
"""simple docstring"""



import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida

from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
    EntryNotFoundError,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    is_jinja_available,
)
from packaging import version
from requests import HTTPError

from .. import __version__
from .constants import (
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    SAFETENSORS_WEIGHTS_NAME,
    WEIGHTS_NAME,
)
from .import_utils import (
    ENV_VARS_TRUE_VALUES,
    _flax_version,
    _jax_version,
    _onnxruntime_version,
    _torch_version,
    is_flax_available,
    is_onnx_available,
    is_torch_available,
)
from .logging import get_logger


UpperCAmelCase__          = get_logger(__name__)


UpperCAmelCase__          = Path(__file__).parent / 'model_card_template.md'
UpperCAmelCase__          = uuida().hex
UpperCAmelCase__          = os.getenv('HF_HUB_OFFLINE', '').upper() in ENV_VARS_TRUE_VALUES
UpperCAmelCase__          = os.getenv('DISABLE_TELEMETRY', '').upper() in ENV_VARS_TRUE_VALUES
UpperCAmelCase__          = HUGGINGFACE_CO_RESOLVE_ENDPOINT + '/api/telemetry/'
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Union[Dict, str, None] = None		)     ->					str:
  _snake_case					     =       f'''diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}'''
  if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
    return ua + "; telemetry/off"
  if is_torch_available():
    ua += f'''; torch/{_torch_version}'''
  if is_flax_available():
    ua += f'''; jax/{_jax_version}'''
    ua += f'''; flax/{_flax_version}'''
  if is_onnx_available():
    ua += f'''; onnxruntime/{_onnxruntime_version}'''
  # CI will set this value to True
  if os.environ.get('''DIFFUSERS_IS_CI''' ,					''''''		).upper() in ENV_VARS_TRUE_VALUES:
    ua += "; is_ci/true"
  if isinstance(__lowerCamelCase ,					__lowerCamelCase		):
    ua += "; " + "; ".join(f'''{k}/{v}''' for k, v in user_agent.items()		)
  elif isinstance(__lowerCamelCase ,					__lowerCamelCase		):
    ua += "; " + user_agent
  return ua
def 					_UpperCAmelCase      ( __lowerCamelCase   :       str ,					__lowerCamelCase   :       Optional[str] = None ,					__lowerCamelCase   :       Optional[str] = None		)     ->					Any:
  if token is None:
    _snake_case					     =       HfFolder.get_token()
  if organization is None:
    _snake_case					     =       whoami(__lowerCamelCase		)['''name''']
    return f'''{username}/{model_id}'''
  else:
    return f'''{organization}/{model_id}'''
def 					_UpperCAmelCase      ( __lowerCamelCase   :       int ,					__lowerCamelCase   :       Tuple		)     ->					Union[str, Any]:
  if not is_jinja_available():
    raise ValueError(
        '''Modelcard rendering is based on Jinja templates.'''
        ''' Please make sure to have `jinja` installed before using `create_model_card`.'''
        ''' To install it, please run `pip install Jinja2`.'''		)

  if hasattr(__lowerCamelCase ,					'''local_rank'''		) and args.local_rank not in [-1, 0]:
    return

  _snake_case					     =       args.hub_token if hasattr(__lowerCamelCase ,					'''hub_token'''		) else None
  _snake_case					     =       get_full_repo_name(__lowerCamelCase ,					token=__lowerCamelCase		)

