
'''simple docstring'''



import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
     from PIL import Image

     from transformers import CLIPSegProcessor, ViTImageProcessor

@require_vision
class    _a				(unittest.TestCase):





       """simple docstring"""




       def        UpperCamelCase				(							self      )    ->			List[Any]:
          _SCREAMING_SNAKE_CASE      =				tempfile.mkdtemp()

          # fmt: off
          _SCREAMING_SNAKE_CASE      =				["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
          # fmt: on
          _SCREAMING_SNAKE_CASE      =				dict(zip(A__       ,							range(len(A__      )      )      )      )
          _SCREAMING_SNAKE_CASE      =				["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
          _SCREAMING_SNAKE_CASE      =				{"""unk_token""": """<unk>"""}

          _SCREAMING_SNAKE_CASE      =				os.path.join(self.tmpdirname       ,							VOCAB_FILES_NAMES["""vocab_file"""]      )
          _SCREAMING_SNAKE_CASE      =				os.path.join(self.tmpdirname       ,							VOCAB_FILES_NAMES["""merges_file"""]      )
          with open(self.vocab_file       ,							"""w"""       ,							encoding="""utf-8"""      ) as fp:
             fp.write(json.dumps(A__      ) + """\n"""      )
          with open(self.merges_file       ,							"""w"""       ,							encoding="""utf-8"""      ) as fp:
             fp.write("""\n""".join(A__      )      )

          _SCREAMING_SNAKE_CASE      =				{
              """do_resize""": True,
              """size""": 20,
              """do_center_crop""": True,
              """crop_size""": 18,
              """do_normalize""": True,
              """image_mean""": [0.4814_5466, 0.457_8275, 0.4082_1073],
              """image_std""": [0.2686_2954, 0.2613_0258, 0.2757_7711],
          }
          _SCREAMING_SNAKE_CASE      =				os.path.join(self.tmpdirname       ,							A__      )
          with open(self.image_processor_file       ,							"""w"""       ,							encoding="""utf-8"""      ) as fp:
             json.dump(A__       ,							A__      )






       def        UpperCamelCase				(							self       ,							**A__      )    ->			Any:
          return CLIPTokenizer.from_pretrained(self.tmpdirname       ,							**A__      )






       def        UpperCamelCase				(							self       ,							**A__      )    ->			str:
          return CLIPTokenizerFast.from_pretrained(self.tmpdirname       ,							**A__      )






       def        UpperCamelCase				(							self       ,							**A__      )    ->			Dict:
          return ViTImageProcessor.from_pretrained(self.tmpdirname       ,							**A__      )






       def        UpperCamelCase				(							self      )    ->			Any:
          shutil.rmtree(self.tmpdirname      )






       def        UpperCamelCase				(							self      )    ->			List[Any]:
          _SCREAMING_SNAKE_CASE      =				[np.random.randint(2_55       ,							size=(3, 30, 4_00)       ,							dtype=np.uinta      )]

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

          return image_inputs






       def        UpperCamelCase				(							self      )    ->			Union[str, Any]:
          _SCREAMING_SNAKE_CASE      =				self.get_tokenizer()
          _SCREAMING_SNAKE_CASE      =				self.get_rust_tokenizer()
          _SCREAMING_SNAKE_CASE      =				self.get_image_processor()

          _SCREAMING_SNAKE_CASE      =				CLIPSegProcessor(tokenizer=A__       ,							image_processor=A__      )
          processor_slow.save_pretrained(self.tmpdirname      )
          _SCREAMING_SNAKE_CASE      =				CLIPSegProcessor.from_pretrained(self.tmpdirname       ,							use_fast=A__      )

