

"""simple docstring"""




import json
import os
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


A_							:			Optional[Any]											= logging.get_logger(__name__)


A_							:			Dict											= {
    "vocab_file": "vocab.json",
    "tokenizer_config_file": "tokenizer_config.json",
    "merges_file": "merges.txt",
}

A_							:			Any											= {
    "vocab_file": {
        "facebook/s2t-wav2vec2-large-en-de": (
            "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json"
        ),
    },
    "tokenizer_config_file": {
        "facebook/s2t-wav2vec2-large-en-de": (
            "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json"
        ),
    },
    "merges_file": {
        "facebook/s2t-wav2vec2-large-en-de": (
            "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt"
        ),
    },
}

A_							:			int											= "</w>"
A_							:			Dict											= "@@ "

def 	A       (     snake_case__    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      set()
			SCREAMING_SNAKE_CASE__								=      word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)    )
						SCREAMING_SNAKE_CASE__								=      char
			return pairs


# Speech2Text2 has no max input length
A_							:			List[str]											= {"facebook/s2t-wav2vec2-large-en-de": 1_024}





class    lowerCamelCase						(A__			):
			lowerCamelCase__   :		int							=							VOCAB_FILES_NAMES
			lowerCamelCase__   :		Optional[Any]							=							PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase__   :		Any							=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase__   :		Dict							=							['input_ids', 'attention_mask']

			def __init__(					self				:						Union[str, Any]    ,				__UpperCAmelCase				:						str    ,				__UpperCAmelCase				:						List[Any]="<s>"    ,				__UpperCAmelCase				:						Optional[Any]="<pad>"    ,				__UpperCAmelCase				:						List[Any]="</s>"    ,				__UpperCAmelCase				:						Optional[Any]="<unk>"    ,				__UpperCAmelCase				:						str=False    ,				__UpperCAmelCase				:						int=None    ,				**__UpperCAmelCase				:						Any    ,				)   ->    Optional[Any]:
						super().__init__(
						    unk_token=__UpperCAmelCase    ,				bos_token=__UpperCAmelCase    ,				eos_token=__UpperCAmelCase    ,				pad_token=__UpperCAmelCase    ,				do_lower_case=__UpperCAmelCase    ,				**__UpperCAmelCase    ,				)

						SCREAMING_SNAKE_CASE__								=      do_lower_case

						with open(__UpperCAmelCase    ,				encoding="""utf-8"""       ) as vocab_handle:
									SCREAMING_SNAKE_CASE__								=      json.load(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      {v: k for k, v in self.encoder.items()}

						if merges_file is None:
									logger.info(F"""No merges files provided. {self.__class__.__name__} can only be used for decoding."""       )

									SCREAMING_SNAKE_CASE__								=      None
									SCREAMING_SNAKE_CASE__								=      None
						else:
									with open(__UpperCAmelCase    ,				encoding="""utf-8"""       ) as merges_handle:
												SCREAMING_SNAKE_CASE__								=      merges_handle.read().split("""\n"""       )[:-1]

									SCREAMING_SNAKE_CASE__								=      [tuple(merge.split()[:2]       ) for merge in merges]
									SCREAMING_SNAKE_CASE__								=      dict(zip(__UpperCAmelCase    ,				range(len(__UpperCAmelCase       )       )       )       )
									SCREAMING_SNAKE_CASE__								=      {}

			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    int:
						return len(self.decoder       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    Dict:
						return dict(self.encoder    ,				**self.added_tokens_encoder       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]    ,				__UpperCAmelCase				:						Union[str, Any]       )   ->    Any:
						SCREAMING_SNAKE_CASE__								=      tuple(token[:-1]       ) + (token[-1] + BPE_TOKEN_MERGES,)
						if token in self.cache:
									return self.cache[token]
						SCREAMING_SNAKE_CASE__								=      get_pairs(__UpperCAmelCase       )

						if not pairs:
									return token

						while True:
									SCREAMING_SNAKE_CASE__								=      min(__UpperCAmelCase    ,				key=lambda __UpperCAmelCase       : self.bpe_ranks.get(__UpperCAmelCase    ,				float("""inf"""       )       )       )
									if bigram not in self.bpe_ranks:
												break
									SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      bigram
									SCREAMING_SNAKE_CASE__								=      []
									SCREAMING_SNAKE_CASE__								=      0
									while i < len(__UpperCAmelCase       ):
												try:
															SCREAMING_SNAKE_CASE__								=      word.index(__UpperCAmelCase    ,				__UpperCAmelCase       )
												except ValueError:
															new_word.extend(word[i:]       )
															break
												else:
															new_word.extend(word[i:j]       )
															SCREAMING_SNAKE_CASE__								=      j

