





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 					lowercase__ (			unittest.TestCase       ):

   '''simple docstring'''


   def    UpperCamelCase__						(					self							)				->   Union[str, Any]:






       """simple docstring"""
       UpperCamelCase__							:       List[str]      =   tempfile.mkdtemp()

       # fmt: off
       UpperCamelCase__							:       List[Any]      =   ['''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__							:       int      =   dict(zip(_A,	range(len(_A							)							)							)							)
       UpperCamelCase__							:       int      =   ['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
       UpperCamelCase__							:       int      =   {'''unk_token''': '''<unk>'''}

       UpperCamelCase__							:       Union[str, Any]      =   os.path.join(self.tmpdirname,	VOCAB_FILES_NAMES['''vocab_file''']							)
       UpperCamelCase__							:       List[Any]      =   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__							:       str      =   {
           '''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__							:       str      =   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,	**__magic_name__							)				->   List[str]:






       """simple docstring"""
       return CLIPTokenizer.from_pretrained(self.tmpdirname,	**_A							)




   def    UpperCamelCase__						(					self,	**__magic_name__							)				->   Dict:






       """simple docstring"""
       return CLIPTokenizerFast.from_pretrained(self.tmpdirname,	**_A							)




   def    UpperCamelCase__						(					self,	**__magic_name__							)				->   List[str]:






       """simple docstring"""
       return CLIPImageProcessor.from_pretrained(self.tmpdirname,	**_A							)




   def    UpperCamelCase__						(					self							)				->   int:






       """simple docstring"""
       shutil.rmtree(self.tmpdirname							)




   def    UpperCamelCase__						(					self							)				->   Optional[int]:






       """simple docstring"""
       UpperCamelCase__							:       Optional[int]      =   [np.random.randint(255,	size=(3, 30, 400),	dtype=np.uinta							)]

       UpperCamelCase__							:       Optional[int]      =   [Image.fromarray(np.moveaxis(_A,	0,	-1							)							) for x in image_inputs]

       return image_inputs




   def    UpperCamelCase__						(					self							)				->   str:






       """simple docstring"""
       UpperCamelCase__							:       List[str]      =   self.get_tokenizer()
       UpperCamelCase__							:       Optional[int]      =   self.get_rust_tokenizer()
       UpperCamelCase__							:       Tuple      =   self.get_image_processor()

       UpperCamelCase__							:       Optional[Any]      =   CLIPProcessor(tokenizer=_A,	image_processor=_A							)
       processor_slow.save_pretrained(self.tmpdirname							)
       UpperCamelCase__							:       List[str]      =   CLIPProcessor.from_pretrained(self.tmpdirname,	use_fast=_A							)

       UpperCamelCase__							:       str      =   CLIPProcessor(tokenizer=_A,	image_processor=_A							)
       processor_fast.save_pretrained(self.tmpdirname							)
       UpperCamelCase__							:       List[str]      =   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    UpperCamelCase__						(					self							)				->   Union[str, Any]:






       """simple docstring"""
       UpperCamelCase__							:       Union[str, Any]      =   CLIPProcessor(tokenizer=self.get_tokenizer(),	image_processor=self.get_image_processor()							)
       processor.save_pretrained(self.tmpdirname							)

       UpperCamelCase__							:       int      =   self.get_tokenizer(bos_token='''(BOS)''',	eos_token='''(EOS)'''							)
       UpperCamelCase__							:       List[str]      =   self.get_image_processor(do_normalize=_A,	padding_value=1.0							)

       UpperCamelCase__							:       Optional[Any]      =   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    UpperCamelCase__						(					self							)				->   Tuple:






       """simple docstring"""
       UpperCamelCase__							:       List[Any]      =   self.get_image_processor()
       UpperCamelCase__							:       str      =   self.get_tokenizer()

       UpperCamelCase__							:       Optional[int]      =   CLIPProcessor(tokenizer=_A,	image_processor=_A							)

       UpperCamelCase__							:       List[Any]      =   self.prepare_image_inputs()

       UpperCamelCase__							:       Dict      =   image_processor(_A,	return_tensors='''np'''							)
       UpperCamelCase__							:       str      =   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    UpperCamelCase__						(					self							)				->   int:






       """simple docstring"""
       UpperCamelCase__							:       str      =   self.get_image_processor()
       UpperCamelCase__							:       List[Any]      =   self.get_tokenizer()

       UpperCamelCase__							:       Optional[Any]      =   CLIPProcessor(tokenizer=_A,	image_processor=_A							)

       UpperCamelCase__							:       Dict      =   '''lower newer'''

       UpperCamelCase__							:       Tuple      =   processor(text=_A							)

       UpperCamelCase__							:       List[Any]      =   tokenizer(_A							)

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




   def    UpperCamelCase__						(					self							)				->   Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:       int      =   self.get_image_processor()
       UpperCamelCase__							:       Dict      =   self.get_tokenizer()

       UpperCamelCase__							:       str      =   CLIPProcessor(tokenizer=_A,	image_processor=_A							)

       UpperCamelCase__							:       int      =   '''lower newer'''
       UpperCamelCase__							:       Union[str, Any]      =   self.prepare_image_inputs()

