import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel

def      lowercase_							(     _lowerCamelCase				:			Optional[Any]						,      _lowerCamelCase				:			int):
    lowercase__   ,  lowercase__   ,  lowercase__					:  str          =							pt_attn_layer.in_proj_weight.chunk(3						,      dim=0)
    lowercase__   ,  lowercase__   ,  lowercase__					:  List[Any]          =							pt_attn_layer.in_proj_bias.chunk(3						,      dim=0)

    lowercase__					:  Dict          =							pt_attn_layer.out_proj.weight
    lowercase__					:  List[str]          =							pt_attn_layer.out_proj.bias

    lowercase__					:  Tuple          =							q_proj
    lowercase__					:  List[str]          =							q_proj_bias

    lowercase__					:  str          =							k_proj
    lowercase__					:  Tuple          =							k_proj_bias

    lowercase__					:  Dict          =							v_proj
    lowercase__					:  str          =							v_proj_bias

    lowercase__					:  Optional[Any]          =							out_proj_weights
    lowercase__					:  str          =							out_proj_bias

def      lowercase_							(     _lowerCamelCase				:			Optional[int]						,      _lowerCamelCase				:			Tuple):
    copy_linear(hf_mlp.fca						,      pt_mlp.c_fc)
    copy_linear(hf_mlp.fca						,      pt_mlp.c_proj)

def      lowercase_							(     _lowerCamelCase				:			Any						,      _lowerCamelCase				:			List[Any]):
    lowercase__					:  Any          =							pt_linear.weight
    lowercase__					:  str          =							pt_linear.bias

def      lowercase_							(     _lowerCamelCase				:			List[Any]						,      _lowerCamelCase				:			List[str]):
    copy_linear(hf_layer.layer_norma						,      pt_layer.ln_a)
    copy_linear(hf_layer.layer_norma						,      pt_layer.ln_a)

    # copy MLP
    copy_mlp(hf_layer.mlp						,      pt_layer.mlp)

    # copy attn
    copy_attn_layer(hf_layer.self_attn						,      pt_layer.attn)

def      lowercase_							(     _lowerCamelCase				:			List[Any]						,      _lowerCamelCase				:			Dict):


    for hf_layer, pt_layer in zip(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE):
        copy_layer(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE)

def      lowercase_							(     _lowerCamelCase				:			Union[str, Any]						,      _lowerCamelCase				:			int):
    lowercase__					:  str          =							pt_model.token_embedding.weight
    lowercase__					:  Optional[Any]          =							pt_model.positional_embedding

    # copy layer norm
    copy_linear(hf_encoder.final_layer_norm						,      pt_model.ln_final)

    # copy hidden layers
    copy_layers(hf_encoder.encoder.layers						,      pt_model.transformer.resblocks)

def      lowercase_							(     _lowerCamelCase				:			List[Any]						,      _lowerCamelCase				:			Any):
    lowercase__					:  Optional[int]          =							pt_model.text_projection.data.T

    # copy text encoder
    copy_encoder(hf_model.text_model						,      _SCREAMING_SNAKE_CASE)

def      lowercase_							(     _lowerCamelCase				:			Tuple						,      _lowerCamelCase				:			List[Any]):
    lowercase__					:  List[Any]          =							pt_model.visual.proj.data.T

    # copy layer norms
    copy_linear(hf_model.vision_model.pre_layrnorm						,      pt_model.visual.ln_pre)
    copy_linear(hf_model.vision_model.post_layernorm						,      pt_model.visual.ln_post)

    # copy embeds
    lowercase__					:  Tuple          =							pt_model.visual.conva.weight.data
    lowercase__					:  Tuple          =							pt_model.visual.class_embedding
    lowercase__					:  List[str]          =							pt_model.visual.positional_embedding.data

    # copy encoder
    copy_layers(hf_model.vision_model.encoder.layers						,      pt_model.visual.transformer.resblocks)

@torch.no_grad()
def      lowercase_							(     _lowerCamelCase				:			List[str]						,      _lowerCamelCase				:			str						,      _lowerCamelCase				:			Optional[int]=None):


    if config_path is not None:
        lowercase__					:  int          =							CLIPConfig.from_pretrained(_SCREAMING_SNAKE_CASE)
    else:
        lowercase__					:  Tuple          =							CLIPConfig(projection_dim=512						,      text_config={}						,      vision_config={})

    lowercase__					:  Any          =							CLIPModel(_SCREAMING_SNAKE_CASE).eval()

    lowercase__   ,  lowercase__					:  Tuple          =							load(_SCREAMING_SNAKE_CASE						,      device="cpu"						,      jit=_SCREAMING_SNAKE_CASE)
    lowercase__					:  List[Any]          =							pt_model.eval()