												if word[i] == first and i < len(__UpperCAmelCase       ) - 1 and word[i + 1] == second:
															new_word.append(first + second       )
															i += 2
												else:
															new_word.append(word[i]       )
															i += 1
									SCREAMING_SNAKE_CASE__								=      tuple(__UpperCAmelCase       )
									SCREAMING_SNAKE_CASE__								=      new_word
									if len(__UpperCAmelCase       ) == 1:
												break
									else:
												SCREAMING_SNAKE_CASE__								=      get_pairs(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      """ """.join(__UpperCAmelCase       )
						if word == "\n  " + BPE_TOKEN_MERGES:
									SCREAMING_SNAKE_CASE__								=      """\n""" + BPE_TOKEN_MERGES

						if word.endswith(__UpperCAmelCase       ):
									SCREAMING_SNAKE_CASE__								=      word.replace(__UpperCAmelCase    ,				""""""       )

						SCREAMING_SNAKE_CASE__								=      word.replace(""" """    ,				__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      word
						return word

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]    ,				__UpperCAmelCase				:						Optional[int]       )   ->    Any:

						if self.bpe_ranks is None:
									raise ValueError(
									    """This tokenizer was instantiated without a `merges.txt` file, so"""
									    """ that it can only be used for decoding, not for encoding."""
									    """Make sure to provide `merges.txt` file at instantiation to enable """
									    """encoding."""       )

						if self.do_lower_case:
									SCREAMING_SNAKE_CASE__								=      text.lower()

						SCREAMING_SNAKE_CASE__								=      text.split()

						SCREAMING_SNAKE_CASE__								=      []
						for token in text:
									if token:
												split_tokens.extend(list(self.bpe(__UpperCAmelCase       ).split(""" """       )       )       )

						return split_tokens

			def 					SCREAMING_SNAKE_CASE				(					self				:						int    ,				__UpperCAmelCase				:						str       )   ->    int:
						return self.encoder.get(__UpperCAmelCase    ,				self.encoder.get(self.unk_token       )       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple    ,				__UpperCAmelCase				:						int       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      self.decoder.get(__UpperCAmelCase    ,				self.unk_token       )
						return result

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]    ,				__UpperCAmelCase				:						List[str]       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      """ """.join(__UpperCAmelCase       )

						# make sure @@ tokens are concatenated
						SCREAMING_SNAKE_CASE__								=      """""".join(string.split(__UpperCAmelCase       )       )

						return string





			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						str    ,				__UpperCAmelCase				:						Optional[str] = None       )   ->    Tuple[str]:
						if not os.path.isdir(__UpperCAmelCase       ):
									logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""       )
									return
						SCREAMING_SNAKE_CASE__								=      os.path.join(
						    __UpperCAmelCase    ,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]       )
						SCREAMING_SNAKE_CASE__								=      os.path.join(
						    __UpperCAmelCase    ,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]       )

						with open(__UpperCAmelCase    ,				"""w"""    ,				encoding="""utf-8"""       ) as f:
									f.write(json.dumps(self.encoder    ,				indent=2    ,				sort_keys=__UpperCAmelCase    ,				ensure_ascii=__UpperCAmelCase       ) + """\n"""       )

						SCREAMING_SNAKE_CASE__								=      0
						if self.bpe_ranks is None:
									return (vocab_file,)

						with open(__UpperCAmelCase    ,				"""w"""    ,				encoding="""utf-8"""       ) as writer:
									for bpe_tokens, token_index in sorted(self.bpe_ranks.items()    ,				key=lambda __UpperCAmelCase       : kv[1]       ):
												if index != token_index:
															logger.warning(
															    F"""Saving vocabulary to {merges_file}: BPE merge indices are not consecutive."""
															    """ Please check that the tokenizer is not corrupted!"""       )
															SCREAMING_SNAKE_CASE__								=      token_index
												writer.write(""" """.join(__UpperCAmelCase       ) + """\n"""       )
												index += 1

						return (vocab_file, merges_file)





