
"""simple docstring"""


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_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipImageProcessor,
    InstructBlipConfig,
    InstructBlipForConditionalGeneration,
    InstructBlipProcessor,
    InstructBlipQFormerConfig,
    InstructBlipVisionConfig,
    LlamaConfig,
    LlamaTokenizerFast,
    TaConfig,
    TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD






def    _snake_case					(		):
	A     = 'https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg'
	A     = Image.open(requests.get(snake_case__						,   stream=snake_case__  ).raw  ).convert('RGB'  )

	return image






def    _snake_case					(		snake_case__ :  Optional[int]  ):
	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.embeddings.layernorm.weight')  )
	rename_keys.append(('Qformer.bert.embeddings.LayerNorm.bias', 'qformer.embeddings.layernorm.bias')  )

	# fmt: on
	return rename_keys






def    _snake_case					(		snake_case__ :  Dict						,   snake_case__ :  Tuple						,   snake_case__ :  Optional[Any]  ):
	A     = dct.pop(snake_case__  )
	A     = val






def    _snake_case					(		snake_case__ :  List[str]						,   snake_case__ :  Optional[Any]  ):
	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(snake_case__						,   requires_grad=snake_case__  ), v_bias)  )
		A     = qkv_bias






def    _snake_case					(		snake_case__ :  Tuple  ):
	A     = 364 if 'coco' in model_name else 224
	A     = InstructBlipVisionConfig(image_size=snake_case__  ).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 "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()
	elif "vicuna-7b" in model_name:
		A     = LlamaConfig.from_pretrained('decapoda-research/llama-7b-hf'						,   vocab_size=3_2001  ).to_dict()
	elif "vicuna-13b" in model_name:
		A     = LlamaConfig.from_pretrained('decapoda-research/llama-13b-hf'						,   vocab_size=3_2001  ).to_dict()
	else:
		raise ValueError('Model name not supported'  )

	# the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
	A     = InstructBlipQFormerConfig(vocab_size=3_0523  ).to_dict()
	A     = InstructBlipConfig(vision_config=snake_case__						,   text_config=snake_case__						,   qformer_config=snake_case__  )

	return config, image_size






@torch.no_grad()
def    _snake_case					(		snake_case__ :  Tuple						,   snake_case__ :  List[Any]=None						,   snake_case__ :  List[Any]=False  ):
	A     = AutoTokenizer.from_pretrained('bert-base-uncased'						,   truncation_side='left'  )
	qformer_tokenizer.add_special_tokens({'bos_token': '[DEC]'}  )

	if "t5" in model_name:
		A     = TaTokenizerFast.from_pretrained('google/flan-t5-xl'						,   truncation_side='left'  )
	elif "vicuna" in model_name:
		# the following was used in the original implementation:
		# tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
		# tokenizer.add_special_tokens({"pad_token": "[PAD]"})
		# tokenizer.add_special_tokens({"bos_token": "</s>"})
		# tokenizer.add_special_tokens({"eos_token": "</s>"})
		# tokenizer.add_special_tokens({"unk_token": "</s>"})
		A     = LlamaTokenizerFast.from_pretrained(
		    'huggyllama/llama-7b'						,   truncation_side='left'						,   bos_token='</s>'						,   unk_token='</s>'  )
		tokenizer.add_special_tokens({'pad_token': '[PAD]'}  )

	A ,   A     = get_blipa_config(snake_case__  )
	A     = InstructBlipForConditionalGeneration(snake_case__  ).eval()

	A     = {
	    'instructblip-vicuna-7b': ('blip2_vicuna_instruct', 'vicuna7b'),
	    'instructblip-vicuna-13b': ('blip2_vicuna_instruct', 'vicuna13b'),
	    'instructblip-flan-t5-xl': ('blip2_t5_instruct', 'flant5xl'),
	    'instructblip-flan-t5-xxl': ('blip2_t5_instruct', 'flant5xxl'),
	}

	A ,   A     = model_name_to_original[model_name]

	# load original model
	print('Loading original model...'  )
	A     = 'cuda:1' if torch.cuda.is_available() else 'cpu'
	A     = 'cuda:2' if torch.cuda.is_available() else 'cpu'
	A ,   A ,   A     = load_model_and_preprocess(
	    name=snake_case__						,   model_type=snake_case__						,   is_eval=snake_case__						,   device=snake_case__  )
	original_model.eval()
	print('Done!'  )

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

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

	# read in qv biases
	read_in_q_v_bias(snake_case__						,   snake_case__  )

	# note: weights get loaded in torch.float32 by default
	hf_model.load_state_dict(snake_case__						,   strict=snake_case__  )

	A     = load_demo_image()
	A     = 'What is unusual about this image?'

