


"""simple docstring"""




import types
from typing import List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


lowerCamelCase						 =	logging.get_logger(__name__)  # pylint: disable=invalid-name




def      a_				(   self :				Any		,      SCREAMING_SNAKE_CASE__ :				int		,      SCREAMING_SNAKE_CASE__ :				Optional[int]		,      SCREAMING_SNAKE_CASE__ :				List[Any]		,      SCREAMING_SNAKE_CASE__ :				Optional[Any]					):
       '''simple docstring'''





       _lowerCamelCase	:		Optional[Any]          =image.to(device=_a					)
       _lowerCamelCase	:		int          =image  # take image as image_embeddings
       _lowerCamelCase	:		Union[str, Any]          =image_embeddings.unsqueeze(1					)

       # duplicate image embeddings for each generation per prompt, using mps friendly method
       _lowerCamelCase	:		int          =image_embeddings.shape
       _lowerCamelCase	:		List[Any]          =image_embeddings.repeat(1		,      _a		,      1					)
       _lowerCamelCase	:		Optional[int]          =image_embeddings.view(bs_embed * num_images_per_prompt		,      _a		,      -1					)

       if do_classifier_free_guidance:
              _lowerCamelCase	:		List[Any]          =torch.zeros_like(_a					)

              # For classifier free guidance, we need to do two forward passes.
              # Here we concatenate the unconditional and text embeddings into a single batch
              # to avoid doing two forward passes
              _lowerCamelCase	:		int          =torch.cat([uncond_embeddings, image_embeddings]					)

       return image_embeddings




class A  (       __A					):






      def __init__( self						:					int      ,       lowercase_						:					PriorTransformer      ,       lowercase_						:					CLIPTokenizer      ,       lowercase_						:					CLIPTextModelWithProjection      ,       lowercase_						:					UnCLIPScheduler      ,       lowercase_						:					Optional[dict] = None      ,       ) ->						List[Any]:
             """simple docstring"""




             super().__init__()

             _lowerCamelCase	:		int          ={"image_encoder": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

             _lowerCamelCase	:		str          =decoder_pipe_kwargs.get('torch_dtype'      ,       lowercase_			) or prior.dtype

             _lowerCamelCase	:		List[str]          =StableDiffusionImageVariationPipeline.from_pretrained(
                 'lambdalabs/sd-image-variations-diffusers'      ,       **lowercase_			)

             # replace `_encode_image` method
             _lowerCamelCase	:		Union[str, Any]          =types.MethodType(_encode_image      ,       self.decoder_pipe			)

             self.register_modules(
                 prior=lowercase_      ,       tokenizer=lowercase_      ,       text_encoder=lowercase_      ,       prior_scheduler=lowercase_      ,       )






      def 	lowerCamelCase	( self						:					Tuple      ,       lowercase_						:					Any      ,       lowercase_						:					str      ,       lowercase_						:					Optional[int]      ,       lowercase_						:					Optional[Any]      ,       lowercase_						:					Optional[Union[CLIPTextModelOutput, Tuple]] = None      ,       lowercase_						:					Optional[torch.Tensor] = None      ,       ) ->						str:
             """simple docstring"""




             if text_model_output is None:
                    _lowerCamelCase	:		List[str]          =len(lowercase_			) if isinstance(lowercase_      ,       lowercase_			) else 1
                    # get prompt text embeddings
                    _lowerCamelCase	:		Any          =self.tokenizer(
                        lowercase_      ,       padding='max_length'      ,       max_length=self.tokenizer.model_max_length      ,       return_tensors='pt'      ,       )
                    _lowerCamelCase	:		Dict          =text_inputs.input_ids
                    _lowerCamelCase	:		List[Any]          =text_inputs.attention_mask.bool().to(lowercase_			)

                    if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                           _lowerCamelCase	:		str          =self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]			)
                           logger.warning(
                               'The following part of your input was truncated because CLIP can only handle sequences up to'
                               F''' {self.tokenizer.model_max_length} tokens: {removed_text}'''			)
                           _lowerCamelCase	:		Optional[int]          =text_input_ids[:, : self.tokenizer.model_max_length]

