'''simple docstring'''

import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path

import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized

from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available

from ..wavaveca.test_feature_extraction_wavaveca import floats_list


if is_pyctcdecode_available():
       from huggingface_hub import snapshot_download
       from pyctcdecode import BeamSearchDecoderCTC

       from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
       from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput

if is_torch_available():
       from transformers import WavaVecaForCTC





@require_pyctcdecode
class 							lowercase						(						unittest.TestCase ):


   def __UpperCAmelCase       (   self				:	Any)      ->					Tuple:
         lowercase_     =      "| <pad> <unk> <s> </s> a b c d e f g h i j k".split()
         lowercase_     =      dict(zip(__lowerCAmelCase   ,		range(len(__lowerCAmelCase))))

         lowercase_     =      {
             "unk_token": "<unk>",
             "bos_token": "<s>",
             "eos_token": "</s>",
         }
         lowercase_     =      {
             "feature_size": 1,
             "padding_value": 0.0,
             "sampling_rate": 1_6000,
             "return_attention_mask": False,
             "do_normalize": True,
         }

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

         with open(self.feature_extraction_file   ,		"w"   ,		encoding="utf-8") as fp:
               fp.write(json.dumps(__lowerCAmelCase) + "\n")

         # load decoder from hub
         lowercase_     =      "hf-internal-testing/ngram-beam-search-decoder"


   def __UpperCAmelCase       (   self				:	Any   ,		**__lowerCAmelCase				:	Optional[int])      ->					List[str]:
         lowercase_     =      self.add_kwargs_tokens_map.copy()
         kwargs.update(__lowerCAmelCase)
         return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname   ,		**__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Optional[Any]   ,		**__lowerCAmelCase				:	str)      ->					int:
         return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname   ,		**__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Optional[Any]   ,		**__lowerCAmelCase				:	List[str])      ->					List[Any]:
         return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name   ,		**__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	int)      ->					Dict:
         shutil.rmtree(self.tmpdirname)


   def __UpperCAmelCase       (   self				:	Optional[int])      ->					Tuple:
         lowercase_     =      self.get_tokenizer()
         lowercase_     =      self.get_feature_extractor()
         lowercase_     =      self.get_decoder()

         lowercase_     =      WavaVecaProcessorWithLM(tokenizer=__lowerCAmelCase   ,		feature_extractor=__lowerCAmelCase   ,		decoder=__lowerCAmelCase)

         processor.save_pretrained(self.tmpdirname)
         lowercase_     =      WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname)

         # tokenizer
         self.assertEqual(processor.tokenizer.get_vocab()   ,		tokenizer.get_vocab())
         self.assertIsInstance(processor.tokenizer   ,		__lowerCAmelCase)

         # feature extractor
         self.assertEqual(processor.feature_extractor.to_json_string()   ,		feature_extractor.to_json_string())
         self.assertIsInstance(processor.feature_extractor   ,		__lowerCAmelCase)

         # decoder
         self.assertEqual(processor.decoder._alphabet.labels   ,		decoder._alphabet.labels)
         self.assertEqual(
             processor.decoder.model_container[decoder._model_key]._unigram_set   ,		decoder.model_container[decoder._model_key]._unigram_set   ,		)
         self.assertIsInstance(processor.decoder   ,		__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Optional[int])      ->					Union[str, Any]:
         lowercase_     =      WavaVecaProcessorWithLM(
             tokenizer=self.get_tokenizer()   ,		feature_extractor=self.get_feature_extractor()   ,		decoder=self.get_decoder())
         processor.save_pretrained(self.tmpdirname)

         # make sure that error is thrown when decoder alphabet doesn't match
         lowercase_     =      WavaVecaProcessorWithLM.from_pretrained(
             self.tmpdirname   ,		alpha=5.0   ,		beta=3.0   ,		score_boundary=-7.0   ,		unk_score_offset=3)