	# create processor
	A     = BlipImageProcessor(
	    size={'height': image_size, 'width': image_size}						,   image_mean=snake_case__						,   image_std=snake_case__  )
	A     = InstructBlipProcessor(
	    image_processor=snake_case__						,   tokenizer=snake_case__						,   qformer_tokenizer=snake_case__						,   )
	A     = processor(images=snake_case__						,   text=snake_case__						,   return_tensors='pt'  ).to(snake_case__  )

	# make sure processor creates exact same pixel values
	A     = vis_processors['eval'](snake_case__  ).unsqueeze(0  ).to(snake_case__  )
	A     = inputs.pixel_values
	assert torch.allclose(original_pixel_values.to(pixel_values.device  )						,   snake_case__  )

	original_model.to(snake_case__  )
	hf_model.to(snake_case__  )
	with torch.no_grad():
		if "vicuna" in model_name:
			A     = original_model({'image': original_pixel_values, 'text_input': [prompt]}  ).logits
			A     = hf_model(**snake_case__  ).logits
		else:
			A     = original_model(
			    {'image': original_pixel_values, 'text_input': [prompt], 'text_output': ['\n']}  ).logits
			A     = tokenizer('\n'						,   return_tensors='pt'  ).input_ids.to(snake_case__  )
			A     = label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id						,   -100  )
			A     = hf_model(**snake_case__						,   labels=snake_case__  ).logits

	print('First values of original logits:'						,   original_logits[0, :3, :3]  )
	print('First values of HF logits:'						,   logits[0, :3, :3]  )

	# assert values
	assert original_logits.shape == logits.shape
	A     = 1e-4 if 'vicuna' in model_name else 1e-5
	assert torch.allclose(original_logits.to(logits.device  )						,   snake_case__						,   atol=snake_case__  )
	print('Looks ok!'  )

	print('Generating with original model...'  )
	A     = original_model.generate({'image': original_pixel_values, 'prompt': prompt}						,   num_beams=5  )

	# important: we need to cast the weights of the HF model to the appropriate type
	print('Generating with HF model...'  )
	A     = hf_model.generate(
	    **snake_case__						,   do_sample=snake_case__						,   num_beams=5						,   max_length=256						,   min_length=1						,   top_p=0.9						,   repetition_penalty=1.5						,   length_penalty=1.0						,   temperature=1						,   )
	if "vicuna" in model_name:
		# convert output id 0 to 2 (eos_token_id)
		# TODO add this in the generate method?
		A     = 2
	print('Original generation:'						,   snake_case__  )
	A     = processor.batch_decode(snake_case__						,   skip_special_tokens=snake_case__  )
	A     = [text.strip() for text in output_text]
	print('HF generation:'						,   snake_case__  )

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

	if push_to_hub:
		processor.push_to_hub(F'Salesforce/{model_name}'  )
		hf_model.push_to_hub(F'Salesforce/{model_name}'  )


if __name__ == "__main__":
		_lowercase				 =					argparse.ArgumentParser()
		_lowercase				 =					[
		    '''instructblip-vicuna-7b''',
		    '''instructblip-vicuna-13b''',
		    '''instructblip-flan-t5-xl''',
		    '''instructblip-flan-t5-xxl''',
		]
		parser.add_argument(
		    '''--model_name''',
		    default='''instructblip-flan-t5-xl''',
		    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				 =					parser.parse_args()