                    _lowerCamelCase	:		Dict          =self.text_encoder(text_input_ids.to(lowercase_			)			)

                    _lowerCamelCase	:		Dict          =text_encoder_output.text_embeds
                    _lowerCamelCase	:		Tuple          =text_encoder_output.last_hidden_state

             else:
                    _lowerCamelCase	:		Tuple          =text_model_output[0].shape[0]
                    _lowerCamelCase	:		List[str]          =text_model_output[0], text_model_output[1]
                    _lowerCamelCase	:		Optional[int]          =text_attention_mask

             _lowerCamelCase	:		Tuple          =text_embeddings.repeat_interleave(lowercase_      ,       dim=0			)
             _lowerCamelCase	:		Union[str, Any]          =text_encoder_hidden_states.repeat_interleave(lowercase_      ,       dim=0			)
             _lowerCamelCase	:		List[Any]          =text_mask.repeat_interleave(lowercase_      ,       dim=0			)

             if do_classifier_free_guidance:
                    _lowerCamelCase	:		Any          =[""] * batch_size

                    _lowerCamelCase	:		List[str]          =self.tokenizer(
                        lowercase_      ,       padding='max_length'      ,       max_length=self.tokenizer.model_max_length      ,       truncation=lowercase_      ,       return_tensors='pt'      ,       )
                    _lowerCamelCase	:		str          =uncond_input.attention_mask.bool().to(lowercase_			)
                    _lowerCamelCase	:		List[str]          =self.text_encoder(uncond_input.input_ids.to(lowercase_			)			)

                    _lowerCamelCase	:		str          =uncond_embeddings_text_encoder_output.text_embeds
                    _lowerCamelCase	:		List[str]          =uncond_embeddings_text_encoder_output.last_hidden_state

                    # duplicate unconditional embeddings for each generation per prompt, using mps friendly method

                    _lowerCamelCase	:		Optional[Any]          =uncond_embeddings.shape[1]
                    _lowerCamelCase	:		List[Any]          =uncond_embeddings.repeat(1      ,       lowercase_			)
                    _lowerCamelCase	:		Dict          =uncond_embeddings.view(batch_size * num_images_per_prompt      ,       lowercase_			)

                    _lowerCamelCase	:		Optional[Any]          =uncond_text_encoder_hidden_states.shape[1]
                    _lowerCamelCase	:		str          =uncond_text_encoder_hidden_states.repeat(1      ,       lowercase_      ,       1			)
                    _lowerCamelCase	:		Tuple          =uncond_text_encoder_hidden_states.view(
                        batch_size * num_images_per_prompt      ,       lowercase_      ,       -1			)
                    _lowerCamelCase	:		List[Any]          =uncond_text_mask.repeat_interleave(lowercase_      ,       dim=0			)

                    # done duplicates

                    # For classifier free guidance, we need to do two forward passes.
                    # Here we concatenate the unconditional and text embeddings into a single batch
                    # to avoid doing two forward passes
                    _lowerCamelCase	:		Any          =torch.cat([uncond_embeddings, text_embeddings]			)
                    _lowerCamelCase	:		List[Any]          =torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]			)

                    _lowerCamelCase	:		Union[str, Any]          =torch.cat([uncond_text_mask, text_mask]			)

             return text_embeddings, text_encoder_hidden_states, text_mask






      @property
      def 	lowerCamelCase	( self						:					Optional[int]			) ->						Optional[int]:
             """simple docstring"""




             if self.device != torch.device('meta'			) or not hasattr(self.prior      ,       '_hf_hook'			):
                    return self.device
             for module in self.prior.modules():
                    if (
                        hasattr(lowercase_      ,       '_hf_hook'			)
                        and hasattr(module._hf_hook      ,       'execution_device'			)
                        and module._hf_hook.execution_device is not None
                    ):
                           return torch.device(module._hf_hook.execution_device			)
             return self.device






