




"""simple docstring"""

import uuid
from typing import Any, Dict, List, Optional, Union

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
  import tensorflow as tf

if is_torch_available():
  import torch


__magic_name__				     =     logging.get_logger(__name__)






class        SCREAMING_SNAKE_CASE__   :
   def __init__(       self			:				Optional[Any]   ,	SCREAMING_SNAKE_CASE_			:				str = None   ,	SCREAMING_SNAKE_CASE_			:				uuid.UUID = None   ,	SCREAMING_SNAKE_CASE_			:				str=None   ,	SCREAMING_SNAKE_CASE_			:				int=None							):
          if not conversation_id:
                 lowerCamelCase__          =						uuid.uuida()
          if past_user_inputs is None:
                 lowerCamelCase__          =						[]
          if generated_responses is None:
                 lowerCamelCase__          =						[]

          lowerCamelCase__          =						conversation_id
          lowerCamelCase__          =						past_user_inputs
          lowerCamelCase__          =						generated_responses
          lowerCamelCase__          =						text
   def __eq__(       self			:				Union[str, Any]   ,	SCREAMING_SNAKE_CASE_			:				Tuple							):
          if not isinstance(UpperCAmelCase__   ,	UpperCAmelCase__							):
                 return False
          if self.uuid == other.uuid:
                 return True
          return (
              self.new_user_input == other.new_user_input
              and self.past_user_inputs == other.past_user_inputs
              and self.generated_responses == other.generated_responses
          )
   def    __UpperCAmelCase       (       self			:				str   ,	SCREAMING_SNAKE_CASE_			:				str   ,	SCREAMING_SNAKE_CASE_			:				bool = False							):
          if self.new_user_input:
                 if overwrite:
                        logger.warning(
                            f"""User input added while unprocessed input was existing: \"{self.new_user_input}\" was overwritten """
                            f"""with: \"{text}\"."""							)
                        lowerCamelCase__          =						text
                 else:
                        logger.warning(
                            f"""User input added while unprocessed input was existing: \"{self.new_user_input}\" new input """
                            f"""ignored: \"{text}\". Set `overwrite` to True to overwrite unprocessed user input"""							)
          else:
                 lowerCamelCase__          =						text
   def    __UpperCAmelCase       (       self			:				List[Any]							):
          if self.new_user_input:
                 self.past_user_inputs.append(self.new_user_input							)
          lowerCamelCase__          =						None
   def    __UpperCAmelCase       (       self			:				List[Any]   ,	SCREAMING_SNAKE_CASE_			:				str							):
          self.generated_responses.append(UpperCAmelCase__							)
   def    __UpperCAmelCase       (       self			:				str							):
          for user_input, generated_response in zip(self.past_user_inputs   ,	self.generated_responses							):
                 yield True, user_input
                 yield False, generated_response
          if self.new_user_input:
                 yield True, self.new_user_input





   def __repr__(       self			:				int							):
          lowerCamelCase__          =						f"""Conversation id: {self.uuid} \n"""
          for is_user, text in self.iter_texts():
                 lowerCamelCase__          =						'''user''' if is_user else '''bot'''
                 output += f"""{name} >> {text} \n"""
          return output






@add_end_docstrings(
    lowercase__ ,				r"\n        min_length_for_response (`int`, *optional*, defaults to 32):\n            The minimum length (in number of tokens) for a response.\n        minimum_tokens (`int`, *optional*, defaults to 10):\n            The minimum length of tokens to leave for a response.\n    " ,				)
class        SCREAMING_SNAKE_CASE__   (						lowercase__      ):
   def __init__(       self			:				Union[str, Any]   ,	*SCREAMING_SNAKE_CASE_			:				Any   ,	**SCREAMING_SNAKE_CASE_			:				str							):
          super().__init__(*UpperCAmelCase__   ,	**UpperCAmelCase__							)
          if self.tokenizer.pad_token_id is None:
                 lowerCamelCase__          =						self.tokenizer.eos_token
   def    __UpperCAmelCase       (       self			:				Tuple   ,	SCREAMING_SNAKE_CASE_			:				Tuple=None   ,	SCREAMING_SNAKE_CASE_			:				Optional[Any]=None   ,	SCREAMING_SNAKE_CASE_			:				Any=None   ,	**SCREAMING_SNAKE_CASE_			:				Union[str, Any]							):
          lowerCamelCase__          =						{}
          lowerCamelCase__          =						{}
          lowerCamelCase__          =						{}