  _snake_case					     =       ModelCard.from_template(
      card_data=ModelCardData(  # Card metadata object that will be converted to YAML block
          language='''en''' ,					license='''apache-2.0''' ,					library_name='''diffusers''' ,					tags=[] ,					datasets=args.dataset_name ,					metrics=[] ,					) ,					template_path=__lowerCamelCase ,					model_name=__lowerCamelCase ,					repo_name=__lowerCamelCase ,					dataset_name=args.dataset_name if hasattr(__lowerCamelCase ,					'''dataset_name'''		) else None ,					learning_rate=args.learning_rate ,					train_batch_size=args.train_batch_size ,					eval_batch_size=args.eval_batch_size ,					gradient_accumulation_steps=(
          args.gradient_accumulation_steps if hasattr(__lowerCamelCase ,					'''gradient_accumulation_steps'''		) else None
      ) ,					adam_betaa=args.adam_betaa if hasattr(__lowerCamelCase ,					'''adam_beta1'''		) else None ,					adam_betaa=args.adam_betaa if hasattr(__lowerCamelCase ,					'''adam_beta2'''		) else None ,					adam_weight_decay=args.adam_weight_decay if hasattr(__lowerCamelCase ,					'''adam_weight_decay'''		) else None ,					adam_epsilon=args.adam_epsilon if hasattr(__lowerCamelCase ,					'''adam_epsilon'''		) else None ,					lr_scheduler=args.lr_scheduler if hasattr(__lowerCamelCase ,					'''lr_scheduler'''		) else None ,					lr_warmup_steps=args.lr_warmup_steps if hasattr(__lowerCamelCase ,					'''lr_warmup_steps'''		) else None ,					ema_inv_gamma=args.ema_inv_gamma if hasattr(__lowerCamelCase ,					'''ema_inv_gamma'''		) else None ,					ema_power=args.ema_power if hasattr(__lowerCamelCase ,					'''ema_power'''		) else None ,					ema_max_decay=args.ema_max_decay if hasattr(__lowerCamelCase ,					'''ema_max_decay'''		) else None ,					mixed_precision=args.mixed_precision ,					)

  _snake_case					     =       os.path.join(args.output_dir ,					'''README.md'''		)
  model_card.save(__lowerCamelCase		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Optional[str] ,					__lowerCamelCase   :       Optional[str] = None		)     ->					str:
  if resolved_file is None or commit_hash is not None:
    return commit_hash
  _snake_case					     =       str(Path(__lowerCamelCase		).as_posix()		)
  _snake_case					     =       re.search(R'''snapshots/([^/]+)/''' ,					__lowerCamelCase		)
  if search is None:
    return None
  _snake_case					     =       search.groups()[0]
  return commit_hash if REGEX_COMMIT_HASH.match(__lowerCamelCase		) else None


# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
UpperCAmelCase__          = os.path.expanduser(
    os.getenv('HF_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'huggingface'))
)
UpperCAmelCase__          = os.path.join(hf_cache_home, 'diffusers')
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Optional[str] = None ,					__lowerCamelCase   :       Optional[str] = None		)     ->					None:
  if new_cache_dir is None:
    _snake_case					     =       DIFFUSERS_CACHE
  if old_cache_dir is None:
    _snake_case					     =       old_diffusers_cache

  _snake_case					     =       Path(__lowerCamelCase		).expanduser()
  _snake_case					     =       Path(__lowerCamelCase		).expanduser()
  for old_blob_path in old_cache_dir.glob('''**/blobs/*'''		):
    if old_blob_path.is_file() and not old_blob_path.is_symlink():
      _snake_case					     =       new_cache_dir / old_blob_path.relative_to(__lowerCamelCase		)
      new_blob_path.parent.mkdir(parents=__lowerCamelCase ,					exist_ok=__lowerCamelCase		)
      os.replace(__lowerCamelCase ,					__lowerCamelCase		)
      try:
        os.symlink(__lowerCamelCase ,					__lowerCamelCase		)
      except OSError:
        logger.warning(
            '''Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded.'''		)
  # At this point, old_cache_dir contains symlinks to the new cache (it can still be used).


UpperCAmelCase__          = os.path.join(DIFFUSERS_CACHE, 'version_diffusers_cache.txt')
if not os.path.isfile(cache_version_file):
 UpperCAmelCase__          = 0
else:
 with open(cache_version_file) as f:
  try:
   UpperCAmelCase__          = int(f.read())
  except ValueError:
   UpperCAmelCase__          = 0

