
'''simple docstring'''







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  SCREAMING_SNAKE_CASE__       (  unittest.TestCase   ):
	lowercase__	    =							MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
	lowercase__	    =							TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
	def __lowerCamelCase    (				self  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase			):



					'''simple docstring'''






					if isinstance(model.config  ,    __UpperCamelCase			):
									__a							,	__a       :     Dict								=	list(tokenizer.lang_code_to_id.keys()			)[:2]
									__a       :     str								=	TranslationPipeline(model=__UpperCamelCase  ,    tokenizer=__UpperCamelCase  ,    src_lang=__UpperCamelCase  ,    tgt_lang=__UpperCamelCase			)
					else:
									__a       :     str								=	TranslationPipeline(model=__UpperCamelCase  ,    tokenizer=__UpperCamelCase			)
					return translator, ["Some string", "Some other text"]
	def __lowerCamelCase    (				self  ,    __UpperCamelCase  ,    __UpperCamelCase			):



					'''simple docstring'''






					__a       :     Union[str, Any]								=	translator("""Some string"""			)
					self.assertEqual(__UpperCamelCase  ,    [{"""translation_text""": ANY(__UpperCamelCase			)}]			)

					__a       :     int								=	translator(["""Some string"""]			)
					self.assertEqual(__UpperCamelCase  ,    [{"""translation_text""": ANY(__UpperCamelCase			)}]			)

					__a       :     List[Any]								=	translator(["""Some string""", """other string"""]			)
					self.assertEqual(__UpperCamelCase  ,    [{"""translation_text""": ANY(__UpperCamelCase			)}, {"""translation_text""": ANY(__UpperCamelCase			)}]			)
	@require_torch
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a       :     Tuple								=	pipeline("""translation_en_to_ro"""  ,    model="""patrickvonplaten/t5-tiny-random"""  ,    framework="""pt"""			)
					__a       :     Dict								=	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 __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a       :     int								=	pipeline("""translation_en_to_ro"""  ,    model="""patrickvonplaten/t5-tiny-random"""  ,    framework="""tf"""			)
					__a       :     Optional[int]								=	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 __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a       :     str								=	pipeline("""translation_en_to_de"""  ,    model="""patrickvonplaten/t5-tiny-random"""  ,    framework="""pt"""			)
					__a       :     str								=	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 __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a       :     int								=	pipeline("""translation_en_to_de"""  ,    model="""patrickvonplaten/t5-tiny-random"""  ,    framework="""tf"""			)
					__a       :     List[str]								=	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  SCREAMING_SNAKE_CASE__       (  unittest.TestCase   ):
	@require_torch
	@slow
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					with self.assertRaises(__UpperCamelCase			):
									pipeline(task="""translation_cn_to_ar"""			)

					# but we do for this one
					__a       :     List[str]								=	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 __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a       :     Tuple								=	MBartForConditionalGeneration.from_pretrained("""facebook/mbart-large-50-many-to-many-mmt"""			)
					__a       :     Optional[int]								=	MBartaaTokenizerFast.from_pretrained("""facebook/mbart-large-50-many-to-many-mmt"""			)

					__a       :     List[Any]								=	pipeline(task="""translation"""  ,    model=__UpperCamelCase  ,    tokenizer=__UpperCamelCase			)
					# Missing src_lang, tgt_lang
					with self.assertRaises(__UpperCamelCase			):
									translator("""This is a test"""			)

					__a       :     Optional[Any]								=	translator("""This is a test"""  ,    src_lang="""en_XX"""  ,    tgt_lang="""ar_AR"""			)
					self.assertEqual(__UpperCamelCase  ,    [{"""translation_text""": """هذا إختبار"""}]			)

					__a       :     List[str]								=	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
					__a       :     str								=	pipeline(task="""translation"""  ,    model=__UpperCamelCase  ,    tokenizer=__UpperCamelCase  ,    src_lang="""en_XX"""  ,    tgt_lang="""ar_AR"""			)
					__a       :     str								=	translator("""This is a test"""			)
					self.assertEqual(__UpperCamelCase  ,    [{"""translation_text""": """هذا إختبار"""}]			)
	@require_torch
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a       :     Optional[Any]								=	"""patrickvonplaten/t5-tiny-random"""
					__a       :     str								=	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 __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a       :     Any								=	"""patrickvonplaten/t5-tiny-random"""
					with pytest.warns(__UpperCamelCase  ,    match=r""".*translation_en_to_de.*"""			):
									__a       :     int								=	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 __lowerCamelCase    (				self			):



					'''simple docstring'''






					with self.assertRaises(__UpperCamelCase			):
									pipeline(task="""translation"""			)

'''simple docstring'''







from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers
class  SCREAMING_SNAKE_CASE__       :
	def __init__(				self  ,    __UpperCamelCase			):



					'''simple docstring'''






					__a       :     Union[str, Any]								=	vocab
					__a       :     int								=	normalizers.BertNormalizer(
					    clean_text=__UpperCamelCase  ,    handle_chinese_chars=__UpperCamelCase  ,    strip_accents=__UpperCamelCase  ,    lowercase=__UpperCamelCase  ,    )
					try:
									import rjieba
					except ImportError:
									raise ImportError(
									    """You need to install rjieba to use RoFormerTokenizer. """
									    """See https://pypi.org/project/rjieba/ for installation."""			)
					__a       :     Optional[Any]								=	rjieba
	def __lowerCamelCase    (				self  ,    __UpperCamelCase  ,    __UpperCamelCase			):



					'''simple docstring'''






					__a       :     Union[str, Any]								=	[]

					# this code slice normalized_string is too slow (6s) but test_alignement_methods can pass
					for token, start, end in self.jieba.tokenize(str(__UpperCamelCase			)  ,    hmm=__UpperCamelCase			):
									if token in self.vocab:
													splits.append(normalized_string[start:end]			)
									else:
													__a       :     str								=	self.normalizers.normalize_str(__UpperCamelCase			).split()
													for token in token_list:
																	if token:
																					__a       :     str								=	start + len(__UpperCamelCase			)
																					splits.append(normalized_string[start:end]			)
																					__a       :     Union[str, Any]								=	end

        # this code test_alignement_methods can't pass but fast (300ms)
        # for token in self.jieba.cut(str(normalized_string), False):
        #     if token in self.vocab:
        #         splits.append(NormalizedString(token))
        #     else:
        #         token_list = self.normalizers.normalize_str(token).split()
        #         for token in token_list:
        #             if token:
        #                 splits.append(NormalizedString(token))

					return splits

	def __lowerCamelCase    (				self  ,    __UpperCamelCase			):



					'''simple docstring'''






					pretok.split(self.jieba_split			)