


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 CLIPImageProcessor, CLIPProcessor






@require_vision
class     UpperCAmelCase__       (					unittest.TestCase		):



    """simple docstring"""






    def _a					(			self    )  ->       str:
         __UpperCamelCase    							=tempfile.mkdtemp()

         # fmt: off
         __UpperCamelCase    							=['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
         __UpperCamelCase    							=dict(zip(A_					,   range(len(A_    )    )    )    )
         __UpperCamelCase    							=['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
         __UpperCamelCase    							={'unk_token': '<unk>'}

         __UpperCamelCase    							=os.path.join(self.tmpdirname					,   VOCAB_FILES_NAMES['vocab_file']    )
         __UpperCamelCase    							=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_    )    )

         __UpperCamelCase    							={
             '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],
         }
         __UpperCamelCase    							=os.path.join(self.tmpdirname					,   A_    )
         with open(self.image_processor_file					,   'w'					,   encoding='utf-8'    ) as fp:
              json.dump(A_					,   A_    )






    def _a					(			self					,   **A_    )  ->       List[Any]:
         return CLIPTokenizer.from_pretrained(self.tmpdirname					,   **A_    )






    def _a					(			self					,   **A_    )  ->       Any:
         return CLIPTokenizerFast.from_pretrained(self.tmpdirname					,   **A_    )






    def _a					(			self					,   **A_    )  ->       Optional[Any]:
         return CLIPImageProcessor.from_pretrained(self.tmpdirname					,   **A_    )






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






    def _a					(			self    )  ->       Tuple:
         __UpperCamelCase    							=[np.random.randint(255					,   size=(3, 30, 400)					,   dtype=np.uinta    )]

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

         return image_inputs






    def _a					(			self    )  ->       List[str]:
         __UpperCamelCase    							=self.get_tokenizer()
         __UpperCamelCase    							=self.get_rust_tokenizer()
         __UpperCamelCase    							=self.get_image_processor()

         __UpperCamelCase    							=CLIPProcessor(tokenizer=A_					,   image_processor=A_    )
         processor_slow.save_pretrained(self.tmpdirname    )
         __UpperCamelCase    							=CLIPProcessor.from_pretrained(self.tmpdirname					,   use_fast=A_    )

         __UpperCamelCase    							=CLIPProcessor(tokenizer=A_					,   image_processor=A_    )
         processor_fast.save_pretrained(self.tmpdirname    )
         __UpperCamelCase    							=CLIPProcessor.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 _a					(			self    )  ->       Optional[Any]:
         __UpperCamelCase    							=CLIPProcessor(tokenizer=self.get_tokenizer()					,   image_processor=self.get_image_processor()    )
         processor.save_pretrained(self.tmpdirname    )

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

         __UpperCamelCase    							=CLIPProcessor.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 _a					(			self    )  ->       Tuple:
         __UpperCamelCase    							=self.get_image_processor()
         __UpperCamelCase    							=self.get_tokenizer()

         __UpperCamelCase    							=CLIPProcessor(tokenizer=A_					,   image_processor=A_    )

         __UpperCamelCase    							=self.prepare_image_inputs()

         __UpperCamelCase    							=image_processor(A_					,   return_tensors='np'    )
         __UpperCamelCase    							=processor(images=A_					,   return_tensors='np'    )

         for key in input_image_proc.keys():
              self.assertAlmostEqual(input_image_proc[key].sum()					,   input_processor[key].sum()					,   delta=1E-2    )






    def _a					(			self    )  ->       Union[str, Any]:
         __UpperCamelCase    							=self.get_image_processor()
         __UpperCamelCase    							=self.get_tokenizer()

         __UpperCamelCase    							=CLIPProcessor(tokenizer=A_					,   image_processor=A_    )

         __UpperCamelCase    							='lower newer'

         __UpperCamelCase    							=processor(text=A_    )

         __UpperCamelCase    							=tokenizer(A_    )

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






    def _a					(			self    )  ->       List[Any]:
         __UpperCamelCase    							=self.get_image_processor()
         __UpperCamelCase    							=self.get_tokenizer()

         __UpperCamelCase    							=CLIPProcessor(tokenizer=A_					,   image_processor=A_    )

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

         __UpperCamelCase    							=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 _a					(			self    )  ->       Union[str, Any]:
         __UpperCamelCase    							=self.get_image_processor()
         __UpperCamelCase    							=self.get_tokenizer()

         __UpperCamelCase    							=CLIPProcessor(tokenizer=A_					,   image_processor=A_    )

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

         __UpperCamelCase    							=processor.batch_decode(A_    )
         __UpperCamelCase    							=tokenizer.batch_decode(A_    )

         self.assertListEqual(A_					,   A_    )






    def _a					(			self    )  ->       Union[str, Any]:
         __UpperCamelCase    							=self.get_image_processor()
         __UpperCamelCase    							=self.get_tokenizer()

         __UpperCamelCase    							=CLIPProcessor(tokenizer=A_					,   image_processor=A_    )

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

         __UpperCamelCase    							=processor(text=A_					,   images=A_    )

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






from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

UpperCamelCase__											=					[
    'python',
    'tqdm',
    'regex',
    'requests',
    'packaging',
    'filelock',
    'numpy',
    'tokenizers',
    'huggingface-hub',
    'safetensors',
    'accelerate',
    'pyyaml',
]

for pkg in pkgs_to_check_at_runtime:
      if pkg in deps:
            if pkg == "tokenizers":
                  # must be loaded here, or else tqdm check may fail
                  from .utils import is_tokenizers_available

                  if not is_tokenizers_available():
                        continue  # not required, check version only if installed
            elif pkg == "accelerate":
                  # must be loaded here, or else tqdm check may fail
                  from .utils import is_accelerate_available

                  # Maybe switch to is_torch_available in the future here so that Accelerate is hard dep of
                  # Transformers with PyTorch
                  if not is_accelerate_available():
                        continue  # not required, check version only if installed

            require_version_core(deps[pkg])
      else:
            raise ValueError(f'''can\'t find {pkg} in {deps.keys()}, check dependency_versions_table.py''')




def    lowerCAmelCase_ (     __A,  __A=None							)     ->				Dict:







      '''simple docstring'''


      require_version(deps[pkg],  __A							)



