



from typing import Dict, List, Optional

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__UpperCAmelCase	       =	logging.get_logger(__name__)


__UpperCAmelCase	       =	{
    '''nielsr/canine-s''': 2_048,
}

# Unicode defines 1,114,112 total “codepoints”
__UpperCAmelCase	       =	1_114_112

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
__UpperCAmelCase	       =	0

__UpperCAmelCase	       =	0XE_000
__UpperCAmelCase	       =	0XE_001
__UpperCAmelCase	       =	0XE_002
__UpperCAmelCase	       =	0XE_003
__UpperCAmelCase	       =	0XE_004

# Maps special codepoints to human-readable names.
__UpperCAmelCase	       =	{
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: '''[CLS]''',
    SEP: '''[SEP]''',
    BOS: '''[BOS]''',
    MASK: '''[MASK]''',
    PAD: '''[PAD]''',
    RESERVED: '''[RESERVED]''',
}

# Maps special codepoint human-readable names to their codepoint values.
__UpperCAmelCase	       =	{name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}



class     lowerCAmelCase_		(  a__							):
       UpperCAmelCase__    : str       					=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       def __init__(      self,      SCREAMING_SNAKE_CASE_=chr(SCREAMING_SNAKE_CASE_	),      SCREAMING_SNAKE_CASE_=chr(SCREAMING_SNAKE_CASE_	),      SCREAMING_SNAKE_CASE_=chr(SCREAMING_SNAKE_CASE_	),      SCREAMING_SNAKE_CASE_=chr(SCREAMING_SNAKE_CASE_	),      SCREAMING_SNAKE_CASE_=chr(SCREAMING_SNAKE_CASE_	),      SCREAMING_SNAKE_CASE_=chr(SCREAMING_SNAKE_CASE_	),      SCREAMING_SNAKE_CASE_=False,      SCREAMING_SNAKE_CASE_=2048,      **SCREAMING_SNAKE_CASE_,      )					->		List[Any]:
          UpperCamelCase   :  Union[str, Any]						     =     AddedToken(SCREAMING_SNAKE_CASE_,      lstrip=SCREAMING_SNAKE_CASE_,      rstrip=SCREAMING_SNAKE_CASE_	) if isinstance(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	) else bos_token
          UpperCamelCase   :  List[str]						     =     AddedToken(SCREAMING_SNAKE_CASE_,      lstrip=SCREAMING_SNAKE_CASE_,      rstrip=SCREAMING_SNAKE_CASE_	) if isinstance(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	) else eos_token
          UpperCamelCase   :  str						     =     AddedToken(SCREAMING_SNAKE_CASE_,      lstrip=SCREAMING_SNAKE_CASE_,      rstrip=SCREAMING_SNAKE_CASE_	) if isinstance(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	) else sep_token
          UpperCamelCase   :  str						     =     AddedToken(SCREAMING_SNAKE_CASE_,      lstrip=SCREAMING_SNAKE_CASE_,      rstrip=SCREAMING_SNAKE_CASE_	) if isinstance(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	) else cls_token
          UpperCamelCase   :  Tuple						     =     AddedToken(SCREAMING_SNAKE_CASE_,      lstrip=SCREAMING_SNAKE_CASE_,      rstrip=SCREAMING_SNAKE_CASE_	) if isinstance(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	) else pad_token

          # Mask token behave like a normal word, i.e. include the space before it
          UpperCamelCase   :  Tuple						     =     AddedToken(SCREAMING_SNAKE_CASE_,      lstrip=SCREAMING_SNAKE_CASE_,      rstrip=SCREAMING_SNAKE_CASE_	) if isinstance(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	) else mask_token

          super().__init__(
              bos_token=SCREAMING_SNAKE_CASE_,      eos_token=SCREAMING_SNAKE_CASE_,      sep_token=SCREAMING_SNAKE_CASE_,      cls_token=SCREAMING_SNAKE_CASE_,      pad_token=SCREAMING_SNAKE_CASE_,      mask_token=SCREAMING_SNAKE_CASE_,      add_prefix_space=SCREAMING_SNAKE_CASE_,      model_max_length=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_,      )

          # Creates a mapping for looking up the IDs of special symbols.
          UpperCamelCase   :  Dict[str, int]						     =     {}
          for codepoint, name in SPECIAL_CODEPOINTS.items():
             UpperCamelCase   :  Union[str, Any]						     =     codepoint

          # Creates a mapping for looking up the string forms of special symbol IDs.
          UpperCamelCase   :  Dict[int, str]						     =     {
              codepoint: name for name, codepoint in self._special_codepoints.items()
          }

          UpperCamelCase   :  Dict						     =     UNICODE_VOCAB_SIZE
          UpperCamelCase   :  Union[str, Any]						     =     len(self._special_codepoints	)
       @property
       def  snake_case_	(      self	)					->		int:
          return self._unicode_vocab_size
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		List[str]:
          return list(SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		int:
          try:
             return ord(SCREAMING_SNAKE_CASE_	)
          except TypeError:
             raise ValueError(F"""invalid token: '{token}'"""	)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		str:
          try:
             if index in SPECIAL_CODEPOINTS:
                return SPECIAL_CODEPOINTS[index]
             return chr(SCREAMING_SNAKE_CASE_	)
          except TypeError:
             raise ValueError(F"""invalid id: {index}"""	)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		List[str]:
          return "".join(SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = None	)					->		List[int]:
          UpperCamelCase   :  Union[str, Any]						     =     [self.sep_token_id]
          UpperCamelCase   :  Any						     =     [self.cls_token_id]

          UpperCamelCase   :  Optional[int]						     =     cls + token_ids_a + sep
          if token_ids_a is not None:
             result += token_ids_a + sep
          return result
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = False	)					->		List[int]:
          if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=SCREAMING_SNAKE_CASE_,      token_ids_a=SCREAMING_SNAKE_CASE_,      already_has_special_tokens=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Dict						     =     [1] + ([0] * len(SCREAMING_SNAKE_CASE_	)) + [1]
          if token_ids_a is not None:
             result += ([0] * len(SCREAMING_SNAKE_CASE_	)) + [1]
          return result
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = None	)					->		List[int]:
          UpperCamelCase   :  Any						     =     [self.sep_token_id]
          UpperCamelCase   :  List[Any]						     =     [self.cls_token_id]

          UpperCamelCase   :  Optional[int]						     =     len(cls + token_ids_a + sep	) * [0]
          if token_ids_a is not None:
             result += len(token_ids_a + sep	) * [1]
          return result






       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = None	)					->		Dict:
          return ()




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'	)



