






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
from ...utils import logging


_a				     =  logging.get_logger(__name__)

_a				     =  {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

_a				     =  {
    '''vocab_file''': {
        '''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json''',
    },
}





class __lowerCamelCase			(				snake_case__):





	"""simple docstring"""






	def __init__(							self	,   UpperCAmelCase=None	,   UpperCAmelCase=None	,   UpperCAmelCase=False	,   UpperCAmelCase=True	,   UpperCAmelCase=False	,   UpperCAmelCase="<s>"	,   UpperCAmelCase="</s>"	,   UpperCAmelCase="<unk>"	,   UpperCAmelCase="<sep>"	,   UpperCAmelCase="<pad>"	,   UpperCAmelCase="<cls>"	,   UpperCAmelCase="<mask>"	,   UpperCAmelCase=["<eop>", "<eod>"]	,   **UpperCAmelCase	,   ):
							"""simple docstring"""







							_UpperCAmelCase     =      AddedToken(UpperCAmelCase	,   lstrip=UpperCAmelCase	,   rstrip=UpperCAmelCase   ) if isinstance(UpperCAmelCase	,   UpperCAmelCase   ) else mask_token

							super().__init__(
							    vocab_file=UpperCAmelCase	,   tokenizer_file=UpperCAmelCase	,   do_lower_case=UpperCAmelCase	,   remove_space=UpperCAmelCase	,   keep_accents=UpperCAmelCase	,   bos_token=UpperCAmelCase	,   eos_token=UpperCAmelCase	,   unk_token=UpperCAmelCase	,   sep_token=UpperCAmelCase	,   pad_token=UpperCAmelCase	,   cls_token=UpperCAmelCase	,   mask_token=UpperCAmelCase	,   additional_special_tokens=UpperCAmelCase	,   **UpperCAmelCase	,   )

							_UpperCAmelCase     =      3
							_UpperCAmelCase     =      do_lower_case
							_UpperCAmelCase     =      remove_space
							_UpperCAmelCase     =      keep_accents
							_UpperCAmelCase     =      vocab_file
							_UpperCAmelCase     =      False if not self.vocab_file else True

							try:
													import jieba
							except ModuleNotFoundError as error:
													raise error.__class__(
													    'You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '
													    'See https://pypi.org/project/jieba/ for installation.'   )
							_UpperCAmelCase     =      jieba
							_UpperCAmelCase     =      str.maketrans(' \n'	,   '\u2582\u2583'   )

	def 	UpperCamelCase   (							self	,   UpperCAmelCase	,   UpperCAmelCase = None   ):
							"""simple docstring"""







							_UpperCAmelCase     =      [self.sep_token_id]
							_UpperCAmelCase     =      [self.cls_token_id]
							if token_ids_a is None:
													return token_ids_a + sep + cls
							return token_ids_a + sep + token_ids_a + sep + cls

	def 	UpperCamelCase   (							self	,   UpperCAmelCase	,   UpperCAmelCase = None   ):
							"""simple docstring"""







							_UpperCAmelCase     =      [self.sep_token_id]
							_UpperCAmelCase     =      [2]

							if token_ids_a is None:
													return len(token_ids_a + sep   ) * [0] + cls_segment_id
							return len(token_ids_a + sep   ) * [0] + len(token_ids_a + sep   ) * [1] + cls_segment_id

	def 	UpperCamelCase   (							self	,   UpperCAmelCase	,   UpperCAmelCase = None   ):
							"""simple docstring"""







							if not self.can_save_slow_tokenizer:
													raise ValueError(
													    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
													    'tokenizer.'   )

							if not os.path.isdir(UpperCAmelCase   ):
													logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""   )
													return
							_UpperCAmelCase     =      os.path.join(
							    UpperCAmelCase	,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )

							if os.path.abspath(self.vocab_file   ) != os.path.abspath(UpperCAmelCase   ):
													copyfile(self.vocab_file	,   UpperCAmelCase   )

							return (out_vocab_file,)

	def 	UpperCamelCase   (							self	,   UpperCAmelCase	,   *UpperCAmelCase	,   **UpperCAmelCase   ):
							"""simple docstring"""







