
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():
			_A  =	get_tests_dir("fixtures/test_sentencepiece.model")


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

_A  =	12_80_22
_A  =	12_80_28





@require_sentencepiece
class       _lowerCAmelCase   (      __a   , unittest.TestCase			):
				_lowercase     						=MaMaaaTokenizer
				_lowercase     						=False
				_lowercase     						=False
				_lowercase     						=True




				def 	__a		(   self	)							-> Any:
							super().setUp()

							lowerCAmelCase_         =      ["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
							lowerCAmelCase_         =      dict(zip(_UpperCamelCase							,							range(len(_UpperCamelCase	)	)	)	)
							lowerCAmelCase_         =      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"]	)

							lowerCAmelCase_         =      MaMaaaTokenizer.from_pretrained(self.tmpdirname	)
							tokenizer.save_pretrained(self.tmpdirname	)




				def 	__a		(   self							,							**_UpperCamelCase	)							-> str:
							return MaMaaaTokenizer.from_pretrained(self.tmpdirname							,							**_UpperCamelCase	)




				def 	__a		(   self							,							_UpperCamelCase	)							-> Any:
							return (
							    "This is a test",
							    "This is a test",
							)




				def 	__a		(   self	)							-> Optional[int]:
							lowerCAmelCase_         =      "</s>"
							lowerCAmelCase_         =      0

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




				def 	__a		(   self	)							-> Optional[Any]:
							lowerCAmelCase_         =      self.get_tokenizer()
							lowerCAmelCase_         =      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 	__a		(   self	)							-> Any:
							pass




				def 	__a		(   self	)							-> List[Any]:
							lowerCAmelCase_         =      self.get_tokenizer()

							lowerCAmelCase_         =      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]							,							)

							lowerCAmelCase_         =      tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]	)
							self.assertListEqual(_UpperCamelCase							,							["▁This", "▁is", "▁a", "▁t", "est"]	)

							lowerCAmelCase_         =      tokenizer.convert_tokens_to_string(_UpperCamelCase	)
							self.assertEqual(_UpperCamelCase							,							"This is a test"	)




				@slow
				def 	__a		(   self	)							-> Union[str, Any]:
							# fmt: off
							lowerCAmelCase_         =      {"input_ids": [[128_022, 110_108, 397, 11, 38_272, 2_247, 124_811, 285, 18_105, 1_586, 207, 7, 39_534, 4_428, 397, 1_019, 18_105, 1_586, 207, 7, 41_337, 16_786, 241, 7, 20_214, 17, 125_690, 10_398, 7, 44_378, 58_069, 68_342, 7_798, 7_343, 11, 299, 33_310, 4, 158, 37_350, 94_077, 4_569, 299, 33_310, 90, 4, 52_840, 290, 4, 31_270, 112, 299, 682, 4, 52_840, 39_953, 14_079, 193, 52_519, 90_894, 17_894, 120_697, 11, 40_445, 551, 17, 1_019, 52_519, 90_894, 17_756, 963, 11, 40_445, 480, 17, 9_792, 1_120, 5_173, 1_393, 6_240, 16_786, 241, 120_996, 28, 1_245, 1_393, 118_240, 11_123, 1_019, 93_612, 2_691, 10_618, 98_058, 120_409, 1_928, 279, 4, 40_683, 367, 178, 207, 1_019, 103, 103_121, 506, 65_296, 5, 2], [128_022, 21_217, 367, 117, 125_450, 128, 719, 7, 7_308, 40, 93_612, 12_669, 1_116, 16_704, 71, 17_785, 3_699, 15_592, 35, 144, 9_584, 241, 11_943, 713, 950, 799, 2_247, 88_427, 150, 149, 118_813, 120_706, 1_019, 106_906, 81_518, 28, 1_224, 22_799, 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], [128_022, 1_658, 123_311, 5_155, 5_578, 4_722, 279, 14_947, 2_366, 1_120, 1_197, 14, 1_348, 9_232, 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       _lowerCAmelCase   (      unittest.TestCase			):
				_lowercase     						='''facebook/m2m100_418M'''
				_lowercase     						=[
				    '''In my opinion, there are two levels of response from the French government.''',
				    '''NSA Affair Emphasizes Complete Lack of Debate on Intelligence''',
				]
				_lowercase     						=[
				    '''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
				_lowercase     						=[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 	__a		(   cls	)							-> List[str]:
							lowerCAmelCase_         =      MaMaaaTokenizer.from_pretrained(
							    cls.checkpoint_name							,							src_lang="en"							,							tgt_lang="fr"	)
							lowerCAmelCase_         =      1
							return cls




				def 	__a		(   self	)							-> List[str]:
							self.assertEqual(self.tokenizer.get_lang_id("ar"	)							,							128_006	)
							self.assertEqual(self.tokenizer.get_lang_id("en"	)							,							128_022	)
							self.assertEqual(self.tokenizer.get_lang_id("ro"	)							,							128_076	)
							self.assertEqual(self.tokenizer.get_lang_id("mr"	)							,							128_063	)




