




from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


UpperCamelCase								=	logging.get_logger(__name__)

UpperCamelCase								=	{
    'EleutherAI/gpt-neo-1.3B': 'https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}





class 					__lowerCamelCase			(       UpperCamelCase__       ):




    """simple docstring"""

    snake_case__   = "gpt_neo"
    snake_case__   = ["past_key_values"]
    snake_case__   = {"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}


    def __init__(			self	:	int  ,      SCREAMING_SNAKE_CASE__	:	Any=50_257  ,      SCREAMING_SNAKE_CASE__	:	Any=2_048  ,      SCREAMING_SNAKE_CASE__	:	Union[str, Any]=2_048  ,      SCREAMING_SNAKE_CASE__	:	int=24  ,      SCREAMING_SNAKE_CASE__	:	Tuple=[[["global", "local"], 12]]  ,      SCREAMING_SNAKE_CASE__	:	str=16  ,      SCREAMING_SNAKE_CASE__	:	Dict=None  ,      SCREAMING_SNAKE_CASE__	:	Dict=256  ,      SCREAMING_SNAKE_CASE__	:	Any="gelu_new"  ,      SCREAMING_SNAKE_CASE__	:	List[str]=0.0  ,      SCREAMING_SNAKE_CASE__	:	Tuple=0.0  ,      SCREAMING_SNAKE_CASE__	:	int=0.0  ,      SCREAMING_SNAKE_CASE__	:	str=0.1  ,      SCREAMING_SNAKE_CASE__	:	Tuple=1e-5  ,      SCREAMING_SNAKE_CASE__	:	List[str]=0.02  ,      SCREAMING_SNAKE_CASE__	:	Union[str, Any]=True  ,      SCREAMING_SNAKE_CASE__	:	Dict=50_256  ,      SCREAMING_SNAKE_CASE__	:	Union[str, Any]=50_256  ,      **SCREAMING_SNAKE_CASE__	:	Union[str, Any]  ,      ) ->			List[str]:
         lowerCAmelCase__            =					vocab_size
         lowerCAmelCase__            =					max_position_embeddings
         lowerCAmelCase__            =					hidden_size
         lowerCAmelCase__            =					num_layers
         lowerCAmelCase__            =					num_heads
         lowerCAmelCase__            =					intermediate_size
         lowerCAmelCase__            =					window_size
         lowerCAmelCase__            =					activation_function
         lowerCAmelCase__            =					resid_dropout
         lowerCAmelCase__            =					embed_dropout
         lowerCAmelCase__            =					attention_dropout
         lowerCAmelCase__            =					classifier_dropout
         lowerCAmelCase__            =					layer_norm_epsilon
         lowerCAmelCase__            =					initializer_range
         lowerCAmelCase__            =					use_cache

         lowerCAmelCase__            =					bos_token_id
         lowerCAmelCase__            =					eos_token_id

         lowerCAmelCase__            =					attention_types
         lowerCAmelCase__            =					self.expand_attention_types_params(SCREAMING_SNAKE_CASE__  )

         if len(self.attention_layers  ) != self.num_layers:
              raise ValueError(
                  "Configuration for convolutional module is incorrect. "
                  "It is required that `len(config.attention_layers)` == `config.num_layers` "
                  f'but is `len(config.attention_layers) = {len(self.attention_layers  )}`, '
                  f'`config.num_layers = {self.num_layers}`. '
                  "`config.attention_layers` is prepared using `config.attention_types`. "
                  "Please verify the value of `config.attention_types` argument."  )

         super().__init__(bos_token_id=SCREAMING_SNAKE_CASE__  ,      eos_token_id=SCREAMING_SNAKE_CASE__  ,      **SCREAMING_SNAKE_CASE__  )


    @staticmethod
    def   a (			SCREAMING_SNAKE_CASE__	:	List[str]  ) ->			List[Any]:
         lowerCAmelCase__            =					[]
         for item in attention_types:
              for _ in range(item[1]  ):
                   attentions.extend(item[0]  )
         return attentions






def 				_A  (		lowerCAmelCase_	:   Any  ,							lowerCAmelCase_	:   str  ,							lowerCAmelCase_	:   Dict  ,							lowerCAmelCase_	:   Union[str, Any]     ):
     """simple docstring"""

     import torch

     lowerCAmelCase__            =					input.size()
     lowerCAmelCase__            =					len(lowerCAmelCase_     )
     lowerCAmelCase__            =					shape[dimension]