							_UpperCAmelCase     =      [
							    ' '.join([x.translate(self.translator   ) for x in self.jieba.cut(UpperCAmelCase	,   cut_all=UpperCAmelCase   )]   )
							    for text in batch_text_or_text_pairs
							]
							return super()._batch_encode_plus(UpperCAmelCase	,   *UpperCAmelCase	,   **UpperCAmelCase   )

	def 	UpperCamelCase   (							self	,   *UpperCAmelCase	,   **UpperCAmelCase   ):
							"""simple docstring"""







							_UpperCAmelCase     =      super()._decode(*UpperCAmelCase	,   **UpperCAmelCase   )
							_UpperCAmelCase     =      text.replace(' '	,   ''   ).replace('\u2582'	,   ' '   ).replace('\u2583'	,   '\n'   )
							return text








import unittest

import pytest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    MBartaaTokenizerFast,
    MBartConfig,
    MBartForConditionalGeneration,
    TranslationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow

from .test_pipelines_common import ANY





@is_pipeline_test
class __lowerCamelCase			(				unittest.TestCase):





	"""simple docstring"""






	UpperCamelCase__								=				MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
	UpperCamelCase__								=				TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

	def 	UpperCamelCase   (							self	,   UpperCAmelCase	,   UpperCAmelCase	,   UpperCAmelCase   ):
							"""simple docstring"""







							if isinstance(model.config	,   UpperCAmelCase   ):
													_UpperCAmelCase ,			_UpperCAmelCase     =      list(tokenizer.lang_code_to_id.keys()   )[:2]
													_UpperCAmelCase     =      TranslationPipeline(model=UpperCAmelCase	,   tokenizer=UpperCAmelCase	,   src_lang=UpperCAmelCase	,   tgt_lang=UpperCAmelCase   )
							else:
													_UpperCAmelCase     =      TranslationPipeline(model=UpperCAmelCase	,   tokenizer=UpperCAmelCase   )
							return translator, ["Some string", "Some other text"]

	def 	UpperCamelCase   (							self	,   UpperCAmelCase	,   UpperCAmelCase   ):
							"""simple docstring"""







							_UpperCAmelCase     =      translator('Some string'   )
							self.assertEqual(UpperCAmelCase	,   [{'translation_text': ANY(UpperCAmelCase   )}]   )

							_UpperCAmelCase     =      translator(['Some string']   )
							self.assertEqual(UpperCAmelCase	,   [{'translation_text': ANY(UpperCAmelCase   )}]   )

							_UpperCAmelCase     =      translator(['Some string', 'other string']   )
							self.assertEqual(UpperCAmelCase	,   [{'translation_text': ANY(UpperCAmelCase   )}, {'translation_text': ANY(UpperCAmelCase   )}]   )

	@require_torch
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      pipeline('translation_en_to_ro'	,   model='patrickvonplaten/t5-tiny-random'	,   framework='pt'   )
							_UpperCAmelCase     =      translator('This is a test string'	,   max_length=20   )
							self.assertEqual(
							    UpperCAmelCase	,   [
							        {
							            'translation_text': (
							                'Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'
							                ' Beide Beide'
							            )
							        }
							    ]	,   )

	@require_tf
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      pipeline('translation_en_to_ro'	,   model='patrickvonplaten/t5-tiny-random'	,   framework='tf'   )
							_UpperCAmelCase     =      translator('This is a test string'	,   max_length=20   )
							self.assertEqual(
							    UpperCAmelCase	,   [
							        {
							            'translation_text': (
							                'Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'
							                ' Beide Beide'
							            )
							        }
							    ]	,   )

