






"""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  __lowerCamelCase     (	unittest.TestCase	):
   a   :					Tuple			    =MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
   a   :					Optional[Any]			    =TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_			,					snake_case_			,					snake_case_   )		->							Optional[int]:






       if isinstance(model.config			,					__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     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_			,					snake_case_   )		->							List[Any]:
       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     SCREAMING_SNAKE_CASE__			(       self   )		->							Dict:
       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     SCREAMING_SNAKE_CASE__			(       self   )		->							Dict:
       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     SCREAMING_SNAKE_CASE__			(       self   )		->							Any:
       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     SCREAMING_SNAKE_CASE__			(       self   )		->							int:
       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	):

   @require_torch
   @slow
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							str:






       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     SCREAMING_SNAKE_CASE__			(       self   )		->							int:
       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     SCREAMING_SNAKE_CASE__			(       self   )		->							Union[str, Any]:
       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     SCREAMING_SNAKE_CASE__			(       self   )		->							Tuple:
       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     SCREAMING_SNAKE_CASE__			(       self   )		->							List[Any]:






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






'''simple docstring'''







import json
import os
import unittest

from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import (
    VOCAB_FILES_NAMES,
    GPTNeoXJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class  SCREAMING_SNAKE_CASE__       (  __UpperCamelCase			,					unittest.TestCase   ):
	lowercase__	    =							GPTNeoXJapaneseTokenizer
	lowercase__	    =							False
	lowercase__	    =							{"do_clean_text": False, "add_prefix_space": False}
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					super().setUp()

					__a       :     Optional[int]								=	[
					    """こん""",
					    """こんに""",
					    """にちは""",
					    """ばんは""",
					    """世界,㔺界""",
					    """、""",
					    """。""",
					    """<BR>""",
					    """<SP>""",
					    """<TAB>""",
					    """<URL>""",
					    """<EMAIL>""",
					    """<TEL>""",
					    """<DATE>""",
					    """<PRICE>""",
					    """<BLOCK>""",
					    """<KIGOU>""",
					    """<U2000U2BFF>""",
					    """<|emoji1|>""",
					    """<unk>""",
					    """<|startoftext|>""",
					    """<|endoftext|>""",
					]
					__a       :     Any								=	{"""emoji""": {"""\ud83d\ude00""": """<|emoji1|>"""}, """emoji_inv""": {"""<|emoji1|>""": """\ud83d\ude00"""}}  # 😀
					__a       :     List[str]								=	{"""unk_token""": """<unk>"""}

					__a       :     Any								=	os.path.join(self.tmpdirname  ,    VOCAB_FILES_NAMES["""vocab_file"""]			)
					__a       :     Tuple								=	os.path.join(self.tmpdirname  ,    VOCAB_FILES_NAMES["""emoji_file"""]			)
					with open(self.vocab_file  ,    """w"""  ,    encoding="""utf-8"""			) as vocab_writer:
									vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]			)			)
					with open(self.emoji_file  ,    """w"""			) as emoji_writer:
									emoji_writer.write(json.dumps(__UpperCamelCase			)			)
	def __lowerCamelCase    (				self  ,    **__UpperCamelCase			):



					'''simple docstring'''






					kwargs.update(self.special_tokens_map			)
					return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname  ,    **__UpperCamelCase			)
	def __lowerCamelCase    (				self  ,    __UpperCamelCase			):



					'''simple docstring'''






					__a       :     Any								=	"""こんにちは、世界。 \nこんばんは、㔺界。😀"""
					__a       :     Optional[int]								=	"""こんにちは、世界。 \nこんばんは、世界。😀"""
					return input_text, output_text
	def __lowerCamelCase    (				self  ,    __UpperCamelCase			):



					'''simple docstring'''






					__a							,	__a       :     List[str]								=	self.get_input_output_texts(__UpperCamelCase			)
					__a       :     List[str]								=	tokenizer.encode(__UpperCamelCase  ,    add_special_tokens=__UpperCamelCase			)
					__a       :     str								=	tokenizer.decode(__UpperCamelCase  ,    clean_up_tokenization_spaces=__UpperCamelCase			)
					return text, ids
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					pass  # TODO add if relevant
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					pass  # TODO add if relevant
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					pass  # TODO add if relevant
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a       :     List[Any]								=	self.get_tokenizer()

					# Testing tokenization
					__a       :     Dict								=	"""こんにちは、世界。　こんばんは、㔺界。"""
					__a       :     Optional[int]								=	["""こん""", """にちは""", """、""", """世界""", """。""", """<SP>""", """こん""", """ばんは""", """、""", """㔺界""", """。"""]
					__a       :     str								=	tokenizer.tokenize(__UpperCamelCase			)
					self.assertListEqual(__UpperCamelCase  ,    __UpperCamelCase			)

					# Testing conversion to ids without special tokens
					__a       :     Optional[Any]								=	[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
					__a       :     Optional[int]								=	tokenizer.convert_tokens_to_ids(__UpperCamelCase			)
					self.assertListEqual(__UpperCamelCase  ,    __UpperCamelCase			)

					# Testing conversion to ids with special tokens
					__a       :     List[str]								=	tokens + [tokenizer.unk_token]
					__a       :     Dict								=	[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
					__a       :     Any								=	tokenizer.convert_tokens_to_ids(__UpperCamelCase			)
					self.assertListEqual(__UpperCamelCase  ,    __UpperCamelCase			)
	@slow
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a       :     int								=	self.tokenizer_class.from_pretrained("""abeja/gpt-neox-japanese-2.7b"""			)

					__a       :     Union[str, Any]								=	tokenizer.encode("""ありがとう。"""  ,    add_special_tokens=__UpperCamelCase			)
					__a       :     Dict								=	tokenizer.encode("""どういたしまして。"""  ,    add_special_tokens=__UpperCamelCase			)

					__a       :     Optional[Any]								=	tokenizer.build_inputs_with_special_tokens(__UpperCamelCase			)
					__a       :     int								=	tokenizer.build_inputs_with_special_tokens(__UpperCamelCase  ,    __UpperCamelCase			)

					assert encoded_sentence == ids_a
					assert encoded_pair == ids_a + ids_a
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					pass

	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					pass