				def 	__a		(   self	)							-> Dict:
							lowerCAmelCase_         =      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 	__a		(   self	)							-> List[Any]:
							lowerCAmelCase_         =      "en"
							lowerCAmelCase_         =      self.tokenizer.batch_encode_plus(self.src_text	).input_ids[0]
							self.assertListEqual(self.expected_src_tokens							,							_UpperCamelCase	)




				def 	__a		(   self	)							-> Dict:
							self.assertIn(_UpperCamelCase							,							self.tokenizer.all_special_ids	)
							# fmt: off
							lowerCAmelCase_         =      [FR_CODE, 5_364, 82, 8_642, 4, 294, 47, 8, 14_028, 136, 3_286, 9_706, 6, 90_797, 6, 144_012, 162, 88_128, 30_061, 5, 2]
							# fmt: on
							lowerCAmelCase_         =      self.tokenizer.decode(_UpperCamelCase							,							skip_special_tokens=_UpperCamelCase	)
							lowerCAmelCase_         =      self.tokenizer.decode(generated_ids[1:]							,							skip_special_tokens=_UpperCamelCase	)
							self.assertEqual(_UpperCamelCase							,							_UpperCamelCase	)
							self.assertNotIn(self.tokenizer.eos_token							,							_UpperCamelCase	)




				def 	__a		(   self	)							-> Optional[Any]:
							lowerCAmelCase_         =      tempfile.mkdtemp()
							lowerCAmelCase_         =      self.tokenizer.lang_token_to_id
							self.tokenizer.save_pretrained(_UpperCamelCase	)
							lowerCAmelCase_         =      MaMaaaTokenizer.from_pretrained(_UpperCamelCase	)
							self.assertDictEqual(new_tok.lang_token_to_id							,							_UpperCamelCase	)




				@require_torch
				def 	__a		(   self	)							-> str:
							lowerCAmelCase_         =      "en"
							lowerCAmelCase_         =      "fr"

							lowerCAmelCase_         =      self.tokenizer(self.src_text							,							text_target=self.tgt_text							,							padding=_UpperCamelCase							,							return_tensors="pt"	)

							lowerCAmelCase_         =      shift_tokens_right(
							    batch["labels"]							,							self.tokenizer.pad_token_id							,							self.tokenizer.eos_token_id	)

							for k in batch:
										lowerCAmelCase_         =      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 	__a		(   self	)							-> Dict:
							lowerCAmelCase_         =      "mr"
							self.assertListEqual(self.tokenizer.prefix_tokens							,							[self.tokenizer.get_lang_id("mr"	)]	)
							self.assertListEqual(self.tokenizer.suffix_tokens							,							[self.tokenizer.eos_token_id]	)

							lowerCAmelCase_         =      "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 	__a		(   self	)							-> List[Any]:
							lowerCAmelCase_         =      "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	)]	)

							lowerCAmelCase_         =      "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 	__a		(   self	)							-> str:
							lowerCAmelCase_         =      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": [[128_022, 58, 4_183, 2]],
							        "attention_mask": [[1, 1, 1, 1]],
							        # ar_AR
							        "forced_bos_token_id": 128_006,
							    }							,							)





from math import ceil






def   lowerCamelCase__							(							__lowerCAmelCase    :    Dict       ,				__lowerCAmelCase    :    int							):




			"""simple docstring"""


			lowerCAmelCase_         =      list(range(0       ,				__lowerCAmelCase							)							)

			lowerCAmelCase_         =      [item for sublist in list(device_map.values()							) for item in sublist]

			# Duplicate check
			lowerCAmelCase_         =      []
			for i in device_map_blocks:
						if device_map_blocks.count(__lowerCAmelCase							) > 1 and i not in duplicate_blocks:
									duplicate_blocks.append(__lowerCAmelCase							)
    # Missing blocks
			lowerCAmelCase_         =      [i for i in blocks if i not in device_map_blocks]
			lowerCAmelCase_         =      [i for i in device_map_blocks if i not in blocks]

			if len(__lowerCAmelCase							) != 0:
						raise ValueError(
						    "Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device."
						    " These attention blocks were specified more than once: " + str(__lowerCAmelCase							)							)
			if len(__lowerCAmelCase							) != 0:
						raise ValueError(
						    "There are attention blocks for this model that are not specified in the device_map. Add these attention "
						    "blocks to a device on the device_map: " + str(__lowerCAmelCase							)							)
			if len(__lowerCAmelCase							) != 0:
						raise ValueError(
						    "The device_map contains more attention blocks than this model has. Remove these from the device_map:"
						    + str(__lowerCAmelCase							)							)






def   lowerCamelCase__							(							__lowerCAmelCase    :    List[str]       ,				__lowerCAmelCase    :    Tuple							):




			"""simple docstring"""


			lowerCAmelCase_         =      list(range(__lowerCAmelCase							)							)
			lowerCAmelCase_         =      int(ceil(n_layers / len(__lowerCAmelCase							)							)							)
			lowerCAmelCase_         =      [layers[i : i + n_blocks] for i in range(0       ,				__lowerCAmelCase       ,				__lowerCAmelCase							)]

			return dict(zip(__lowerCAmelCase       ,				__lowerCAmelCase							)							)