     lowerCAmelCase__            =					torch.arange(0  ,							lowerCAmelCase_  ,							lowerCAmelCase_     )
     lowerCAmelCase__            =					torch.div(sizedim - size  ,							lowerCAmelCase_  ,							rounding_mode="floor"     ) + 1
     lowerCAmelCase__            =					torch.arange(lowerCAmelCase_     ) + low_indices[:min_length][:, None]

     lowerCAmelCase__            =					[slice(lowerCAmelCase_     )] * rank
     lowerCAmelCase__            =					indices
     lowerCAmelCase__            =					input[s]

     lowerCAmelCase__            =					list(range(0  ,							rank + 1     )     )
     perm.append(perm.pop(dimension + 1     )     )

     return sliced.permute(lowerCAmelCase_     )



def 				_A  (		lowerCAmelCase_	:   Optional[int]  ,							lowerCAmelCase_	:   Tuple     ):
     """simple docstring"""

     import torch

     lowerCAmelCase__            =					torch.arange(1  ,							lowerCAmelCase_     )
     lowerCAmelCase__            =					torch.remainder(lowerCAmelCase_  ,							lowerCAmelCase_     )
     lowerCAmelCase__            =					remainders == 0
     lowerCAmelCase__            =					candidates[divisor_indices]
     lowerCAmelCase__            =					torch.max(lowerCAmelCase_     )
     return largest_divisor, torch.div(lowerCAmelCase_  ,							lowerCAmelCase_  ,							rounding_mode="floor"     )






class 					__lowerCamelCase			(       UpperCamelCase__       ):




    """simple docstring"""

    @property
    def   a (			self	:	str  ) ->			Mapping[str, Mapping[int, str]]:
         lowerCAmelCase__            =					OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}  )
         if self.use_past:
              self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE__  ,      direction="inputs"  )
              lowerCAmelCase__            =					{0: "batch", 1: "past_sequence + sequence"}
         else:
              lowerCAmelCase__            =					{0: "batch", 1: "sequence"}

         return common_inputs


    @property
    def   a (			self	:	List[str]  ) ->			int:
         return self._config.num_heads


    def   a (			self	:	Optional[int]  ,      SCREAMING_SNAKE_CASE__	:	PreTrainedTokenizer  ,      SCREAMING_SNAKE_CASE__	:	int = -1  ,      SCREAMING_SNAKE_CASE__	:	int = -1  ,      SCREAMING_SNAKE_CASE__	:	bool = False  ,      SCREAMING_SNAKE_CASE__	:	Optional[TensorType] = None  ,      ) ->			Mapping[str, Any]:
         lowerCAmelCase__            =					super(SCREAMING_SNAKE_CASE__  ,      self  ).generate_dummy_inputs(
             SCREAMING_SNAKE_CASE__  ,      batch_size=SCREAMING_SNAKE_CASE__  ,      seq_length=SCREAMING_SNAKE_CASE__  ,      is_pair=SCREAMING_SNAKE_CASE__  ,      framework=SCREAMING_SNAKE_CASE__  )

         # We need to order the input in the way they appears in the forward()
         lowerCAmelCase__            =					OrderedDict({"input_ids": common_inputs["input_ids"]}  )

         # Need to add the past_keys
         if self.use_past:
              if not is_torch_available():
                   raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."  )
              else:
                   import torch

                   lowerCAmelCase__      ,						lowerCAmelCase__            =					common_inputs["input_ids"].shape
                   # Not using the same length for past_key_values
                   lowerCAmelCase__            =					seqlen + 2
                   lowerCAmelCase__            =					(
                       batch,
                       self.num_attention_heads,
                       past_key_values_length,
                       self._config.hidden_size // self.num_attention_heads,
                   )
                   lowerCAmelCase__            =					[
                       (torch.zeros(SCREAMING_SNAKE_CASE__  ), torch.zeros(SCREAMING_SNAKE_CASE__  )) for _ in range(self.num_layers  )
                   ]

         lowerCAmelCase__            =					common_inputs["attention_mask"]
         if self.use_past:
              lowerCAmelCase__            =					ordered_inputs["attention_mask"].dtype
              lowerCAmelCase__            =					torch.cat(
                  [ordered_inputs["attention_mask"], torch.ones(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  ,      dtype=SCREAMING_SNAKE_CASE__  )]  ,      dim=1  )

         return ordered_inputs


    @property
    def   a (			self	:	str  ) ->			int:
         return 13


'''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():
						lowercase__						       =      get_tests_dir("fixtures/test_sentencepiece.model")


