

"""simple docstring"""


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
			from PIL import Image

			from transformers import CLIPImageProcessor, CLIPProcessor





@require_vision
class    snake_case     (    unittest.TestCase  ):

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



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      tempfile.mkdtemp()

						# fmt: off
						__lowerCAmelCase:	Union[str, Any]   				=      ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
						# fmt: on
						__lowerCAmelCase:	int   				=      dict(zip(UpperCamelCase__					,  range(len(UpperCamelCase__))))
						__lowerCAmelCase:	str   				=      ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
						__lowerCAmelCase:	Tuple   				=      {"unk_token": "<unk>"}

						__lowerCAmelCase:	str   				=      os.path.join(self.tmpdirname					,  VOCAB_FILES_NAMES["vocab_file"])
						__lowerCAmelCase:	Optional[int]   				=      os.path.join(self.tmpdirname					,  VOCAB_FILES_NAMES["merges_file"])
						with open(self.vocab_file					,  "w"					,  encoding="utf-8") as fp:
								fp.write(json.dumps(UpperCamelCase__) + "\n")
						with open(self.merges_file					,  "w"					,  encoding="utf-8") as fp:
								fp.write("\n".join(UpperCamelCase__))

						__lowerCAmelCase:	List[str]   				=      {
						    "do_resize": True,
						    "size": 2_0,
						    "do_center_crop": True,
						    "crop_size": 1_8,
						    "do_normalize": True,
						    "image_mean": [0.48145466, 0.4578275, 0.40821073],
						    "image_std": [0.26862954, 0.26130258, 0.27577711],
						}
						__lowerCAmelCase:	List[str]   				=      os.path.join(self.tmpdirname					,  UpperCamelCase__)
						with open(self.image_processor_file					,  "w"					,  encoding="utf-8") as fp:
								json.dump(UpperCamelCase__					,  UpperCamelCase__)

				def 							lowercase_					(     self							:    int					,  **UpperCamelCase__							:    Tuple)->       List[Any]:



						'''simple docstring'''


						return CLIPTokenizer.from_pretrained(self.tmpdirname					,  **UpperCamelCase__)

				def 							lowercase_					(     self							:    int					,  **UpperCamelCase__							:    int)->       Dict:



						'''simple docstring'''


						return CLIPTokenizerFast.from_pretrained(self.tmpdirname					,  **UpperCamelCase__)

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



						'''simple docstring'''


						return CLIPImageProcessor.from_pretrained(self.tmpdirname					,  **UpperCamelCase__)

				def 							lowercase_					(     self							:    Union[str, Any])->       int:



						'''simple docstring'''


						shutil.rmtree(self.tmpdirname)

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



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      [np.random.randint(2_5_5					,  size=(3, 3_0, 4_0_0)					,  dtype=np.uinta)]

						__lowerCAmelCase:	Any   				=      [Image.fromarray(np.moveaxis(UpperCamelCase__					,  0					,  -1)) for x in image_inputs]

						return image_inputs

				def 							lowercase_					(     self							:    int)->       int:



						'''simple docstring'''


						__lowerCAmelCase:	int   				=      self.get_tokenizer()
						__lowerCAmelCase:	List[Any]   				=      self.get_rust_tokenizer()
						__lowerCAmelCase:	Union[str, Any]   				=      self.get_image_processor()

						__lowerCAmelCase:	Tuple   				=      CLIPProcessor(tokenizer=UpperCamelCase__					,  image_processor=UpperCamelCase__)
						processor_slow.save_pretrained(self.tmpdirname)
						__lowerCAmelCase:	Optional[Any]   				=      CLIPProcessor.from_pretrained(self.tmpdirname					,  use_fast=UpperCamelCase__)

						__lowerCAmelCase:	List[Any]   				=      CLIPProcessor(tokenizer=UpperCamelCase__					,  image_processor=UpperCamelCase__)
						processor_fast.save_pretrained(self.tmpdirname)
						__lowerCAmelCase:	Dict   				=      CLIPProcessor.from_pretrained(self.tmpdirname)

						self.assertEqual(processor_slow.tokenizer.get_vocab()					,  tokenizer_slow.get_vocab())
						self.assertEqual(processor_fast.tokenizer.get_vocab()					,  tokenizer_fast.get_vocab())
						self.assertEqual(tokenizer_slow.get_vocab()					,  tokenizer_fast.get_vocab())
						self.assertIsInstance(processor_slow.tokenizer					,  UpperCamelCase__)
						self.assertIsInstance(processor_fast.tokenizer					,  UpperCamelCase__)

						self.assertEqual(processor_slow.image_processor.to_json_string()					,  image_processor.to_json_string())
						self.assertEqual(processor_fast.image_processor.to_json_string()					,  image_processor.to_json_string())
						self.assertIsInstance(processor_slow.image_processor					,  UpperCamelCase__)
						self.assertIsInstance(processor_fast.image_processor					,  UpperCamelCase__)

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



						'''simple docstring'''


