v biases
										a					     =   state_dict.pop(f'''visual_encoder.blocks.{i}.attn.q_bias'''       )
										a					     =   state_dict.pop(f'''visual_encoder.blocks.{i}.attn.v_bias'''       )

										# next, set bias in the state dict
										a					     =   torch.cat((q_bias, torch.zeros_like(A ,     requires_grad=A       ), v_bias)       )
										a					     =   qkv_bias






def     a(      A       :    Union[str, Any] ,     A       :    Tuple       )		->       str:
					"""simple docstring"""





					a					     =   364 if "coco" in model_name else 224
					a					     =   BlipaVisionConfig(image_size=A       ).to_dict()

					# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
					# seems like flan-T5 models don't have bos_token_id properly set?
					if "opt-2.7b" in model_name:
										a					     =   OPTConfig.from_pretrained("facebook/opt-2.7b" ,     eos_token_id=A       ).to_dict()
					elif "opt-6.7b" in model_name:
										a					     =   OPTConfig.from_pretrained("facebook/opt-6.7b" ,     eos_token_id=A       ).to_dict()
					elif "t5-xl" in model_name:
										a					     =   TaConfig.from_pretrained("google/flan-t5-xl" ,     dense_act_fn="gelu" ,     bos_token_id=1       ).to_dict()
					elif "t5-xxl" in model_name:
										a					     =   TaConfig.from_pretrained("google/flan-t5-xxl" ,     dense_act_fn="gelu" ,     bos_token_id=1       ).to_dict()

					a					     =   BlipaConfig(vision_config=A ,     text_config=A       )

					return config, image_size






@torch.no_grad()
def     a(      A       :    Optional[int] ,     A       :    List[str]=None ,     A       :    Optional[int]=False       )		->       Any:
					"""simple docstring"""





					a					     =   (
					    AutoTokenizer.from_pretrained("facebook/opt-2.7b"       )
					    if "opt" in model_name
					    else AutoTokenizer.from_pretrained("google/flan-t5-xl"       )
					)
					a					     =   tokenizer("\n" ,     add_special_tokens=A       ).input_ids[0]
					a     ,				a					     =   get_blipa_config(A ,     eos_token_id=A       )

					a					     =   BlipaForConditionalGeneration(A       ).eval()

					a					     =   {
					    "blip2-opt-2.7b": ("blip2_opt", "pretrain_opt2.7b"),
					    "blip2-opt-6.7b": ("blip2_opt", "pretrain_opt6.7b"),
					    "blip2-opt-2.7b-coco": ("blip2_opt", "caption_coco_opt2.7b"),
					    "blip2-opt-6.7b-coco": ("blip2_opt", "caption_coco_opt6.7b"),
					    "blip2-flan-t5-xl": ("blip2_t5", "pretrain_flant5xl"),
					    "blip2-flan-t5-xl-coco": ("blip2_t5", "caption_coco_flant5xl"),
					    "blip2-flan-t5-xxl": ("blip2_t5", "pretrain_flant5xxl"),
					}

					a     ,				a					     =   model_name_to_original[model_name]

					# load original model
					print("Loading original model..."       )
					a					     =   "cuda" if torch.cuda.is_available() else "cpu"
					a     ,				a     ,				a					     =   load_model_and_preprocess(
					    name=A ,     model_type=A ,     is_eval=A ,     device=A       )
					original_model.eval()
					print("Done!"       )

					# update state dict keys
					a					     =   original_model.state_dict()
					a					     =   create_rename_keys(A       )
					for src, dest in rename_keys:
										rename_key(A ,     A ,     A       )

					# some keys can be renamed efficiently
					for key, val in state_dict.copy().items():
										a					     =   state_dict.pop(A       )
										if key.startswith("Qformer.bert"       ):
															a					     =   key.replace("Qformer.bert" ,     "qformer"       )
										if "attention.self" in key:
															a					     =   key.replace("self" ,     "attention"       )
										if "opt_proj" in key:
															a					     =   key.replace("opt_proj" ,     "language_projection"       )
										if "t5_proj" in key:
															a					     =   key.replace("t5_proj" ,     "language_projection"       )
										if key.startswith("opt"       ):
															a					     =   key.replace("opt" ,     "language"       )
										if key.startswith("t5"       ):
															a					     =   key.replace("t5" ,     "language"       )
										a					     =   val