         # decoder
         self.assertEqual(processor.language_model.alpha   ,		5.0)
         self.assertEqual(processor.language_model.beta   ,		3.0)
         self.assertEqual(processor.language_model.score_boundary   ,		-7.0)
         self.assertEqual(processor.language_model.unk_score_offset   ,		3)


   def __UpperCAmelCase       (   self				:	Any)      ->					Dict:
         lowercase_     =      self.get_tokenizer()
         # add token to trigger raise
         tokenizer.add_tokens(["xx"])
         with self.assertRaisesRegex(__lowerCAmelCase   ,		"include"):
               WavaVecaProcessorWithLM(
                   tokenizer=__lowerCAmelCase   ,		feature_extractor=self.get_feature_extractor()   ,		decoder=self.get_decoder())


   def __UpperCAmelCase       (   self				:	int)      ->					Optional[int]:
         lowercase_     =      self.get_feature_extractor()
         lowercase_     =      self.get_tokenizer()
         lowercase_     =      self.get_decoder()

         lowercase_     =      WavaVecaProcessorWithLM(tokenizer=__lowerCAmelCase   ,		feature_extractor=__lowerCAmelCase   ,		decoder=__lowerCAmelCase)

         lowercase_     =      floats_list((3, 1000))

         lowercase_     =      feature_extractor(__lowerCAmelCase   ,		return_tensors="np")
         lowercase_     =      processor(__lowerCAmelCase   ,		return_tensors="np")

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


   def __UpperCAmelCase       (   self				:	Dict)      ->					Union[str, Any]:
         lowercase_     =      self.get_feature_extractor()
         lowercase_     =      self.get_tokenizer()
         lowercase_     =      self.get_decoder()

         lowercase_     =      WavaVecaProcessorWithLM(tokenizer=__lowerCAmelCase   ,		feature_extractor=__lowerCAmelCase   ,		decoder=__lowerCAmelCase)

         lowercase_     =      "This is a test string"

         lowercase_     =      processor(text=__lowerCAmelCase)

         lowercase_     =      tokenizer(__lowerCAmelCase)

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


   def __UpperCAmelCase       (   self				:	Tuple   ,		__lowerCAmelCase				:	Union[str, Any]=(2, 10, 16)   ,		__lowerCAmelCase				:	Any=77)      ->					List[Any]:
         np.random.seed(__lowerCAmelCase)
         return np.random.rand(*__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Tuple)      ->					Dict:
         lowercase_     =      self.get_feature_extractor()
         lowercase_     =      self.get_tokenizer()
         lowercase_     =      self.get_decoder()

         lowercase_     =      WavaVecaProcessorWithLM(tokenizer=__lowerCAmelCase   ,		feature_extractor=__lowerCAmelCase   ,		decoder=__lowerCAmelCase)

         lowercase_     =      self._get_dummy_logits(shape=(10, 16)   ,		seed=13)

         lowercase_     =      processor.decode(__lowerCAmelCase)

         lowercase_     =      decoder.decode_beams(__lowerCAmelCase)[0]

         self.assertEqual(decoded_decoder[0]   ,		decoded_processor.text)
         self.assertEqual("</s> <s> </s>"   ,		decoded_processor.text)
         self.assertEqual(decoded_decoder[-2]   ,		decoded_processor.logit_score)
         self.assertEqual(decoded_decoder[-1]   ,		decoded_processor.lm_score)


   @parameterized.expand([[None], ["fork"], ["spawn"]])
   def __UpperCAmelCase       (   self				:	Optional[Any]   ,		__lowerCAmelCase				:	int)      ->					Any:
         lowercase_     =      self.get_feature_extractor()
         lowercase_     =      self.get_tokenizer()
         lowercase_     =      self.get_decoder()

         lowercase_     =      WavaVecaProcessorWithLM(tokenizer=__lowerCAmelCase   ,		feature_extractor=__lowerCAmelCase   ,		decoder=__lowerCAmelCase)

         lowercase_     =      self._get_dummy_logits()

         # note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
         #       otherwise, the LM won't be available to the pool's sub-processes.
         # manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
         if pool_context is None:
               lowercase_     =      processor.batch_decode(__lowerCAmelCase)
         else:
               with get_context(__lowerCAmelCase).Pool() as pool:
                     lowercase_     =      processor.batch_decode(__lowerCAmelCase   ,		__lowerCAmelCase)

         lowercase_     =      list(__lowerCAmelCase)

         with get_context("fork").Pool() as p:
               lowercase_     =      decoder.decode_beams_batch(__lowerCAmelCase   ,		__lowerCAmelCase)

