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 						snake_case_ (	unittest.TestCase ):
   __A			:	str        =       MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
   __A			:	Tuple        =       TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING






   def    __UpperCamelCase  (   self       :  str			,		lowercase_       :  Dict			,		lowercase_       :  int			,		lowercase_       :  List[str]						)      ->		Optional[int]:
       if isinstance(model.config			,		_lowerCAmelCase						):
           lowercase__				:     List[str]					      =       list(tokenizer.lang_code_to_id.keys()						)[:2]
           lowercase__				:     Tuple					      =       TranslationPipeline(model=_lowerCAmelCase			,		tokenizer=_lowerCAmelCase			,		src_lang=_lowerCAmelCase			,		tgt_lang=_lowerCAmelCase						)
       else:
           lowercase__				:     List[Any]					      =       TranslationPipeline(model=_lowerCAmelCase			,		tokenizer=_lowerCAmelCase						)
       return translator, ["Some string", "Some other text"]






   def    __UpperCamelCase  (   self       :  Union[str, Any]			,		lowercase_       :  int			,		lowercase_       :  str						)      ->		Optional[int]:
       lowercase__				:     Dict					      =       translator("Some string"						)
       self.assertEqual(_lowerCAmelCase			,		[{"translation_text": ANY(_lowerCAmelCase						)}]						)

       lowercase__				:     Dict					      =       translator(["Some string"]						)
       self.assertEqual(_lowerCAmelCase			,		[{"translation_text": ANY(_lowerCAmelCase						)}]						)

       lowercase__				:     Optional[int]					      =       translator(["Some string", "other string"]						)
       self.assertEqual(_lowerCAmelCase			,		[{"translation_text": ANY(_lowerCAmelCase						)}, {"translation_text": ANY(_lowerCAmelCase						)}]						)






   @require_torch
   def    __UpperCamelCase  (   self       :  int						)      ->		int:
       lowercase__				:     List[Any]					      =       pipeline("translation_en_to_ro"			,		model="patrickvonplaten/t5-tiny-random"			,		framework="pt"						)
       lowercase__				:     Union[str, Any]					      =       translator("This is a test string"			,		max_length=20						)
       self.assertEqual(
           _lowerCAmelCase			,		[
               {
                   "translation_text": (
                       "Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"
                       " Beide Beide"
                   )
               }
           ]			,		)






   @require_tf
   def    __UpperCamelCase  (   self       :  int						)      ->		Optional[int]:
       lowercase__				:     Optional[int]					      =       pipeline("translation_en_to_ro"			,		model="patrickvonplaten/t5-tiny-random"			,		framework="tf"						)
       lowercase__				:     Tuple					      =       translator("This is a test string"			,		max_length=20						)
       self.assertEqual(
           _lowerCAmelCase			,		[
               {
                   "translation_text": (
                       "Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"
                       " Beide Beide"
                   )
               }
           ]			,		)






   @require_torch
   def    __UpperCamelCase  (   self       :  Union[str, Any]						)      ->		Union[str, Any]:
       lowercase__				:     Optional[int]					      =       pipeline("translation_en_to_de"			,		model="patrickvonplaten/t5-tiny-random"			,		framework="pt"						)
       lowercase__				:     str					      =       translator("This is a test string"			,		max_length=20						)
       self.assertEqual(
           _lowerCAmelCase			,		[
               {
                   "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       :  Optional[Any]						)      ->		Optional[Any]:
       lowercase__				:     str					      =       pipeline("translation_en_to_de"			,		model="patrickvonplaten/t5-tiny-random"			,		framework="tf"						)
       lowercase__				:     Optional[Any]					      =       translator("This is a test string"			,		max_length=20						)
       self.assertEqual(
           _lowerCAmelCase			,		[
               {
                   "translation_text": (
                       "monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"
                       " urine urine urine urine urine urine urine"
                   )
               }
           ]			,		)



class 						snake_case_ (	unittest.TestCase ):