					# read in qv biases
					read_in_q_v_bias(A ,     A       )

					a     ,				a					     =   hf_model.load_state_dict(A ,     strict=A       )
					assert len(A       ) == 0
					assert unexpected_keys == ["qformer.embeddings.position_ids"]

					a					     =   load_demo_image()
					a					     =   vis_processors["eval"](A       ).unsqueeze(0       ).to(A       )
					a					     =   tokenizer(["\n"] ,     return_tensors="pt"       ).input_ids.to(A       )

					# create processor
					a					     =   BlipImageProcessor(
					    size={"height": image_size, "width": image_size} ,     image_mean=A ,     image_std=A       )
					a					     =   BlipaProcessor(image_processor=A ,     tokenizer=A       )
					a					     =   processor(images=A ,     return_tensors="pt"       ).pixel_values.to(A       )

					# make sure processor creates exact same pixel values
					assert torch.allclose(A ,     A       )

					original_model.to(A       )
					hf_model.to(A       )
					with torch.no_grad():
										if "opt" in model_name:
															a					     =   original_model({"image": original_pixel_values, "text_input": [""]}       ).logits
															a					     =   hf_model(A ,     A       ).logits
										else:
															a					     =   original_model(
															    {"image": original_pixel_values, "text_input": ["\n"], "text_output": ["\n"]}       ).logits
															a					     =   input_ids.masked_fill(input_ids == tokenizer.pad_token_id ,     -100       )
															a					     =   hf_model(A ,     A ,     labels=A       ).logits

					assert original_logits.shape == logits.shape
					print("First values of original logits:" ,     original_logits[0, :3, :3]       )
					print("First values of HF logits:" ,     logits[0, :3, :3]       )

					# assert values
					if model_name == "blip2-flan-t5-xl":
										a					     =   torch.tensor(
										    [[-41.5_850, -4.4_440, -8.9_922], [-47.4_322, -5.9_143, -1.7_340]] ,     device=A       )
										assert torch.allclose(logits[0, :3, :3] ,     A ,     atol=1e-4       )
					elif model_name == "blip2-flan-t5-xl-coco":
										a					     =   torch.tensor(
										    [[-57.0_109, -9.8_967, -12.6_280], [-68.6_578, -12.7_191, -10.5_065]] ,     device=A       )
					else:
										# cast to same type
										a					     =   logits.dtype
										assert torch.allclose(original_logits.to(A       ) ,     A ,     atol=1e-2       )
					print("Looks ok!"       )

					print("Generating a caption..."       )
					a					     =   ""
					a					     =   tokenizer(A ,     return_tensors="pt"       ).input_ids.to(A       )

					a					     =   original_model.generate({"image": original_pixel_values}       )
					a					     =   hf_model.generate(
					    A ,     A ,     do_sample=A ,     num_beams=5 ,     max_length=30 ,     min_length=1 ,     top_p=0.9 ,     repetition_penalty=1.0 ,     length_penalty=1.0 ,     temperature=1 ,     )
					print("Original generation:" ,     A       )
					a					     =   input_ids.shape[1]
					a					     =   processor.batch_decode(outputs[:, prompt_length:] ,     skip_special_tokens=A       )
					a					     =   [text.strip() for text in output_text]
					print("HF generation:" ,     A       )

					if pytorch_dump_folder_path is not None:
										processor.save_pretrained(A       )
										hf_model.save_pretrained(A       )

					if push_to_hub:
										processor.push_to_hub(f'''nielsr/{model_name}'''       )
										hf_model.push_to_hub(f'''nielsr/{model_name}'''       )


if __name__ == "__main__":
				_lowercase: Dict      			=		argparse.ArgumentParser()
				_lowercase: Tuple      			=		[
				    "blip2-opt-2.7b",
				    "blip2-opt-6.7b",
				    "blip2-opt-2.7b-coco",
				    "blip2-opt-6.7b-coco",
				    "blip2-flan-t5-xl",
				    "blip2-flan-t5-xl-coco",
				    "blip2-flan-t5-xxl",
				]
				parser.add_argument(
				    "--model_name",
				    default="blip2-opt-2.7b",
				    choices=choices,
				    type=str,
				    help="Path to hf config.json of model to convert",
				)
				parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
				parser.add_argument(
				    "--push_to_hub",
				    action="store_true",
				    help="Whether to push the model and processor to the hub after converting",
				)

				_lowercase: Any      			=		parser.parse_args()

				convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