if cache_version < 1:
 UpperCAmelCase__          = os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
 if old_cache_is_not_empty:
  logger.warning(
      'The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your '
      'existing cached models. This is a one-time operation, you can interrupt it or run it '
      'later by calling `diffusers.utils.hub_utils.move_cache()`.'
  )
  try:
   move_cache()
  except Exception as e:
   UpperCAmelCase__          = '\n'.join(traceback.format_tb(e.__traceback__))
   logger.error(
       F"There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease "
       'file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole '
       'message and we will do our best to help.'
   )

if cache_version < 1:
 try:
  os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
  with open(cache_version_file, 'w') as f:
   f.write('1')
 except Exception:
  logger.warning(
      F"There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure "
      'the directory exists and can be written to.'
  )
def 					_UpperCAmelCase      ( __lowerCamelCase   :       str ,					__lowerCamelCase   :       Optional[str] = None		)     ->					str:
  if variant is not None:
    _snake_case					     =       weights_name.split('''.'''		)
    _snake_case					     =       splits[:-1] + [variant] + splits[-1:]
    _snake_case					     =       '''.'''.join(__lowerCamelCase		)

  return weights_name

def 					_UpperCAmelCase      ( __lowerCamelCase   :       List[str] ,					*,
    __lowerCamelCase   :       Tuple ,					__lowerCamelCase   :       int ,					__lowerCamelCase   :       Optional[int] ,					__lowerCamelCase   :       Dict ,					__lowerCamelCase   :       Union[str, Any] ,					__lowerCamelCase   :       Tuple ,					__lowerCamelCase   :       Union[str, Any] ,					__lowerCamelCase   :       Optional[Any] ,					__lowerCamelCase   :       Optional[int] ,					__lowerCamelCase   :       int ,					__lowerCamelCase   :       Any=None ,					)     ->					Any:
  _snake_case					     =       str(__lowerCamelCase		)
  if os.path.isfile(__lowerCamelCase		):
    return pretrained_model_name_or_path
  elif os.path.isdir(__lowerCamelCase		):
    if os.path.isfile(os.path.join(__lowerCamelCase ,					__lowerCamelCase		)		):
      # Load from a PyTorch checkpoint
      _snake_case					     =       os.path.join(__lowerCamelCase ,					__lowerCamelCase		)
      return model_file
    elif subfolder is not None and os.path.isfile(
        os.path.join(__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase		)		):
      _snake_case					     =       os.path.join(__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase		)
      return model_file
    else:
      raise EnvironmentError(
          f'''Error no file named {weights_name} found in directory {pretrained_model_name_or_path}.'''		)
  else:
    # 1. First check if deprecated way of loading from branches is used
    if (
        revision in DEPRECATED_REVISION_ARGS
        and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
        and version.parse(version.parse(__lowerCamelCase		).base_version		) >= version.parse('''0.20.0'''		)
    ):
      try:
        _snake_case					     =       hf_hub_download(
            __lowerCamelCase ,					filename=_add_variant(__lowerCamelCase ,					__lowerCamelCase		) ,					cache_dir=__lowerCamelCase ,					force_download=__lowerCamelCase ,					proxies=__lowerCamelCase ,					resume_download=__lowerCamelCase ,					local_files_only=__lowerCamelCase ,					use_auth_token=__lowerCamelCase ,					user_agent=__lowerCamelCase ,					subfolder=__lowerCamelCase ,					revision=revision or commit_hash ,					)
        warnings.warn(
            f'''Loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'` is deprecated. Loading instead from `revision=\'main\'` with `variant={revision}`. Loading model variants via `revision=\'{revision}\'` will be removed in diffusers v1. Please use `variant=\'{revision}\'` instead.''' ,					__lowerCamelCase ,					)
        return model_file
      except:  # noqa: E722
        warnings.warn(
            f'''You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant=\'{revision}\'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(__lowerCamelCase ,					__lowerCamelCase		)} file in the \'main\' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title \'{pretrained_model_name_or_path} is missing {_add_variant(__lowerCamelCase ,					__lowerCamelCase		)}\' so that the correct variant file can be added.''' ,					__lowerCamelCase ,					)
    try:
      # 2. Load model file as usual
      _snake_case					     =       hf_hub_download(
          __lowerCamelCase ,					filename=__lowerCamelCase ,					cache_dir=__lowerCamelCase ,					force_download=__lowerCamelCase ,					proxies=__lowerCamelCase ,					resume_download=__lowerCamelCase ,					local_files_only=__lowerCamelCase ,					use_auth_token=__lowerCamelCase ,					user_agent=__lowerCamelCase ,					subfolder=__lowerCamelCase ,					revision=revision or commit_hash ,					)
      return model_file

