

"""simple docstring"""


import json
import os
import re
import unittest

from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class    snake_case     (    __snake_case,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_       :				Dict       				=  CodeGenTokenizer
				SCREAMING_SNAKE_CASE_       :				Any       				=  CodeGenTokenizerFast
				SCREAMING_SNAKE_CASE_       :				Union[str, Any]       				=  True
				SCREAMING_SNAKE_CASE_       :				List[Any]       				=  {"""add_prefix_space""": True}
				SCREAMING_SNAKE_CASE_       :				List[str]       				=  False

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



						'''simple docstring'''


						super().setUp()

						# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
						__lowerCAmelCase:	int   				=      [
						    "l",
						    "o",
						    "w",
						    "e",
						    "r",
						    "s",
						    "t",
						    "i",
						    "d",
						    "n",
						    "\u0120",
						    "\u0120l",
						    "\u0120n",
						    "\u0120lo",
						    "\u0120low",
						    "er",
						    "\u0120lowest",
						    "\u0120newer",
						    "\u0120wider",
						    "<unk>",
						    "<|endoftext|>",
						]
						__lowerCAmelCase:	List[Any]   				=      dict(zip(UpperCamelCase__					,  range(len(UpperCamelCase__))))
						__lowerCAmelCase:	List[Any]   				=      ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
						__lowerCAmelCase:	Optional[int]   				=      {"unk_token": "<unk>"}

						__lowerCAmelCase:	List[Any]   				=      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__))

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



						'''simple docstring'''


						kwargs.update(self.special_tokens_map)
						return CodeGenTokenizer.from_pretrained(self.tmpdirname					,  **UpperCamelCase__)

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



						'''simple docstring'''


						kwargs.update(self.special_tokens_map)
						return CodeGenTokenizerFast.from_pretrained(self.tmpdirname					,  **UpperCamelCase__)

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



						'''simple docstring'''


						__lowerCAmelCase:	Any   				=      "lower newer"
						__lowerCAmelCase:	Dict   				=      "lower newer"
						return input_text, output_text

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



						'''simple docstring'''


						__lowerCAmelCase:	Optional[Any]   				=      CodeGenTokenizer(self.vocab_file					,  self.merges_file					,  **self.special_tokens_map)
						__lowerCAmelCase:	Union[str, Any]   				=      "lower newer"
						__lowerCAmelCase:	Optional[Any]   				=      ["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
						__lowerCAmelCase:	Tuple   				=      tokenizer.tokenize(UpperCamelCase__					,  add_prefix_space=UpperCamelCase__)
						self.assertListEqual(UpperCamelCase__					,  UpperCamelCase__)

						__lowerCAmelCase:	Tuple   				=      tokens + [tokenizer.unk_token]
						__lowerCAmelCase:	List[Any]   				=      [1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__)					,  UpperCamelCase__)

				def 							lowercase_					(     self							:    Tuple)->       Any:



						'''simple docstring'''


						if not self.test_rust_tokenizer:
								return

						__lowerCAmelCase:	int   				=      self.get_tokenizer()
						__lowerCAmelCase:	str   				=      self.get_rust_tokenizer(add_prefix_space=UpperCamelCase__)

						__lowerCAmelCase:	List[Any]   				=      "lower newer"

						# Testing tokenization
						__lowerCAmelCase:	Optional[Any]   				=      tokenizer.tokenize(UpperCamelCase__					,  add_prefix_space=UpperCamelCase__)
						__lowerCAmelCase:	Dict   				=      rust_tokenizer.tokenize(UpperCamelCase__)
						self.assertListEqual(UpperCamelCase__					,  UpperCamelCase__)

						# Testing conversion to ids without special tokens
						__lowerCAmelCase:	Optional[Any]   				=      tokenizer.encode(UpperCamelCase__					,  add_special_tokens=UpperCamelCase__					,  add_prefix_space=UpperCamelCase__)
						__lowerCAmelCase:	str   				=      rust_tokenizer.encode(UpperCamelCase__					,  add_special_tokens=UpperCamelCase__)
						self.assertListEqual(UpperCamelCase__					,  UpperCamelCase__)

