


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

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
						from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


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

__lowerCAmelCase	: Optional[int]  				={'target_lang': 'fi', 'source_lang': 'en'}
__lowerCAmelCase	: Optional[int]  				='>>zh<<'
__lowerCAmelCase	: Tuple  				='Helsinki-NLP/'

if is_torch_available():
						__lowerCAmelCase	: int  				='pt'
elif is_tf_available():
						__lowerCAmelCase	: Any  				='tf'
else:
						__lowerCAmelCase	: str  				='jax'
@require_sentencepiece
class        _lowercase      (			lowerCamelCase_						,			unittest.TestCase       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			int						=		MarianTokenizer
				SCREAMING_SNAKE_CASE__				:			Any						=		False
				SCREAMING_SNAKE_CASE__				:			Any						=		True


				def 		__magic_name__(       self			:List[str]	)		->			Dict:
										super().setUp()
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		['''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''', '''\u0120''', '''<pad>''']
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		dict(zip(__a			,			range(len(__a	)	)	)	)
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		Path(self.tmpdirname	)
										save_json(__a			,			save_dir / VOCAB_FILES_NAMES['''vocab''']	)
										save_json(__a			,			save_dir / VOCAB_FILES_NAMES['''tokenizer_config_file''']	)
										if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
																copyfile(__a			,			save_dir / VOCAB_FILES_NAMES['''source_spm''']	)
																copyfile(__a			,			save_dir / VOCAB_FILES_NAMES['''target_spm''']	)

										__SCREAMING_SNAKE_CASE						:     Dict					=		MarianTokenizer.from_pretrained(self.tmpdirname	)
										tokenizer.save_pretrained(self.tmpdirname	)


				def 		__magic_name__(       self			:Optional[int]			,			**lowerCAmelCase__			:Any	)		->			Any:
										return MarianTokenizer.from_pretrained(self.tmpdirname			,			**__a	)


				def 		__magic_name__(       self			:int			,			lowerCAmelCase__			:List[Any]	)		->			Dict:
										return (
										    "This is a test",
										    "This is a test",
										)


				def 		__magic_name__(       self			:Any	)		->			List[str]:
										__SCREAMING_SNAKE_CASE						:     Any					=		'''</s>'''
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		0

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


				def 		__magic_name__(       self			:int	)		->			Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:     str					=		list(self.get_tokenizer().get_vocab().keys()	)

										self.assertEqual(vocab_keys[0]			,			'''</s>'''	)
										self.assertEqual(vocab_keys[1]			,			'''<unk>'''	)
										self.assertEqual(vocab_keys[-1]			,			'''<pad>'''	)
										self.assertEqual(len(__a	)			,			9	)


				def 		__magic_name__(       self			:Dict	)		->			Dict:
										self.assertEqual(self.get_tokenizer().vocab_size			,			9	)


				def 		__magic_name__(       self			:List[Any]	)		->			Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:     List[str]					=		MarianTokenizer.from_pretrained(f'''{ORG_NAME}opus-mt-en-de'''	)
										__SCREAMING_SNAKE_CASE						:     Any					=		en_de_tokenizer(['''I am a small frog''']			,			return_tensors=__a	)
										self.assertIsInstance(__a			,			__a	)
										__SCREAMING_SNAKE_CASE						:     Any					=		[38, 121, 14, 697, 38_848, 0]
										self.assertListEqual(__a			,			batch.input_ids[0]	)

										__SCREAMING_SNAKE_CASE						:     Tuple					=		tempfile.mkdtemp()
										en_de_tokenizer.save_pretrained(__a	)
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		[x.name for x in Path(__a	).glob('''*'''	)]
										self.assertIn('''source.spm'''			,			__a	)
										MarianTokenizer.from_pretrained(__a	)


				def 		__magic_name__(       self			:List[str]	)		->			Optional[int]:
										__SCREAMING_SNAKE_CASE						:     Tuple					=		self.get_tokenizer()

										__SCREAMING_SNAKE_CASE						:     Tuple					=		tok(
										    ['''I am a small frog''' * 1_000, '''I am a small frog''']			,			padding=__a			,			truncation=__a			,			return_tensors=__a	)
										self.assertIsInstance(__a			,			__a	)
										self.assertEqual(batch.input_ids.shape			,			(2, 512)	)


				def 		__magic_name__(       self			:str	)		->			Any:
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		self.get_tokenizer()
										__SCREAMING_SNAKE_CASE						:     Dict					=		tok(['''I am a tiny frog''', '''I am a small frog''']			,			padding=__a			,			return_tensors=__a	)
										self.assertIsInstance(__a			,			__a	)
										self.assertEqual(batch_smaller.input_ids.shape			,			(2, 10)	)


