




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase							:						Dict					       =  {
    "configuration_clip": [
        "CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "CLIPConfig",
        "CLIPOnnxConfig",
        "CLIPTextConfig",
        "CLIPVisionConfig",
    ],
    "processing_clip": ["CLIPProcessor"],
    "tokenization_clip": ["CLIPTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase							:						Optional[Any]					       =  ["CLIPTokenizerFast"]

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase							:						str					       =  ["CLIPFeatureExtractor"]
 lowerCamelCase							:						List[str]					       =  ["CLIPImageProcessor"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase							:						List[str]					       =  [
     "CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
     "CLIPModel",
     "CLIPPreTrainedModel",
     "CLIPTextModel",
     "CLIPTextModelWithProjection",
     "CLIPVisionModel",
     "CLIPVisionModelWithProjection",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase							:						Optional[Any]					       =  [
     "TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFCLIPModel",
     "TFCLIPPreTrainedModel",
     "TFCLIPTextModel",
     "TFCLIPVisionModel",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase							:						Optional[int]					       =  [
     "FlaxCLIPModel",
     "FlaxCLIPPreTrainedModel",
     "FlaxCLIPTextModel",
     "FlaxCLIPTextPreTrainedModel",
     "FlaxCLIPVisionModel",
     "FlaxCLIPVisionPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_clip import (
     CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
     CLIPConfig,
     CLIPOnnxConfig,
     CLIPTextConfig,
     CLIPVisionConfig,
 )
 from .processing_clip import CLIPProcessor
 from .tokenization_clip import CLIPTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_clip_fast import CLIPTokenizerFast

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_clip import CLIPFeatureExtractor
  from .image_processing_clip import CLIPImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_clip import (
      CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
      CLIPModel,
      CLIPPreTrainedModel,
      CLIPTextModel,
      CLIPTextModelWithProjection,
      CLIPVisionModel,
      CLIPVisionModelWithProjection,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_clip import (
      TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFCLIPModel,
      TFCLIPPreTrainedModel,
      TFCLIPTextModel,
      TFCLIPVisionModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_clip import (
      FlaxCLIPModel,
      FlaxCLIPPreTrainedModel,
      FlaxCLIPTextModel,
      FlaxCLIPTextPreTrainedModel,
      FlaxCLIPVisionModel,
      FlaxCLIPVisionPreTrainedModel,
  )


else:
 import sys

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









'''simple docstring'''


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_      :  Optional[Any]				     =  get_tests_dir('fixtures/test_sentencepiece.model')

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

if is_torch_available():
 lowerCAmelCase_      :  Dict				     =  'pt'
elif is_tf_available():
 lowerCAmelCase_      :  Union[str, Any]				     =  'tf'
else:
 lowerCAmelCase_      :  int				     =  'jax'







@require_sentencepiece
class      SCREAMING_SNAKE_CASE    (						snake_case_							,      unittest.TestCase				):
      __magic_name__  :       Dict   							=					MarianTokenizer
      __magic_name__  :       Any   							=					False
      __magic_name__  :       str   							=					True






      def 							lowercase_						( self   :  Any       ):


           '''simple docstring'''

           super().setUp()
           a_     :							Optional[Any]     						=  ["""</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est""", """\u0120""", """<pad>"""]
           a_     :							Optional[int]     						=  dict(zip(lowercase__						,						range(len(lowercase__       )       )       )       )
           a_     :							List[str]     						=  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_     :							Dict     						=  MarianTokenizer.from_pretrained(self.tmpdirname       )
           tokenizer.save_pretrained(self.tmpdirname       )






      def 							lowercase_						( self   :  Tuple						,						**lowercase__   :  int       ):


           '''simple docstring'''

           return MarianTokenizer.from_pretrained(self.tmpdirname						,						**lowercase__       )






      def 							lowercase_						( self   :  int						,						lowercase__   :  int       ):


           '''simple docstring'''

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






      def 							lowercase_						( self   :  List[str]       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  """</s>"""
           a_     :							Optional[int]     						=  0

