



import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
    from .tokenization_fnet import FNetTokenizer
else:
    __UpperCAmelCase	       =	None

__UpperCAmelCase	       =	logging.get_logger(__name__)
__UpperCAmelCase	       =	{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

__UpperCAmelCase	       =	{
    '''vocab_file''': {
        '''google/fnet-base''': '''https://huggingface.co/google/fnet-base/resolve/main/spiece.model''',
        '''google/fnet-large''': '''https://huggingface.co/google/fnet-large/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''google/fnet-base''': '''https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json''',
        '''google/fnet-large''': '''https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json''',
    },
}

__UpperCAmelCase	       =	{
    '''google/fnet-base''': 512,
    '''google/fnet-large''': 512,
}

__UpperCAmelCase	       =	'''▁'''



class     lowerCAmelCase_		(  a__							):
       UpperCAmelCase__    : str       					=  VOCAB_FILES_NAMES
       UpperCAmelCase__    : Dict       					=  PRETRAINED_VOCAB_FILES_MAP
       UpperCAmelCase__    : Optional[int]       					=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCAmelCase__    : int       					=  ["input_ids", "token_type_ids"]
       UpperCAmelCase__    : Union[str, Any]       					=  FNetTokenizer
       def __init__(      self,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=False,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_="<unk>",      SCREAMING_SNAKE_CASE_="[SEP]",      SCREAMING_SNAKE_CASE_="<pad>",      SCREAMING_SNAKE_CASE_="[CLS]",      SCREAMING_SNAKE_CASE_="[MASK]",      **SCREAMING_SNAKE_CASE_,      )					->		Any:
          # Mask token behave like a normal word, i.e. include the space before it and
          # is included in the raw text, there should be a match in a non-normalized sentence.
          UpperCamelCase   :  List[str]						     =     (
              AddedToken(SCREAMING_SNAKE_CASE_,      lstrip=SCREAMING_SNAKE_CASE_,      rstrip=SCREAMING_SNAKE_CASE_,      normalized=SCREAMING_SNAKE_CASE_	)
              if isinstance(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
              else mask_token
          )

          super().__init__(
              SCREAMING_SNAKE_CASE_,      tokenizer_file=SCREAMING_SNAKE_CASE_,      do_lower_case=SCREAMING_SNAKE_CASE_,      remove_space=SCREAMING_SNAKE_CASE_,      keep_accents=SCREAMING_SNAKE_CASE_,      unk_token=SCREAMING_SNAKE_CASE_,      sep_token=SCREAMING_SNAKE_CASE_,      pad_token=SCREAMING_SNAKE_CASE_,      cls_token=SCREAMING_SNAKE_CASE_,      mask_token=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_,      )

          UpperCamelCase   :  Any						     =     do_lower_case
          UpperCamelCase   :  List[str]						     =     remove_space
          UpperCamelCase   :  Dict						     =     keep_accents
          UpperCamelCase   :  str						     =     vocab_file
          UpperCamelCase   :  Optional[int]						     =     False if not self.vocab_file else True
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = None	)					->		List[int]:
          UpperCamelCase   :  int						     =     [self.sep_token_id]
          UpperCamelCase   :  Optional[int]						     =     [self.cls_token_id]
          if token_ids_a is None:
             return cls + token_ids_a + sep
          return cls + token_ids_a + sep + token_ids_a + sep
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = None	)					->		List[int]:
          UpperCamelCase   :  Dict						     =     [self.sep_token_id]
          UpperCamelCase   :  List[str]						     =     [self.cls_token_id]

          if token_ids_a is None:
             return len(cls + token_ids_a + sep	) * [0]
          return len(cls + token_ids_a + sep	) * [0] + len(token_ids_a + sep	) * [1]






       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = None	)					->		Tuple[str]:
          if not os.path.isdir(SCREAMING_SNAKE_CASE_	):
             logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""	)
             return
          UpperCamelCase   :  List[str]						     =     os.path.join(
              SCREAMING_SNAKE_CASE_,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)

          if os.path.abspath(self.vocab_file	) != os.path.abspath(SCREAMING_SNAKE_CASE_	):
             copyfile(self.vocab_file,      SCREAMING_SNAKE_CASE_	)

          return (out_vocab_file,)








import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image
else:



    class     lowerCAmelCase_		:
           @staticmethod
           def  snake_case_	(      *SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)					->		Dict:
              pass
def 		UpperCamelCase     (       snake_case__	:   Image     )     ->					str:
   UpperCamelCase   :  Tuple						     =     hashlib.mda(image.tobytes()     )
   return m.hexdigest()



@is_pipeline_test
@require_vision
@require_timm
@require_torch
class     lowerCAmelCase_		(  unittest.TestCase							):
       UpperCAmelCase__    : str       					=  MODEL_FOR_DEPTH_ESTIMATION_MAPPING
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		Optional[Any]:
          UpperCamelCase   :  List[str]						     =     DepthEstimationPipeline(model=SCREAMING_SNAKE_CASE_,      image_processor=SCREAMING_SNAKE_CASE_	)
          return depth_estimator, [
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
          ]
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		Union[str, Any]:
          UpperCamelCase   :  Union[str, Any]						     =     depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
          self.assertEqual({'predicted_depth': ANY(torch.Tensor	), 'depth': ANY(Image.Image	)},      SCREAMING_SNAKE_CASE_	)
          import datasets

          UpperCamelCase   :  int						     =     datasets.load_dataset('hf-internal-testing/fixtures_image_utils',      'image',      split='test'	)
          UpperCamelCase   :  Tuple						     =     depth_estimator(
              [
                  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	),
                  'http://images.cocodataset.org/val2017/000000039769.jpg',
                  # RGBA
                  dataset[0]['file'],
                  # LA
                  dataset[1]['file'],
                  # L
                  dataset[2]['file'],
              ]	)
          self.assertEqual(
              [
                  {'predicted_depth': ANY(torch.Tensor	), 'depth': ANY(Image.Image	)},
                  {'predicted_depth': ANY(torch.Tensor	), 'depth': ANY(Image.Image	)},
                  {'predicted_depth': ANY(torch.Tensor	), 'depth': ANY(Image.Image	)},
                  {'predicted_depth': ANY(torch.Tensor	), 'depth': ANY(Image.Image	)},
                  {'predicted_depth': ANY(torch.Tensor	), 'depth': ANY(Image.Image	)},
              ],      SCREAMING_SNAKE_CASE_,      )
       @require_tf
       @unittest.skip('Depth estimation is not implemented in TF'	)
       def  snake_case_	(      self	)					->		Optional[int]:
          pass
       @slow
       @require_torch
       def  snake_case_	(      self	)					->		List[Any]:
          UpperCamelCase   :  Optional[Any]						     =     'Intel/dpt-large'
          UpperCamelCase   :  Tuple						     =     pipeline('depth-estimation',      model=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Any						     =     depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'	)
          UpperCamelCase   :  int						     =     hashimage(outputs['depth']	)

          # This seems flaky.
          # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
          self.assertEqual(nested_simplify(outputs['predicted_depth'].max().item()	),      29.3_04	)
          self.assertEqual(nested_simplify(outputs['predicted_depth'].min().item()	),      2.6_62	)






       @require_torch
       def  snake_case_	(      self	)					->		Optional[int]:
          # This is highly irregular to have no small tests.
          self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT'	)



