

"""simple docstring"""




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

import datasets


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

__UpperCAmelCase =				'\\nTER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a\nhypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu\n(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found\nhere: https://github.com/jhclark/tercom.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.\n'

__UpperCAmelCase =				'\nProduces TER scores alongside the number of edits and reference length.\n\nArgs:\n    predictions (list of str): The system stream (a sequence of segments).\n    references (list of list of str): A list of one or more reference streams (each a sequence of segments).\n    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,\n                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.\n                                    Only applies if `normalized = True`. Defaults to `False`.\n    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.\n\nReturns:\n    \'score\' (float): TER score (num_edits / sum_ref_lengths * 100)\n    \'num_edits\' (int): The cumulative number of edits\n    \'ref_length\' (float): The cumulative average reference length\n\nExamples:\n    Example 1:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?",\n        ...                     "What did the TER metric user say to the developer?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],\n        ...             ["Your jokes are...", "...TERrible"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {\'score\': 150.0, \'num_edits\': 15, \'ref_length\': 10.0}\n\n    Example 2:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {\'score\': 62.5, \'num_edits\': 5, \'ref_length\': 8.0}\n\n    Example 3:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         normalized=True,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {\'score\': 57.14285714285714, \'num_edits\': 6, \'ref_length\': 10.5}\n\n    Example 4:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {\'score\': 0.0, \'num_edits\': 0, \'ref_length\': 8.0}\n\n    Example 5:\n        >>> predictions = ["does this sentence match??",\n        ...                    "what about this sentence?",\n        ...                    "What did the TER metric user say to the developer?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],\n        ...             ["Your jokes are...", "...TERrible"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {\'score\': 100.0, \'num_edits\': 10, \'ref_length\': 10.0}\n'







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

  def    __lowercase       (  self		:			List[Any]    ):






   '''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    __lowercase       (  self		:			List[str]					,A		:			Dict					,A		:			int					,A		:			bool = False					,A		:			bool = False					,A		:			bool = False					,A		:			bool = False					,):






   '''simple docstring'''
   UpperCAmelCase__					:  Optional[Any]					   =					len(references[0]    )
   if any(len(A    ) != references_per_prediction for refs in references    ):
    raise ValueError("""Sacrebleu requires the same number of references for each prediction"""    )
   UpperCAmelCase__					:  List[str]					   =					[[refs[i] for refs in references] for i in range(A    )]

   UpperCAmelCase__					:  Dict					   =					TER(
       normalized=A					,no_punct=A					,asian_support=A					,case_sensitive=A					,)
   UpperCAmelCase__					:  str					   =					sb_ter.corpus_score(A					,A    )

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








"""simple docstring"""




from typing import List, Optional, Union

import torch
from transformers import (
    XLMRobertaTokenizer,
)

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .text_encoder import MultilingualCLIP


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

__UpperCAmelCase =				'\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior")\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "red cat, 4k photo"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> negative_image_emb = out.negative_image_embeds\n\n        >>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")\n        >>> pipe.to("cuda")\n\n        >>> image = pipe(\n        ...     prompt,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=negative_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ... ).images\n\n        >>> image[0].save("cat.png")\n        ```\n'
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase=8      ):
 '''simple docstring'''





 UpperCAmelCase__					:  Tuple					   =					h // scale_factor**2
 if h % scale_factor**2 != 0:
  new_h += 1
 UpperCAmelCase__					:  Any					   =					w // scale_factor**2
 if w % scale_factor**2 != 0:
  new_w += 1
 return new_h * scale_factor, new_w * scale_factor







class      __lowercase			(    __lowerCamelCase					):

  def __init__(  self		:			str					,A		:			MultilingualCLIP					,A		:			XLMRobertaTokenizer					,A		:			UNetaDConditionModel					,A		:			Union[DDIMScheduler, DDPMScheduler]					,A		:			VQModel					,):