						# Testing conversion to ids with special tokens
						__lowerCAmelCase:	Dict   				=      self.get_rust_tokenizer(add_prefix_space=UpperCamelCase__)
						__lowerCAmelCase:	List[Any]   				=      tokenizer.encode(UpperCamelCase__					,  add_prefix_space=UpperCamelCase__)
						__lowerCAmelCase:	Union[str, Any]   				=      rust_tokenizer.encode(UpperCamelCase__)
						self.assertListEqual(UpperCamelCase__					,  UpperCamelCase__)

						# Testing the unknown token
						__lowerCAmelCase:	Any   				=      tokens + [rust_tokenizer.unk_token]
						__lowerCAmelCase:	List[Any]   				=      [1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
						self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(UpperCamelCase__)					,  UpperCamelCase__)

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



						'''simple docstring'''


						pass

				def 							lowercase_					(     self							:    Any					,  UpperCamelCase__							:    int=1_5)->       int:



						'''simple docstring'''


						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
								with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
										__lowerCAmelCase:	List[str]   				=      self.rust_tokenizer_class.from_pretrained(UpperCamelCase__					,  **UpperCamelCase__)

										# Simple input
										__lowerCAmelCase:	Optional[int]   				=      "This is a simple input"
										__lowerCAmelCase:	Dict   				=      ["This is a simple input 1", "This is a simple input 2"]
										__lowerCAmelCase:	Tuple   				=      ("This is a simple input", "This is a pair")
										__lowerCAmelCase:	List[Any]   				=      [
										    ("This is a simple input 1", "This is a simple input 2"),
										    ("This is a simple pair 1", "This is a simple pair 2"),
										]

										# Simple input tests
										self.assertRaises(UpperCamelCase__					,  tokenizer_r.encode					,  UpperCamelCase__					,  max_length=UpperCamelCase__					,  padding="max_length")

										# Simple input
										self.assertRaises(UpperCamelCase__					,  tokenizer_r.encode_plus					,  UpperCamelCase__					,  max_length=UpperCamelCase__					,  padding="max_length")

										# Simple input
										self.assertRaises(
										    UpperCamelCase__					,  tokenizer_r.batch_encode_plus					,  UpperCamelCase__					,  max_length=UpperCamelCase__					,  padding="max_length"					,  )

										# Pair input
										self.assertRaises(UpperCamelCase__					,  tokenizer_r.encode					,  UpperCamelCase__					,  max_length=UpperCamelCase__					,  padding="max_length")

										# Pair input
										self.assertRaises(UpperCamelCase__					,  tokenizer_r.encode_plus					,  UpperCamelCase__					,  max_length=UpperCamelCase__					,  padding="max_length")

										# Pair input
										self.assertRaises(
										    UpperCamelCase__					,  tokenizer_r.batch_encode_plus					,  UpperCamelCase__					,  max_length=UpperCamelCase__					,  padding="max_length"					,  )

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



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      CodeGenTokenizer.from_pretrained(self.tmpdirname					,  pad_token="<pad>")

						# Simple input
						__lowerCAmelCase:	Dict   				=      "This is a simple input"
						__lowerCAmelCase:	List[str]   				=      ["This is a simple input looooooooong", "This is a simple input"]
						__lowerCAmelCase:	str   				=      ("This is a simple input", "This is a pair")
						__lowerCAmelCase:	Tuple   				=      [
						    ("This is a simple input loooooong", "This is a simple input"),
						    ("This is a simple pair loooooong", "This is a simple pair"),
						]

						__lowerCAmelCase:	Union[str, Any]   				=      tokenizer.pad_token_id

						__lowerCAmelCase:	List[Any]   				=      tokenizer(UpperCamelCase__					,  padding="max_length"					,  max_length=3_0					,  return_tensors="np")
						__lowerCAmelCase:	List[Any]   				=      tokenizer(UpperCamelCase__					,  padding=UpperCamelCase__					,  truncate=UpperCamelCase__					,  return_tensors="np")
						__lowerCAmelCase:	Dict   				=      tokenizer(*UpperCamelCase__					,  padding="max_length"					,  max_length=6_0					,  return_tensors="np")
						__lowerCAmelCase:	Dict   				=      tokenizer(UpperCamelCase__					,  padding=UpperCamelCase__					,  truncate=UpperCamelCase__					,  return_tensors="np")