          if min_length_for_response is not None:
                 lowerCamelCase__          =						min_length_for_response
          if minimum_tokens is not None:
                 lowerCamelCase__          =						minimum_tokens

          if "max_length" in generate_kwargs:
                 lowerCamelCase__          =						generate_kwargs['''max_length''']
                 # self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
          if clean_up_tokenization_spaces is not None:
                 lowerCamelCase__          =						clean_up_tokenization_spaces

          if generate_kwargs:
                 forward_params.update(UpperCAmelCase__							)
          return preprocess_params, forward_params, postprocess_params
   def __call__(       self			:				str   ,	SCREAMING_SNAKE_CASE_			:				Union[Conversation, List[Conversation]]   ,	SCREAMING_SNAKE_CASE_			:				Any=0   ,	**SCREAMING_SNAKE_CASE_			:				Any							):
          lowerCamelCase__          =						super().__call__(UpperCAmelCase__   ,	num_workers=UpperCAmelCase__   ,	**UpperCAmelCase__							)
          if isinstance(UpperCAmelCase__   ,	UpperCAmelCase__							) and len(UpperCAmelCase__							) == 1:
                 return outputs[0]
          return outputs
   def    __UpperCAmelCase       (       self			:				List[Any]   ,	SCREAMING_SNAKE_CASE_			:				Conversation   ,	SCREAMING_SNAKE_CASE_			:				Any=32							):
          if not isinstance(UpperCAmelCase__   ,	UpperCAmelCase__							):
                 raise ValueError("""ConversationalPipeline, expects Conversation as inputs"""							)
          if conversation.new_user_input is None:
                 raise ValueError(
                     f"""Conversation with UUID {type(conversation.uuid							)} does not contain new user input to process. """
                     """Add user inputs with the conversation\'s `add_user_input` method"""							)
          if hasattr(self.tokenizer   ,	"""_build_conversation_input_ids"""							):
                 lowerCamelCase__          =						self.tokenizer._build_conversation_input_ids(UpperCAmelCase__							)
          else:
                 # If the tokenizer cannot handle conversations, we default to only the old version
                 lowerCamelCase__          =						self._legacy_parse_and_tokenize(UpperCAmelCase__							)

          if self.framework == "pt":
                 lowerCamelCase__          =						torch.LongTensor([input_ids]							)
          elif self.framework == "tf":
                 lowerCamelCase__          =						tf.constant([input_ids]							)
          return {"input_ids": input_ids, "conversation": conversation}
   def    __UpperCAmelCase       (       self			:				List[str]   ,	SCREAMING_SNAKE_CASE_			:				Optional[int]   ,	SCREAMING_SNAKE_CASE_			:				int=10   ,	**SCREAMING_SNAKE_CASE_			:				Tuple							):
          lowerCamelCase__          =						generate_kwargs.get("""max_length"""   ,	self.model.config.max_length							)

          lowerCamelCase__          =						model_inputs['''input_ids'''].shape[1]
          if max_length - minimum_tokens < n:
                 logger.warning(f"""Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})"""							)
                 lowerCamelCase__          =						max_length - minimum_tokens
                 lowerCamelCase__          =						model_inputs['''input_ids'''][:, -trim:]
                 if "attention_mask" in model_inputs:
                        lowerCamelCase__          =						model_inputs['''attention_mask'''][:, -trim:]
          lowerCamelCase__          =						model_inputs.pop("""conversation"""							)
          lowerCamelCase__          =						max_length
          lowerCamelCase__          =						self.model.generate(**UpperCAmelCase__   ,	**UpperCAmelCase__							)
          if self.model.config.is_encoder_decoder:
                 lowerCamelCase__          =						1
          else:
                 lowerCamelCase__          =						n
          return {"output_ids": output_ids[:, start_position:], "conversation": conversation}
   def    __UpperCAmelCase       (       self			:				Dict   ,	SCREAMING_SNAKE_CASE_			:				str   ,	SCREAMING_SNAKE_CASE_			:				List[Any]=True							):
          lowerCamelCase__          =						model_outputs['''output_ids''']
          lowerCamelCase__          =						self.tokenizer.decode(
              output_ids[0]   ,	skip_special_tokens=UpperCAmelCase__   ,	clean_up_tokenization_spaces=UpperCAmelCase__   ,	)
          lowerCamelCase__          =						model_outputs['''conversation''']
          conversation.mark_processed()
          conversation.append_response(UpperCAmelCase__							)
          return conversation





