
import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
						from PIL import Image

						from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor
@require_tokenizers
@require_vision
class  lowercase__(				unittest.TestCase							):



	"""simple docstring"""




	def 						_lowercase  (       self  :			Any       )       ->     str:
					lowercase_       =					tempfile.mkdtemp()

					# fmt: off
					lowercase_       =					['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
					# fmt: on
					lowercase_       =					os.path.join(self.tmpdirname       ,							VOCAB_FILES_NAMES['''vocab_file''']       )
					with open(self.vocab_file       ,							'''w'''       ,							encoding='''utf-8'''       ) as vocab_writer:
									vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]       )       )

					lowercase_       =					{
					    '''do_resize''': True,
					    '''size''': {'''height''': 1_8, '''width''': 1_8},
					    '''do_normalize''': True,
					    '''image_mean''': [0.5, 0.5, 0.5],
					    '''image_std''': [0.5, 0.5, 0.5],
					}
					lowercase_       =					os.path.join(self.tmpdirname       ,							SCREAMING_SNAKE_CASE_       )
					with open(self.image_processor_file       ,							'''w'''       ,							encoding='''utf-8'''       ) as fp:
									json.dump(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			Any       ,							**SCREAMING_SNAKE_CASE_  :			Any       )       ->     Optional[Any]:
					return BertTokenizer.from_pretrained(self.tmpdirname       ,							**SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			Optional[int]       ,							**SCREAMING_SNAKE_CASE_  :			List[Any]       )       ->     Optional[int]:
					return ViTImageProcessor.from_pretrained(self.tmpdirname       ,							**SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			str       )       ->     Optional[int]:
					shutil.rmtree(self.tmpdirname       )
	def 						_lowercase  (       self  :			Union[str, Any]       )       ->     Union[str, Any]:
					lowercase_       =					[np.random.randint(2_5_5       ,							size=(3, 3_0, 4_0_0)       ,							dtype=np.uinta       )]

					lowercase_       =					[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE_       ,							0       ,							-1       )       ) for x in image_inputs]

					return image_inputs
	def 						_lowercase  (       self  :			Dict       )       ->     Optional[int]:
					lowercase_       =					self.get_tokenizer()
					lowercase_       =					self.get_image_processor()

					lowercase_       =					VisionTextDualEncoderProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,							image_processor=SCREAMING_SNAKE_CASE_       )

					processor.save_pretrained(self.tmpdirname       )
					lowercase_       =					VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname       )

					self.assertEqual(processor.tokenizer.get_vocab()       ,							tokenizer.get_vocab()       )
					self.assertIsInstance(processor.tokenizer       ,							(BertTokenizer, BertTokenizerFast)       )

					self.assertEqual(processor.image_processor.to_json_string()       ,							image_processor.to_json_string()       )
					self.assertIsInstance(processor.image_processor       ,							SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			Union[str, Any]       )       ->     List[Any]:
					lowercase_       =					VisionTextDualEncoderProcessor(
					    tokenizer=self.get_tokenizer()       ,							image_processor=self.get_image_processor()       )
					processor.save_pretrained(self.tmpdirname       )

					lowercase_       =					self.get_tokenizer(bos_token='''(BOS)'''       ,							eos_token='''(EOS)'''       )
					lowercase_       =					self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE_       ,							padding_value=1.0       )

					lowercase_       =					VisionTextDualEncoderProcessor.from_pretrained(
					    self.tmpdirname       ,							bos_token='''(BOS)'''       ,							eos_token='''(EOS)'''       ,							do_normalize=SCREAMING_SNAKE_CASE_       ,							padding_value=1.0       )

					self.assertEqual(processor.tokenizer.get_vocab()       ,							tokenizer_add_kwargs.get_vocab()       )
					self.assertIsInstance(processor.tokenizer       ,							(BertTokenizer, BertTokenizerFast)       )

					self.assertEqual(processor.image_processor.to_json_string()       ,							image_processor_add_kwargs.to_json_string()       )
					self.assertIsInstance(processor.image_processor       ,							SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			Union[str, Any]       )       ->     Optional[int]:
					lowercase_       =					self.get_image_processor()
					lowercase_       =					self.get_tokenizer()

					lowercase_       =					VisionTextDualEncoderProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,							image_processor=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					self.prepare_image_inputs()

					lowercase_       =					image_processor(SCREAMING_SNAKE_CASE_       ,							return_tensors='''np'''       )
					lowercase_       =					processor(images=SCREAMING_SNAKE_CASE_       ,							return_tensors='''np'''       )

					for key in input_feat_extract.keys():
									self.assertAlmostEqual(input_feat_extract[key].sum()       ,							input_processor[key].sum()       ,							delta=1e-2       )
	def 						_lowercase  (       self  :			List[str]       )       ->     Optional[int]:
					lowercase_       =					self.get_image_processor()
					lowercase_       =					self.get_tokenizer()

					lowercase_       =					VisionTextDualEncoderProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,							image_processor=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					'''lower newer'''