         lowercase_ ,							lowercase_ ,							lowercase_     =      [], [], []
         for beams in decoded_beams:
               texts_decoder.append(beams[0][0])
               logit_scores_decoder.append(beams[0][-2])
               lm_scores_decoder.append(beams[0][-1])

         self.assertListEqual(__lowerCAmelCase   ,		decoded_processor.text)
         self.assertListEqual(["<s> <s> </s>", "<s> <s> <s>"]   ,		decoded_processor.text)
         self.assertListEqual(__lowerCAmelCase   ,		decoded_processor.logit_score)
         self.assertListEqual(__lowerCAmelCase   ,		decoded_processor.lm_score)


   def __UpperCAmelCase       (   self				:	Any)      ->					Optional[int]:
         lowercase_     =      self.get_feature_extractor()
         lowercase_     =      self.get_tokenizer()
         lowercase_     =      self.get_decoder()

         lowercase_     =      WavaVecaProcessorWithLM(tokenizer=__lowerCAmelCase   ,		feature_extractor=__lowerCAmelCase   ,		decoder=__lowerCAmelCase)

         lowercase_     =      self._get_dummy_logits()

         lowercase_     =      15
         lowercase_     =      -20.0
         lowercase_     =      -4.0

         lowercase_     =      processor.batch_decode(
             __lowerCAmelCase   ,		beam_width=__lowerCAmelCase   ,		beam_prune_logp=__lowerCAmelCase   ,		token_min_logp=__lowerCAmelCase   ,		)
         lowercase_     =      decoded_processor_out.text

         lowercase_     =      list(__lowerCAmelCase)

         with get_context("fork").Pool() as pool:
               lowercase_     =      decoder.decode_beams_batch(
                   __lowerCAmelCase   ,		__lowerCAmelCase   ,		beam_width=__lowerCAmelCase   ,		beam_prune_logp=__lowerCAmelCase   ,		token_min_logp=__lowerCAmelCase   ,		)

         lowercase_     =      [d[0][0] for d in decoded_decoder_out]
         lowercase_     =      [d[0][2] for d in decoded_decoder_out]
         lowercase_     =      [d[0][3] for d in decoded_decoder_out]

         self.assertListEqual(__lowerCAmelCase   ,		__lowerCAmelCase)
         self.assertListEqual(["</s> <s> <s>", "<s> <s> <s>"]   ,		__lowerCAmelCase)

         self.assertTrue(np.array_equal(__lowerCAmelCase   ,		decoded_processor_out.logit_score))
         self.assertTrue(np.allclose([-20.054, -18.447]   ,		__lowerCAmelCase   ,		atol=1e-3))

         self.assertTrue(np.array_equal(__lowerCAmelCase   ,		decoded_processor_out.lm_score))
         self.assertTrue(np.allclose([-15.554, -13.9474]   ,		__lowerCAmelCase   ,		atol=1e-3))


   def __UpperCAmelCase       (   self				:	str)      ->					Union[str, Any]:
         lowercase_     =      self.get_feature_extractor()
         lowercase_     =      self.get_tokenizer()
         lowercase_     =      self.get_decoder()

         lowercase_     =      WavaVecaProcessorWithLM(tokenizer=__lowerCAmelCase   ,		feature_extractor=__lowerCAmelCase   ,		decoder=__lowerCAmelCase)

         lowercase_     =      self._get_dummy_logits()

         lowercase_     =      2.0
         lowercase_     =      5.0
         lowercase_     =      -20.0
         lowercase_     =      True

         lowercase_     =      processor.batch_decode(
             __lowerCAmelCase   ,		alpha=__lowerCAmelCase   ,		beta=__lowerCAmelCase   ,		unk_score_offset=__lowerCAmelCase   ,		lm_score_boundary=__lowerCAmelCase   ,		)
         lowercase_     =      decoded_processor_out.text

         lowercase_     =      list(__lowerCAmelCase)
         decoder.reset_params(
             alpha=__lowerCAmelCase   ,		beta=__lowerCAmelCase   ,		unk_score_offset=__lowerCAmelCase   ,		lm_score_boundary=__lowerCAmelCase   ,		)

         with get_context("fork").Pool() as pool:
               lowercase_     =      decoder.decode_beams_batch(
                   __lowerCAmelCase   ,		__lowerCAmelCase   ,		)

         lowercase_     =      [d[0][0] for d in decoded_decoder_out]