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






      def 							lowercase_						( self   :  Tuple       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  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 							lowercase_						( self   :  List[Any]       ):


           '''simple docstring'''

           self.assertEqual(self.get_tokenizer().vocab_size						,						9       )






      def 							lowercase_						( self   :  str       ):


           '''simple docstring'''

           a_     :							str     						=  MarianTokenizer.from_pretrained(F"{ORG_NAME}opus-mt-en-de"       )
           a_     :							Any     						=  en_de_tokenizer(["""I am a small frog"""]						,						return_tensors=lowercase__       )
           self.assertIsInstance(lowercase__						,						lowercase__       )
           a_     :							str     						=  [38, 121, 14, 697, 3_8848, 0]
           self.assertListEqual(lowercase__						,						batch.input_ids[0]       )

           a_     :							Union[str, Any]     						=  tempfile.mkdtemp()
           en_de_tokenizer.save_pretrained(lowercase__       )
           a_     :							Union[str, Any]     						=  [x.name for x in Path(lowercase__       ).glob("""*"""       )]
           self.assertIn("""source.spm"""						,						lowercase__       )
           MarianTokenizer.from_pretrained(lowercase__       )






      def 							lowercase_						( self   :  str       ):


           '''simple docstring'''

           a_     :							int     						=  self.get_tokenizer()

           a_     :							Dict     						=  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 							lowercase_						( self   :  Optional[Any]       ):


           '''simple docstring'''

           a_     :							List[str]     						=  self.get_tokenizer()
           a_     :							Dict     						=  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 							lowercase_						( self   :  List[Any]       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  {"""input_ids""": [[4_3495, 462, 20, 4_2164, 1369, 52, 464, 132, 1703, 492, 13, 7491, 3_8999, 6, 8, 464, 132, 1703, 492, 13, 4669, 3_7867, 13, 7525, 27, 1593, 988, 13, 3_3972, 7029, 6, 20, 8251, 383, 2, 270, 5866, 3788, 2, 2353, 8251, 1_2338, 2, 1_3958, 387, 2, 3629, 6953, 188, 2900, 2, 1_3958, 8011, 1_1501, 23, 8460, 4073, 3_4009, 20, 435, 1_1439, 27, 8, 8460, 4073, 6004, 20, 9988, 375, 27, 33, 266, 1945, 1076, 1350, 3_7867, 3288, 5, 577, 1076, 4374, 8, 5082, 5, 2_6453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 1_0767, 6, 316, 304, 4239, 3, 0], [148, 1_5722, 19, 1839, 12, 1350, 13, 2_2327, 5082, 5418, 4_7567, 3_5938, 59, 318, 1_9552, 108, 2183, 54, 1_4976, 4835, 32, 547, 1114, 8, 315, 2417, 5, 92, 1_9088, 3, 0, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100], [36, 6395, 1_2570, 3_9147, 1_1597, 6, 266, 4, 4_5405, 7296, 3, 0, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100]], """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 							lowercase_						( self   :  int       ):


           '''simple docstring'''

           a_     :							Tuple     						=  MarianTokenizer.from_pretrained("""hf-internal-testing/test-marian-two-vocabs"""       )

           a_     :							Tuple     						=  """Tämä on testi"""
           a_     :							Union[str, Any]     						=  """This is a test"""

           a_     :							Union[str, Any]     						=  [76, 7, 2047, 2]
           a_     :							Optional[int]     						=  [69, 12, 11, 940, 2]

           a_     :							Optional[Any]     						=  tokenizer(lowercase__       ).input_ids
           self.assertListEqual(lowercase__						,						lowercase__       )

           a_     :							Optional[int]     						=  tokenizer(text_target=lowercase__       ).input_ids
           self.assertListEqual(lowercase__						,						lowercase__       )

           a_     :							str     						=  tokenizer.decode(lowercase__						,						skip_special_tokens=lowercase__       )
           self.assertEqual(lowercase__						,						lowercase__       )