       UpperCamelCase__							:       List[Any]      =   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							)				->   Tuple:






       """simple docstring"""
       UpperCamelCase__							:       int      =   self.get_image_processor()
       UpperCamelCase__							:       Optional[int]      =   self.get_tokenizer()

       UpperCamelCase__							:       Tuple      =   CLIPProcessor(tokenizer=_A,	image_processor=_A							)

       UpperCamelCase__							:       int      =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       UpperCamelCase__							:       str      =   processor.batch_decode(_A							)
       UpperCamelCase__							:       List[str]      =   tokenizer.batch_decode(_A							)

       self.assertListEqual(_A,	_A							)




   def    UpperCamelCase__						(					self							)				->   Dict:






       """simple docstring"""
       UpperCamelCase__							:       List[Any]      =   self.get_image_processor()
       UpperCamelCase__							:       List[Any]      =   self.get_tokenizer()

       UpperCamelCase__							:       List[Any]      =   CLIPProcessor(tokenizer=_A,	image_processor=_A							)

       UpperCamelCase__							:       str      =   '''lower newer'''
       UpperCamelCase__							:       List[Any]      =   self.prepare_image_inputs()

       UpperCamelCase__							:       Dict      =   processor(text=_A,	images=_A							)

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








# Lint as: python3
import itertools
import os
import re


UpperCAmelCase_  							=				re.compile(R'([A-Z]+)([A-Z][a-z])')
UpperCAmelCase_  							=				re.compile(R'([a-z\d])([A-Z])')

UpperCAmelCase_  							=				re.compile(R'(?<!_)_(?!_)')
UpperCAmelCase_  							=				re.compile(R'(_{2,})')

UpperCAmelCase_  							=				R'^\w+(\.\w+)*$'

UpperCAmelCase_  							=				R'<>:/\|?*'




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Optional[int]   )   ->			List[Any]:
    UpperCamelCase__							:       int      =   _uppercase_uppercase_re.sub(r'''\1_\2'''	,     __UpperCAmelCase   )
    UpperCamelCase__							:       Tuple      =   _lowercase_uppercase_re.sub(r'''\1_\2'''	,     __UpperCAmelCase   )
    return name.lower()




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Any   )   ->			Any:
    UpperCamelCase__							:       Optional[int]      =   _single_underscore_re.split(__UpperCAmelCase   )
    UpperCamelCase__							:       Dict      =   [_multiple_underscores_re.split(__UpperCAmelCase   ) for n in name]
    return "".join(n.capitalize() for n in itertools.chain.from_iterable(__UpperCAmelCase   ) if n != ''''''   )




def 	lowerCAmelCase_					(						__UpperCAmelCase:    int   )   ->			Union[str, Any]:
    if os.path.basename(__UpperCAmelCase   ) != name:
        raise ValueError(f"Should be a dataset name, not a path: {name}"   )
    return camelcase_to_snakecase(__UpperCAmelCase   )




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Dict	,     __UpperCAmelCase:    Optional[int]   )   ->			Optional[Any]:
    if os.path.basename(__UpperCAmelCase   ) != name:
        raise ValueError(f"Should be a dataset name, not a path: {name}"   )
    if not re.match(_split_re	,     __UpperCAmelCase   ):
        raise ValueError(f"Split name should match '{_split_re}'' but got '{split}'."   )
    return f"{filename_prefix_for_name(__UpperCAmelCase   )}-{split}"




def 	lowerCAmelCase_					(						__UpperCAmelCase:    str	,     __UpperCAmelCase:    Dict	,     __UpperCAmelCase:    List[Any]	,     __UpperCAmelCase:    List[Any]=None   )   ->			Union[str, Any]:
    UpperCamelCase__							:       Optional[Any]      =   filename_prefix_for_split(__UpperCAmelCase	,     __UpperCAmelCase   )
    if filetype_suffix:
        prefix += f".{filetype_suffix}"
    UpperCamelCase__							:       Dict      =   os.path.join(__UpperCAmelCase	,     __UpperCAmelCase   )
    return f"{filepath}*"





def 	lowerCAmelCase_					(						__UpperCAmelCase:    List[str]	,     __UpperCAmelCase:    List[str]	,     __UpperCAmelCase:    int	,     __UpperCAmelCase:    str=None	,     __UpperCAmelCase:    Tuple=None   )   ->			Tuple:
    UpperCamelCase__							:       str      =   filename_prefix_for_split(__UpperCAmelCase	,     __UpperCAmelCase   )
    UpperCamelCase__							:       Tuple      =   os.path.join(__UpperCAmelCase	,     __UpperCAmelCase   )

    if shard_lengths:
        UpperCamelCase__							:       Optional[Any]      =   len(__UpperCAmelCase   )
        UpperCamelCase__							:       List[str]      =   [f"{prefix}-{shard_id:05d}-of-{num_shards:05d}" for shard_id in range(__UpperCAmelCase   )]
        if filetype_suffix:
            UpperCamelCase__							:       Union[str, Any]      =   [filename + f".{filetype_suffix}" for filename in filenames]
        return filenames
    else:
        UpperCamelCase__							:       Tuple      =   prefix
        if filetype_suffix:
            filename += f".{filetype_suffix}"
        return [filename]