   def    __UpperCAmelCase       (       self			:				Any   ,	SCREAMING_SNAKE_CASE_			:				Conversation							):
          lowerCamelCase__          =						self.tokenizer.eos_token_id
          lowerCamelCase__          =						[]
          for is_user, text in conversation.iter_texts():
                 if eos_token_id is not None:
                        input_ids.extend(self.tokenizer.encode(UpperCAmelCase__   ,	add_special_tokens=UpperCAmelCase__							) + [eos_token_id]							)
                 else:
                        input_ids.extend(self.tokenizer.encode(UpperCAmelCase__   ,	add_special_tokens=UpperCAmelCase__							)							)

          if len(UpperCAmelCase__							) > self.tokenizer.model_max_length:
                 lowerCamelCase__          =						input_ids[-self.tokenizer.model_max_length :]
          return input_ids









'''simple docstring'''





import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)
from transformers.utils import is_sentencepiece_available


if is_sentencepiece_available():
							from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


if is_sentencepiece_available():
							UpperCamelCase_    						=       get_tests_dir("""fixtures/test_sentencepiece.model""")


if is_torch_available():
							from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

UpperCamelCase_    						=       128022
UpperCamelCase_    						=       128028





@require_sentencepiece
class 		__SCREAMING_SNAKE_CASE							(			lowercase__						,							unittest.TestCase      ):
				lowerCamelCase_		       =    MaMaaaTokenizer
				lowerCamelCase_		       =    False
				lowerCamelCase_		       =    False
				lowerCamelCase_		       =    True







				def  lowerCamelCase_	(       self     :		Dict   ):
										'''simple docstring'''
										super().setUp()

										lowercase :							Dict    		=['''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''', '''\u0120''', '''<pad>''']
										lowercase :							List[Any]    		=dict(zip(UpperCAmelCase__		,					range(len(UpperCAmelCase__   )   )   )   )
										lowercase :							List[Any]    		=Path(self.tmpdirname   )
										save_json(UpperCAmelCase__		,					save_dir / VOCAB_FILES_NAMES['''vocab_file''']   )
										if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
																copyfile(UpperCAmelCase__		,					save_dir / VOCAB_FILES_NAMES['''spm_file''']   )

										lowercase :							Tuple    		=MaMaaaTokenizer.from_pretrained(self.tmpdirname   )
										tokenizer.save_pretrained(self.tmpdirname   )







				def  lowerCamelCase_	(       self     :		Any		,					**UpperCAmelCase__     :		int   ):
										'''simple docstring'''
										return MaMaaaTokenizer.from_pretrained(self.tmpdirname		,					**UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		List[Any]		,					UpperCAmelCase__     :		Dict   ):
										'''simple docstring'''
										return (
										    "This is a test",
										    "This is a test",
										)







				def  lowerCamelCase_	(       self     :		Any   ):
										'''simple docstring'''
										lowercase :							Tuple    		='''</s>'''
										lowercase :							Union[str, Any]    		=0

										self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase__   )		,					UpperCAmelCase__   )
										self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase__   )		,					UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										lowercase :							List[Any]    		=self.get_tokenizer()
										lowercase :							Optional[Any]    		=list(tokenizer.get_vocab().keys()   )

										self.assertEqual(vocab_keys[0]		,					'''</s>'''   )
										self.assertEqual(vocab_keys[1]		,					'''<unk>'''   )
										self.assertEqual(vocab_keys[-1]		,					'''<s>'''   )
										self.assertEqual(len(UpperCAmelCase__   )		,					tokenizer.vocab_size + len(tokenizer.get_added_vocab()   )   )







				@unittest.skip('''Skip this test while all models are still to be uploaded.'''   )
				def  lowerCamelCase_	(       self     :		Optional[int]   ):
										'''simple docstring'''
										pass







				def  lowerCamelCase_	(       self     :		List[Any]   ):
										'''simple docstring'''
										lowercase :							Union[str, Any]    		=self.get_tokenizer()

										lowercase :							str    		=tokenizer.tokenize('''This is a test'''   )
										self.assertListEqual(UpperCAmelCase__		,					['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']   )

										self.assertListEqual(
										    tokenizer.convert_tokens_to_ids(UpperCAmelCase__   )		,					[2, 3, 4, 5, 6]		,					)

										lowercase :							Optional[int]    		=tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]   )
										self.assertListEqual(UpperCAmelCase__		,					['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']   )

										lowercase :							Tuple    		=tokenizer.convert_tokens_to_string(UpperCAmelCase__   )
										self.assertEqual(UpperCAmelCase__		,					'''This is a test'''   )