   @require_torch
   @slow
   def    __UpperCamelCase  (   self       :  Dict						)      ->		Any:
       # We don't provide a default for this pair
       with self.assertRaises(_lowerCAmelCase						):
           pipeline(task="translation_cn_to_ar"						)

       # but we do for this one
       lowercase__				:     int					      =       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       :  Optional[int]						)      ->		Dict:
       lowercase__				:     str					      =       MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"						)
       lowercase__				:     Dict					      =       MBartaaTokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"						)

       lowercase__				:     Optional[int]					      =       pipeline(task="translation"			,		model=_lowerCAmelCase			,		tokenizer=_lowerCAmelCase						)
       # Missing src_lang, tgt_lang
       with self.assertRaises(_lowerCAmelCase						):
           translator("This is a test"						)

       lowercase__				:     Tuple					      =       translator("This is a test"			,		src_lang="en_XX"			,		tgt_lang="ar_AR"						)
       self.assertEqual(_lowerCAmelCase			,		[{"translation_text": "هذا إختبار"}]						)

       lowercase__				:     List[str]					      =       translator("This is a test"			,		src_lang="en_XX"			,		tgt_lang="hi_IN"						)
       self.assertEqual(_lowerCAmelCase			,		[{"translation_text": "यह एक परीक्षण है"}]						)

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






   @require_torch
   def    __UpperCamelCase  (   self       :  List[str]						)      ->		Tuple:
       lowercase__				:     Union[str, Any]					      =       """patrickvonplaten/t5-tiny-random"""
       lowercase__				:     Optional[Any]					      =       pipeline(task="translation_cn_to_ar"			,		model=_lowerCAmelCase						)
       self.assertEqual(translator._preprocess_params["src_lang"]			,		"cn"						)
       self.assertEqual(translator._preprocess_params["tgt_lang"]			,		"ar"						)






   @require_torch
   def    __UpperCamelCase  (   self       :  List[str]						)      ->		Dict:
       lowercase__				:     Optional[Any]					      =       """patrickvonplaten/t5-tiny-random"""
       with pytest.warns(_lowerCAmelCase			,		match=R".*translation_en_to_de.*"						):
           lowercase__				:     Union[str, Any]					      =       pipeline(task="translation"			,		model=_lowerCAmelCase						)
       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       :  Dict						)      ->		Any:
       with self.assertRaises(_lowerCAmelCase						):
           pipeline(task="translation"						)




import os
from typing import List, Union

import tensorflow as tf
from tensorflow_text import BertTokenizer as BertTokenizerLayer
from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs

from .tokenization_bert import BertTokenizer



class 						snake_case_ (	tf.keras.layers.Layer ):






   def __init__(   self       :  Optional[int]			,		lowercase_       :  List			,		lowercase_       :  bool			,		lowercase_       :  int = None			,		lowercase_       :  int = None			,		lowercase_       :  int = None			,		lowercase_       :  str = "longest"			,		lowercase_       :  bool = True			,		lowercase_       :  int = 5_12			,		lowercase_       :  int = None			,		lowercase_       :  bool = True			,		lowercase_       :  bool = True			,		lowercase_       :  bool = True			,		**lowercase_       :  Tuple			,		)      ->		Any:
       super().__init__()
       if use_fast_bert_tokenizer:
           lowercase__				:     Optional[Any]					      =       FastBertTokenizer(
               lowercase_			,		token_out_type=tf.intaa			,		lower_case_nfd_strip_accents=lowercase_			,		**lowercase_						)
       else:
           lowercase__				:     List[str]					      =       tf.lookup.StaticVocabularyTable(
               tf.lookup.KeyValueTensorInitializer(
                   keys=lowercase_			,		key_dtype=tf.string			,		values=tf.range(tf.size(lowercase_			,		out_type=tf.intaa						)			,		dtype=tf.intaa						)			,		value_dtype=tf.intaa			,		)			,		num_oov_buckets=1			,		)
           lowercase__				:     Optional[Any]					      =       BertTokenizerLayer(
               lowercase_			,		token_out_type=tf.intaa			,		lower_case=lowercase_			,		**lowercase_						)

