
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        =  logging.get_logger(__name__)


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

# See all BART models at https://huggingface.co/models?filter=bart
lowercase        =  {
    '''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        =  {
    '''facebook/bart-base''': 1_0_2_4,
    '''facebook/bart-large''': 1_0_2_4,
    '''facebook/bart-large-mnli''': 1_0_2_4,
    '''facebook/bart-large-cnn''': 1_0_2_4,
    '''facebook/bart-large-xsum''': 1_0_2_4,
    '''yjernite/bart_eli5''': 1_0_2_4,
}





class       __A(				UpperCAmelCase       ):
					SCREAMING_SNAKE_CASE			    =    VOCAB_FILES_NAMES
					SCREAMING_SNAKE_CASE			    =    PRETRAINED_VOCAB_FILES_MAP
					SCREAMING_SNAKE_CASE			    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					SCREAMING_SNAKE_CASE			    =    ['''input_ids''', '''attention_mask''']
					SCREAMING_SNAKE_CASE			    =    BartTokenizer

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

												lowerCamelCase_							  =					json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
												if pre_tok_state.get("""add_prefix_space"""   ,					__UpperCamelCase   ) != add_prefix_space:
																			lowerCamelCase_							  =					getattr(__UpperCamelCase   ,					pre_tok_state.pop("""type"""   )   )
																			lowerCamelCase_							  =					add_prefix_space
																			lowerCamelCase_							  =					pre_tok_class(**__UpperCamelCase   )

												lowerCamelCase_							  =					add_prefix_space

												# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
												lowerCamelCase_							  =					"""post_processor"""
												lowerCamelCase_							  =					getattr(self.backend_tokenizer   ,					__UpperCamelCase   ,					__UpperCamelCase   )
												if tokenizer_component_instance:
																			lowerCamelCase_							  =					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:
																										lowerCamelCase_							  =					tuple(state["""sep"""]   )
																			if "cls" in state:
																										lowerCamelCase_							  =					tuple(state["""cls"""]   )

																			lowerCamelCase_							  =					False

																			if state.get("""add_prefix_space"""   ,					__UpperCamelCase   ) != add_prefix_space:
																										lowerCamelCase_							  =					add_prefix_space
																										lowerCamelCase_							  =					True

																			if state.get("""trim_offsets"""   ,					__UpperCamelCase   ) != trim_offsets:
																										lowerCamelCase_							  =					trim_offsets
																										lowerCamelCase_							  =					True

																			if changes_to_apply:
																										lowerCamelCase_							  =					getattr(__UpperCamelCase   ,					state.pop("""type"""   )   )
																										lowerCamelCase_							  =					component_class(**__UpperCamelCase   )
																										setattr(self.backend_tokenizer   ,					__UpperCamelCase   ,					__UpperCamelCase   )

					@property
					def    lowercase__      (	self					:     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    lowercase__      (	self					:     int   ,					__UpperCamelCase					:     Any   ):
												lowerCamelCase_							  =					AddedToken(__UpperCamelCase   ,					lstrip=__UpperCamelCase   ,					rstrip=__UpperCamelCase   ) if isinstance(__UpperCamelCase   ,					__UpperCamelCase   ) else value
												lowerCamelCase_							  =					value

					def    lowercase__      (	self					:     Optional[int]   ,					*__UpperCamelCase					:     int   ,					**__UpperCamelCase					:     Dict   ):
												lowerCamelCase_							  =					kwargs.get("""is_split_into_words"""   ,					__UpperCamelCase   )

												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(*__UpperCamelCase   ,					**__UpperCamelCase   )

					def    lowercase__      (	self					:     List[str]   ,					*__UpperCamelCase					:     Any   ,					**__UpperCamelCase					:     Dict   ):
												lowerCamelCase_							  =					kwargs.get("""is_split_into_words"""   ,					__UpperCamelCase   )

												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(*__UpperCamelCase   ,					**__UpperCamelCase   )

					def    lowercase__      (	self					:     Optional[Any]   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     Optional[str] = None   ):
												lowerCamelCase_							  =					self._tokenizer.model.save(__UpperCamelCase   ,					name=__UpperCamelCase   )
												return tuple(__UpperCamelCase   )

					def    lowercase__      (	self					:     Tuple   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     Optional[int]=None   ):
												lowerCamelCase_							  =					[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    lowercase__      (	self					:     Dict   ,					__UpperCamelCase					:     List[int]   ,					__UpperCamelCase					:     Optional[List[int]] = None   ):
												lowerCamelCase_							  =					[self.sep_token_id]
												lowerCamelCase_							  =					[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       __A:

							@staticmethod
							def    lowercase__      (	*__UpperCamelCase					:     str   ,					**__UpperCamelCase					:     Dict   ):
														pass



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Image       )  ->    str:
							lowerCamelCase_							  =					hashlib.mda(image.tobytes()       )
							return m.hexdigest()





@is_pipeline_test
@require_vision
@require_timm
@require_torch
class       __A(				unittest.TestCase       ):
					SCREAMING_SNAKE_CASE			    =    MODEL_FOR_DEPTH_ESTIMATION_MAPPING

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

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

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

					@require_tf
					@unittest.skip("""Depth estimation is not implemented in TF"""   )
					def    lowercase__      (	self					:     Optional[int]   ):
												pass

					@slow
					@require_torch
					def    lowercase__      (	self					:     List[Any]   ):
												lowerCamelCase_							  =					"""Intel/dpt-large"""
												lowerCamelCase_							  =					pipeline("""depth-estimation"""   ,					model=__UpperCamelCase   )
												lowerCamelCase_							  =					depth_estimator("""http://images.cocodataset.org/val2017/000000039769.jpg"""   )
												lowerCamelCase_							  =					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    lowercase__      (	self					:     Dict   ):
												# This is highly irregular to have no small tests.
												self.skipTest("""There is not hf-internal-testing tiny model for either GLPN nor DPT"""   )