         self.assertListEqual(__lowerCAmelCase   ,		__lowerCAmelCase)
         self.assertListEqual(["<s> </s> <s> </s> </s>", "</s> </s> <s> </s> </s>"]   ,		__lowerCAmelCase)
         lowercase_     =      processor.decoder.model_container[processor.decoder._model_key]
         self.assertEqual(lm_model.alpha   ,		2.0)
         self.assertEqual(lm_model.beta   ,		5.0)
         self.assertEqual(lm_model.unk_score_offset   ,		-20.0)
         self.assertEqual(lm_model.score_boundary   ,		__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Tuple)      ->					Any:
         lowercase_     =      WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm")

         lowercase_     =      processor.decoder.model_container[processor.decoder._model_key]
         lowercase_     =      Path(language_model._kenlm_model.path.decode("utf-8")).parent.parent.absolute()

         lowercase_     =      os.listdir(__lowerCAmelCase)
         lowercase_     =      ["alphabet.json", "language_model"]

         downloaded_decoder_files.sort()
         expected_decoder_files.sort()

         # test that only decoder relevant files from
         # https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
         # are downloaded and none of the rest (e.g. README.md, ...)
         self.assertListEqual(__lowerCAmelCase   ,		__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Dict)      ->					Union[str, Any]:
         lowercase_     =      snapshot_download("hf-internal-testing/processor_with_lm")

         lowercase_     =      WavaVecaProcessorWithLM.from_pretrained(__lowerCAmelCase)

         lowercase_     =      processor.decoder.model_container[processor.decoder._model_key]
         lowercase_     =      Path(language_model._kenlm_model.path.decode("utf-8")).parent.parent.absolute()

         lowercase_     =      os.listdir(__lowerCAmelCase)
         lowercase_     =      os.listdir(__lowerCAmelCase)

         local_decoder_files.sort()
         expected_decoder_files.sort()

         # test that both decoder form hub and local files in cache are the same
         self.assertListEqual(__lowerCAmelCase   ,		__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Optional[Any])      ->					Any:
         lowercase_     =      WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm")
         lowercase_     =      AutoProcessor.from_pretrained("hf-internal-testing/processor_with_lm")

         lowercase_     =      floats_list((3, 1000))

         lowercase_     =      processor_wavaveca(__lowerCAmelCase   ,		return_tensors="np")
         lowercase_     =      processor_auto(__lowerCAmelCase   ,		return_tensors="np")

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

         lowercase_     =      self._get_dummy_logits()

         lowercase_     =      processor_wavaveca.batch_decode(__lowerCAmelCase)
         lowercase_     =      processor_auto.batch_decode(__lowerCAmelCase)

         self.assertListEqual(decoded_wavaveca.text   ,		decoded_auto.text)


   def __UpperCAmelCase       (   self				:	Optional[Any])      ->					int:
         lowercase_     =      self.get_feature_extractor()
         lowercase_     =      self.get_tokenizer()
         lowercase_     =      self.get_decoder()

         lowercase_     =      WavaVecaProcessorWithLM(tokenizer=__lowerCAmelCase   ,		feature_extractor=__lowerCAmelCase   ,		decoder=__lowerCAmelCase)

         self.assertListEqual(
             processor.model_input_names   ,		feature_extractor.model_input_names   ,		msg="`processor` and `feature_extractor` model input names do not match"   ,		)


   @staticmethod
   def __UpperCAmelCase       (   __lowerCAmelCase				:	Dict   ,		__lowerCAmelCase				:	Optional[Any])      ->					Dict:
         lowercase_     =      [d[key] for d in offsets]
         return retrieved_list


   def __UpperCAmelCase       (   self				:	Union[str, Any])      ->					List[Any]:
         lowercase_     =      WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm")
         lowercase_     =      self._get_dummy_logits()[0]

         lowercase_     =      processor.decode(__lowerCAmelCase   ,		output_word_offsets=__lowerCAmelCase)
         # check Wav2Vec2CTCTokenizerOutput keys for word
         self.assertEqual(len(outputs.keys())   ,		4)
         self.assertTrue("text" in outputs)
         self.assertTrue("word_offsets" in outputs)
         self.assertTrue(isinstance(__lowerCAmelCase   ,		__lowerCAmelCase))