          _SCREAMING_SNAKE_CASE      =				CLIPSegProcessor(tokenizer=A__       ,							image_processor=A__      )
          processor_fast.save_pretrained(self.tmpdirname      )
          _SCREAMING_SNAKE_CASE      =				CLIPSegProcessor.from_pretrained(self.tmpdirname      )

          self.assertEqual(processor_slow.tokenizer.get_vocab()       ,							tokenizer_slow.get_vocab()      )
          self.assertEqual(processor_fast.tokenizer.get_vocab()       ,							tokenizer_fast.get_vocab()      )
          self.assertEqual(tokenizer_slow.get_vocab()       ,							tokenizer_fast.get_vocab()      )
          self.assertIsInstance(processor_slow.tokenizer       ,							A__      )
          self.assertIsInstance(processor_fast.tokenizer       ,							A__      )

          self.assertEqual(processor_slow.image_processor.to_json_string()       ,							image_processor.to_json_string()      )
          self.assertEqual(processor_fast.image_processor.to_json_string()       ,							image_processor.to_json_string()      )
          self.assertIsInstance(processor_slow.image_processor       ,							A__      )
          self.assertIsInstance(processor_fast.image_processor       ,							A__      )






       def        UpperCamelCase				(							self      )    ->			Optional[Any]:
          _SCREAMING_SNAKE_CASE      =				CLIPSegProcessor(tokenizer=self.get_tokenizer()       ,							image_processor=self.get_image_processor()      )
          processor.save_pretrained(self.tmpdirname      )

          _SCREAMING_SNAKE_CASE      =				self.get_tokenizer(bos_token="""(BOS)"""       ,							eos_token="""(EOS)"""      )
          _SCREAMING_SNAKE_CASE      =				self.get_image_processor(do_normalize=A__       ,							padding_value=1.0      )

          _SCREAMING_SNAKE_CASE      =				CLIPSegProcessor.from_pretrained(
              self.tmpdirname       ,							bos_token="""(BOS)"""       ,							eos_token="""(EOS)"""       ,							do_normalize=A__       ,							padding_value=1.0      )

          self.assertEqual(processor.tokenizer.get_vocab()       ,							tokenizer_add_kwargs.get_vocab()      )
          self.assertIsInstance(processor.tokenizer       ,							A__      )

          self.assertEqual(processor.image_processor.to_json_string()       ,							image_processor_add_kwargs.to_json_string()      )
          self.assertIsInstance(processor.image_processor       ,							A__      )






       def        UpperCamelCase				(							self      )    ->			Dict:
          _SCREAMING_SNAKE_CASE      =				self.get_image_processor()
          _SCREAMING_SNAKE_CASE      =				self.get_tokenizer()

          _SCREAMING_SNAKE_CASE      =				CLIPSegProcessor(tokenizer=A__       ,							image_processor=A__      )

          _SCREAMING_SNAKE_CASE      =				self.prepare_image_inputs()

          _SCREAMING_SNAKE_CASE      =				image_processor(A__       ,							return_tensors="""np"""      )
          _SCREAMING_SNAKE_CASE      =				processor(images=A__       ,							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        UpperCamelCase				(							self      )    ->			Tuple:
          _SCREAMING_SNAKE_CASE      =				self.get_image_processor()
          _SCREAMING_SNAKE_CASE      =				self.get_tokenizer()

          _SCREAMING_SNAKE_CASE      =				CLIPSegProcessor(tokenizer=A__       ,							image_processor=A__      )

          _SCREAMING_SNAKE_CASE      =				"""lower newer"""

          _SCREAMING_SNAKE_CASE      =				processor(text=A__      )

          _SCREAMING_SNAKE_CASE      =				tokenizer(A__      )

          for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key]       ,							encoded_processor[key]      )






       def        UpperCamelCase				(							self      )    ->			Any:
          _SCREAMING_SNAKE_CASE      =				self.get_image_processor()
          _SCREAMING_SNAKE_CASE      =				self.get_tokenizer()

          _SCREAMING_SNAKE_CASE      =				CLIPSegProcessor(tokenizer=A__       ,							image_processor=A__      )

          _SCREAMING_SNAKE_CASE      =				"""lower newer"""
          _SCREAMING_SNAKE_CASE      =				self.prepare_image_inputs()

          _SCREAMING_SNAKE_CASE      =				processor(text=A__       ,							images=A__      )

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

          # test if it raises when no input is passed
          with pytest.raises(A__      ):
             processor()






       def        UpperCamelCase				(							self      )    ->			int:
          _SCREAMING_SNAKE_CASE      =				self.get_image_processor()
          _SCREAMING_SNAKE_CASE      =				self.get_tokenizer()