						# s
						# test single string max_length padding
						self.assertEqual(out_s["input_ids"].shape[-1]					,  3_0)
						self.assertTrue(pad_token_id in out_s["input_ids"])
						self.assertTrue(0 in out_s["attention_mask"])

						# s2
						# test automatic padding
						self.assertEqual(out_sa["input_ids"].shape[-1]					,  3_3)
						# long slice doesn't have padding
						self.assertFalse(pad_token_id in out_sa["input_ids"][0])
						self.assertFalse(0 in out_sa["attention_mask"][0])
						# short slice does have padding
						self.assertTrue(pad_token_id in out_sa["input_ids"][1])
						self.assertTrue(0 in out_sa["attention_mask"][1])

						# p
						# test single pair max_length padding
						self.assertEqual(out_p["input_ids"].shape[-1]					,  6_0)
						self.assertTrue(pad_token_id in out_p["input_ids"])
						self.assertTrue(0 in out_p["attention_mask"])

						# p2
						# test automatic padding pair
						self.assertEqual(out_pa["input_ids"].shape[-1]					,  5_2)
						# long slice pair doesn't have padding
						self.assertFalse(pad_token_id in out_pa["input_ids"][0])
						self.assertFalse(0 in out_pa["attention_mask"][0])
						# short slice pair does have padding
						self.assertTrue(pad_token_id in out_pa["input_ids"][1])
						self.assertTrue(0 in out_pa["attention_mask"][1])

				def 							lowercase_					(     self							:    Any)->       str:



						'''simple docstring'''


						__lowerCAmelCase:	Any   				=      "$$$"
						__lowerCAmelCase:	Dict   				=      CodeGenTokenizer.from_pretrained(self.tmpdirname					,  bos_token=UpperCamelCase__					,  add_bos_token=UpperCamelCase__)

						__lowerCAmelCase:	int   				=      "This is a simple input"
						__lowerCAmelCase:	int   				=      ["This is a simple input 1", "This is a simple input 2"]

						__lowerCAmelCase:	int   				=      tokenizer.bos_token_id

						__lowerCAmelCase:	Optional[int]   				=      tokenizer(UpperCamelCase__)
						__lowerCAmelCase:	List[str]   				=      tokenizer(UpperCamelCase__)

						self.assertEqual(out_s.input_ids[0]					,  UpperCamelCase__)
						self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids))

						__lowerCAmelCase:	Tuple   				=      tokenizer.decode(out_s.input_ids)
						__lowerCAmelCase:	Optional[int]   				=      tokenizer.batch_decode(out_sa.input_ids)

						self.assertEqual(decode_s.split()[0]					,  UpperCamelCase__)
						self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa))

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



						'''simple docstring'''


						__lowerCAmelCase:	Optional[int]   				=      CodeGenTokenizer.from_pretrained("Salesforce/codegen-350M-mono")

						__lowerCAmelCase:	List[str]   				=      "\nif len_a > len_b:\n    result = a\nelse:\n    result = b\n\n\n\n#"
						__lowerCAmelCase:	int   				=      "\nif len_a > len_b:      result = a\nelse:      result = b"

						__lowerCAmelCase:	List[Any]   				=      tokenizer.encode(UpperCamelCase__)
						__lowerCAmelCase:	List[Any]   				=      ["^#", re.escape("<|endoftext|>"), "^'''", "^\"\"\"", "\n\n\n"]
						__lowerCAmelCase:	Tuple   				=      tokenizer.decode(UpperCamelCase__					,  truncate_before_pattern=UpperCamelCase__)
						self.assertEqual(UpperCamelCase__					,  UpperCamelCase__)




				def 							lowercase_					(     self							:    Dict)->       str:



						'''simple docstring'''


						pass





"""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}


