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		:       Union[str, Any]									=     get_tests_dir('fixtures/test_sentencepiece.model')


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

__UpperCamelCase		:       Optional[Any]									=     12_8022
__UpperCamelCase		:       Optional[int]									=     12_8028





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


						def        __A       (       self	:				int						):





												'''simple docstring'''





												super().setUp()

												SCREAMING_SNAKE_CASE							:    Tuple							=							['''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''', '''\u0120''', '''<pad>''']
												SCREAMING_SNAKE_CASE							:    List[str]							=							dict(zip(UpperCamelCase__    ,		range(len(UpperCamelCase__						)						)						)						)
												SCREAMING_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''']						)

												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							MaMaaaTokenizer.from_pretrained(self.tmpdirname						)
												tokenizer.save_pretrained(self.tmpdirname						)


						def        __A       (       self	:				List[str]    ,		**UpperCamelCase__	:				Tuple						):





												'''simple docstring'''





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


						def        __A       (       self	:				Optional[Any]    ,		UpperCamelCase__	:				Tuple						):





												'''simple docstring'''





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


						def        __A       (       self	:				Tuple						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[Any]							=							'''</s>'''
												SCREAMING_SNAKE_CASE							:    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        __A       (       self	:				Tuple						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    int							=							self.get_tokenizer()
												SCREAMING_SNAKE_CASE							:    Tuple							=							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	:				Optional[Any]						):





												'''simple docstring'''





												pass


						def        __A       (       self	:				Tuple						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Dict							=							self.get_tokenizer()

												SCREAMING_SNAKE_CASE							:    Tuple							=							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]    ,		)

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

												SCREAMING_SNAKE_CASE							:    List[str]							=							tokenizer.convert_tokens_to_string(UpperCamelCase__						)
												self.assertEqual(UpperCamelCase__    ,		'''This is a test'''						)



						@slow
						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							{'''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 					lowercase__						(   unittest.TestCase):
						UpperCamelCase_     =		"""facebook/m2m100_418M"""
						UpperCamelCase_     =		[
						    """In my opinion, there are two levels of response from the French government.""",
						    """NSA Affair Emphasizes Complete Lack of Debate on Intelligence""",
						]
						UpperCamelCase_     =		[
						    """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
						UpperCamelCase_     =		[EN_CODE, 593, 1_949, 115_781, 4, 71_586, 4_234, 60_633, 126_233, 432, 123_808, 15_592, 1_197, 117_132, 120_618, 5, 2]


						@classmethod
						def        __A       (       cls	:				Tuple						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    MaMaaaTokenizer							=							MaMaaaTokenizer.from_pretrained(
												    cls.checkpoint_name    ,		src_lang='''en'''    ,		tgt_lang='''fr'''						)
												SCREAMING_SNAKE_CASE							:    int							=							1
												return cls


						def        __A       (       self	:				Optional[int]						):





												'''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        __A       (       self	:				str						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Dict							=							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	:				str						):





												'''simple docstring'''





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


						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												self.assertIn(UpperCamelCase__    ,		self.tokenizer.all_special_ids						)
												# fmt: off
												SCREAMING_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
												SCREAMING_SNAKE_CASE							:    List[Any]							=							self.tokenizer.decode(UpperCamelCase__    ,		skip_special_tokens=UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							self.tokenizer.decode(generated_ids[1:]    ,		skip_special_tokens=UpperCamelCase__						)
												self.assertEqual(UpperCamelCase__    ,		UpperCamelCase__						)
												self.assertNotIn(self.tokenizer.eos_token    ,		UpperCamelCase__						)


						def        __A       (       self	:				Any						):





												'''simple docstring'''





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


						@require_torch
						def        __A       (       self	:				Dict						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Dict							=							'''en'''
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							'''fr'''

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

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

												for k in batch:
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							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	:				Optional[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							'''mr'''
												self.assertListEqual(self.tokenizer.prefix_tokens    ,		[self.tokenizer.get_lang_id('''mr'''						)]						)
												self.assertListEqual(self.tokenizer.suffix_tokens    ,		[self.tokenizer.eos_token_id]						)

												SCREAMING_SNAKE_CASE							:    Optional[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        __A       (       self	:				List[Any]						):





												'''simple docstring'''





												SCREAMING_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						)]						)

												SCREAMING_SNAKE_CASE							:    Dict							=							'''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	:				List[str]						):





												'''simple docstring'''





												SCREAMING_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,
												    }    ,		)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__UpperCamelCase		:       List[Any]									=     {'configuration_glpn': ['GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GLPNConfig']}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       Optional[Any]									=     ['GLPNFeatureExtractor']
					__UpperCamelCase		:       List[str]									=     ['GLPNImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       str									=     [
					    'GLPN_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'GLPNForDepthEstimation',
					    'GLPNLayer',
					    'GLPNModel',
					    'GLPNPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_glpn import GLPNFeatureExtractor
										from .image_processing_glpn import GLPNImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_glpn import (
										    GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GLPNForDepthEstimation,
										    GLPNLayer,
										    GLPNModel,
										    GLPNPreTrainedModel,
										)


else:
					import sys

					__UpperCamelCase		:       Optional[Any]									=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





