
import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


lowercase						:      Tuple									= logging.get_logger(__name__)


lowercase						:      Tuple									= {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

# See all BART models at https://huggingface.co/models?filter=bart
lowercase						:      str									= {
    'vocab_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
    },
    'merges_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json',
    },
}

lowercase						:      Dict									= {
    'facebook/bart-base': 1024,
    'facebook/bart-large': 1024,
    'facebook/bart-large-mnli': 1024,
    'facebook/bart-large-cnn': 1024,
    'facebook/bart-large-xsum': 1024,
    'yjernite/bart_eli5': 1024,
}




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  VOCAB_FILES_NAMES
	_A			=  PRETRAINED_VOCAB_FILES_MAP
	_A			=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_A			=  ['input_ids', 'attention_mask']
	_A			=  BartTokenizer







	def __init__(      self	:List[Any]  ,     a	:Dict=None  ,     a	:Union[str, Any]=None  ,     a	:Dict=None  ,     a	:Dict="replace"  ,     a	:List[str]="<s>"  ,     a	:Tuple="</s>"  ,     a	:Union[str, Any]="</s>"  ,     a	:Optional[int]="<s>"  ,     a	:Optional[int]="<unk>"  ,     a	:Optional[Any]="<pad>"  ,     a	:Any="<mask>"  ,     a	:Union[str, Any]=False  ,     a	:int=True  ,     **a	:Tuple  ,     )	->				Any:
								super().__init__(
								    a  ,     a  ,     tokenizer_file=a  ,     errors=a  ,     bos_token=a  ,     eos_token=a  ,     sep_token=a  ,     cls_token=a  ,     unk_token=a  ,     pad_token=a  ,     mask_token=a  ,     add_prefix_space=a  ,     trim_offsets=a  ,     **a  ,     )

								__UpperCamelCase			:     List[str]         =      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
								if pre_tok_state.get("add_prefix_space"  ,     a		) != add_prefix_space:
															__UpperCamelCase			:     Dict         =      getattr(a  ,     pre_tok_state.pop("type"		)		)
															__UpperCamelCase			:     Optional[Any]         =      add_prefix_space
															__UpperCamelCase			:     List[str]         =      pre_tok_class(**a		)

								__UpperCamelCase			:     Any         =      add_prefix_space

								# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
								__UpperCamelCase			:     Tuple         =      "post_processor"
								__UpperCamelCase			:     Tuple         =      getattr(self.backend_tokenizer  ,     a  ,     a		)
								if tokenizer_component_instance:
															__UpperCamelCase			:     Dict         =      json.loads(tokenizer_component_instance.__getstate__()		)

															# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
															if "sep" in state:
																						__UpperCamelCase			:     Optional[int]         =      tuple(state["sep"]		)
															if "cls" in state:
																						__UpperCamelCase			:     Dict         =      tuple(state["cls"]		)

															__UpperCamelCase			:     List[str]         =      False

															if state.get("add_prefix_space"  ,     a		) != add_prefix_space:
																						__UpperCamelCase			:     Tuple         =      add_prefix_space
																						__UpperCamelCase			:     List[Any]         =      True

															if state.get("trim_offsets"  ,     a		) != trim_offsets:
																						__UpperCamelCase			:     Union[str, Any]         =      trim_offsets
																						__UpperCamelCase			:     Any         =      True

															if changes_to_apply:
																						__UpperCamelCase			:     List[str]         =      getattr(a  ,     state.pop("type"		)		)
																						__UpperCamelCase			:     str         =      component_class(**a		)
																						setattr(self.backend_tokenizer  ,     a  ,     a		)







	@property
	def      _lowerCamelCase       (      self	:Dict		)	->				str:
								if self._mask_token is None:
															if self.verbose:
																						logger.error("Using mask_token, but it is not set yet."		)
															return None
								return str(self._mask_token		)







