



import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


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




class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     def __init__(       self  :  Tuple, _snake_case  :  WhisperForConditionalGeneration, _snake_case  :  WhisperProcessor, _snake_case  :  AutoencoderKL, _snake_case  :  CLIPTextModel, _snake_case  :  CLIPTokenizer, _snake_case  :  UNetaDConditionModel, _snake_case  :  Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler], _snake_case  :  StableDiffusionSafetyChecker, _snake_case  :  CLIPImageProcessor, )   ->Optional[int]:
            super().__init__()

            if safety_checker is None:
                   logger.warning(
                       F'''You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'''
                       ' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'
                       ' results in services or applications open to the public. Both the diffusers team and Hugging Face'
                       ' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'
                       ' it only for use-cases that involve analyzing network behavior or auditing its results. For more'
                       ' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .'					)

            self.register_modules(
                speech_model=_snake_case, speech_processor=_snake_case, vae=_snake_case, text_encoder=_snake_case, tokenizer=_snake_case, unet=_snake_case, scheduler=_snake_case, feature_extractor=_snake_case, )






     def    lowercase_   (       self  :  List[str], _snake_case  :  Optional[Union[str, int]] = "auto"					)   ->Union[str, Any]:
            if slice_size == "auto":
                   snake_case__							:	Any   	=	self.unet.config.attention_head_dim // 2
            self.unet.set_attention_slice(_snake_case					)






     def    lowercase_   (       self  :  Any					)   ->Any:
            self.enable_attention_slicing(_snake_case					)






     @torch.no_grad()
     def __call__(       self  :  List[Any], _snake_case  :  str, _snake_case  :  Dict=1_6_0_0_0, _snake_case  :  int = 5_1_2, _snake_case  :  int = 5_1_2, _snake_case  :  int = 5_0, _snake_case  :  float = 7.5, _snake_case  :  Optional[Union[str, List[str]]] = None, _snake_case  :  Optional[int] = 1, _snake_case  :  float = 0.0, _snake_case  :  Optional[torch.Generator] = None, _snake_case  :  Optional[torch.FloatTensor] = None, _snake_case  :  Optional[str] = "pil", _snake_case  :  bool = True, _snake_case  :  Optional[Callable[[int, int, torch.FloatTensor], None]] = None, _snake_case  :  int = 1, **_snake_case  :  Any, )   ->int:
            snake_case__							:	Optional[int]   	=	self.speech_processor.feature_extractor(
                _snake_case, return_tensors='pt', sampling_rate=_snake_case					).input_features.to(self.device					)
            snake_case__							:	Dict   	=	self.speech_model.generate(_snake_case, max_length=4_8_0_0_0_0					)

            snake_case__							:	Optional[int]   	=	self.speech_processor.tokenizer.batch_decode(_snake_case, skip_special_tokens=_snake_case, normalize=_snake_case					)[
                0
            ]

            if isinstance(_snake_case, _snake_case					):
                   snake_case__							:	Union[str, Any]   	=	1
            elif isinstance(_snake_case, _snake_case					):
                   snake_case__							:	int   	=	len(_snake_case					)
            else:
                   raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(_snake_case					)}'''					)

            if height % 8 != 0 or width % 8 != 0:
                   raise ValueError(F'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''					)

            if (callback_steps is None) or (
                callback_steps is not None and (not isinstance(_snake_case, _snake_case					) or callback_steps <= 0)
            ):
                   raise ValueError(
                       F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
                       F''' {type(_snake_case					)}.'''					)

            # get prompt text embeddings
            snake_case__							:	Dict   	=	self.tokenizer(
                _snake_case, padding='max_length', max_length=self.tokenizer.model_max_length, return_tensors='pt', )
            snake_case__							:	Dict   	=	text_inputs.input_ids

            if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                   snake_case__							:	List[Any]   	=	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}'''					)
                   snake_case__							:	Union[str, Any]   	=	text_input_ids[:, : self.tokenizer.model_max_length]
            snake_case__							:	Optional[Any]   	=	self.text_encoder(text_input_ids.to(self.device					)					)[0]

