





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():
							__lowerCAmelCase :Tuple     =    get_tests_dir('fixtures/test_sentencepiece.model')


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

__lowerCAmelCase :Union[str, Any]     =    12_80_22
__lowerCAmelCase :str     =    12_80_28






@require_sentencepiece
class        _a(    __A	,   unittest.TestCase    ):
		lowerCamelCase__     :str   				=		MaMaaaTokenizer
		lowerCamelCase__     :str   				=		False
		lowerCamelCase__     :List[Any]   				=		False
		lowerCamelCase__     :List[str]   				=		True







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


							'''simple docstring'''

							super().setUp()

							_snake_case       :					Optional[Any]				       =   ["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
							_snake_case       :					Tuple				       =   dict(zip(__snake_case ,						range(len(__snake_case				)				)				)				)
							_snake_case       :					Union[str, Any]				       =   Path(self.tmpdirname				)
							save_json(__snake_case ,						save_dir / VOCAB_FILES_NAMES["vocab_file"]				)
							if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
												copyfile(__snake_case ,						save_dir / VOCAB_FILES_NAMES["spm_file"]				)

							_snake_case       :					Dict				       =   MaMaaaTokenizer.from_pretrained(self.tmpdirname				)
							tokenizer.save_pretrained(self.tmpdirname				)







		def        lowercase       (    self ,						**__snake_case				)      ->      Optional[Any]:


							'''simple docstring'''

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







		def        lowercase       (    self ,						__snake_case				)      ->      Optional[Any]:


							'''simple docstring'''

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







		def        lowercase       (    self				)      ->      Dict:


							'''simple docstring'''

							_snake_case       :					Union[str, Any]				       =   "</s>"
							_snake_case       :					int				       =   0

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







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


							'''simple docstring'''

							_snake_case       :					Dict				       =   self.get_tokenizer()
							_snake_case       :					Union[str, 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(__snake_case				) ,						tokenizer.vocab_size + len(tokenizer.get_added_vocab()				)				)







		@unittest.skip("Skip this test while all models are still to be uploaded."				)
		def        lowercase       (    self				)      ->      List[Any]:


							'''simple docstring'''

							pass







		def        lowercase       (    self				)      ->      int:


							'''simple docstring'''

							_snake_case       :					Tuple				       =   self.get_tokenizer()

							_snake_case       :					Dict				       =   tokenizer.tokenize("This is a test"				)
							self.assertListEqual(__snake_case ,						["▁This", "▁is", "▁a", "▁t", "est"]				)

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

							_snake_case       :					Union[str, Any]				       =   tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]				)
							self.assertListEqual(__snake_case ,						["▁This", "▁is", "▁a", "▁t", "est"]				)

							_snake_case       :					str				       =   tokenizer.convert_tokens_to_string(__snake_case				)
							self.assertEqual(__snake_case ,						"This is a test"				)







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


							'''simple docstring'''

							_snake_case       :					Any				       =   {"input_ids": [[1_2_8_0_2_2, 1_1_0_1_0_8, 3_9_7, 1_1, 3_8_2_7_2, 2_2_4_7, 1_2_4_8_1_1, 2_8_5, 1_8_1_0_5, 1_5_8_6, 2_0_7, 7, 3_9_5_3_4, 4_4_2_8, 3_9_7, 1_0_1_9, 1_8_1_0_5, 1_5_8_6, 2_0_7, 7, 4_1_3_3_7, 1_6_7_8_6, 2_4_1, 7, 2_0_2_1_4, 1_7, 1_2_5_6_9_0, 1_0_3_9_8, 7, 4_4_3_7_8, 5_8_0_6_9, 6_8_3_4_2, 7_7_9_8, 7_3_4_3, 1_1, 2_9_9, 3_3_3_1_0, 4, 1_5_8, 3_7_3_5_0, 9_4_0_7_7, 4_5_6_9, 2_9_9, 3_3_3_1_0, 9_0, 4, 5_2_8_4_0, 2_9_0, 4, 3_1_2_7_0, 1_1_2, 2_9_9, 6_8_2, 4, 5_2_8_4_0, 3_9_9_5_3, 1_4_0_7_9, 1_9_3, 5_2_5_1_9, 9_0_8_9_4, 1_7_8_9_4, 1_2_0_6_9_7, 1_1, 4_0_4_4_5, 5_5_1, 1_7, 1_0_1_9, 5_2_5_1_9, 9_0_8_9_4, 1_7_7_5_6, 9_6_3, 1_1, 4_0_4_4_5, 4_8_0, 1_7, 9_7_9_2, 1_1_2_0, 5_1_7_3, 1_3_9_3, 6_2_4_0, 1_6_7_8_6, 2_4_1, 1_2_0_9_9_6, 2_8, 1_2_4_5, 1_3_9_3, 1_1_8_2_4_0, 1_1_1_2_3, 1_0_1_9, 9_3_6_1_2, 2_6_9_1, 1_0_6_1_8, 9_8_0_5_8, 1_2_0_4_0_9, 1_9_2_8, 2_7_9, 4, 4_0_6_8_3, 3_6_7, 1_7_8, 2_0_7, 1_0_1_9, 1_0_3, 1_0_3_1_2_1, 5_0_6, 6_5_2_9_6, 5, 2], [1_2_8_0_2_2, 2_1_2_1_7, 3_6_7, 1_1_7, 1_2_5_4_5_0, 1_2_8, 7_1_9, 7, 7_3_0_8, 4_0, 9_3_6_1_2, 1_2_6_6_9, 1_1_1_6, 1_6_7_0_4, 7_1, 1_7_7_8_5, 3_6_9_9, 1_5_5_9_2, 3_5, 1_4_4, 9_5_8_4, 2_4_1, 1_1_9_4_3, 7_1_3, 9_5_0, 7_9_9, 2_2_4_7, 8_8_4_2_7, 1_5_0, 1_4_9, 1_1_8_8_1_3, 1_2_0_7_0_6, 1_0_1_9, 1_0_6_9_0_6, 8_1_5_1_8, 2_8, 1_2_2_4, 2_2_7_9_9, 3_9_7, 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_2_8_0_2_2, 1_6_5_8, 1_2_3_3_1_1, 5_1_5_5, 5_5_7_8, 4_7_2_2, 2_7_9, 1_4_9_4_7, 2_3_6_6, 1_1_2_0, 1_1_9_7, 1_4, 1_3_4_8, 9_2_3_2, 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=__snake_case ,						model_name="facebook/m2m100_418M" ,						revision="c168bae485c864188cf9aa0e4108b0b6934dc91e" ,						)