"""simple docstring"""




import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch

import numpy as np
import pytest
from absl.testing import parameterized

import datasets
from datasets import load_metric

from .utils import for_all_test_methods, local, slow


# mark all tests as integration
A_							:			Optional[Any]											= pytest.mark.integration


A_							:			Union[str, Any]											= {"comet"}
A_							:			str											= importlib.util.find_spec("fairseq") is not None

A_							:			Any											= {"code_eval"}
A_							:			Tuple											= os.name == "nt"

A_							:			List[Any]											= {"bertscore", "frugalscore", "perplexity"}
A_							:			Any											= importlib.util.find_spec("transformers") is not None

def 	A       (     snake_case__    ):




			'''simple docstring'''


			@wraps(snake_case__    )
			def wrapper(self		,				snake_case__    ):
						if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
									self.skipTest("""\"test requires Fairseq\""""    )
						else:
									test_case(self		,				snake_case__    )

			return wrapper

def 	A       (     snake_case__    ):




			'''simple docstring'''


			@wraps(snake_case__    )
			def wrapper(self		,				snake_case__    ):
						if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
									self.skipTest("""\"test requires transformers\""""    )
						else:
									test_case(self		,				snake_case__    )

			return wrapper

def 	A       (     snake_case__    ):




			'''simple docstring'''


			@wraps(snake_case__    )
			def wrapper(self		,				snake_case__    ):
						if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
									self.skipTest("""\"test not supported on Windows\""""    )
						else:
									test_case(self		,				snake_case__    )

			return wrapper

def 	A       (     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      [metric_dir.split(os.sep    )[-2] for metric_dir in glob.glob("""./metrics/*/"""    )]
			return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"]  # gleu is unfinished





@parameterized.named_parameters(get_local_metric_names()			)
@for_all_test_methods(
    A__			,A__			,A__			)
@local
class    lowerCamelCase						(parameterized.TestCase			):
			lowerCamelCase__   :		int							=							{}
			lowerCamelCase__   :		Dict							=							None

			@pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning"""       )
			@pytest.mark.filterwarnings("""ignore:load_metric is deprecated:FutureWarning"""       )
			def 					SCREAMING_SNAKE_CASE				(					self				:						str    ,				__UpperCAmelCase				:						str       )   ->    Union[str, Any]:
						SCREAMING_SNAKE_CASE__								=      """[...]"""
						SCREAMING_SNAKE_CASE__								=      importlib.import_module(
						    datasets.load.metric_module_factory(os.path.join("""metrics"""    ,				__UpperCAmelCase       )       ).module_path       )
						SCREAMING_SNAKE_CASE__								=      datasets.load.import_main_class(metric_module.__name__    ,				dataset=__UpperCAmelCase       )
						# check parameters
						SCREAMING_SNAKE_CASE__								=      inspect.signature(metric._compute       ).parameters
						self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values()       )       )  # no **kwargs
						# run doctest
						with self.patch_intensive_calls(__UpperCAmelCase    ,				metric_module.__name__       ):
									with self.use_local_metrics():
												try:
															SCREAMING_SNAKE_CASE__								=      doctest.testmod(__UpperCAmelCase    ,				verbose=__UpperCAmelCase    ,				raise_on_error=__UpperCAmelCase       )
												except doctest.UnexpectedException as e:
															raise e.exc_info[1]  # raise the exception that doctest caught
						self.assertEqual(results.failed    ,				0       )
						self.assertGreater(results.attempted    ,				1       )

			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						Optional[Any]       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      """[...]"""
						SCREAMING_SNAKE_CASE__								=      importlib.import_module(
						    datasets.load.metric_module_factory(os.path.join("""metrics"""    ,				__UpperCAmelCase       )       ).module_path       )
						# run doctest
						with self.use_local_metrics():
									SCREAMING_SNAKE_CASE__								=      doctest.testmod(__UpperCAmelCase    ,				verbose=__UpperCAmelCase    ,				raise_on_error=__UpperCAmelCase       )
						self.assertEqual(results.failed    ,				0       )
						self.assertGreater(results.attempted    ,				1       )

			@contextmanager
			def 					SCREAMING_SNAKE_CASE				(					self				:						Any    ,				__UpperCAmelCase				:						List[str]    ,				__UpperCAmelCase				:						Optional[Any]       )   ->    str:
						if metric_name in self.INTENSIVE_CALLS_PATCHER:
									with self.INTENSIVE_CALLS_PATCHER[metric_name](__UpperCAmelCase       ):
												yield
						else:
									yield