					lowercase_       =					processor(text=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					tokenizer(SCREAMING_SNAKE_CASE_       )

					for key in encoded_tok.keys():
									self.assertListEqual(encoded_tok[key]       ,							encoded_processor[key]       )
	def 						_lowercase  (       self  :			List[Any]       )       ->     Optional[int]:
					lowercase_       =					self.get_image_processor()
					lowercase_       =					self.get_tokenizer()

					lowercase_       =					VisionTextDualEncoderProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,							image_processor=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					'''lower newer'''
					lowercase_       =					self.prepare_image_inputs()

					lowercase_       =					processor(text=SCREAMING_SNAKE_CASE_       ,							images=SCREAMING_SNAKE_CASE_       )

					self.assertListEqual(list(inputs.keys()       )       ,							['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values''']       )

					# test if it raises when no input is passed
					with self.assertRaises(SCREAMING_SNAKE_CASE_       ):
									processor()
	def 						_lowercase  (       self  :			Tuple       )       ->     Optional[Any]:
					lowercase_       =					self.get_image_processor()
					lowercase_       =					self.get_tokenizer()

					lowercase_       =					VisionTextDualEncoderProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,							image_processor=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					lowercase_       =					processor.batch_decode(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					tokenizer.batch_decode(SCREAMING_SNAKE_CASE_       )

					self.assertListEqual(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )




	def 						_lowercase  (       self  :			Optional[Any]       )       ->     Dict:
					lowercase_       =					self.get_image_processor()
					lowercase_       =					self.get_tokenizer()

					lowercase_       =					VisionTextDualEncoderProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,							image_processor=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					'''lower newer'''
					lowercase_       =					self.prepare_image_inputs()

					lowercase_       =					processor(text=SCREAMING_SNAKE_CASE_       ,							images=SCREAMING_SNAKE_CASE_       )

					self.assertListEqual(list(inputs.keys()       )       ,							processor.model_input_names       )












from pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




				lowercase__											=       """"""
				lowercase__											=       """hf-legacy"""  # "hf://"" is reserved for hffs




				def __init__(     self						:    List[str],  lowerCamelCase						:    Optional[DatasetInfo] = None,  lowerCamelCase						:    Optional[str] = None,  **lowerCamelCase						:    str,  ):

								'''simple docstring'''
								super().__init__(self,  **lowerCamelCase  )
								lowercase__    =			repo_info
								lowercase__    =			token
								lowercase__    =			None




				def 				lowercase__   (     self						:    Optional[Any]  ):

								'''simple docstring'''
								if self.dir_cache is None:
												lowercase__    =			{}
												for hf_file in self.repo_info.siblings:
																# TODO(QL): add sizes
																lowercase__    =			{
																    '''name''': hf_file.rfilename,
																    '''size''': None,
																    '''type''': '''file''',
																}
																self.dir_cache.update(
																    {
																        str(lowerCamelCase  ): {'''name''': str(lowerCamelCase  ), '''size''': None, '''type''': '''directory'''}
																        for d in list(PurePosixPath(hf_file.rfilename  ).parents  )[:-1]
																    }  )




				def 				lowercase__   (     self						:    Any,  lowerCamelCase						:    str,  lowerCamelCase						:    str = "rb",  **lowerCamelCase						:    Any,  ):

								'''simple docstring'''
								if not isinstance(self.repo_info,  lowerCamelCase  ):
												raise NotImplementedError(F"""Open is only implemented for dataset repositories, but got {self.repo_info}"""  )
								lowercase__    =			hf_hub_url(self.repo_info.id,  lowerCamelCase,  revision=self.repo_info.sha  )
								return fsspec.open(
								    lowerCamelCase,  mode=lowerCamelCase,  headers=get_authentication_headers_for_url(lowerCamelCase,  use_auth_token=self.token  ),  client_kwargs={'''trust_env''': True},  ).open()




				def 				lowercase__   (     self						:    Dict,  lowerCamelCase						:    Any,  **lowerCamelCase						:    int  ):

								'''simple docstring'''
								self._get_dirs()
								lowercase__    =			self._strip_protocol(lowerCamelCase  )
								if path in self.dir_cache:
												return self.dir_cache[path]
								else:
												raise FileNotFoundError(lowerCamelCase  )




				def 				lowercase__   (     self						:    Optional[Any],  lowerCamelCase						:    List[str],  lowerCamelCase						:    Optional[int]=False,  **lowerCamelCase						:    str  ):

								'''simple docstring'''
								self._get_dirs()
								lowercase__    =			PurePosixPath(path.strip('''/'''  )  )
								lowercase__    =			{}
								for p, f in self.dir_cache.items():
												lowercase__    =			PurePosixPath(p.strip('''/'''  )  )
												lowercase__    =			p.parent
												if root == path:
																lowercase__    =			f
								lowercase__    =			list(paths.values()  )
								if detail:
												return out
								else:
												return sorted(f['''name'''] for f in out  )