         self.assertEqual(" ".join(self.get_from_offsets(outputs["word_offsets"]   ,		"word"))   ,		outputs.text)
         self.assertListEqual(self.get_from_offsets(outputs["word_offsets"]   ,		"word")   ,		["<s>", "<s>", "</s>"])
         self.assertListEqual(self.get_from_offsets(outputs["word_offsets"]   ,		"start_offset")   ,		[0, 2, 4])
         self.assertListEqual(self.get_from_offsets(outputs["word_offsets"]   ,		"end_offset")   ,		[1, 3, 5])


   def __UpperCAmelCase       (   self				:	int)      ->					Optional[Any]:
         lowercase_     =      WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm")
         lowercase_     =      self._get_dummy_logits()

         lowercase_     =      processor.batch_decode(__lowerCAmelCase   ,		output_word_offsets=__lowerCAmelCase)

         # check Wav2Vec2CTCTokenizerOutput keys for word
         self.assertEqual(len(outputs.keys())   ,		4)
         self.assertTrue("text" in outputs)
         self.assertTrue("word_offsets" in outputs)
         self.assertTrue(isinstance(__lowerCAmelCase   ,		__lowerCAmelCase))

         self.assertListEqual(
             [" ".join(self.get_from_offsets(__lowerCAmelCase   ,		"word")) for o in outputs["word_offsets"]]   ,		outputs.text)
         self.assertListEqual(self.get_from_offsets(outputs["word_offsets"][0]   ,		"word")   ,		["<s>", "<s>", "</s>"])
         self.assertListEqual(self.get_from_offsets(outputs["word_offsets"][0]   ,		"start_offset")   ,		[0, 2, 4])
         self.assertListEqual(self.get_from_offsets(outputs["word_offsets"][0]   ,		"end_offset")   ,		[1, 3, 5])


   @slow
   @require_torch
   @require_torchaudio
   def __UpperCAmelCase       (   self				:	str)      ->					List[Any]:
         import torch

         lowercase_     =      load_dataset("common_voice"   ,		"en"   ,		split="train"   ,		streaming=__lowerCAmelCase)
         lowercase_     =      ds.cast_column("audio"   ,		datasets.Audio(sampling_rate=1_6000))
         lowercase_     =      iter(__lowerCAmelCase)
         lowercase_     =      next(__lowerCAmelCase)

         lowercase_     =      AutoProcessor.from_pretrained("patrickvonplaten/wav2vec2-base-100h-with-lm")
         lowercase_     =      WavaVecaForCTC.from_pretrained("patrickvonplaten/wav2vec2-base-100h-with-lm")

         # compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
         lowercase_     =      processor(sample["audio"]["array"]   ,		return_tensors="pt").input_values

         with torch.no_grad():
               lowercase_     =      model(__lowerCAmelCase).logits.cpu().numpy()

         lowercase_     =      processor.decode(logits[0]   ,		output_word_offsets=__lowerCAmelCase)

         lowercase_     =      model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
         lowercase_     =      [
             {
                 "start_time": d["start_offset"] * time_offset,
                 "end_time": d["end_offset"] * time_offset,
                 "word": d["word"],
             }
             for d in output["word_offsets"]
         ]

         lowercase_     =      "WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL"

         # output words
         self.assertEqual(" ".join(self.get_from_offsets(__lowerCAmelCase   ,		"word"))   ,		__lowerCAmelCase)
         self.assertEqual(" ".join(self.get_from_offsets(__lowerCAmelCase   ,		"word"))   ,		output.text)

         # output times
         lowercase_     =      torch.tensor(self.get_from_offsets(__lowerCAmelCase   ,		"start_time"))
         lowercase_     =      torch.tensor(self.get_from_offsets(__lowerCAmelCase   ,		"end_time"))

         # fmt: off
         lowercase_     =      torch.tensor([1.4199, 1.6599, 2.2599, 3.0, 3.24, 3.5999, 3.7999, 4.0999, 4.26, 4.94, 5.28, 5.6599, 5.78, 5.94, 6.32, 6.5399, 6.6599])
         lowercase_     =      torch.tensor([1.5399, 1.8999, 2.9, 3.16, 3.5399, 3.72, 4.0199, 4.1799, 4.76, 5.1599, 5.5599, 5.6999, 5.86, 6.1999, 6.38, 6.6199, 6.94])
         # fmt: on

         self.assertTrue(torch.allclose(__lowerCAmelCase   ,		__lowerCAmelCase   ,		atol=0.01))
         self.assertTrue(torch.allclose(__lowerCAmelCase   ,		__lowerCAmelCase   ,		atol=0.01))