				@slow
				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										# fmt: off
										lowercase :							int    		={'''input_ids''': [[128022, 110108, 397, 11, 38272, 2247, 124811, 285, 18105, 1586, 207, 7, 39534, 4428, 397, 1019, 18105, 1586, 207, 7, 41337, 16786, 241, 7, 20214, 17, 125690, 10398, 7, 44378, 58069, 68342, 7798, 7343, 11, 299, 33310, 4, 158, 37350, 94077, 4569, 299, 33310, 90, 4, 52840, 290, 4, 31270, 112, 299, 682, 4, 52840, 39953, 14079, 193, 52519, 90894, 17894, 120697, 11, 40445, 551, 17, 1019, 52519, 90894, 17756, 963, 11, 40445, 480, 17, 9792, 1120, 5173, 1393, 6240, 16786, 241, 120996, 28, 1245, 1393, 118240, 11123, 1019, 93612, 2691, 10618, 98058, 120409, 1928, 279, 4, 40683, 367, 178, 207, 1019, 103, 103121, 506, 65296, 5, 2], [128022, 21217, 367, 117, 125450, 128, 719, 7, 7308, 40, 93612, 12669, 1116, 16704, 71, 17785, 3699, 15592, 35, 144, 9584, 241, 11943, 713, 950, 799, 2247, 88427, 150, 149, 118813, 120706, 1019, 106906, 81518, 28, 1224, 22799, 397, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [128022, 1658, 123311, 5155, 5578, 4722, 279, 14947, 2366, 1120, 1197, 14, 1348, 9232, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
										# fmt: on

										self.tokenizer_integration_test_util(
										    expected_encoding=UpperCAmelCase__		,					model_name='''facebook/m2m100_418M'''		,					revision='''c168bae485c864188cf9aa0e4108b0b6934dc91e'''		,					)





@require_torch
@require_sentencepiece
@require_tokenizers
class 		__SCREAMING_SNAKE_CASE							(			unittest.TestCase      ):
				lowerCamelCase_		       =    'facebook/m2m100_418M'
				lowerCamelCase_		       =    [
				    'In my opinion, there are two levels of response from the French government.',
				    'NSA Affair Emphasizes Complete Lack of Debate on Intelligence',
				]
				lowerCamelCase_		       =    [
				    'Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.',
				    'L\'affaire NSA souligne l\'absence totale de débat sur le renseignement',
				]

				# fmt: off
				lowerCamelCase_		       =    [EN_CODE, 5_93, 19_49, 11_57_81, 4, 7_15_86, 42_34, 6_06_33, 12_62_33, 4_32, 12_38_08, 1_55_92, 11_97, 11_71_32, 12_06_18, 5, 2]







				@classmethod
				def  lowerCamelCase_	(       cls     :		Optional[Any]   ):
										'''simple docstring'''
										lowercase :							MaMaaaTokenizer    		=MaMaaaTokenizer.from_pretrained(
										    cls.checkpoint_name		,					src_lang='''en'''		,					tgt_lang='''fr'''   )
										lowercase :							Optional[int]    		=1
										return cls







				def  lowerCamelCase_	(       self     :		List[Any]   ):
										'''simple docstring'''
										self.assertEqual(self.tokenizer.get_lang_id('''ar'''   )		,					128006   )
										self.assertEqual(self.tokenizer.get_lang_id('''en'''   )		,					128022   )
										self.assertEqual(self.tokenizer.get_lang_id('''ro'''   )		,					128076   )
										self.assertEqual(self.tokenizer.get_lang_id('''mr'''   )		,					128063   )