    except RepositoryNotFoundError:
      raise EnvironmentError(
          f'''{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier '''
          '''listed on \'https://huggingface.co/models\'\nIf this is a private repository, make sure to pass a '''
          '''token having permission to this repo with `use_auth_token` or log in with `huggingface-cli '''
          '''login`.'''		)
    except RevisionNotFoundError:
      raise EnvironmentError(
          f'''{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for '''
          '''this model name. Check the model page at '''
          f'''\'https://huggingface.co/{pretrained_model_name_or_path}\' for available revisions.'''		)
    except EntryNotFoundError:
      raise EnvironmentError(
          f'''{pretrained_model_name_or_path} does not appear to have a file named {weights_name}.'''		)
    except HTTPError as err:
      raise EnvironmentError(
          f'''There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}'''		)
    except ValueError:
      raise EnvironmentError(
          f'''We couldn\'t connect to \'{HUGGINGFACE_CO_RESOLVE_ENDPOINT}\' to load this model, couldn\'t find it'''
          f''' in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a'''
          f''' directory containing a file named {weights_name} or'''
          ''' \nCheckout your internet connection or see how to run the library in'''
          ''' offline mode at \'https://huggingface.co/docs/diffusers/installation#offline-mode\'.'''		)
    except EnvironmentError:
      raise EnvironmentError(
          f'''Can\'t load the model for \'{pretrained_model_name_or_path}\'. If you were trying to load it from '''
          '''\'https://huggingface.co/models\', make sure you don\'t have a local directory with the same name. '''
          f'''Otherwise, make sure \'{pretrained_model_name_or_path}\' is the correct path to a directory '''
          f'''containing a file named {weights_name}'''		)





"""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 CLIPImageProcessor, CLIPProcessor

@require_vision
class       lowerCAmelCase__				(						unittest.TestCase	):







     def      lowercase	(							self			:			Any			):
       _snake_case					     =       tempfile.mkdtemp()

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

       _snake_case					     =       os.path.join(self.tmpdirname    ,      VOCAB_FILES_NAMES['''vocab_file''']			)
       _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(_lowerCamelCase			) + '''\n'''			)
       with open(self.merges_file    ,      '''w'''    ,      encoding='''utf-8'''			) as fp:
         fp.write('''\n'''.join(_lowerCamelCase			)			)

       _snake_case					     =       {
           '''do_resize''': True,
           '''size''': 20,
           '''do_center_crop''': True,
           '''crop_size''': 18,
           '''do_normalize''': True,
           '''image_mean''': [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
           '''image_std''': [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
       }
       _snake_case					     =       os.path.join(self.tmpdirname    ,      _lowerCamelCase			)
       with open(self.image_processor_file    ,      '''w'''    ,      encoding='''utf-8'''			) as fp:
         json.dump(_lowerCamelCase    ,      _lowerCamelCase			)







     def      lowercase	(							self			:			Tuple    ,      **_lowerCamelCase			:			Any			):
       return CLIPTokenizer.from_pretrained(self.tmpdirname    ,      **_lowerCamelCase			)







     def      lowercase	(							self			:			str    ,      **_lowerCamelCase			:			Any			):
       return CLIPTokenizerFast.from_pretrained(self.tmpdirname    ,      **_lowerCamelCase			)







     def      lowercase	(							self			:			int    ,      **_lowerCamelCase			:			Optional[int]			):
       return CLIPImageProcessor.from_pretrained(self.tmpdirname    ,      **_lowerCamelCase			)







     def      lowercase	(							self			:			Union[str, Any]			):
       shutil.rmtree(self.tmpdirname			)







     def      lowercase	(							self			:			Any			):
       _snake_case					     =       [np.random.randint(255    ,      size=(3, 30, 400)    ,      dtype=np.uinta			)]