   '''simple docstring'''
   super().__init__()

   self.register_modules(
       text_encoder=A					,tokenizer=A					,unet=A					,scheduler=A					,movq=A					,)
   UpperCAmelCase__					:  Optional[Any]					   =					2 ** (len(self.movq.config.block_out_channels    ) - 1)

  def    __lowercase       (  self		:			Dict					,A		:			Any					,A		:			Tuple					,A		:			Dict					,A		:			int					,A		:			str					,A		:			List[str]    ):






   '''simple docstring'''
   if latents is None:
    UpperCAmelCase__					:  Any					   =					randn_tensor(A					,generator=A					,device=A					,dtype=A    )
   else:
    if latents.shape != shape:
     raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}"    )
    UpperCAmelCase__					:  int					   =					latents.to(A    )

   UpperCAmelCase__					:  Any					   =					latents * scheduler.init_noise_sigma
   return latents

  def    __lowercase       (  self		:			Optional[int]					,A		:			List[Any]					,A		:			Optional[Any]					,A		:			str					,A		:			Optional[Any]					,A		:			str=None					,):






   '''simple docstring'''
   UpperCAmelCase__					:  List[Any]					   =					len(A    ) if isinstance(A					,A    ) else 1
   # get prompt text embeddings
   UpperCAmelCase__					:  List[Any]					   =					self.tokenizer(
       A					,padding="""max_length"""					,truncation=A					,max_length=77					,return_attention_mask=A					,add_special_tokens=A					,return_tensors="""pt"""					,)

   UpperCAmelCase__					:  List[str]					   =					text_inputs.input_ids
   UpperCAmelCase__					:  Any					   =					self.tokenizer(A					,padding="""longest"""					,return_tensors="""pt"""    ).input_ids

   if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(A					,A    ):
    UpperCAmelCase__					:  List[str]					   =					self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]    )
    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}"    )

   UpperCAmelCase__					:  str					   =					text_input_ids.to(A    )
   UpperCAmelCase__					:  Optional[Any]					   =					text_inputs.attention_mask.to(A    )

   UpperCAmelCase__   ,      UpperCAmelCase__					:  Tuple					   =					self.text_encoder(
       input_ids=A					,attention_mask=A    )

   UpperCAmelCase__					:  Optional[int]					   =					prompt_embeds.repeat_interleave(A					,dim=0    )
   UpperCAmelCase__					:  Optional[int]					   =					text_encoder_hidden_states.repeat_interleave(A					,dim=0    )
   UpperCAmelCase__					:  List[str]					   =					text_mask.repeat_interleave(A					,dim=0    )

   if do_classifier_free_guidance:
    UpperCAmelCase__					:  List[str]
    if negative_prompt is None:
     UpperCAmelCase__					:  List[Any]					   =					[""""""] * batch_size
    elif type(A    ) is not type(A    ):
     raise TypeError(
         f"`negative_prompt` should be the same type to `prompt`, but got {type(A    )} !="
         f" {type(A    )}."    )
    elif isinstance(A					,A    ):
     UpperCAmelCase__					:  Any					   =					[negative_prompt]
    elif batch_size != len(A    ):
     raise ValueError(
         f"`negative_prompt`: {negative_prompt} has batch size {len(A    )}, but `prompt`:"
         f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
         """ the batch size of `prompt`."""    )
    else:
     UpperCAmelCase__					:  List[Any]					   =					negative_prompt

    UpperCAmelCase__					:  Any					   =					self.tokenizer(
        A					,padding="""max_length"""					,max_length=77					,truncation=A					,return_attention_mask=A					,add_special_tokens=A					,return_tensors="""pt"""					,)
    UpperCAmelCase__					:  Optional[int]					   =					uncond_input.input_ids.to(A    )
    UpperCAmelCase__					:  str					   =					uncond_input.attention_mask.to(A    )

    UpperCAmelCase__   ,      UpperCAmelCase__					:  Dict					   =					self.text_encoder(
        input_ids=A					,attention_mask=A    )

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

    UpperCAmelCase__					:  Any					   =					negative_prompt_embeds.shape[1]
    UpperCAmelCase__					:  Any					   =					negative_prompt_embeds.repeat(1					,A    )
    UpperCAmelCase__					:  str					   =					negative_prompt_embeds.view(batch_size * num_images_per_prompt					,A    )