          _SCREAMING_SNAKE_CASE      =				CLIPSegProcessor(tokenizer=A__       ,							image_processor=A__      )

          _SCREAMING_SNAKE_CASE      =				self.prepare_image_inputs()
          _SCREAMING_SNAKE_CASE      =				self.prepare_image_inputs()

          _SCREAMING_SNAKE_CASE      =				processor(images=A__       ,							visual_prompt=A__      )

          self.assertListEqual(list(inputs.keys()      )       ,							["""pixel_values""", """conditional_pixel_values"""]      )

          # test if it raises when no input is passed
          with pytest.raises(A__      ):
             processor()






       def        UpperCamelCase				(							self      )    ->			Optional[Any]:
          _SCREAMING_SNAKE_CASE      =				self.get_image_processor()
          _SCREAMING_SNAKE_CASE      =				self.get_tokenizer()

          _SCREAMING_SNAKE_CASE      =				CLIPSegProcessor(tokenizer=A__       ,							image_processor=A__      )

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

          _SCREAMING_SNAKE_CASE      =				processor.batch_decode(A__      )
          _SCREAMING_SNAKE_CASE      =				tokenizer.batch_decode(A__      )

          self.assertListEqual(A__       ,							A__      )








'''simple docstring'''



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    _a				(_lowerCamelCase):





       """simple docstring"""




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






       def __init__(							self       ,							A__ = None       ,							A__ = None       ,							**A__       ,							)    ->			Optional[int]:
          super().__init__(self       ,							**A__      )
          _SCREAMING_SNAKE_CASE      =				repo_info
          _SCREAMING_SNAKE_CASE      =				token
          _SCREAMING_SNAKE_CASE      =				None






       def        UpperCamelCase				(							self      )    ->			Tuple:
          if self.dir_cache is None:
             _SCREAMING_SNAKE_CASE      =				{}
             for hf_file in self.repo_info.siblings:
                # TODO(QL): add sizes
                _SCREAMING_SNAKE_CASE      =				{
                    """name""": hf_file.rfilename,
                    """size""": None,
                    """type""": """file""",
                }
                self.dir_cache.update(
                    {
                        str(A__      ): {"""name""": str(A__      ), """size""": None, """type""": """directory"""}
                        for d in list(PurePosixPath(hf_file.rfilename      ).parents      )[:-1]
                    }      )






       def        UpperCamelCase				(							self       ,							A__       ,							A__ = "rb"       ,							**A__       ,							)    ->			Optional[int]:
          if not isinstance(self.repo_info       ,							A__      ):
             raise NotImplementedError(F"Open is only implemented for dataset repositories, but got {self.repo_info}"      )
          _SCREAMING_SNAKE_CASE      =				hf_hub_url(self.repo_info.id       ,							A__       ,							revision=self.repo_info.sha      )
          return fsspec.open(
              A__       ,							mode=A__       ,							headers=get_authentication_headers_for_url(A__       ,							use_auth_token=self.token      )       ,							client_kwargs={"""trust_env""": True}       ,							).open()






       def        UpperCamelCase				(							self       ,							A__       ,							**A__      )    ->			str:
          self._get_dirs()
          _SCREAMING_SNAKE_CASE      =				self._strip_protocol(A__      )
          if path in self.dir_cache:
             return self.dir_cache[path]
          else:
             raise FileNotFoundError(A__      )






       def        UpperCamelCase				(							self       ,							A__       ,							A__=False       ,							**A__      )    ->			List[Any]:
          self._get_dirs()
          _SCREAMING_SNAKE_CASE      =				PurePosixPath(path.strip("""/"""      )      )
          _SCREAMING_SNAKE_CASE      =				{}
          for p, f in self.dir_cache.items():
             _SCREAMING_SNAKE_CASE      =				PurePosixPath(p.strip("""/"""      )      )
             _SCREAMING_SNAKE_CASE      =				p.parent
             if root == path:
                _SCREAMING_SNAKE_CASE      =				f
          _SCREAMING_SNAKE_CASE      =				list(paths.values()      )
          if detail:
             return out
          else:
             return sorted(f["""name"""] for f in out      )