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






import sacrebleu as scb
from packaging import version
from sacrebleu import TER

import datasets


SCREAMING_SNAKE_CASE__							:      List[Any]         =    """\
@inproceedings{snover-etal-2006-study,
    title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",
    author = \"Snover, Matthew  and
      Dorr, Bonnie  and
      Schwartz, Rich  and
      Micciulla, Linnea  and
      Makhoul, John\",
    booktitle = \"Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers\",
    month = aug # \" 8-12\",
    year = \"2006\",
    address = \"Cambridge, Massachusetts, USA\",
    publisher = \"Association for Machine Translation in the Americas\",
    url = \"https://aclanthology.org/2006.amta-papers.25\",
    pages = \"223--231\",
}
@inproceedings{post-2018-call,
    title = \"A Call for Clarity in Reporting {BLEU} Scores\",
    author = \"Post, Matt\",
    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",
    month = oct,
    year = \"2018\",
    address = \"Belgium, Brussels\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W18-6319\",
    pages = \"186--191\",
}
"""

SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    """\
TER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a
hypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu
(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found
here: https://github.com/jhclark/tercom.

The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534

See the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.
"""

SCREAMING_SNAKE_CASE__							:      int         =    """
Produces TER scores alongside the number of edits and reference length.

Args:
    predictions (list of str): The system stream (a sequence of segments).
    references (list of list of str): A list of one or more reference streams (each a sequence of segments).
    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,
                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.
                                    Only applies if `normalized = True`. Defaults to `False`.
    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.

Returns:
    'score' (float): TER score (num_edits / sum_ref_lengths * 100)
    'num_edits' (int): The cumulative number of edits
    'ref_length' (float): The cumulative average reference length

Examples:
    Example 1:
        >>> predictions = [\"does this sentence match??\",
        ...                     \"what about this sentence?\",
        ...                     \"What did the TER metric user say to the developer?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],
        ...             [\"Your jokes are...\", \"...TERrible\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {'score': 150.0, 'num_edits': 15, 'ref_length': 10.0}

    Example 2:
        >>> predictions = [\"does this sentence match??\",
        ...                     \"what about this sentence?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {'score': 62.5, 'num_edits': 5, 'ref_length': 8.0}

    Example 3:
        >>> predictions = [\"does this sentence match??\",
        ...                     \"what about this sentence?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         normalized=True,
        ...                         case_sensitive=True)
        >>> print(results)
        {'score': 57.14285714285714, 'num_edits': 6, 'ref_length': 10.5}

    Example 4:
        >>> predictions = [\"does this sentence match??\",
        ...                     \"what about this sentence?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {'score': 0.0, 'num_edits': 0, 'ref_length': 8.0}

    Example 5:
        >>> predictions = [\"does this sentence match??\",
        ...                    \"what about this sentence?\",
        ...                    \"What did the TER metric user say to the developer?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],
        ...             [\"Your jokes are...\", \"...TERrible\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {'score': 100.0, 'num_edits': 10, 'ref_length': 10.0}
"""







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





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





      if version.parse(scb.__version__   ) < version.parse("1.4.12"   ):
         raise ImportWarning(
             "To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"
             "You can install it with `pip install \"sacrebleu>=1.4.12\"`."   )
      return datasets.MetricInfo(
          description=_DESCRIPTION       ,      citation=_CITATION       ,      homepage="http://www.cs.umd.edu/~snover/tercom/"       ,      inputs_description=_KWARGS_DESCRIPTION       ,      features=datasets.Features(
              {
                  "predictions": datasets.Value("string"       ,      id="sequence"   ),
                  "references": datasets.Sequence(datasets.Value("string"       ,      id="sequence"   )       ,      id="references"   ),
              }   )       ,      codebase_urls=["https://github.com/mjpost/sacreBLEU#ter"]       ,      reference_urls=[
              "https://github.com/jhclark/tercom",
          ]       ,      )





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case = False       ,      snake_case = False       ,      snake_case = False       ,      snake_case = False       ,      )					->	Optional[Any]:
      """simple docstring"""





      a__						:    int   		=	len(references[0]   )
      if any(len(snake_case   ) != references_per_prediction for refs in references   ):
         raise ValueError("Sacrebleu requires the same number of references for each prediction"   )
      a__						:    int   		=	[[refs[i] for refs in references] for i in range(snake_case   )]

      a__						:    Optional[Any]   		=	TER(
          normalized=snake_case       ,      no_punct=snake_case       ,      asian_support=snake_case       ,      case_sensitive=snake_case       ,      )
      a__						:    Union[str, Any]   		=	sb_ter.corpus_score(snake_case       ,      snake_case   )

      return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}