'''simple docstring'''

import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation


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

lowerCAmelCase_ : Any						  =      {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

lowerCAmelCase_ : Tuple						  =      {
    "vocab_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
    },
    "merges_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/tokenizer.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/tokenizer.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/tokenizer.json",
    },
}

lowerCAmelCase_ : Dict						  =      {
    "gpt2": 1_024,
    "gpt2-medium": 1_024,
    "gpt2-large": 1_024,
    "gpt2-xl": 1_024,
    "distilgpt2": 1_024,
}





class 							lowercase						(						__lowerCamelCase ):
   lowerCamelCase_    					=VOCAB_FILES_NAMES
   lowerCamelCase_    					=PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase_    					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase_    					=['input_ids', 'attention_mask']
   lowerCamelCase_    					=GPTaTokenizer


   def __init__(   self				:	Dict   ,		__lowerCAmelCase				:	List[Any]=None   ,		__lowerCAmelCase				:	Optional[Any]=None   ,		__lowerCAmelCase				:	Optional[int]=None   ,		__lowerCAmelCase				:	Any="<|endoftext|>"   ,		__lowerCAmelCase				:	Union[str, Any]="<|endoftext|>"   ,		__lowerCAmelCase				:	List[Any]="<|endoftext|>"   ,		__lowerCAmelCase				:	Optional[Any]=False   ,		**__lowerCAmelCase				:	Dict   ,		)      ->					int:
         super().__init__(
             __lowerCAmelCase   ,		__lowerCAmelCase   ,		tokenizer_file=__lowerCAmelCase   ,		unk_token=__lowerCAmelCase   ,		bos_token=__lowerCAmelCase   ,		eos_token=__lowerCAmelCase   ,		add_prefix_space=__lowerCAmelCase   ,		**__lowerCAmelCase   ,		)

         lowercase_     =      kwargs.pop("add_bos_token"   ,		__lowerCAmelCase)

         lowercase_     =      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
         if pre_tok_state.get("add_prefix_space"   ,		__lowerCAmelCase) != add_prefix_space:
               lowercase_     =      getattr(__lowerCAmelCase   ,		pre_tok_state.pop("type"))
               lowercase_     =      add_prefix_space
               lowercase_     =      pre_tok_class(**__lowerCAmelCase)

         lowercase_     =      add_prefix_space


   def __UpperCAmelCase       (   self				:	List[str]   ,		*__lowerCAmelCase				:	List[str]   ,		**__lowerCAmelCase				:	str)      ->					BatchEncoding:
         lowercase_     =      kwargs.get("is_split_into_words"   ,		__lowerCAmelCase)
         assert self.add_prefix_space or not is_split_into_words, (
             F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
             "to use it with pretokenized inputs."
         )

         return super()._batch_encode_plus(*__lowerCAmelCase   ,		**__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	List[Any]   ,		*__lowerCAmelCase				:	Union[str, Any]   ,		**__lowerCAmelCase				:	Union[str, Any])      ->					BatchEncoding:
         lowercase_     =      kwargs.get("is_split_into_words"   ,		__lowerCAmelCase)

         assert self.add_prefix_space or not is_split_into_words, (
             F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
             "to use it with pretokenized inputs."
         )

         return super()._encode_plus(*__lowerCAmelCase   ,		**__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Optional[int]   ,		__lowerCAmelCase				:	str   ,		__lowerCAmelCase				:	Optional[str] = None)      ->					Tuple[str]:
         lowercase_     =      self._tokenizer.model.save(__lowerCAmelCase   ,		name=__lowerCAmelCase)
         return tuple(__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Tuple   ,		__lowerCAmelCase				:	"Conversation")      ->					List[int]:
         lowercase_     =      []
         for is_user, text in conversation.iter_texts():
               input_ids.extend(self.encode(__lowerCAmelCase   ,		add_special_tokens=__lowerCAmelCase) + [self.eos_token_id])

         if len(__lowerCAmelCase) > self.model_max_length:
               lowercase_     =      input_ids[-self.model_max_length :]
         return input_ids






