

"""simple docstring"""


import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError

from transformers import (
    AlbertTokenizer,
    AutoTokenizer,
    BertTokenizer,
    BertTokenizerFast,
    GPTaTokenizerFast,
    is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie


sys.path.append(str(Path(__file__).parent.parent / "utils"))

from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
			from test_module.custom_tokenization_fast import CustomTokenizerFast





class    snake_case     (    unittest.TestCase  ):

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



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      mock.Mock()
						__lowerCAmelCase:	Union[str, Any]   				=      5_0_0
						__lowerCAmelCase:	List[Any]   				=      {}
						__lowerCAmelCase:	Optional[Any]   				=      HTTPError
						__lowerCAmelCase:	Dict   				=      {}

						# Download this model to make sure it's in the cache.
						__lowerCAmelCase:	Optional[int]   				=      BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert")

						# Under the mock environment we get a 500 error when trying to reach the tokenizer.
						with mock.patch("requests.Session.request"					,  return_value=UpperCamelCase__) as mock_head:
								__lowerCAmelCase:	Union[str, Any]   				=      BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert")
								# This check we did call the fake head request
								mock_head.assert_called()

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



						'''simple docstring'''


						__lowerCAmelCase:	int   				=      mock.Mock()
						__lowerCAmelCase:	List[str]   				=      5_0_0
						__lowerCAmelCase:	Any   				=      {}
						__lowerCAmelCase:	Optional[int]   				=      HTTPError
						__lowerCAmelCase:	Optional[int]   				=      {}

						# Download this model to make sure it's in the cache.
						__lowerCAmelCase:	int   				=      GPTaTokenizerFast.from_pretrained("gpt2")

						# Under the mock environment we get a 500 error when trying to reach the tokenizer.
						with mock.patch("requests.Session.request"					,  return_value=UpperCamelCase__) as mock_head:
								__lowerCAmelCase:	List[str]   				=      GPTaTokenizerFast.from_pretrained("gpt2")
								# This check we did call the fake head request
								mock_head.assert_called()

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



						'''simple docstring'''


						try:
								__lowerCAmelCase:	List[Any]   				=      tempfile.mktemp()
								with open(UpperCamelCase__					,  "wb") as f:
										http_get("https://huggingface.co/albert-base-v1/resolve/main/spiece.model"					,  UpperCamelCase__)

								__lowerCAmelCase:	Optional[Any]   				=      AlbertTokenizer.from_pretrained(UpperCamelCase__)
						finally:
								os.remove(UpperCamelCase__)

						# Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
						# the current folder and have the right name.
						if os.path.isfile("tokenizer.json"):
								# We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
								return
						try:
								with open("tokenizer.json"					,  "wb") as f:
										http_get("https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json"					,  UpperCamelCase__)
								__lowerCAmelCase:	List[Any]   				=      AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
								# The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
								self.assertEqual(tokenizer.vocab_size					,  1_0_0_0)
								# Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.

						finally:
								os.remove("tokenizer.json")




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



						'''simple docstring'''


						__lowerCAmelCase:	Union[str, Any]   				=      AlbertTokenizer.from_pretrained("https://huggingface.co/albert-base-v1/resolve/main/spiece.model")





@is_staging_test
class    snake_case     (    unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_       :				Union[str, Any]       				=  ["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """bla""", """blou"""]

				@classmethod
				def 							lowercase_					(     cls							:    List[Any])->       Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      TOKEN
						HfFolder.save_token(UpperCamelCase__)

				@classmethod
				def 							lowercase_					(     cls							:    Any)->       Dict:



						'''simple docstring'''


						try:
								delete_repo(token=cls._token					,  repo_id="test-tokenizer")
						except HTTPError:
								pass

						try:
								delete_repo(token=cls._token					,  repo_id="valid_org/test-tokenizer-org")
						except HTTPError:
								pass

						try:
								delete_repo(token=cls._token					,  repo_id="test-dynamic-tokenizer")
						except HTTPError:
								pass