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

lowercase__						       =      1_2_8_0_2_2
lowercase__						       =      1_2_8_0_2_8






@require_sentencepiece
class 	snake_case__       (      __SCREAMING_SNAKE_CASE    ,					unittest.TestCase					):







		"""simple docstring"""

		lowerCamelCase     							=						MaMaaaTokenizer
		lowerCamelCase     							=						False
		lowerCamelCase     							=						False
		lowerCamelCase     							=						True



		def       lowerCAmelCase				(      self				:			Dict      )						->			Optional[Any]:






				"""simple docstring"""







				super().setUp()

				snake_case  :       int     			=						['''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''', '''\u0120''', '''<pad>''']
				snake_case  :       int     			=						dict(zip(UpperCamelCase__  ,	range(len(UpperCamelCase__      )      )      )      )
				snake_case  :       Optional[int]     			=						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''']      )

				snake_case  :       Optional[int]     			=						MaMaaaTokenizer.from_pretrained(self.tmpdirname      )
				tokenizer.save_pretrained(self.tmpdirname      )



		def       lowerCAmelCase				(      self				:			Dict  ,	**UpperCamelCase__				:			Optional[int]      )						->			Any:






				"""simple docstring"""







				return MaMaaaTokenizer.from_pretrained(self.tmpdirname  ,	**UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			Tuple  ,	UpperCamelCase__				:			Tuple      )						->			str:






				"""simple docstring"""







				return (
				    "This is a test",
				    "This is a test",
				)



		def       lowerCAmelCase				(      self				:			List[str]      )						->			Optional[Any]:






				"""simple docstring"""







				snake_case  :       Optional[int]     			=						'''</s>'''
				snake_case  :       str     			=						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				:			Dict      )						->			int:






				"""simple docstring"""







				snake_case  :       Tuple     			=						self.get_tokenizer()
				snake_case  :       Dict     			=						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				:			str      )						->			List[str]:






				"""simple docstring"""







				pass



		def       lowerCAmelCase				(      self				:			int      )						->			Union[str, Any]:






				"""simple docstring"""







				snake_case  :       Optional[Any]     			=						self.get_tokenizer()

				snake_case  :       Optional[Any]     			=						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]  ,	)

				snake_case  :       Any     			=						tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]      )
				self.assertListEqual(UpperCamelCase__  ,	['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']      )

				snake_case  :       Optional[int]     			=						tokenizer.convert_tokens_to_string(UpperCamelCase__      )
				self.assertEqual(UpperCamelCase__  ,	'''This is a test'''      )



		@slow
		def       lowerCAmelCase				(      self				:			Union[str, Any]      )						->			List[Any]:






				"""simple docstring"""







				snake_case  :       Tuple     			=						{'''input_ids''': [[12_8022, 11_0108, 397, 11, 3_8272, 2247, 12_4811, 285, 1_8105, 1586, 207, 7, 3_9534, 4428, 397, 1019, 1_8105, 1586, 207, 7, 4_1337, 1_6786, 241, 7, 2_0214, 17, 12_5690, 1_0398, 7, 4_4378, 5_8069, 6_8342, 7798, 7343, 11, 299, 3_3310, 4, 158, 3_7350, 9_4077, 4569, 299, 3_3310, 90, 4, 5_2840, 290, 4, 3_1270, 112, 299, 682, 4, 5_2840, 3_9953, 1_4079, 193, 5_2519, 9_0894, 1_7894, 12_0697, 11, 4_0445, 551, 17, 1019, 5_2519, 9_0894, 1_7756, 963, 11, 4_0445, 480, 17, 9792, 1120, 5173, 1393, 6240, 1_6786, 241, 12_0996, 28, 1245, 1393, 11_8240, 1_1123, 1019, 9_3612, 2691, 1_0618, 9_8058, 12_0409, 1928, 279, 4, 4_0683, 367, 178, 207, 1019, 103, 10_3121, 506, 6_5296, 5, 2], [12_8022, 2_1217, 367, 117, 12_5450, 128, 719, 7, 7308, 40, 9_3612, 1_2669, 1116, 1_6704, 71, 1_7785, 3699, 1_5592, 35, 144, 9584, 241, 1_1943, 713, 950, 799, 2247, 8_8427, 150, 149, 11_8813, 12_0706, 1019, 10_6906, 8_1518, 28, 1224, 2_2799, 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], [12_8022, 1658, 12_3311, 5155, 5578, 4722, 279, 1_4947, 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 	snake_case__       (      unittest.TestCase					):