       lowercase__				:     Optional[int]					      =       vocab_list
       lowercase__				:     List[Any]					      =       do_lower_case
       lowercase__				:     Dict					      =       cls_token_id or vocab_list.index("[CLS]"						)
       lowercase__				:     Any					      =       sep_token_id or vocab_list.index("[SEP]"						)
       lowercase__				:     Union[str, Any]					      =       pad_token_id or vocab_list.index("[PAD]"						)
       lowercase__				:     List[str]					      =       ShrinkLongestTrimmer(max_length - 3			,		axis=1						)  # Allow room for special tokens
       lowercase__				:     Dict					      =       max_length
       lowercase__				:     List[str]					      =       padding
       lowercase__				:     Tuple					      =       truncation
       lowercase__				:     Tuple					      =       pad_to_multiple_of
       lowercase__				:     Optional[Any]					      =       return_token_type_ids
       lowercase__				:     Dict					      =       return_attention_mask






   @classmethod
   def    __UpperCamelCase  (   cls       :  Union[str, Any]			,		lowercase_       :  "PreTrainedTokenizerBase"			,		**lowercase_       :  Optional[Any]						)      ->		int:  # noqa: F821
       lowercase__				:     Optional[Any]					      =       kwargs.pop("do_lower_case"			,		lowercase_						)
       lowercase__				:     Optional[Any]					      =       tokenizer.do_lower_case if do_lower_case is None else do_lower_case
       lowercase__				:     Union[str, Any]					      =       kwargs.pop("cls_token_id"			,		lowercase_						)
       lowercase__				:     List[Any]					      =       tokenizer.cls_token_id if cls_token_id is None else cls_token_id
       lowercase__				:     Dict					      =       kwargs.pop("sep_token_id"			,		lowercase_						)
       lowercase__				:     Dict					      =       tokenizer.sep_token_id if sep_token_id is None else sep_token_id
       lowercase__				:     Any					      =       kwargs.pop("pad_token_id"			,		lowercase_						)
       lowercase__				:     Tuple					      =       tokenizer.pad_token_id if pad_token_id is None else pad_token_id

       lowercase__				:     Union[str, Any]					      =       tokenizer.get_vocab()
       lowercase__				:     Tuple					      =       sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]			,		key=lambda lowercase_						: x[1]						)
       lowercase__				:     Tuple					      =       [entry[0] for entry in vocab]
       return cls(
           vocab_list=lowercase_			,		do_lower_case=lowercase_			,		cls_token_id=lowercase_			,		sep_token_id=lowercase_			,		pad_token_id=lowercase_			,		**lowercase_			,		)






   @classmethod
   def    __UpperCamelCase  (   cls       :  Any			,		lowercase_       :  Union[str, os.PathLike]			,		*lowercase_       :  Optional[Any]			,		**lowercase_       :  List[Any]						)      ->		Optional[int]:
       try:
           lowercase__				:     str					      =       BertTokenizer.from_pretrained(lowercase_			,		*lowercase_			,		**lowercase_						)
       except:  # noqa: E722
           from .tokenization_bert_fast import BertTokenizerFast

           lowercase__				:     int					      =       BertTokenizerFast.from_pretrained(lowercase_			,		*lowercase_			,		**lowercase_						)
       return cls.from_tokenizer(lowercase_			,		**lowercase_						)






   def    __UpperCamelCase  (   self       :  List[str]			,		lowercase_       :  List[Any]						)      ->		Any:
       if self.do_lower_case:
           lowercase__				:     Optional[int]					      =       case_fold_utfa(lowercase_						)
       lowercase__				:     Optional[Any]					      =       self.tf_tokenizer.tokenize(lowercase_						)
       return tokens.merge_dims(1			,		-1						)