    UpperCAmelCase__					:  Dict					   =					uncond_text_encoder_hidden_states.shape[1]
    UpperCAmelCase__					:  Any					   =					uncond_text_encoder_hidden_states.repeat(1					,A					,1    )
    UpperCAmelCase__					:  Any					   =					uncond_text_encoder_hidden_states.view(
        batch_size * num_images_per_prompt					,A					,-1    )
    UpperCAmelCase__					:  List[Any]					   =					uncond_text_mask.repeat_interleave(A					,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
    UpperCAmelCase__					:  Any					   =					torch.cat([negative_prompt_embeds, prompt_embeds]    )
    UpperCAmelCase__					:  int					   =					torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]    )

    UpperCAmelCase__					:  str					   =					torch.cat([uncond_text_mask, text_mask]    )

   return prompt_embeds, text_encoder_hidden_states, text_mask

  def    __lowercase       (  self		:			Tuple					,A		:			Dict=0    ):






   '''simple docstring'''
   if is_accelerate_available():
    from accelerate import cpu_offload
   else:
    raise ImportError("""Please install accelerate via `pip install accelerate`"""    )

   UpperCAmelCase__					:  str					   =					torch.device(f"cuda:{gpu_id}"    )

   UpperCAmelCase__					:  Tuple					   =					[
       self.unet,
       self.text_encoder,
       self.movq,
   ]
   for cpu_offloaded_model in models:
    if cpu_offloaded_model is not None:
     cpu_offload(A					,A    )

  def    __lowercase       (  self		:			int					,A		:			Optional[Any]=0    ):






   '''simple docstring'''
   if is_accelerate_available() and is_accelerate_version(""">="""					,"""0.17.0.dev0"""    ):
    from accelerate import cpu_offload_with_hook
   else:
    raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""    )

   UpperCAmelCase__					:  List[str]					   =					torch.device(f"cuda:{gpu_id}"    )

   if self.device.type != "cpu":
    self.to("""cpu"""					,silence_dtype_warnings=A    )
    torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

   UpperCAmelCase__					:  List[str]					   =					None
   for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
    UpperCAmelCase__   ,      UpperCAmelCase__					:  Union[str, Any]					   =					cpu_offload_with_hook(A					,A					,prev_module_hook=A    )

   if self.safety_checker is not None:
    UpperCAmelCase__   ,      UpperCAmelCase__					:  Optional[int]					   =					cpu_offload_with_hook(self.safety_checker					,A					,prev_module_hook=A    )

   # We'll offload the last model manually.
   UpperCAmelCase__					:  Any					   =					hook

  @property
  # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
  def    __lowercase       (  self		:			List[Any]    ):






   '''simple docstring'''
   if not hasattr(self.unet					,"""_hf_hook"""    ):
    return self.device
   for module in self.unet.modules():
    if (
        hasattr(A					,"""_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




  @torch.no_grad()
  @replace_example_docstring(A    )
  def __call__(  self		:			Union[str, Any]					,A		:			Union[str, List[str]]					,A		:			Union[torch.FloatTensor, List[torch.FloatTensor]]					,A		:			Union[torch.FloatTensor, List[torch.FloatTensor]]					,A		:			Optional[Union[str, List[str]]] = None					,A		:			int = 512					,A		:			int = 512					,A		:			int = 100					,A		:			float = 4.0					,A		:			int = 1					,A		:			Optional[Union[torch.Generator, List[torch.Generator]]] = None					,A		:			Optional[torch.FloatTensor] = None					,A		:			Optional[str] = "pil"					,A		:			bool = True					,):






   '''simple docstring'''

   if isinstance(A					,A    ):
    UpperCAmelCase__					:  str					   =					1
   elif isinstance(A					,A    ):
    UpperCAmelCase__					:  Tuple					   =					len(A    )
   else:
    raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(A    )}"    )