						__lowerCAmelCase:	Any   				=      CLIPProcessor(tokenizer=self.get_tokenizer()					,  image_processor=self.get_image_processor())
						processor.save_pretrained(self.tmpdirname)

						__lowerCAmelCase:	Union[str, Any]   				=      self.get_tokenizer(bos_token="(BOS)"					,  eos_token="(EOS)")
						__lowerCAmelCase:	Tuple   				=      self.get_image_processor(do_normalize=UpperCamelCase__					,  padding_value=1.0)

						__lowerCAmelCase:	Optional[Any]   				=      CLIPProcessor.from_pretrained(
						    self.tmpdirname					,  bos_token="(BOS)"					,  eos_token="(EOS)"					,  do_normalize=UpperCamelCase__					,  padding_value=1.0)

						self.assertEqual(processor.tokenizer.get_vocab()					,  tokenizer_add_kwargs.get_vocab())
						self.assertIsInstance(processor.tokenizer					,  UpperCamelCase__)

						self.assertEqual(processor.image_processor.to_json_string()					,  image_processor_add_kwargs.to_json_string())
						self.assertIsInstance(processor.image_processor					,  UpperCamelCase__)

				def 							lowercase_					(     self							:    Union[str, Any])->       int:



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      self.get_image_processor()
						__lowerCAmelCase:	Dict   				=      self.get_tokenizer()

						__lowerCAmelCase:	Any   				=      CLIPProcessor(tokenizer=UpperCamelCase__					,  image_processor=UpperCamelCase__)

						__lowerCAmelCase:	int   				=      self.prepare_image_inputs()

						__lowerCAmelCase:	Tuple   				=      image_processor(UpperCamelCase__					,  return_tensors="np")
						__lowerCAmelCase:	Union[str, Any]   				=      processor(images=UpperCamelCase__					,  return_tensors="np")

						for key in input_image_proc.keys():
								self.assertAlmostEqual(input_image_proc[key].sum()					,  input_processor[key].sum()					,  delta=1e-2)

				def 							lowercase_					(     self							:    Union[str, Any])->       Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Optional[int]   				=      self.get_image_processor()
						__lowerCAmelCase:	str   				=      self.get_tokenizer()

						__lowerCAmelCase:	List[str]   				=      CLIPProcessor(tokenizer=UpperCamelCase__					,  image_processor=UpperCamelCase__)

						__lowerCAmelCase:	str   				=      "lower newer"

						__lowerCAmelCase:	Optional[int]   				=      processor(text=UpperCamelCase__)

						__lowerCAmelCase:	int   				=      tokenizer(UpperCamelCase__)

						for key in encoded_tok.keys():
								self.assertListEqual(encoded_tok[key]					,  encoded_processor[key])

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



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      self.get_image_processor()
						__lowerCAmelCase:	List[str]   				=      self.get_tokenizer()

						__lowerCAmelCase:	Any   				=      CLIPProcessor(tokenizer=UpperCamelCase__					,  image_processor=UpperCamelCase__)

						__lowerCAmelCase:	int   				=      "lower newer"
						__lowerCAmelCase:	str   				=      self.prepare_image_inputs()

						__lowerCAmelCase:	int   				=      processor(text=UpperCamelCase__					,  images=UpperCamelCase__)

						self.assertListEqual(list(inputs.keys())					,  ["input_ids", "attention_mask", "pixel_values"])

						# test if it raises when no input is passed
						with pytest.raises(UpperCamelCase__):
								processor()

				def 							lowercase_					(     self							:    Tuple)->       Any:



						'''simple docstring'''


						__lowerCAmelCase:	List[str]   				=      self.get_image_processor()
						__lowerCAmelCase:	Dict   				=      self.get_tokenizer()

						__lowerCAmelCase:	Dict   				=      CLIPProcessor(tokenizer=UpperCamelCase__					,  image_processor=UpperCamelCase__)

						__lowerCAmelCase:	Optional[int]   				=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						__lowerCAmelCase:	Optional[Any]   				=      processor.batch_decode(UpperCamelCase__)
						__lowerCAmelCase:	int   				=      tokenizer.batch_decode(UpperCamelCase__)

						self.assertListEqual(UpperCamelCase__					,  UpperCamelCase__)




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



						'''simple docstring'''


						__lowerCAmelCase:	int   				=      self.get_image_processor()
						__lowerCAmelCase:	Union[str, Any]   				=      self.get_tokenizer()

						__lowerCAmelCase:	List[str]   				=      CLIPProcessor(tokenizer=UpperCamelCase__					,  image_processor=UpperCamelCase__)

						__lowerCAmelCase:	List[Any]   				=      "lower newer"
						__lowerCAmelCase:	Optional[int]   				=      self.prepare_image_inputs()

						__lowerCAmelCase:	str   				=      processor(text=UpperCamelCase__					,  images=UpperCamelCase__)

						self.assertListEqual(list(inputs.keys())					,  processor.model_input_names)





"""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}