			@contextmanager
			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    Union[str, Any]:
						def load_local_metric(__UpperCAmelCase				:						int    ,				*__UpperCAmelCase				:						int    ,				**__UpperCAmelCase				:						Optional[int]       ):
									return load_metric(os.path.join("""metrics"""    ,				__UpperCAmelCase       )    ,				*__UpperCAmelCase    ,				**__UpperCAmelCase       )

						with patch("""datasets.load_metric"""       ) as mock_load_metric:
									SCREAMING_SNAKE_CASE__								=      load_local_metric
									yield





			@classmethod
			def 					SCREAMING_SNAKE_CASE				(					cls				:						Dict    ,				__UpperCAmelCase				:						int       )   ->    Optional[Any]:
						def wrapper(__UpperCAmelCase				:						List[str]       ):
									SCREAMING_SNAKE_CASE__								=      contextmanager(__UpperCAmelCase       )
									SCREAMING_SNAKE_CASE__								=      patcher
									return patcher

						return wrapper

@LocalMetricTest.register_intensive_calls_patcher("""bleurt"""    )
def 	A       (     snake_case__    ):




			'''simple docstring'''


			import tensorflow.compat.va as tf
			from bleurt.score import Predictor

			tf.flags.DEFINE_string("""sv"""		,				""""""		,				""""""    )  # handle pytest cli flags





			class    lowerCamelCase						(A__			):

						def 					SCREAMING_SNAKE_CASE				(					self				:						Any    ,				__UpperCAmelCase				:						Union[str, Any]       )   ->    List[str]:
									assert len(input_dict["""input_ids"""]       ) == 2
									return np.array([1.03, 1.04]       )

    # mock predict_fn which is supposed to do a forward pass with a bleurt model
			with patch("""bleurt.score._create_predictor"""    ) as mock_create_predictor:
						SCREAMING_SNAKE_CASE__								=      MockedPredictor()
						yield

@LocalMetricTest.register_intensive_calls_patcher("""bertscore"""    )
def 	A       (     snake_case__    ):




			'''simple docstring'''


			import torch

			def bert_cos_score_idf(snake_case__		,				snake_case__		,				*snake_case__		,				**snake_case__    ):
						return torch.tensor([[1.0, 1.0, 1.0]] * len(snake_case__    )    )

			# mock get_model which is supposed to do download a bert model
			# mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
			with patch("""bert_score.scorer.get_model"""    ), patch(
			    """bert_score.scorer.bert_cos_score_idf"""    ) as mock_bert_cos_score_idf:
						SCREAMING_SNAKE_CASE__								=      bert_cos_score_idf
						yield

@LocalMetricTest.register_intensive_calls_patcher("""comet"""    )
def 	A       (     snake_case__    ):




			'''simple docstring'''


			def load_from_checkpoint(snake_case__    ):





						class    lowerCamelCase						:

									def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]    ,				__UpperCAmelCase				:						Any    ,				*__UpperCAmelCase				:						str    ,				**__UpperCAmelCase				:						List[Any]       )   ->    Optional[int]:
												assert len(__UpperCAmelCase       ) == 2
												SCREAMING_SNAKE_CASE__								=      [0.19, 0.92]
												return scores, sum(__UpperCAmelCase       ) / len(__UpperCAmelCase       )
						return Model()

			# mock load_from_checkpoint which is supposed to do download a bert model
			# mock load_from_checkpoint which is supposed to do download a bert model
			with patch("""comet.download_model"""    ) as mock_download_model:
						SCREAMING_SNAKE_CASE__								=      None
						with patch("""comet.load_from_checkpoint"""    ) as mock_load_from_checkpoint:
									SCREAMING_SNAKE_CASE__								=      load_from_checkpoint
									yield




def 	A       (     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      load_metric(os.path.join("""metrics"""		,				"""seqeval"""    )    )
			SCREAMING_SNAKE_CASE__								=      """ERROR"""
			SCREAMING_SNAKE_CASE__								=      f"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}"""
			with pytest.raises(snake_case__		,				match=re.escape(snake_case__    )    ):
						metric.compute(predictions=[]		,				references=[]		,				scheme=snake_case__    )