      def 	lowerCamelCase	( self						:					Any      ,       lowercase_						:					Union[str, Any]      ,       lowercase_						:					Tuple      ,       lowercase_						:					List[Any]      ,       lowercase_						:					Union[str, Any]      ,       lowercase_						:					Optional[int]      ,       lowercase_						:					Optional[Any]			) ->						List[str]:
             """simple docstring"""




             if latents is None:
                    _lowerCamelCase	:		Optional[Any]          =randn_tensor(lowercase_      ,       generator=lowercase_      ,       device=lowercase_      ,       dtype=lowercase_			)
             else:
                    if latents.shape != shape:
                           raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''			)
                    _lowerCamelCase	:		int          =latents.to(lowercase_			)

             _lowerCamelCase	:		List[str]          =latents * scheduler.init_noise_sigma
             return latents






      def 	lowerCamelCase	( self						:					List[Any]      ,       lowercase_						:					Optional[Union[str, torch.device]] = None			) ->						Optional[Any]:
             """simple docstring"""




             self.decoder_pipe.to(lowercase_			)
             super().to(lowercase_			)






      @torch.no_grad()
      def __call__( self						:					Union[str, Any]      ,       lowercase_						:					Optional[Union[str, List[str]]] = None      ,       lowercase_						:					Optional[int] = None      ,       lowercase_						:					Optional[int] = None      ,       lowercase_						:					int = 1      ,       lowercase_						:					int = 25      ,       lowercase_						:					Optional[torch.Generator] = None      ,       lowercase_						:					Optional[torch.FloatTensor] = None      ,       lowercase_						:					Optional[Union[CLIPTextModelOutput, Tuple]] = None      ,       lowercase_						:					Optional[torch.Tensor] = None      ,       lowercase_						:					float = 4.0      ,       lowercase_						:					float = 8.0      ,       lowercase_						:					int = 50      ,       lowercase_						:					Optional[int] = 1      ,       lowercase_						:					float = 0.0      ,       lowercase_						:					Optional[str] = "pil"      ,       lowercase_						:					bool = True      ,       ) ->						int:
             """simple docstring"""




             if prompt is not None:
                    if isinstance(lowercase_      ,       lowercase_			):
                           _lowerCamelCase	:		Tuple          =1
                    elif isinstance(lowercase_      ,       lowercase_			):
                           _lowerCamelCase	:		Union[str, Any]          =len(lowercase_			)
                    else:
                           raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(lowercase_			)}'''			)
             else:
                    _lowerCamelCase	:		List[Any]          =text_model_output[0].shape[0]

             _lowerCamelCase	:		str          =self._execution_device

             _lowerCamelCase	:		List[str]          =batch_size * num_images_per_prompt

             _lowerCamelCase	:		Optional[int]          =prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

             _lowerCamelCase	:		Tuple          =self._encode_prompt(
                 lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_			)

             # prior

             self.prior_scheduler.set_timesteps(lowercase_      ,       device=lowercase_			)
             _lowerCamelCase	:		str          =self.prior_scheduler.timesteps

             _lowerCamelCase	:		List[Any]          =self.prior.config.embedding_dim

             _lowerCamelCase	:		str          =self.prepare_latents(
                 (batch_size, embedding_dim)      ,       text_embeddings.dtype      ,       lowercase_      ,       lowercase_      ,       lowercase_      ,       self.prior_scheduler      ,       )

             for i, t in enumerate(self.progress_bar(lowercase_			)			):
                    # expand the latents if we are doing classifier free guidance
                    _lowerCamelCase	:		Optional[int]          =torch.cat([prior_latents] * 2			) if do_classifier_free_guidance else prior_latents