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



						'''simple docstring'''


						with tempfile.TemporaryDirectory() as tmp_dir:
								__lowerCAmelCase:	Union[str, Any]   				=      os.path.join(UpperCamelCase__					,  "vocab.txt")
								with open(UpperCamelCase__					,  "w"					,  encoding="utf-8") as vocab_writer:
										vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]))
								__lowerCAmelCase:	List[str]   				=      BertTokenizer(UpperCamelCase__)

						tokenizer.push_to_hub("test-tokenizer"					,  use_auth_token=self._token)
						__lowerCAmelCase:	Optional[int]   				=      BertTokenizer.from_pretrained(f"{USER}/test-tokenizer")
						self.assertDictEqual(new_tokenizer.vocab					,  tokenizer.vocab)

						# Reset repo
						delete_repo(token=self._token					,  repo_id="test-tokenizer")

						# Push to hub via save_pretrained
						with tempfile.TemporaryDirectory() as tmp_dir:
								tokenizer.save_pretrained(UpperCamelCase__					,  repo_id="test-tokenizer"					,  push_to_hub=UpperCamelCase__					,  use_auth_token=self._token)

						__lowerCAmelCase:	Dict   				=      BertTokenizer.from_pretrained(f"{USER}/test-tokenizer")
						self.assertDictEqual(new_tokenizer.vocab					,  tokenizer.vocab)

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



						'''simple docstring'''


						with tempfile.TemporaryDirectory() as tmp_dir:
								__lowerCAmelCase:	Optional[Any]   				=      os.path.join(UpperCamelCase__					,  "vocab.txt")
								with open(UpperCamelCase__					,  "w"					,  encoding="utf-8") as vocab_writer:
										vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]))
								__lowerCAmelCase:	Any   				=      BertTokenizer(UpperCamelCase__)

						tokenizer.push_to_hub("valid_org/test-tokenizer-org"					,  use_auth_token=self._token)
						__lowerCAmelCase:	Optional[Any]   				=      BertTokenizer.from_pretrained("valid_org/test-tokenizer-org")
						self.assertDictEqual(new_tokenizer.vocab					,  tokenizer.vocab)

						# Reset repo
						delete_repo(token=self._token					,  repo_id="valid_org/test-tokenizer-org")

						# Push to hub via save_pretrained
						with tempfile.TemporaryDirectory() as tmp_dir:
								tokenizer.save_pretrained(
								    UpperCamelCase__					,  repo_id="valid_org/test-tokenizer-org"					,  push_to_hub=UpperCamelCase__					,  use_auth_token=self._token)

						__lowerCAmelCase:	Optional[int]   				=      BertTokenizer.from_pretrained("valid_org/test-tokenizer-org")
						self.assertDictEqual(new_tokenizer.vocab					,  tokenizer.vocab)




				@require_tokenizers
				def 							lowercase_					(     self							:    Optional[Any])->       Dict:



						'''simple docstring'''


						CustomTokenizer.register_for_auto_class()
						with tempfile.TemporaryDirectory() as tmp_dir:
								__lowerCAmelCase:	str   				=      os.path.join(UpperCamelCase__					,  "vocab.txt")
								with open(UpperCamelCase__					,  "w"					,  encoding="utf-8") as vocab_writer:
										vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]))
								__lowerCAmelCase:	Any   				=      CustomTokenizer(UpperCamelCase__)

						# No fast custom tokenizer
						tokenizer.push_to_hub("test-dynamic-tokenizer"					,  use_auth_token=self._token)

						__lowerCAmelCase:	Optional[int]   				=      AutoTokenizer.from_pretrained(f"{USER}/test-dynamic-tokenizer"					,  trust_remote_code=UpperCamelCase__)
						# Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
						self.assertEqual(tokenizer.__class__.__name__					,  "CustomTokenizer")

						# Fast and slow custom tokenizer
						CustomTokenizerFast.register_for_auto_class()
						with tempfile.TemporaryDirectory() as tmp_dir:
								__lowerCAmelCase:	Tuple   				=      os.path.join(UpperCamelCase__					,  "vocab.txt")
								with open(UpperCamelCase__					,  "w"					,  encoding="utf-8") as vocab_writer:
										vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]))