            # duplicate text embeddings for each generation per prompt, using mps friendly method
            snake_case__	,				snake_case__	,				snake_case__							:	Optional[int]   	=	text_embeddings.shape
            snake_case__							:	Optional[int]   	=	text_embeddings.repeat(1, _snake_case, 1					)
            snake_case__							:	str   	=	text_embeddings.view(bs_embed * num_images_per_prompt, _snake_case, -1					)

            # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
            # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
            # corresponds to doing no classifier free guidance.
            snake_case__							:	Optional[Any]   	=	guidance_scale > 1.0
            # get unconditional embeddings for classifier free guidance
            if do_classifier_free_guidance:
                   snake_case__							:	List[str]
                   if negative_prompt is None:
                          snake_case__							:	Optional[Any]   	=	[''] * batch_size
                   elif type(_snake_case					) is not type(_snake_case					):
                          raise TypeError(
                              F'''`negative_prompt` should be the same type to `prompt`, but got {type(_snake_case					)} !='''
                              F''' {type(_snake_case					)}.'''					)
                   elif isinstance(_snake_case, _snake_case					):
                          snake_case__							:	int   	=	[negative_prompt]
                   elif batch_size != len(_snake_case					):
                          raise ValueError(
                              F'''`negative_prompt`: {negative_prompt} has batch size {len(_snake_case					)}, but `prompt`:'''
                              F''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
                              ' the batch size of `prompt`.'					)
                   else:
                          snake_case__							:	Tuple   	=	negative_prompt

                   snake_case__							:	Dict   	=	text_input_ids.shape[-1]
                   snake_case__							:	int   	=	self.tokenizer(
                       _snake_case, padding='max_length', max_length=_snake_case, truncation=_snake_case, return_tensors='pt', )
                   snake_case__							:	Optional[int]   	=	self.text_encoder(uncond_input.input_ids.to(self.device					)					)[0]

                   # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
                   snake_case__							:	List[Any]   	=	uncond_embeddings.shape[1]
                   snake_case__							:	str   	=	uncond_embeddings.repeat(1, _snake_case, 1					)
                   snake_case__							:	Union[str, Any]   	=	uncond_embeddings.view(batch_size * num_images_per_prompt, _snake_case, -1					)

                   # 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
                   snake_case__							:	Tuple   	=	torch.cat([uncond_embeddings, text_embeddings]					)

            # get the initial random noise unless the user supplied it

            # Unlike in other pipelines, latents need to be generated in the target device
            # for 1-to-1 results reproducibility with the CompVis implementation.
            # However this currently doesn't work in `mps`.
            snake_case__							:	str   	=	(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
            snake_case__							:	str   	=	text_embeddings.dtype
            if latents is None:
                   if self.device.type == "mps":
                          # randn does not exist on mps
                          snake_case__							:	Dict   	=	torch.randn(_snake_case, generator=_snake_case, device='cpu', dtype=_snake_case					).to(
                              self.device					)
                   else:
                          snake_case__							:	str   	=	torch.randn(_snake_case, generator=_snake_case, device=self.device, dtype=_snake_case					)
            else:
                   if latents.shape != latents_shape:
                          raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''					)
                   snake_case__							:	int   	=	latents.to(self.device					)

            # set timesteps
            self.scheduler.set_timesteps(_snake_case					)

            # Some schedulers like PNDM have timesteps as arrays
            # It's more optimized to move all timesteps to correct device beforehand
            snake_case__							:	Any   	=	self.scheduler.timesteps.to(self.device					)

            # scale the initial noise by the standard deviation required by the scheduler
            snake_case__							:	Any   	=	latents * self.scheduler.init_noise_sigma

            # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
            # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
            # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
            # and should be between [0, 1]
            snake_case__							:	List[str]   	=	'eta' in set(inspect.signature(self.scheduler.step					).parameters.keys()					)
            snake_case__							:	Any   	=	{}
            if accepts_eta:
                   snake_case__							:	Optional[int]   	=	eta

            for i, t in enumerate(self.progress_bar(_snake_case					)					):
                   # expand the latents if we are doing classifier free guidance
                   snake_case__							:	List[Any]   	=	torch.cat([latents] * 2					) if do_classifier_free_guidance else latents
                   snake_case__							:	Tuple   	=	self.scheduler.scale_model_input(_snake_case, _snake_case					)

                   # predict the noise residual
                   snake_case__							:	str   	=	self.unet(_snake_case, _snake_case, encoder_hidden_states=_snake_case					).sample

                   # perform guidance
                   if do_classifier_free_guidance:
                          snake_case__	,				snake_case__							:	List[str]   	=	noise_pred.chunk(2					)
                          snake_case__							:	Optional[int]   	=	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                   # compute the previous noisy sample x_t -> x_t-1
                   snake_case__							:	Union[str, Any]   	=	self.scheduler.step(_snake_case, _snake_case, _snake_case, **_snake_case					).prev_sample

                   # call the callback, if provided
                   if callback is not None and i % callback_steps == 0:
                          callback(_snake_case, _snake_case, _snake_case					)

            snake_case__							:	Optional[int]   	=	1 / 0.1_8_2_1_5 * latents
            snake_case__							:	Tuple   	=	self.vae.decode(_snake_case					).sample

            snake_case__							:	Tuple   	=	(image / 2 + 0.5).clamp(0, 1					)

            # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
            snake_case__							:	int   	=	image.cpu().permute(0, 2, 3, 1					).float().numpy()

            if output_type == "pil":
                   snake_case__							:	str   	=	self.numpy_to_pil(_snake_case					)

            if not return_dict:
                   return image

            return StableDiffusionPipelineOutput(images=_snake_case, nsfw_content_detected=_snake_case					)











from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


a_   :Any        =      "\\n@misc{wu2016googles,\n      title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n      author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey\n              and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin\n              Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto\n              Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and\n              Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes\n              and Jeffrey Dean},\n      year={2016},\n      eprint={1609.08144},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n"

a_   :List[str]        =      "\\nThe BLEU score has some undesirable properties when used for single\nsentences, as it was designed to be a corpus measure. We therefore\nuse a slightly different score for our RL experiments which we call\nthe 'GLEU score'. For the GLEU score, we record all sub-sequences of\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\ncompute a recall, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the target (ground truth) sequence,\nand a precision, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the generated output sequence. Then\nGLEU score is simply the minimum of recall and precision. This GLEU\nscore's range is always between 0 (no matches) and 1 (all match) and\nit is symmetrical when switching output and target. According to\nour experiments, GLEU score correlates quite well with the BLEU\nmetric on a corpus level but does not have its drawbacks for our per\nsentence reward objective.\n"

a_   :List[str]        =      "\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\n\nArgs:\n    predictions (list of str): list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references (list of list of str): list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\n    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\n\nReturns:\n    'google_bleu': google_bleu score\n\nExamples:\n    Example 1:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.44\n\n    Example 2:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n        ...          'heed', 'the', 'cat', 'commands']\n        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n        ...          'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.61\n\n    Example 3:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n        ...          'heed', 'the', 'cat', 'commands']\n        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n        ...          'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.53\n\n    Example 4:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n        ...          'heed', 'the', 'cat', 'commands']\n        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n        ...          'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.4\n"




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






     """simple docstring"""


     def    lowercase_   (       self  :  str					)   ->MetricInfo:
            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'					),
                    }					), )






     def    lowercase_   (       self  :  str, _snake_case  :  List[List[List[str]]], _snake_case  :  List[List[str]], _snake_case  :  int = 1, _snake_case  :  int = 4, )   ->Dict[str, float]:
            return {
                "google_bleu": gleu_score.corpus_gleu(
                    list_of_references=_snake_case, hypotheses=_snake_case, min_len=_snake_case, max_len=_snake_case					)
            }