				@slow
				def 		__magic_name__(       self			:List[Any]	)		->			List[str]:
										# fmt: off
										__SCREAMING_SNAKE_CASE						:     Dict					=		{'''input_ids''': [[43_495, 462, 20, 42_164, 1_369, 52, 464, 132, 1_703, 492, 13, 7_491, 38_999, 6, 8, 464, 132, 1_703, 492, 13, 4_669, 37_867, 13, 7_525, 27, 1_593, 988, 13, 33_972, 7_029, 6, 20, 8_251, 383, 2, 270, 5_866, 3_788, 2, 2_353, 8_251, 12_338, 2, 13_958, 387, 2, 3_629, 6_953, 188, 2_900, 2, 13_958, 8_011, 11_501, 23, 8_460, 4_073, 34_009, 20, 435, 11_439, 27, 8, 8_460, 4_073, 6_004, 20, 9_988, 375, 27, 33, 266, 1_945, 1_076, 1_350, 37_867, 3_288, 5, 577, 1_076, 4_374, 8, 5_082, 5, 26_453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 10_767, 6, 316, 304, 4_239, 3, 0], [148, 15_722, 19, 1_839, 12, 1_350, 13, 22_327, 5_082, 5_418, 47_567, 35_938, 59, 318, 19_552, 108, 2_183, 54, 14_976, 4_835, 32, 547, 1_114, 8, 315, 2_417, 5, 92, 19_088, 3, 0, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100], [36, 6_395, 12_570, 39_147, 11_597, 6, 266, 4, 45_405, 7_296, 3, 0, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100]], '''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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=__a			,			model_name='''Helsinki-NLP/opus-mt-en-de'''			,			revision='''1a8c2263da11e68e50938f97e10cd57820bd504c'''			,			decode_kwargs={'''use_source_tokenizer''': True}			,			)



				def 		__magic_name__(       self			:List[Any]	)		->			Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:     List[str]					=		MarianTokenizer.from_pretrained('''hf-internal-testing/test-marian-two-vocabs'''	)

										__SCREAMING_SNAKE_CASE						:     Dict					=		'''Tämä on testi'''
										__SCREAMING_SNAKE_CASE						:     str					=		'''This is a test'''

										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		[76, 7, 2_047, 2]
										__SCREAMING_SNAKE_CASE						:     List[str]					=		[69, 12, 11, 940, 2]

										__SCREAMING_SNAKE_CASE						:     List[str]					=		tokenizer(__a	).input_ids
										self.assertListEqual(__a			,			__a	)

										__SCREAMING_SNAKE_CASE						:     Tuple					=		tokenizer(text_target=__a	).input_ids
										self.assertListEqual(__a			,			__a	)

										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		tokenizer.decode(__a			,			skip_special_tokens=__a	)
										self.assertEqual(__a			,			__a	)







'''simple docstring'''







import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_       ,							lowerCamelCase_      ):






				"""simple docstring"""







				@register_to_config
				def __init__(  self     :					Dict					,				*,
				    __a     :					int = 4					,				__a     :					int = 7_68					,				__a     :					int					,				__a     :					int					,				):
								super().__init__()

								_a								= nn.Parameter(torch.zeros(__a					)					)

								# parameters for additional clip time embeddings
								_a								= nn.Linear(__a					,				__a					)
								_a								= nn.Linear(__a					,				__a					)

								# parameters for encoder hidden states
								_a								= clip_extra_context_tokens
								_a								= nn.Linear(
								    __a					,				self.clip_extra_context_tokens * cross_attention_dim					)
								_a								= nn.Linear(__a					,				__a					)
								_a								= nn.LayerNorm(__a					)




				def   UpperCamelCase__   (  self     :					Optional[Any]					,				*, __a     :					Tuple					,				__a     :					Union[str, Any]					,				__a     :					Any					,				__a     :					List[Any]					):
								if do_classifier_free_guidance:
												# Add the classifier free guidance embeddings to the image embeddings
												_a								= image_embeddings.shape[0]
												_a								= self.learned_classifier_free_guidance_embeddings.unsqueeze(0					)
												_a								= classifier_free_guidance_embeddings.expand(
												    __a					,				-1					)
												_a								= torch.cat([classifier_free_guidance_embeddings, image_embeddings]					,				dim=0					)

								# The image embeddings batch size and the text embeddings batch size are equal
								assert image_embeddings.shape[0] == prompt_embeds.shape[0]

								_a								= prompt_embeds.shape[0]

								# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
								# adding CLIP embeddings to the existing timestep embedding, ...
								_a								= self.embedding_proj(__a					)
								_a								= self.clip_image_embeddings_project_to_time_embeddings(__a					)
								_a								= time_projected_image_embeddings + time_projected_prompt_embeds

								# ... and by projecting CLIP embeddings into four
								# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
								_a								= self.clip_extra_context_tokens_proj(__a					)
								_a								= clip_extra_context_tokens.reshape(__a					,				-1					,				self.clip_extra_context_tokens					)
								_a								= clip_extra_context_tokens.permute(0					,				2					,				1					)

								_a								= self.encoder_hidden_states_proj(__a					)
								_a								= self.text_encoder_hidden_states_norm(__a					)
								_a								= torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]					,				dim=1					)

								return text_encoder_hidden_states, additive_clip_time_embeddings