								__lowerCAmelCase:	Dict   				=      BertTokenizerFast.from_pretrained(UpperCamelCase__)
								bert_tokenizer.save_pretrained(UpperCamelCase__)
								__lowerCAmelCase:	Tuple   				=      CustomTokenizerFast.from_pretrained(UpperCamelCase__)

						tokenizer.push_to_hub("test-dynamic-tokenizer"					,  use_auth_token=self._token)

						__lowerCAmelCase:	Union[str, Any]   				=      AutoTokenizer.from_pretrained(f"{USER}/test-dynamic-tokenizer"					,  trust_remote_code=UpperCamelCase__)
						# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
						self.assertEqual(tokenizer.__class__.__name__					,  "CustomTokenizerFast")
						__lowerCAmelCase:	int   				=      AutoTokenizer.from_pretrained(
						    f"{USER}/test-dynamic-tokenizer"					,  use_fast=UpperCamelCase__					,  trust_remote_code=UpperCamelCase__)
						# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
						self.assertEqual(tokenizer.__class__.__name__					,  "CustomTokenizer")





class    snake_case     (    unittest.TestCase  ):

				def 							lowercase_					(     self							:    int)->       str:



						'''simple docstring'''


						__lowerCAmelCase:	Optional[Any]   				=      Trie()
						trie.add("Hello 友達")
						self.assertEqual(trie.data					,  {"H": {"e": {"l": {"l": {"o": {" ": {"友": {"達": {"": 1}}}}}}}}})
						trie.add("Hello")
						trie.data
						self.assertEqual(trie.data					,  {"H": {"e": {"l": {"l": {"o": {"": 1, " ": {"友": {"達": {"": 1}}}}}}}}})

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



						'''simple docstring'''


						__lowerCAmelCase:	Union[str, Any]   				=      Trie()
						self.assertEqual(trie.split("[CLS] This is a extra_id_100")					,  ["[CLS] This is a extra_id_100"])
						trie.add("[CLS]")
						trie.add("extra_id_1")
						trie.add("extra_id_100")
						self.assertEqual(trie.split("[CLS] This is a extra_id_100")					,  ["[CLS]", " This is a ", "extra_id_100"])

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



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      Trie()
						trie.add("A")
						self.assertEqual(trie.split("ABC")					,  ["A", "BC"])
						self.assertEqual(trie.split("BCA")					,  ["BC", "A"])

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



						'''simple docstring'''


						__lowerCAmelCase:	List[Any]   				=      Trie()
						trie.add("TOKEN]")
						trie.add("[SPECIAL_TOKEN]")
						self.assertEqual(trie.split("This is something [SPECIAL_TOKEN]")					,  ["This is something ", "[SPECIAL_TOKEN]"])

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



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      Trie()
						trie.add("A")
						trie.add("P")
						trie.add("[SPECIAL_TOKEN]")
						self.assertEqual(trie.split("This is something [SPECIAL_TOKEN]")					,  ["This is something ", "[SPECIAL_TOKEN]"])

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



						'''simple docstring'''


						__lowerCAmelCase:	Optional[int]   				=      Trie()
						trie.add("AB")
						trie.add("B")
						trie.add("C")
						self.assertEqual(trie.split("ABC")					,  ["AB", "C"])

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



						'''simple docstring'''


						__lowerCAmelCase:	Optional[Any]   				=      Trie()
						trie.add("ABC")
						trie.add("B")
						trie.add("CD")
						self.assertEqual(trie.split("ABCD")					,  ["ABC", "D"])




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



						'''simple docstring'''


						__lowerCAmelCase:	List[str]   				=      Trie()
						__lowerCAmelCase:	Optional[Any]   				=      trie.cut_text("ABC"					,  [0, 0, 2, 1, 2, 3])
						self.assertEqual(UpperCamelCase__					,  ["AB", "C"])





"""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}