                    _lowerCamelCase	:		Optional[int]          =self.prior(
                        lowercase_      ,       timestep=lowercase_      ,       proj_embedding=lowercase_      ,       encoder_hidden_states=lowercase_      ,       attention_mask=lowercase_      ,       ).predicted_image_embedding

                    if do_classifier_free_guidance:
                           _lowerCamelCase	:		Any          =predicted_image_embedding.chunk(2			)
                           _lowerCamelCase	:		Dict          =predicted_image_embedding_uncond + prior_guidance_scale * (
                               predicted_image_embedding_text - predicted_image_embedding_uncond
                           )

                    if i + 1 == prior_timesteps_tensor.shape[0]:
                           _lowerCamelCase	:		Union[str, Any]          =None
                    else:
                           _lowerCamelCase	:		Any          =prior_timesteps_tensor[i + 1]

                    _lowerCamelCase	:		str          =self.prior_scheduler.step(
                        lowercase_      ,       timestep=lowercase_      ,       sample=lowercase_      ,       generator=lowercase_      ,       prev_timestep=lowercase_      ,       ).prev_sample

             _lowerCamelCase	:		Union[str, Any]          =self.prior.post_process_latents(lowercase_			)

             _lowerCamelCase	:		List[Any]          =prior_latents

             _lowerCamelCase	:		List[str]          =self.decoder_pipe(
                 image=lowercase_      ,       height=lowercase_      ,       width=lowercase_      ,       num_inference_steps=lowercase_      ,       guidance_scale=lowercase_      ,       generator=lowercase_      ,       output_type=lowercase_      ,       return_dict=lowercase_      ,       num_images_per_prompt=lowercase_      ,       eta=lowercase_      ,       )
             return output






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

import datasets


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

lowerCamelCase						 =	'\\nFrugalScore 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.\n'


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




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






      def 	lowerCamelCase	( self						:					Tuple			) ->						Optional[Any]:
             """simple docstring"""




             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 	lowerCamelCase	( self						:					Optional[int]      ,       lowercase_						:					Union[str, Any]			) ->						int:
             """simple docstring"""




             if self.config_name == "default":
                    _lowerCamelCase	:		Optional[int]          ='moussaKam/frugalscore_tiny_bert-base_bert-score'
             else:
                    _lowerCamelCase	:		List[Any]          =self.config_name
             _lowerCamelCase	:		str          =AutoModelForSequenceClassification.from_pretrained(lowercase_			)
             _lowerCamelCase	:		str          =AutoTokenizer.from_pretrained(lowercase_			)






      def 	lowerCamelCase	( self						:					List[Any]      ,       lowercase_						:					List[str]      ,       lowercase_						:					str      ,       lowercase_						:					Optional[int]=32      ,       lowercase_						:					int=128      ,       lowercase_						:					Tuple=None      ,       ) ->						str:
             """simple docstring"""




             assert len(lowercase_			) == len(
                 lowercase_			), "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:
                    _lowerCamelCase	:		List[str]          ='gpu' if torch.cuda.is_available() else 'cpu'
             _lowerCamelCase	:		List[str]          =TrainingArguments(
                 'trainer'      ,       fpaa=(device == 'gpu')      ,       per_device_eval_batch_size=lowercase_      ,       report_to='all'      ,       no_cuda=(device == 'cpu')      ,       log_level='warning'      ,       )
             _lowerCamelCase	:		str          ={'sentence1': predictions, 'sentence2': references}
             _lowerCamelCase	:		Tuple          =datasets.Dataset.from_dict(lowercase_			)

             def tokenize_function(lowercase_						:					Any			):
                    return self.tokenizer(
                        data['sentence1']      ,       data['sentence2']      ,       max_length=lowercase_      ,       truncation=lowercase_      ,       padding=lowercase_			)

             _lowerCamelCase	:		Dict          =raw_datasets.map(lowercase_      ,       batched=lowercase_			)
             tokenized_datasets.remove_columns(['sentence1', 'sentence2']			)
             _lowerCamelCase	:		Union[str, Any]          =Trainer(self.model      ,       lowercase_      ,       tokenizer=self.tokenizer			)
             _lowerCamelCase	:		List[str]          =trainer.predict(lowercase_			)
             return {"scores": list(predictions.predictions.squeeze(-1			)			)}


