


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:						List[str]   				=       get_tests_dir('fixtures/test_sentencepiece.model')

_lowerCAmelCase:						List[Any]   				=       {'target_lang': 'fi', 'source_lang': 'en'}
_lowerCAmelCase:						int   				=       '>>zh<<'
_lowerCAmelCase:						int   				=       'Helsinki-NLP/'

if is_torch_available():
	_lowerCAmelCase:						str   				=       'pt'
elif is_tf_available():
	_lowerCAmelCase:						Optional[int]   				=       'tf'
else:
	_lowerCAmelCase:						List[Any]   				=       'jax'




@require_sentencepiece
class       lowercase_					(lowercase__      ,					unittest.TestCase   ):
				snake_case	    =MarianTokenizer
				snake_case	    =False
				snake_case	    =True





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

											a__      =MarianTokenizer.from_pretrained(self.tmpdirname)
											tokenizer.save_pretrained(self.tmpdirname)





				def 						__UpperCamelCase    (				self       ,  **lowercase_)  -> MarianTokenizer:
											return MarianTokenizer.from_pretrained(self.tmpdirname       ,  **lowercase_)





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> int:
											return (
											    "This is a test",
											    "This is a test",
											)





				def 						__UpperCamelCase    (				self)  -> Union[str, Any]:
											a__      ='</s>'
											a__      =0

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





				def 						__UpperCamelCase    (				self)  -> Dict:
											a__      =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(lowercase_)       ,  9)





				def 						__UpperCamelCase    (				self)  -> List[str]:
											self.assertEqual(self.get_tokenizer().vocab_size       ,  9)





				def 						__UpperCamelCase    (				self)  -> Any:
											a__      =MarianTokenizer.from_pretrained(F"""{ORG_NAME}opus-mt-en-de""")
											a__      =en_de_tokenizer(['I am a small frog']       ,  return_tensors=lowercase_)
											self.assertIsInstance(lowercase_       ,  lowercase_)
											a__      =[38, 121, 14, 697, 38848, 0]
											self.assertListEqual(lowercase_       ,  batch.input_ids[0])

											a__      =tempfile.mkdtemp()
											en_de_tokenizer.save_pretrained(lowercase_)
											a__      =[x.name for x in Path(lowercase_).glob('*')]
											self.assertIn('source.spm'       ,  lowercase_)
											MarianTokenizer.from_pretrained(lowercase_)





				def 						__UpperCamelCase    (				self)  -> Tuple:
											a__      =self.get_tokenizer()

											a__      =tok(
											    ['I am a small frog' * 1000, 'I am a small frog']       ,  padding=lowercase_       ,  truncation=lowercase_       ,  return_tensors=lowercase_)
											self.assertIsInstance(lowercase_       ,  lowercase_)
											self.assertEqual(batch.input_ids.shape       ,  (2, 512))





				def 						__UpperCamelCase    (				self)  -> List[str]:
											a__      =self.get_tokenizer()
											a__      =tok(['I am a tiny frog', 'I am a small frog']       ,  padding=lowercase_       ,  return_tensors=lowercase_)
											self.assertIsInstance(lowercase_       ,  lowercase_)
											self.assertEqual(batch_smaller.input_ids.shape       ,  (2, 10))





				@slow
				def 						__UpperCamelCase    (				self)  -> Dict:
											# fmt: off
											a__      ={'input_ids': [[43495, 462, 20, 42164, 1369, 52, 464, 132, 1703, 492, 13, 7491, 38999, 6, 8, 464, 132, 1703, 492, 13, 4669, 37867, 13, 7525, 27, 1593, 988, 13, 33972, 7029, 6, 20, 8251, 383, 2, 270, 5866, 3788, 2, 2353, 8251, 12338, 2, 13958, 387, 2, 3629, 6953, 188, 2900, 2, 13958, 8011, 11501, 23, 8460, 4073, 34009, 20, 435, 11439, 27, 8, 8460, 4073, 6004, 20, 9988, 375, 27, 33, 266, 1945, 1076, 1350, 37867, 3288, 5, 577, 1076, 4374, 8, 5082, 5, 26453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 10767, 6, 316, 304, 4239, 3, 0], [148, 15722, 19, 1839, 12, 1350, 13, 22327, 5082, 5418, 47567, 35938, 59, 318, 19552, 108, 2183, 54, 14976, 4835, 32, 547, 1114, 8, 315, 2417, 5, 92, 19088, 3, 0, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100], [36, 6395, 12570, 39147, 11597, 6, 266, 4, 45405, 7296, 3, 0, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100]], '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=lowercase_       ,  model_name='Helsinki-NLP/opus-mt-en-de'       ,  revision='1a8c2263da11e68e50938f97e10cd57820bd504c'       ,  decode_kwargs={'use_source_tokenizer': True}       ,  )





				def 						__UpperCamelCase    (				self)  -> List[Any]:
											a__      =MarianTokenizer.from_pretrained('hf-internal-testing/test-marian-two-vocabs')

											a__      ='Tämä on testi'
											a__      ='This is a test'

											a__      =[76, 7, 2047, 2]
											a__      =[69, 12, 11, 940, 2]

											a__      =tokenizer(lowercase_).input_ids
											self.assertListEqual(lowercase_       ,  lowercase_)

											a__      =tokenizer(text_target=lowercase_).input_ids
											self.assertListEqual(lowercase_       ,  lowercase_)

											a__      =tokenizer.decode(lowercase_       ,  skip_special_tokens=lowercase_)
											self.assertEqual(lowercase_       ,  lowercase_)





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_lowerCAmelCase:						Optional[Any]   				=       {
    'configuration_swiftformer': [
        'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SwiftFormerConfig',
        'SwiftFormerOnnxConfig',
    ]
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCAmelCase:						List[str]   				=       [
	    'SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'SwiftFormerForImageClassification',
	    'SwiftFormerModel',
	    'SwiftFormerPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_swiftformer import (
	    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    SwiftFormerConfig,
	    SwiftFormerOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_swiftformer import (
		    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SwiftFormerForImageClassification,
		    SwiftFormerModel,
		    SwiftFormerPreTrainedModel,
		)

else:
	import sys

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

