

"""simple docstring"""


from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
			from PIL import Image

			from ..image_utils import load_image

if is_torch_available():
			import torch

			from transformers.modeling_outputs import BaseModelOutput

			from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

__A            =      logging.get_logger(__name__)





@add_end_docstrings(SCREAMING_SNAKE_CASE_  )
class    snake_case     (    SCREAMING_SNAKE_CASE_  ):

				def __init__(     self							:    Dict					,  **UpperCamelCase__							:    Dict)->       List[str]:



						'''simple docstring'''


						super().__init__(**__a)

						if self.framework == "tf":
								raise ValueError(f"The {self.__class__} is only available in PyTorch.")

						requires_backends(self					,  "vision")
						self.check_model_type(__a)

				def __call__(     self							:    Dict					,  UpperCamelCase__							:    Union[str, "Image.Image", List[Dict[str, Any]]]					,  UpperCamelCase__							:    Union[str, List[str]] = None					,  **UpperCamelCase__							:    Any					,  )->       str:



						'''simple docstring'''


						if "text_queries" in kwargs:
								__lowerCAmelCase:	Tuple   				=      kwargs.pop("text_queries")

						if isinstance(__a					,  (str, Image.Image)):
								__lowerCAmelCase:	List[str]   				=      {'image': image, 'candidate_labels': candidate_labels}
						else:
								__lowerCAmelCase:	Optional[int]   				=      image
						__lowerCAmelCase:	Union[str, Any]   				=      super().__call__(__a					,  **__a)
						return results

				def 							lowercase_					(     self							:    List[str]					,  **UpperCamelCase__							:    int)->       Optional[int]:



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      {}
						if "threshold" in kwargs:
								__lowerCAmelCase:	int   				=      kwargs['threshold']
						if "top_k" in kwargs:
								__lowerCAmelCase:	int   				=      kwargs['top_k']
						return {}, {}, postprocess_params

				def 							lowercase_					(     self							:    List[str]					,  UpperCamelCase__							:    Union[str, Any])->       Tuple:



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      load_image(inputs["image"])
						__lowerCAmelCase:	Optional[int]   				=      inputs['candidate_labels']
						if isinstance(__a					,  __a):
								__lowerCAmelCase:	Dict   				=      candidate_labels.split(",")

						__lowerCAmelCase:	Tuple   				=      torch.tensor([[image.height, image.width]]					,  dtype=torch.intaa)
						for i, candidate_label in enumerate(__a):
								__lowerCAmelCase:	str   				=      self.tokenizer(__a					,  return_tensors=self.framework)
								__lowerCAmelCase:	Tuple   				=      self.image_processor(__a					,  return_tensors=self.framework)
								yield {
								    "is_last": i == len(__a) - 1,
								    "target_size": target_size,
								    "candidate_label": candidate_label,
								    **text_inputs,
								    **image_features,
								}

				def 							lowercase_					(     self							:    Optional[Any]					,  UpperCamelCase__							:    Optional[int])->       Optional[int]:



						'''simple docstring'''


						__lowerCAmelCase:	Any   				=      model_inputs.pop("target_size")
						__lowerCAmelCase:	Tuple   				=      model_inputs.pop("candidate_label")
						__lowerCAmelCase:	List[Any]   				=      model_inputs.pop("is_last")

						__lowerCAmelCase:	Any   				=      self.model(**__a)

						__lowerCAmelCase:	Optional[int]   				=      {'target_size': target_size, 'candidate_label': candidate_label, 'is_last': is_last, **outputs}
						return model_outputs

				def 							lowercase_					(     self							:    Union[str, Any]					,  UpperCamelCase__							:    Optional[Any]					,  UpperCamelCase__							:    List[Any]=0.1					,  UpperCamelCase__							:    Dict=None)->       Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      []
						for model_output in model_outputs:
								__lowerCAmelCase:	Optional[Any]   				=      model_output['candidate_label']
								__lowerCAmelCase:	List[str]   				=      BaseModelOutput(__a)
								__lowerCAmelCase:	Optional[int]   				=      self.image_processor.post_process_object_detection(
								    outputs=__a					,  threshold=__a					,  target_sizes=model_output["target_size"])[0]

								for index in outputs["scores"].nonzero():
										__lowerCAmelCase:	Dict   				=      outputs['scores'][index].item()
										__lowerCAmelCase:	List[Any]   				=      self._get_bounding_box(outputs["boxes"][index][0])