	@require_torch
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      pipeline('translation_en_to_de'	,   model='patrickvonplaten/t5-tiny-random'	,   framework='pt'   )
							_UpperCAmelCase     =      translator('This is a test string'	,   max_length=20   )
							self.assertEqual(
							    UpperCAmelCase	,   [
							        {
							            'translation_text': (
							                'monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine'
							                ' urine urine urine urine urine urine urine'
							            )
							        }
							    ]	,   )

	@require_tf
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      pipeline('translation_en_to_de'	,   model='patrickvonplaten/t5-tiny-random'	,   framework='tf'   )
							_UpperCAmelCase     =      translator('This is a test string'	,   max_length=20   )
							self.assertEqual(
							    UpperCAmelCase	,   [
							        {
							            'translation_text': (
							                'monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine'
							                ' urine urine urine urine urine urine urine'
							            )
							        }
							    ]	,   )





class __lowerCamelCase			(				unittest.TestCase):





	"""simple docstring"""






	@require_torch
	@slow
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							with self.assertRaises(UpperCAmelCase   ):
													pipeline(task='translation_cn_to_ar'   )

							# but we do for this one
							_UpperCAmelCase     =      pipeline(task='translation_en_to_de'   )
							self.assertEqual(translator._preprocess_params['src_lang']	,   'en'   )
							self.assertEqual(translator._preprocess_params['tgt_lang']	,   'de'   )

	@require_torch
	@slow
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      MBartForConditionalGeneration.from_pretrained('facebook/mbart-large-50-many-to-many-mmt'   )
							_UpperCAmelCase     =      MBartaaTokenizerFast.from_pretrained('facebook/mbart-large-50-many-to-many-mmt'   )

							_UpperCAmelCase     =      pipeline(task='translation'	,   model=UpperCAmelCase	,   tokenizer=UpperCAmelCase   )
							# Missing src_lang, tgt_lang
							with self.assertRaises(UpperCAmelCase   ):
													translator('This is a test'   )

							_UpperCAmelCase     =      translator('This is a test'	,   src_lang='en_XX'	,   tgt_lang='ar_AR'   )
							self.assertEqual(UpperCAmelCase	,   [{'translation_text': 'هذا إختبار'}]   )

							_UpperCAmelCase     =      translator('This is a test'	,   src_lang='en_XX'	,   tgt_lang='hi_IN'   )
							self.assertEqual(UpperCAmelCase	,   [{'translation_text': 'यह एक परीक्षण है'}]   )

							# src_lang, tgt_lang can be defined at pipeline call time
							_UpperCAmelCase     =      pipeline(task='translation'	,   model=UpperCAmelCase	,   tokenizer=UpperCAmelCase	,   src_lang='en_XX'	,   tgt_lang='ar_AR'   )
							_UpperCAmelCase     =      translator('This is a test'   )
							self.assertEqual(UpperCAmelCase	,   [{'translation_text': 'هذا إختبار'}]   )

	@require_torch
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      'patrickvonplaten/t5-tiny-random'
							_UpperCAmelCase     =      pipeline(task='translation_cn_to_ar'	,   model=UpperCAmelCase   )
							self.assertEqual(translator._preprocess_params['src_lang']	,   'cn'   )
							self.assertEqual(translator._preprocess_params['tgt_lang']	,   'ar'   )

	@require_torch
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      'patrickvonplaten/t5-tiny-random'
							with pytest.warns(UpperCAmelCase	,   match=R'.*translation_en_to_de.*'   ):
													_UpperCAmelCase     =      pipeline(task='translation'	,   model=UpperCAmelCase   )
							self.assertEqual(translator.task	,   'translation_en_to_de'   )
							self.assertEqual(translator._preprocess_params['src_lang']	,   'en'   )
							self.assertEqual(translator._preprocess_params['tgt_lang']	,   'de'   )

	@require_torch
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							with self.assertRaises(UpperCAmelCase   ):
													pipeline(task='translation'   )