   def    __UpperCamelCase  (   self       :  List[str]			,		lowercase_       :  Optional[int]			,		lowercase_       :  Dict=None			,		lowercase_       :  Optional[int]=None			,		lowercase_       :  Optional[int]=None			,		lowercase_       :  Tuple=None			,		lowercase_       :  Union[str, Any]=None			,		lowercase_       :  Optional[Any]=None			,		lowercase_       :  List[Any]=None			,		)      ->		int:
       if padding is None:
           lowercase__				:     str					      =       self.padding
       if padding not in ("longest", "max_length"):
           raise ValueError("Padding must be either 'longest' or 'max_length'!"						)
       if max_length is not None and text_pair is not None:
           # Because we have to instantiate a Trimmer to do it properly
           raise ValueError("max_length cannot be overridden at call time when truncating paired texts!"						)
       if max_length is None:
           lowercase__				:     int					      =       self.max_length
       if truncation is None:
           lowercase__				:     int					      =       self.truncation
       if pad_to_multiple_of is None:
           lowercase__				:     Union[str, Any]					      =       self.pad_to_multiple_of
       if return_token_type_ids is None:
           lowercase__				:     Dict					      =       self.return_token_type_ids
       if return_attention_mask is None:
           lowercase__				:     Dict					      =       self.return_attention_mask
       if not isinstance(lowercase_			,		tf.Tensor						):
           lowercase__				:     Union[str, Any]					      =       tf.convert_to_tensor(lowercase_						)
       if text_pair is not None and not isinstance(lowercase_			,		tf.Tensor						):
           lowercase__				:     Optional[int]					      =       tf.convert_to_tensor(lowercase_						)
       if text_pair is not None:
           if text.shape.rank > 1:
               raise ValueError("text argument should not be multidimensional when a text pair is supplied!"						)
           if text_pair.shape.rank > 1:
               raise ValueError("text_pair should not be multidimensional!"						)
       if text.shape.rank == 2:
           lowercase__      ,     lowercase__				:     List[Any]					      =       text[:, 0], text[:, 1]
       lowercase__				:     List[Any]					      =       self.unpaired_tokenize(lowercase_						)
       if text_pair is None:  # Unpaired text
           if truncation:
               lowercase__				:     Any					      =       text[:, : max_length - 2]  # Allow room for special tokens
           lowercase__      ,     lowercase__				:     Dict					      =       combine_segments(
               (text,)			,		start_of_sequence_id=self.cls_token_id			,		end_of_segment_id=self.sep_token_id						)
       else:  # Paired text
           lowercase__				:     Any					      =       self.unpaired_tokenize(lowercase_						)
           if truncation:
               lowercase__      ,     lowercase__				:     List[Any]					      =       self.paired_trimmer.trim([text, text_pair]						)
           lowercase__      ,     lowercase__				:     str					      =       combine_segments(
               (text, text_pair)			,		start_of_sequence_id=self.cls_token_id			,		end_of_segment_id=self.sep_token_id						)
       if padding == "longest":
           lowercase__				:     List[str]					      =       input_ids.bounding_shape(axis=1						)
           if pad_to_multiple_of is not None:
               # No ceiling division in tensorflow, so we negate floordiv instead
               lowercase__				:     List[Any]					      =       pad_to_multiple_of * (-tf.math.floordiv(-pad_length			,		lowercase_						))
       else:
           lowercase__				:     Optional[Any]					      =       max_length

       lowercase__      ,     lowercase__				:     str					      =       pad_model_inputs(lowercase_			,		max_seq_length=lowercase_			,		pad_value=self.pad_token_id						)
       lowercase__				:     str					      =       {"input_ids": input_ids}
       if return_attention_mask:
           lowercase__				:     List[Any]					      =       attention_mask
       if return_token_type_ids:
           lowercase__      ,     lowercase__				:     List[str]					      =       pad_model_inputs(
               lowercase_			,		max_seq_length=lowercase_			,		pad_value=self.pad_token_id						)
           lowercase__				:     List[Any]					      =       token_type_ids
       return output







   def    __UpperCamelCase  (   self       :  Any						)      ->		Optional[Any]:
       return {
           "vocab_list": self.vocab_list,
           "do_lower_case": self.do_lower_case,
           "cls_token_id": self.cls_token_id,
           "sep_token_id": self.sep_token_id,
           "pad_token_id": self.pad_token_id,
       }



