





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   A			:
								'''simple docstring'''




								@staticmethod
								def    a_       (			*__lowerCAmelCase   :    int							,	**__lowerCAmelCase   :    str   )	->	Union[str, Any]:







										"""simple docstring"""






										pass







def    __lowerCamelCase					(							__a   :Image    )		->				str:






		"""simple docstring"""

		A__        =   hashlib.mda(image.tobytes()    )
		return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class   A			(unittest.TestCase    ):
			'''simple docstring'''




			__lowerCamelCase	:       Tuple							=    MODEL_FOR_DEPTH_ESTIMATION_MAPPING






			def    a_       (			self   :    Optional[int]							,	__lowerCAmelCase   :    str							,	__lowerCAmelCase   :    List[str]							,	__lowerCAmelCase   :    Optional[Any]   )	->	List[str]:







					"""simple docstring"""






					A__        =   DepthEstimationPipeline(model=__lowerCAmelCase							,	image_processor=__lowerCAmelCase   )
					return depth_estimator, [
					    "./tests/fixtures/tests_samples/COCO/000000039769.png",
					    "./tests/fixtures/tests_samples/COCO/000000039769.png",
					]






			def    a_       (			self   :    Optional[Any]							,	__lowerCAmelCase   :    Any							,	__lowerCAmelCase   :    Dict   )	->	str:







					"""simple docstring"""






					A__        =   depth_estimator("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
					self.assertEqual({"""predicted_depth""": ANY(torch.Tensor   ), """depth""": ANY(Image.Image   )}							,	__lowerCAmelCase   )
					import datasets

					A__        =   datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""							,	"""image"""							,	split="""test"""   )
					A__        =   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   )},
					    ]							,	__lowerCAmelCase							,	)






			@require_tf
			@unittest.skip("""Depth estimation is not implemented in TF"""   )
			def    a_       (			self   :    Any   )	->	List[str]:







					"""simple docstring"""






					pass






			@slow
			@require_torch
			def    a_       (			self   :    List[Any]   )	->	List[str]:







					"""simple docstring"""






					A__        =   """Intel/dpt-large"""
					A__        =   pipeline("""depth-estimation"""							,	model=__lowerCAmelCase   )
					A__        =   depth_estimator("""http://images.cocodataset.org/val2017/000000039769.jpg"""   )
					A__        =   hashimage(outputs["""depth"""]   )

					# This seems flaky.
					# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
					self.assertEqual(nested_simplify(outputs["""predicted_depth"""].max().item()   )							,	2_9.3_0_4   )
					self.assertEqual(nested_simplify(outputs["""predicted_depth"""].min().item()   )							,	2.6_6_2   )







			@require_torch
			def    a_       (			self   :    Dict   )	->	Union[str, Any]:







					"""simple docstring"""






					self.skipTest("""There is not hf-internal-testing tiny model for either GLPN nor DPT"""   )










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_albert import AlbertTokenizer
else:
					A    :					Tuple =      None

A    :					Optional[Any] =      logging.get_logger(__name__)
A    :					Tuple =      {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

A    :					List[str] =      {
    '''vocab_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json''',
    },
}

A    :					List[str] =      {
    '''albert-base-v1''': 5_1_2,
    '''albert-large-v1''': 5_1_2,
    '''albert-xlarge-v1''': 5_1_2,
    '''albert-xxlarge-v1''': 5_1_2,
    '''albert-base-v2''': 5_1_2,
    '''albert-large-v2''': 5_1_2,
    '''albert-xlarge-v2''': 5_1_2,
    '''albert-xxlarge-v2''': 5_1_2,
}

A    :					Optional[int] =      '''▁'''


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			__lowerCamelCase	:       str							=    VOCAB_FILES_NAMES
			__lowerCamelCase	:       List[Any]							=    PRETRAINED_VOCAB_FILES_MAP
			__lowerCamelCase	:       Dict							=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__lowerCamelCase	:       List[str]							=    AlbertTokenizer






			def __init__(			self   :    Tuple							,	__lowerCAmelCase   :    Tuple=None							,	__lowerCAmelCase   :    List[str]=None							,	__lowerCAmelCase   :    Optional[Any]=True							,	__lowerCAmelCase   :    List[Any]=True							,	__lowerCAmelCase   :    str=False							,	__lowerCAmelCase   :    Union[str, Any]="[CLS]"							,	__lowerCAmelCase   :    int="[SEP]"							,	__lowerCAmelCase   :    Dict="<unk>"							,	__lowerCAmelCase   :    Dict="[SEP]"							,	__lowerCAmelCase   :    Union[str, Any]="<pad>"							,	__lowerCAmelCase   :    str="[CLS]"							,	__lowerCAmelCase   :    int="[MASK]"							,	**__lowerCAmelCase   :    Optional[Any]							,	)	->	Optional[Any]:







					"""simple docstring"""






					A__        =   (
					    AddedToken(__lowerCAmelCase							,	lstrip=__lowerCAmelCase							,	rstrip=__lowerCAmelCase							,	normalized=__lowerCAmelCase   )
					    if isinstance(__lowerCAmelCase							,	__lowerCAmelCase   )
					    else mask_token
					)

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

					A__        =   do_lower_case
					A__        =   remove_space
					A__        =   keep_accents
					A__        =   vocab_file
					A__        =   False if not self.vocab_file else True






			def    a_       (			self   :    List[Any]							,	__lowerCAmelCase   :    List[int]							,	__lowerCAmelCase   :    Optional[List[int]] = None   )	->	List[int]:







					"""simple docstring"""






					A__        =   [self.sep_token_id]
					A__        =   [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    a_       (			self   :    Union[str, Any]							,	__lowerCAmelCase   :    List[int]							,	__lowerCAmelCase   :    Optional[List[int]] = None   )	->	List[int]:







					"""simple docstring"""






					A__        =   [self.sep_token_id]
					A__        =   [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    a_       (			self   :    Tuple							,	__lowerCAmelCase   :    str							,	__lowerCAmelCase   :    Optional[str] = None   )	->	Tuple[str]:







					"""simple docstring"""






					if not self.can_save_slow_tokenizer:
							raise ValueError(
							    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
							    """tokenizer."""   )

					if not os.path.isdir(__lowerCAmelCase   ):
							logger.error(f'Vocabulary path ({save_directory}) should be a directory'   )
							return
					A__        =   os.path.join(
					    __lowerCAmelCase							,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )

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

					return (out_vocab_file,)