										__lowerCAmelCase:	Any   				=      {'score': score, 'label': label, 'box': box}
										results.append(__a)

						__lowerCAmelCase:	str   				=      sorted(__a					,  key=lambda UpperCamelCase__: x["score"]					,  reverse=__a)
						if top_k:
								__lowerCAmelCase:	Any   				=      results[:top_k]

						return results




				def 							lowercase_					(     self							:    Optional[int]					,  UpperCamelCase__							:    "torch.Tensor")->       Dict[str, int]:



						'''simple docstring'''


						if self.framework != "pt":
								raise ValueError("The ZeroShotObjectDetectionPipeline is only available in PyTorch.")
						__lowerCAmelCase:	Dict   				=      box.int().tolist()
						__lowerCAmelCase:	str   				=      {
						    'xmin': xmin,
						    'ymin': ymin,
						    'xmax': xmax,
						    'ymax': ymax,
						}
						return bbox





"""simple docstring"""


import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


__A						       =      datasets.logging.get_logger(__name__)


__A						       =      "\\n@inproceedings{bleurt,\n  title={BLEURT: Learning Robust Metrics for Text Generation},\n  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},\n  booktitle={ACL},\n  year={2020},\n  url={https://arxiv.org/abs/2004.04696}\n}\n"

__A						       =      "\\nBLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)\nand then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune\nit for your specific application (the latter is expected to perform better).\n\nSee the project's README at https://github.com/google-research/bleurt#readme for more information.\n"

__A						       =      "\nBLEURT score.\n\nArgs:\n    `predictions` (list of str): prediction/candidate sentences\n    `references` (list of str): reference sentences\n    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.\n\nReturns:\n    'scores': List of scores.\nExamples:\n\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> bleurt = datasets.load_metric(\"bleurt\")\n    >>> results = bleurt.compute(predictions=predictions, references=references)\n    >>> print([round(v, 2) for v in results[\"scores\"]])\n    [1.03, 1.04]\n"

__A						       =      {
    "bleurt-tiny-128": "https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip",
    "bleurt-tiny-512": "https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip",
    "bleurt-base-128": "https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip",
    "bleurt-base-512": "https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip",
    "bleurt-large-128": "https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip",
    "bleurt-large-512": "https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip",
    "BLEURT-20-D3": "https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip",
    "BLEURT-20-D6": "https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip",
    "BLEURT-20-D12": "https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip",
    "BLEURT-20": "https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip",
}





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

				def 							lowercase_					(     self							:    Optional[int])->       Any:



						'''simple docstring'''


						return datasets.MetricInfo(
						    description=_DESCRIPTION					,  citation=_CITATION					,  homepage="https://github.com/google-research/bleurt"					,  inputs_description=_KWARGS_DESCRIPTION					,  features=datasets.Features(
						        {
						            "predictions": datasets.Value("string"					,  id="sequence"),
						            "references": datasets.Value("string"					,  id="sequence"),
						        })					,  codebase_urls=["https://github.com/google-research/bleurt"]					,  reference_urls=["https://github.com/google-research/bleurt", "https://arxiv.org/abs/2004.04696"]					,  )

				def 							lowercase_					(     self							:    int					,  UpperCamelCase__							:    Dict)->       List[str]:



						'''simple docstring'''


						if self.config_name == "default":
								logger.warning(
								    "Using default BLEURT-Base checkpoint for sequence maximum length 128. "
								    "You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512').")
								__lowerCAmelCase:	List[str]   				=      "bleurt-base-128"

						if self.config_name.lower() in CHECKPOINT_URLS:
								__lowerCAmelCase:	Optional[int]   				=      self.config_name.lower()

						elif self.config_name.upper() in CHECKPOINT_URLS:
								__lowerCAmelCase:	Tuple   				=      self.config_name.upper()

						else:
								raise KeyError(
								    f"{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}")

						# download the model checkpoint specified by self.config_name and set up the scorer
						__lowerCAmelCase:	Union[str, Any]   				=      dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name])
						__lowerCAmelCase:	Dict   				=      score.BleurtScorer(os.path.join(UpperCamelCase__					,  UpperCamelCase__))




				def 							lowercase_					(     self							:    Optional[int]					,  UpperCamelCase__							:    Tuple					,  UpperCamelCase__							:    int)->       str:



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      self.scorer.score(references=UpperCamelCase__					,  candidates=UpperCamelCase__)
						return {"scores": scores}