	@mask_token.setter
	def      _lowerCamelCase       (      self	:Optional[Any]  ,     a	:int		)	->				List[str]:
								__UpperCamelCase			:     Optional[int]         =      AddedToken(a  ,     lstrip=a  ,     rstrip=a		) if isinstance(a  ,     a		) else value
								__UpperCamelCase			:     List[Any]         =      value







	def      _lowerCamelCase       (      self	:Union[str, Any]  ,     *a	:Optional[Any]  ,     **a	:Dict		)	->				BatchEncoding:
								__UpperCamelCase			:     Tuple         =      kwargs.get("is_split_into_words"  ,     a		)

								if is_split_into_words and not self.add_prefix_space:
															raise ValueError(
															    f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
															    "to use it with pretokenized inputs."		)

								return super()._batch_encode_plus(*a  ,     **a		)







	def      _lowerCamelCase       (      self	:List[Any]  ,     *a	:List[str]  ,     **a	:Optional[Any]		)	->				BatchEncoding:
								__UpperCamelCase			:     Union[str, Any]         =      kwargs.get("is_split_into_words"  ,     a		)

								if is_split_into_words and not self.add_prefix_space:
															raise ValueError(
															    f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
															    "to use it with pretokenized inputs."		)

								return super()._encode_plus(*a  ,     **a		)







	def      _lowerCamelCase       (      self	:Dict  ,     a	:str  ,     a	:Optional[str] = None		)	->				Tuple[str]:
								__UpperCamelCase			:     int         =      self._tokenizer.model.save(a  ,     name=a		)
								return tuple(a		)







	def      _lowerCamelCase       (      self	:Optional[int]  ,     a	:Optional[Any]  ,     a	:List[Any]=None		)	->				Any:
								__UpperCamelCase			:     str         =      [self.bos_token_id] + token_ids_a + [self.eos_token_id]
								if token_ids_a is None:
															return output

								return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







	def      _lowerCamelCase       (      self	:Any  ,     a	:List[int]  ,     a	:Optional[List[int]] = None		)	->				List[int]:
								__UpperCamelCase			:     int         =      [self.sep_token_id]
								__UpperCamelCase			:     Union[str, Any]         =      [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 + sep + token_ids_a + sep		) * [0]

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__							:




			'''simple docstring'''




			@staticmethod
			def      _lowerCamelCase       (      *a	:Union[str, Any]  ,     **a	:int		)	->				str:
										pass




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Image)      ->					str:






							'''simple docstring'''





							__UpperCamelCase			:     Optional[int]         =      hashlib.mda(image.tobytes())
							return m.hexdigest()




@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 				lowerCamelCase__							(       unittest.TestCase):




	'''simple docstring'''




	_A			=  MODEL_FOR_DEPTH_ESTIMATION_MAPPING







	def      _lowerCamelCase       (      self	:Union[str, Any]  ,     a	:Optional[Any]  ,     a	:Optional[Any]  ,     a	:Optional[int]		)	->				Optional[Any]:
								__UpperCamelCase			:     Any         =      DepthEstimationPipeline(model=a  ,     image_processor=a		)
								return depth_estimator, [
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								]







	def      _lowerCamelCase       (      self	:Union[str, Any]  ,     a	:Optional[Any]  ,     a	:List[Any]		)	->				Dict:
								__UpperCamelCase			:     str         =      depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
								self.assertEqual({"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)}  ,     a		)
								import datasets

								__UpperCamelCase			:     List[Any]         =      datasets.load_dataset("hf-internal-testing/fixtures_image_utils"  ,     "image"  ,     split="test"		)
								__UpperCamelCase			:     int         =      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		)},
								    ]  ,     a  ,     )







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







	@slow
	@require_torch
	def      _lowerCamelCase       (      self	:List[Any]		)	->				List[Any]:
								__UpperCamelCase			:     Dict         =      "Intel/dpt-large"
								__UpperCamelCase			:     int         =      pipeline("depth-estimation"  ,     model=a		)
								__UpperCamelCase			:     Optional[int]         =      depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg"		)
								__UpperCamelCase			:     Optional[int]         =      hashimage(outputs["depth"]		)

								# This seems flaky.
								# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
								self.assertEqual(nested_simplify(outputs["predicted_depth"].max().item()		)  ,     29.304		)
								self.assertEqual(nested_simplify(outputs["predicted_depth"].min().item()		)  ,     2.662		)







	@require_torch
	def      _lowerCamelCase       (      self	:List[Any]		)	->				List[str]:
								# This is highly irregular to have no small tests.
								self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT"		)