				def  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										lowercase :							List[str]    		=self.tokenizer.get_vocab()
										self.assertEqual(len(UpperCAmelCase__   )		,					self.tokenizer.vocab_size   )
										self.assertEqual(vocab['''<unk>''']		,					3   )
										self.assertIn(self.tokenizer.get_lang_token('''en'''   )		,					UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							List[Any]    		='''en'''
										lowercase :							Optional[Any]    		=self.tokenizer.batch_encode_plus(self.src_text   ).input_ids[0]
										self.assertListEqual(self.expected_src_tokens		,					UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										self.assertIn(UpperCAmelCase__		,					self.tokenizer.all_special_ids   )
										# fmt: off
										lowercase :							str    		=[FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 14028, 136, 3286, 9706, 6, 90797, 6, 144012, 162, 88128, 30061, 5, 2]
										# fmt: on
										lowercase :							Optional[Any]    		=self.tokenizer.decode(UpperCAmelCase__		,					skip_special_tokens=UpperCAmelCase__   )
										lowercase :							Optional[Any]    		=self.tokenizer.decode(generated_ids[1:]		,					skip_special_tokens=UpperCAmelCase__   )
										self.assertEqual(UpperCAmelCase__		,					UpperCAmelCase__   )
										self.assertNotIn(self.tokenizer.eos_token		,					UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										lowercase :							Any    		=tempfile.mkdtemp()
										lowercase :							Tuple    		=self.tokenizer.lang_token_to_id
										self.tokenizer.save_pretrained(UpperCAmelCase__   )
										lowercase :							Union[str, Any]    		=MaMaaaTokenizer.from_pretrained(UpperCAmelCase__   )
										self.assertDictEqual(new_tok.lang_token_to_id		,					UpperCAmelCase__   )







				@require_torch
				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										lowercase :							List[str]    		='''en'''
										lowercase :							int    		='''fr'''

										lowercase :							Union[str, Any]    		=self.tokenizer(self.src_text		,					text_target=self.tgt_text		,					padding=UpperCAmelCase__		,					return_tensors='''pt'''   )

										lowercase :							str    		=shift_tokens_right(
										    batch['''labels''']		,					self.tokenizer.pad_token_id		,					self.tokenizer.eos_token_id   )

										for k in batch:
																lowercase :							int    		=batch[k].tolist()
										# batch = {k: v.tolist() for k,v in batch.items()}
										# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
										# batch.decoder_inputs_ids[0][0] ==
										assert batch.input_ids[1][0] == EN_CODE
										assert batch.input_ids[1][-1] == 2
										assert batch.labels[1][0] == FR_CODE
										assert batch.labels[1][-1] == 2
										assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]







				@require_torch
				def  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										lowercase :							Optional[int]    		='''mr'''
										self.assertListEqual(self.tokenizer.prefix_tokens		,					[self.tokenizer.get_lang_id('''mr'''   )]   )
										self.assertListEqual(self.tokenizer.suffix_tokens		,					[self.tokenizer.eos_token_id]   )

										lowercase :							Union[str, Any]    		='''zh'''
										self.assertListEqual(self.tokenizer.prefix_tokens		,					[self.tokenizer.get_lang_id('''zh'''   )]   )
										self.assertListEqual(self.tokenizer.suffix_tokens		,					[self.tokenizer.eos_token_id]   )







				@require_torch
				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							int    		='''mr'''
										self.tokenizer._switch_to_target_mode()
										self.assertListEqual(self.tokenizer.prefix_tokens		,					[self.tokenizer.get_lang_id('''mr'''   )]   )
										self.assertListEqual(self.tokenizer.suffix_tokens		,					[self.tokenizer.eos_token_id]   )
										self.tokenizer._switch_to_input_mode()
										self.assertListEqual(self.tokenizer.prefix_tokens		,					[self.tokenizer.get_lang_id(self.tokenizer.src_lang   )]   )

										lowercase :							Optional[Any]    		='''zh'''
										self.tokenizer._switch_to_target_mode()
										self.assertListEqual(self.tokenizer.prefix_tokens		,					[self.tokenizer.get_lang_id('''zh'''   )]   )
										self.assertListEqual(self.tokenizer.suffix_tokens		,					[self.tokenizer.eos_token_id]   )
										self.tokenizer._switch_to_input_mode()
										self.assertListEqual(self.tokenizer.prefix_tokens		,					[self.tokenizer.get_lang_id(self.tokenizer.src_lang   )]   )







				@require_torch
				def  lowerCamelCase_	(       self     :		Any   ):
										'''simple docstring'''
										lowercase :							Optional[Any]    		=self.tokenizer._build_translation_inputs('''A test'''		,					return_tensors='''pt'''		,					src_lang='''en'''		,					tgt_lang='''ar'''   )

										self.assertEqual(
										    nested_simplify(UpperCAmelCase__   )		,					{
										        # en_XX, A, test, EOS
										        '''input_ids''': [[128022, 58, 4183, 2]],
										        '''attention_mask''': [[1, 1, 1, 1]],
										        # ar_AR
										        '''forced_bos_token_id''': 128006,
										    }		,					)