    copy_text_model_and_projection(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE)
    copy_vison_model_and_projection(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE)
    lowercase__					:  int          =							pt_model.logit_scale

    lowercase__					:  List[str]          =							torch.arange(0						,      77).unsqueeze(0)
    lowercase__					:  List[str]          =							torch.randn(1						,      3						,      224						,      224)

    lowercase__					:  Any          =							hf_model(input_ids=_SCREAMING_SNAKE_CASE						,      pixel_values=_SCREAMING_SNAKE_CASE						,      return_dict=_SCREAMING_SNAKE_CASE)
    lowercase__					:  Union[str, Any]          =							hf_outputs.logits_per_image
    lowercase__					:  List[str]          =							hf_outputs.logits_per_text
    lowercase__   ,  lowercase__					:  Dict          =							pt_model(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE)

    assert torch.allclose(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      atol=1E-3)
    assert torch.allclose(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      atol=1E-3)

    hf_model.save_pretrained(_SCREAMING_SNAKE_CASE)


if __name__ == "__main__":
      UpperCamelCase    				=		argparse.ArgumentParser()
      parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
      parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
      parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
      UpperCamelCase    				=		parser.parse_args()

      convert_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)










"""simple docstring"""





import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments

import datasets


lowerCAmelCase__        =      '''\
@article{eddine2021frugalscore,
  title={FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation},
  author={Eddine, Moussa Kamal and Shang, Guokan and Tixier, Antoine J-P and Vazirgiannis, Michalis},
  journal={arXiv preprint arXiv:2110.08559},
  year={2021}
}
'''

lowerCAmelCase__        =      '''\
FrugalScore is a reference-based metric for NLG models evaluation. It is based on a distillation approach that allows to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance.
'''


lowerCAmelCase__        =      '''
Calculates how good are predictions given some references, using certain scores.
Args:
    predictions (list of str): list of predictions to score. Each predictions
        should be a string.
    references (list of str): list of reference for each prediction. Each
        reference should be a string.
    batch_size (int): the batch size for predictions.
    max_length (int): maximum sequence length.
    device (str): either gpu or cpu
Returns:
    scores (list of int): list of scores.
Examples:
    >>> frugalscore = datasets.load_metric("frugalscore")
    >>> results = frugalscore.compute(predictions=[\'hello there\', \'huggingface\'], references=[\'hello world\', \'hugging face\'])
    >>> print([round(s, 3) for s in results["scores"]])
    [0.631, 0.645]
'''





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


						def      snake_case_ (self		)			-> Tuple:
								return datasets.MetricInfo(
								    description=_DESCRIPTION ,				citation=_CITATION ,				inputs_description=_KWARGS_DESCRIPTION ,				features=datasets.Features(
								        {
								            "predictions": datasets.Value("string"		),
								            "references": datasets.Value("string"		),
								        }		) ,				homepage="https://github.com/moussaKam/FrugalScore" ,				)


						def      snake_case_ (self ,				__a		)			-> Optional[int]:
								if self.config_name == "default":
										UpperCamelCase =						"moussaKam/frugalscore_tiny_bert-base_bert-score"
								else:
										UpperCamelCase =						self.config_name
								UpperCamelCase =						AutoModelForSequenceClassification.from_pretrained(__a		)
								UpperCamelCase =						AutoTokenizer.from_pretrained(__a		)




						def      snake_case_ (self ,				__a ,				__a ,				__a=32 ,				__a=1_28 ,				__a=None ,				)			-> Optional[int]:
								assert len(__a		) == len(
								    __a		), "predictions and references should have the same number of sentences."
								if device is not None:
										assert device in ["gpu", "cpu"], "device should be either gpu or cpu."
								else:
										UpperCamelCase =						"gpu" if torch.cuda.is_available() else "cpu"
								UpperCamelCase =						TrainingArguments(
								    "trainer" ,				fpaa=(device == "gpu") ,				per_device_eval_batch_size=__a ,				report_to="all" ,				no_cuda=(device == "cpu") ,				log_level="warning" ,				)
								UpperCamelCase =						{"sentence1": predictions, "sentence2": references}
								UpperCamelCase =						datasets.Dataset.from_dict(__a		)

								def tokenize_function(__a		):
										return self.tokenizer(
										    data["sentence1"] ,				data["sentence2"] ,				max_length=__a ,				truncation=__a ,				padding=__a		)

								UpperCamelCase =						raw_datasets.map(__a ,				batched=__a		)
								tokenized_datasets.remove_columns(["sentence1", "sentence2"]		)
								UpperCamelCase =						Trainer(self.model ,				__a ,				tokenizer=self.tokenizer		)
								UpperCamelCase =						trainer.predict(__a		)
								return {"scores": list(predictions.predictions.squeeze(-1		)		)}