@require_torch
@require_sentencepiece
@require_tokenizers
class        _a(    unittest.TestCase    ):
		lowerCamelCase__     :Optional[int]   				=		'facebook/m2m100_418M'
		lowerCamelCase__     :Dict   				=		[
		    'In my opinion, there are two levels of response from the French government.',
		    'NSA Affair Emphasizes Complete Lack of Debate on Intelligence',
		]
		lowerCamelCase__     :List[Any]   				=		[
		    '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__     :Optional[Any]   				=		[EN_CODE, 593, 1949, 115781, 4, 71586, 4234, 60633, 126233, 432, 123808, 15592, 1197, 117132, 120618, 5, 2]







		@classmethod
		def        lowercase       (    cls				)      ->      Tuple:


							'''simple docstring'''

							_snake_case       :					MaMaaaTokenizer				       =   MaMaaaTokenizer.from_pretrained(
							    cls.checkpoint_name ,						src_lang="en" ,						tgt_lang="fr"				)
							_snake_case       :					Any				       =   1
							return cls







		def        lowercase       (    self				)      ->      Optional[int]:


							'''simple docstring'''

							self.assertEqual(self.tokenizer.get_lang_id("ar"				) ,						1_2_8_0_0_6				)
							self.assertEqual(self.tokenizer.get_lang_id("en"				) ,						1_2_8_0_2_2				)
							self.assertEqual(self.tokenizer.get_lang_id("ro"				) ,						1_2_8_0_7_6				)
							self.assertEqual(self.tokenizer.get_lang_id("mr"				) ,						1_2_8_0_6_3				)







		def        lowercase       (    self				)      ->      Tuple:


							'''simple docstring'''

							_snake_case       :					Optional[Any]				       =   self.tokenizer.get_vocab()
							self.assertEqual(len(__snake_case				) ,						self.tokenizer.vocab_size				)
							self.assertEqual(vocab["<unk>"] ,						3				)
							self.assertIn(self.tokenizer.get_lang_token("en"				) ,						__snake_case				)







		def        lowercase       (    self				)      ->      Optional[Any]:


							'''simple docstring'''

							_snake_case       :					Any				       =   "en"
							_snake_case       :					Union[str, Any]				       =   self.tokenizer.batch_encode_plus(self.src_text				).input_ids[0]
							self.assertListEqual(self.expected_src_tokens ,						__snake_case				)







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


							'''simple docstring'''

							self.assertIn(__snake_case ,						self.tokenizer.all_special_ids				)
							# fmt: off
							_snake_case       :					Union[str, Any]				       =   [FR_CODE, 5_3_6_4, 8_2, 8_6_4_2, 4, 2_9_4, 4_7, 8, 1_4_0_2_8, 1_3_6, 3_2_8_6, 9_7_0_6, 6, 9_0_7_9_7, 6, 1_4_4_0_1_2, 1_6_2, 8_8_1_2_8, 3_0_0_6_1, 5, 2]
							# fmt: on
							_snake_case       :					List[Any]				       =   self.tokenizer.decode(__snake_case ,						skip_special_tokens=__snake_case				)
							_snake_case       :					Tuple				       =   self.tokenizer.decode(generated_ids[1:] ,						skip_special_tokens=__snake_case				)
							self.assertEqual(__snake_case ,						__snake_case				)
							self.assertNotIn(self.tokenizer.eos_token ,						__snake_case				)







		def        lowercase       (    self				)      ->      Optional[Any]:


							'''simple docstring'''

							_snake_case       :					List[str]				       =   tempfile.mkdtemp()
							_snake_case       :					str				       =   self.tokenizer.lang_token_to_id
							self.tokenizer.save_pretrained(__snake_case				)
							_snake_case       :					List[str]				       =   MaMaaaTokenizer.from_pretrained(__snake_case				)
							self.assertDictEqual(new_tok.lang_token_to_id ,						__snake_case				)







		@require_torch
		def        lowercase       (    self				)      ->      Any:


							'''simple docstring'''

							_snake_case       :					int				       =   "en"
							_snake_case       :					Union[str, Any]				       =   "fr"

							_snake_case       :					Optional[int]				       =   self.tokenizer(self.src_text ,						text_target=self.tgt_text ,						padding=__snake_case ,						return_tensors="pt"				)

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

							for k in batch:
												_snake_case       :					str				       =   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        lowercase       (    self				)      ->      List[Any]:


							'''simple docstring'''

							_snake_case       :					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]				)

							_snake_case       :					Tuple				       =   "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        lowercase       (    self				)      ->      str:


							'''simple docstring'''

							_snake_case       :					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				)]				)

							_snake_case       :					Tuple				       =   "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        lowercase       (    self				)      ->      int:


							'''simple docstring'''

							_snake_case       :					Optional[Any]				       =   self.tokenizer._build_translation_inputs("A test" ,						return_tensors="pt" ,						src_lang="en" ,						tgt_lang="ar"				)

							self.assertEqual(
							    nested_simplify(__snake_case				) ,						{
							        # en_XX, A, test, EOS
							        "input_ids": [[1_2_8_0_2_2, 5_8, 4_1_8_3, 2]],
							        "attention_mask": [[1, 1, 1, 1]],
							        # ar_AR
							        "forced_bos_token_id": 1_2_8_0_0_6,
							    } ,						)






import argparse
import os
from io import BytesIO
from pathlib import Path

import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					List[str]				       =   1.5
					_snake_case       :					Union[str, Any]				       =   int(factor * num_class_images				)
					_snake_case       :					Dict				       =   ClipClient(
					    url="https://knn.laion.ai/knn-service"  ,  indice_name="laion_400m"  ,  num_images=UpperCAmelCase  ,  aesthetic_weight=0.1				)

					os.makedirs(F"""{class_data_dir}/images"""  ,  exist_ok=UpperCAmelCase				)
					if len(list(Path(F"""{class_data_dir}/images"""				).iterdir()				)				) >= num_class_images:
										return

					while True:
										_snake_case       :					Optional[int]				       =   client.query(text=UpperCAmelCase				)
										if len(UpperCAmelCase				) >= factor * num_class_images or num_images > 1e4:
															break
										else:
															_snake_case       :					str				       =   int(factor * num_images				)
															_snake_case       :					Union[str, Any]				       =   ClipClient(
															    url="https://knn.laion.ai/knn-service"  ,  indice_name="laion_400m"  ,  num_images=UpperCAmelCase  ,  aesthetic_weight=0.1  ,  )

					_snake_case       :					List[Any]				       =   0
					_snake_case       :					Any				       =   0
					_snake_case       :					List[Any]				       =   tqdm(desc="downloading real regularization images"  ,  total=UpperCAmelCase				)

					with open(F"""{class_data_dir}/caption.txt"""  ,  "w"				) as fa, open(F"""{class_data_dir}/urls.txt"""  ,  "w"				) as fa, open(
					    F"""{class_data_dir}/images.txt"""  ,  "w"				) as fa:
										while total < num_class_images:
															_snake_case       :					Optional[Any]				       =   class_images[count]
															count += 1
															try:
																				_snake_case       :					Tuple				       =   requests.get(images["url"]				)
																				if img.status_code == 200:
																									_snake_case       :					int				       =   Image.open(BytesIO(img.content				)				)
																									with open(F"""{class_data_dir}/images/{total}.jpg"""  ,  "wb"				) as f:
																														f.write(img.content				)
																									fa.write(images["caption"] + "\n"				)
																									fa.write(images["url"] + "\n"				)
																									fa.write(F"""{class_data_dir}/images/{total}.jpg""" + "\n"				)
																									total += 1
																									pbar.update(1				)
																				else:
																									continue
															except Exception:
																				continue
					return







def    A				(							):
					_snake_case       :					Any				       =   argparse.ArgumentParser(""  ,  add_help=UpperCAmelCase				)
					parser.add_argument("--class_prompt"  ,  help="text prompt to retrieve images"  ,  required=UpperCAmelCase  ,  type=UpperCAmelCase				)
					parser.add_argument("--class_data_dir"  ,  help="path to save images"  ,  required=UpperCAmelCase  ,  type=UpperCAmelCase				)
					parser.add_argument("--num_class_images"  ,  help="number of images to download"  ,  default=200  ,  type=UpperCAmelCase				)
					return parser.parse_args()


if __name__ == "__main__":
							__lowerCAmelCase :List[Any]     =    parse_args()
							retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)