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

       return image_inputs







     def      lowercase	(							self			:			Optional[Any]			):
       _snake_case					     =       self.get_tokenizer()
       _snake_case					     =       self.get_rust_tokenizer()
       _snake_case					     =       self.get_image_processor()

       _snake_case					     =       CLIPProcessor(tokenizer=_lowerCamelCase    ,      image_processor=_lowerCamelCase			)
       processor_slow.save_pretrained(self.tmpdirname			)
       _snake_case					     =       CLIPProcessor.from_pretrained(self.tmpdirname    ,      use_fast=_lowerCamelCase			)

       _snake_case					     =       CLIPProcessor(tokenizer=_lowerCamelCase    ,      image_processor=_lowerCamelCase			)
       processor_fast.save_pretrained(self.tmpdirname			)
       _snake_case					     =       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    ,      _lowerCamelCase			)
       self.assertIsInstance(processor_fast.tokenizer    ,      _lowerCamelCase			)

       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    ,      _lowerCamelCase			)
       self.assertIsInstance(processor_fast.image_processor    ,      _lowerCamelCase			)







     def      lowercase	(							self			:			List[Any]			):
       _snake_case					     =       CLIPProcessor(tokenizer=self.get_tokenizer()    ,      image_processor=self.get_image_processor()			)
       processor.save_pretrained(self.tmpdirname			)

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

       _snake_case					     =       CLIPProcessor.from_pretrained(
           self.tmpdirname    ,      bos_token='''(BOS)'''    ,      eos_token='''(EOS)'''    ,      do_normalize=_lowerCamelCase    ,      padding_value=1.0			)

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

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







     def      lowercase	(							self			:			int			):
       _snake_case					     =       self.get_image_processor()
       _snake_case					     =       self.get_tokenizer()

       _snake_case					     =       CLIPProcessor(tokenizer=_lowerCamelCase    ,      image_processor=_lowerCamelCase			)

       _snake_case					     =       self.prepare_image_inputs()

       _snake_case					     =       image_processor(_lowerCamelCase    ,      return_tensors='''np'''			)
       _snake_case					     =       processor(images=_lowerCamelCase    ,      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      lowercase	(							self			:			Any			):
       _snake_case					     =       self.get_image_processor()
       _snake_case					     =       self.get_tokenizer()

       _snake_case					     =       CLIPProcessor(tokenizer=_lowerCamelCase    ,      image_processor=_lowerCamelCase			)

       _snake_case					     =       '''lower newer'''

       _snake_case					     =       processor(text=_lowerCamelCase			)

       _snake_case					     =       tokenizer(_lowerCamelCase			)

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







     def      lowercase	(							self			:			Any			):
       _snake_case					     =       self.get_image_processor()
       _snake_case					     =       self.get_tokenizer()

       _snake_case					     =       CLIPProcessor(tokenizer=_lowerCamelCase    ,      image_processor=_lowerCamelCase			)

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

       _snake_case					     =       processor(text=_lowerCamelCase    ,      images=_lowerCamelCase			)

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

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







     def      lowercase	(							self			:			List[str]			):
       _snake_case					     =       self.get_image_processor()
       _snake_case					     =       self.get_tokenizer()

       _snake_case					     =       CLIPProcessor(tokenizer=_lowerCamelCase    ,      image_processor=_lowerCamelCase			)

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

       _snake_case					     =       processor.batch_decode(_lowerCamelCase			)
       _snake_case					     =       tokenizer.batch_decode(_lowerCamelCase			)

       self.assertListEqual(_lowerCamelCase    ,      _lowerCamelCase			)







     def      lowercase	(							self			:			List[Any]			):
       _snake_case					     =       self.get_image_processor()
       _snake_case					     =       self.get_tokenizer()

       _snake_case					     =       CLIPProcessor(tokenizer=_lowerCamelCase    ,      image_processor=_lowerCamelCase			)

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

       _snake_case					     =       processor(text=_lowerCamelCase    ,      images=_lowerCamelCase			)

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