   UpperCAmelCase__					:  Optional[int]					   =					self._execution_device

   UpperCAmelCase__					:  Dict					   =					batch_size * num_images_per_prompt
   UpperCAmelCase__					:  Union[str, Any]					   =					guidance_scale > 1.0

   UpperCAmelCase__   ,      UpperCAmelCase__   ,      UpperCAmelCase__					:  Optional[int]					   =					self._encode_prompt(
       A					,A					,A					,A					,A    )

   if isinstance(A					,A    ):
    UpperCAmelCase__					:  Dict					   =					torch.cat(A					,dim=0    )
   if isinstance(A					,A    ):
    UpperCAmelCase__					:  Dict					   =					torch.cat(A					,dim=0    )

   if do_classifier_free_guidance:
    UpperCAmelCase__					:  Optional[int]					   =					image_embeds.repeat_interleave(A					,dim=0    )
    UpperCAmelCase__					:  Tuple					   =					negative_image_embeds.repeat_interleave(A					,dim=0    )

   UpperCAmelCase__					:  List[str]					   =					torch.cat([negative_image_embeds, image_embeds]					,dim=0    ).to(
       dtype=prompt_embeds.dtype					,device=A    )

   self.scheduler.set_timesteps(A					,device=A    )
   UpperCAmelCase__					:  int					   =					self.scheduler.timesteps

   UpperCAmelCase__					:  Union[str, Any]					   =					self.unet.config.in_channels

   UpperCAmelCase__   ,      UpperCAmelCase__					:  Dict					   =					get_new_h_w(A					,A					,self.movq_scale_factor    )

   # create initial latent
   UpperCAmelCase__					:  Dict					   =					self.prepare_latents(
       (batch_size, num_channels_latents, height, width)					,text_encoder_hidden_states.dtype					,A					,A					,A					,self.scheduler					,)

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

    UpperCAmelCase__					:  Optional[Any]					   =					{"""text_embeds""": prompt_embeds, """image_embeds""": image_embeds}
    UpperCAmelCase__					:  Any					   =					self.unet(
        sample=A					,timestep=A					,encoder_hidden_states=A					,added_cond_kwargs=A					,return_dict=A					,)[0]

    if do_classifier_free_guidance:
     UpperCAmelCase__   ,      UpperCAmelCase__					:  List[str]					   =					noise_pred.split(latents.shape[1]					,dim=1    )
     UpperCAmelCase__   ,      UpperCAmelCase__					:  Any					   =					noise_pred.chunk(2    )
     UpperCAmelCase__   ,      UpperCAmelCase__					:  str					   =					variance_pred.chunk(2    )
     UpperCAmelCase__					:  int					   =					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
     UpperCAmelCase__					:  List[Any]					   =					torch.cat([noise_pred, variance_pred_text]					,dim=1    )

    if not (
        hasattr(self.scheduler.config					,"""variance_type"""    )
        and self.scheduler.config.variance_type in ["learned", "learned_range"]
    ):
     UpperCAmelCase__   ,      UpperCAmelCase__					:  List[Any]					   =					noise_pred.split(latents.shape[1]					,dim=1    )

    # compute the previous noisy sample x_t -> x_t-1
    UpperCAmelCase__					:  Optional[Any]					   =					self.scheduler.step(
        A					,A					,A					,generator=A					,).prev_sample
   # post-processing
   UpperCAmelCase__					:  List[Any]					   =					self.movq.decode(A					,force_not_quantize=A    )["""sample"""]

   if output_type not in ["pt", "np", "pil"]:
    raise ValueError(f"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"    )

   if output_type in ["np", "pil"]:
    UpperCAmelCase__					:  Union[str, Any]					   =					image * 0.5 + 0.5
    UpperCAmelCase__					:  Optional[Any]					   =					image.clamp(0					,1    )
    UpperCAmelCase__					:  Dict					   =					image.cpu().permute(0					,2					,3					,1    ).float().numpy()

   if output_type == "pil":
    UpperCAmelCase__					:  List[str]					   =					self.numpy_to_pil(A    )

   if not return_dict:
    return (image,)

   return ImagePipelineOutput(images=A    )