		"""simple docstring"""

		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, 593, 1949, 115781, 4, 71586, 4234, 60633, 126233, 432, 123808, 15592, 1197, 117132, 120618, 5, 2]



		@classmethod
		def       lowerCAmelCase				(      cls				:			List[Any]      )						->			int:






				"""simple docstring"""







				snake_case  :       MaMaaaTokenizer     			=						MaMaaaTokenizer.from_pretrained(
				    cls.checkpoint_name  ,	src_lang='''en'''  ,	tgt_lang='''fr'''      )
				snake_case  :       List[str]     			=						1
				return cls



		def       lowerCAmelCase				(      self				:			Union[str, Any]      )						->			str:






				"""simple docstring"""







				self.assertEqual(self.tokenizer.get_lang_id('''ar'''      )  ,	12_8006      )
				self.assertEqual(self.tokenizer.get_lang_id('''en'''      )  ,	12_8022      )
				self.assertEqual(self.tokenizer.get_lang_id('''ro'''      )  ,	12_8076      )
				self.assertEqual(self.tokenizer.get_lang_id('''mr'''      )  ,	12_8063      )



		def       lowerCAmelCase				(      self				:			Any      )						->			Dict:






				"""simple docstring"""







				snake_case  :       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				:			Optional[Any]      )						->			List[Any]:






				"""simple docstring"""







				snake_case  :       Dict     			=						'''en'''
				snake_case  :       Dict     			=						self.tokenizer.batch_encode_plus(self.src_text      ).input_ids[0]
				self.assertListEqual(self.expected_src_tokens  ,	UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			List[str]      )						->			List[Any]:






				"""simple docstring"""







				self.assertIn(UpperCamelCase__  ,	self.tokenizer.all_special_ids      )
				# fmt: off
				snake_case  :       str     			=						[FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 1_4028, 136, 3286, 9706, 6, 9_0797, 6, 14_4012, 162, 8_8128, 3_0061, 5, 2]
				# fmt: on
				snake_case  :       int     			=						self.tokenizer.decode(UpperCamelCase__  ,	skip_special_tokens=UpperCamelCase__      )
				snake_case  :       List[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				:			Optional[int]      )						->			Tuple:






				"""simple docstring"""







				snake_case  :       Union[str, Any]     			=						tempfile.mkdtemp()
				snake_case  :       Union[str, Any]     			=						self.tokenizer.lang_token_to_id
				self.tokenizer.save_pretrained(UpperCamelCase__      )
				snake_case  :       Optional[int]     			=						MaMaaaTokenizer.from_pretrained(UpperCamelCase__      )
				self.assertDictEqual(new_tok.lang_token_to_id  ,	UpperCamelCase__      )



		@require_torch
		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			Union[str, Any]:






				"""simple docstring"""







				snake_case  :       Optional[Any]     			=						'''en'''
				snake_case  :       int     			=						'''fr'''

				snake_case  :       Dict     			=						self.tokenizer(self.src_text  ,	text_target=self.tgt_text  ,	padding=UpperCamelCase__  ,	return_tensors='''pt'''      )

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

				for k in batch:
						snake_case  :       Tuple     			=						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      )						->			Union[str, Any]:






				"""simple docstring"""







				snake_case  :       List[str]     			=						'''mr'''
				self.assertListEqual(self.tokenizer.prefix_tokens  ,	[self.tokenizer.get_lang_id('''mr'''      )]      )
				self.assertListEqual(self.tokenizer.suffix_tokens  ,	[self.tokenizer.eos_token_id]      )

				snake_case  :       Optional[int]     			=						'''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]      )						->			Dict:






				"""simple docstring"""







				snake_case  :       List[Any]     			=						'''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      )]      )

				snake_case  :       List[str]     			=						'''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				:			Tuple      )						->			str:






				"""simple docstring"""







				snake_case  :       int     			=						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''': [[12_8022, 58, 4183, 2]],
				        '''attention_mask''': [[1, 1, 1, 1]],
				        # ar_AR
				        '''forced_bos_token_id''': 12_8006,
				    }  ,	)


