




'''simple docstring'''


from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class      A       (						_a       ):
							lowercase_							     =							['image_processor', 'tokenizer']
							lowercase_							     =							'BlipImageProcessor'
							lowercase_							     =							('BertTokenizer', 'BertTokenizerFast')







							def __init__(    self			:    Dict				,     lowerCAmelCase_			:    int				,     lowerCAmelCase_			:    List[Any]   )     ->				List[str]:


														"""simple docstring"""


														_a      	=							False
														super().__init__(lowerCAmelCase_				,     lowerCAmelCase_   )
														_a      	=							self.image_processor







							def __call__(    self			:    str				,     lowerCAmelCase_			:    ImageInput = None				,     lowerCAmelCase_			:    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None				,     lowerCAmelCase_			:    bool = True				,     lowerCAmelCase_			:    Union[bool, str, PaddingStrategy] = False				,     lowerCAmelCase_			:    Union[bool, str, TruncationStrategy] = None				,     lowerCAmelCase_			:    Optional[int] = None				,     lowerCAmelCase_			:    int = 0				,     lowerCAmelCase_			:    Optional[int] = None				,     lowerCAmelCase_			:    Optional[bool] = None				,     lowerCAmelCase_			:    bool = False				,     lowerCAmelCase_			:    bool = False				,     lowerCAmelCase_			:    bool = False				,     lowerCAmelCase_			:    bool = False				,     lowerCAmelCase_			:    bool = False				,     lowerCAmelCase_			:    bool = True				,     lowerCAmelCase_			:    Optional[Union[str, TensorType]] = None				,     **lowerCAmelCase_			:    Union[str, Any]				,     )     ->				BatchEncoding:


														"""simple docstring"""


														if images is None and text is None:
																					raise ValueError('''You have to specify either images or text.'''   )

														# Get only text
														if images is None:
																					_a      	=							self.tokenizer
																					_a      	=							self.tokenizer(
																					    text=lowerCAmelCase_				,     add_special_tokens=lowerCAmelCase_				,     padding=lowerCAmelCase_				,     truncation=lowerCAmelCase_				,     max_length=lowerCAmelCase_				,     stride=lowerCAmelCase_				,     pad_to_multiple_of=lowerCAmelCase_				,     return_attention_mask=lowerCAmelCase_				,     return_overflowing_tokens=lowerCAmelCase_				,     return_special_tokens_mask=lowerCAmelCase_				,     return_offsets_mapping=lowerCAmelCase_				,     return_token_type_ids=lowerCAmelCase_				,     return_length=lowerCAmelCase_				,     verbose=lowerCAmelCase_				,     return_tensors=lowerCAmelCase_				,     **lowerCAmelCase_				,     )
																					return text_encoding

														# add pixel_values
														_a      	=							self.image_processor(lowerCAmelCase_				,     return_tensors=lowerCAmelCase_   )

														if text is not None:
																					_a      	=							self.tokenizer(
																					    text=lowerCAmelCase_				,     add_special_tokens=lowerCAmelCase_				,     padding=lowerCAmelCase_				,     truncation=lowerCAmelCase_				,     max_length=lowerCAmelCase_				,     stride=lowerCAmelCase_				,     pad_to_multiple_of=lowerCAmelCase_				,     return_attention_mask=lowerCAmelCase_				,     return_overflowing_tokens=lowerCAmelCase_				,     return_special_tokens_mask=lowerCAmelCase_				,     return_offsets_mapping=lowerCAmelCase_				,     return_token_type_ids=lowerCAmelCase_				,     return_length=lowerCAmelCase_				,     verbose=lowerCAmelCase_				,     return_tensors=lowerCAmelCase_				,     **lowerCAmelCase_				,     )
														else:
																					_a      	=							None

														if text_encoding is not None:
																					encoding_image_processor.update(lowerCAmelCase_   )

														return encoding_image_processor







							def   __lowerCAmelCase     (    self			:    List[str]				,     *lowerCAmelCase_			:    Union[str, Any]				,     **lowerCAmelCase_			:    List[Any]   )     ->				Optional[int]:


														"""simple docstring"""


														return self.tokenizer.batch_decode(*lowerCAmelCase_				,     **lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Any				,     *lowerCAmelCase_			:    str				,     **lowerCAmelCase_			:    int   )     ->				Optional[int]:


														"""simple docstring"""


														return self.tokenizer.decode(*lowerCAmelCase_				,     **lowerCAmelCase_   )







							@property
							def   __lowerCAmelCase     (    self			:    str   )     ->				Optional[Any]:


														"""simple docstring"""


														_a      	=							self.tokenizer.model_input_names
														_a      	=							self.image_processor.model_input_names
														return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )







'''simple docstring'''


import os
import re
import shutil
import sys
import tempfile
import unittest

import black


_snake_case : str  				=						os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
_snake_case : List[str]  				=						'    \"""\n    Output class for the scheduler\'s step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"""\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n'

class      A       (						unittest.TestCase       ):







							def   __lowerCAmelCase     (    self			:    int   )     ->				List[Any]:


														"""simple docstring"""


														_a      	=							tempfile.mkdtemp()
														os.makedirs(os.path.join(self.diffusers_dir				,     '''schedulers/'''   )   )
														_a      	=							self.diffusers_dir
														shutil.copy(
														    os.path.join(lowerCAmelCase_				,     '''src/diffusers/schedulers/scheduling_ddpm.py'''   )				,     os.path.join(self.diffusers_dir				,     '''schedulers/scheduling_ddpm.py'''   )				,     )







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				int:


														"""simple docstring"""


														_a      	=							'''src/diffusers'''
														shutil.rmtree(self.diffusers_dir   )







							def   __lowerCAmelCase     (    self			:    int				,     lowerCAmelCase_			:    str				,     lowerCAmelCase_			:    List[Any]				,     lowerCAmelCase_			:    Union[str, Any]				,     lowerCAmelCase_			:    str=None   )     ->				Union[str, Any]:


														"""simple docstring"""


														_a      	=							comment + F'\nclass {class_name}(nn.Module):\n' + class_code
														if overwrite_result is not None:
																					_a      	=							comment + F'\nclass {class_name}(nn.Module):\n' + overwrite_result
														_a      	=							black.Mode(target_versions={black.TargetVersion.PYaa}				,     line_length=1_19   )
														_a      	=							black.format_str(lowerCAmelCase_				,     mode=lowerCAmelCase_   )
														_a      	=							os.path.join(self.diffusers_dir				,     '''new_code.py'''   )
														with open(lowerCAmelCase_				,     '''w'''				,     newline='''\n'''   ) as f:
																					f.write(lowerCAmelCase_   )
														if overwrite_result is None:
																					self.assertTrue(len(check_copies.is_copy_consistent(lowerCAmelCase_   )   ) == 0   )
														else:
																					check_copies.is_copy_consistent(f.name				,     overwrite=lowerCAmelCase_   )
																					with open(lowerCAmelCase_				,     '''r'''   ) as f:
																												self.assertTrue(f.read()				,     lowerCAmelCase_   )







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


														"""simple docstring"""


														_a      	=							check_copies.find_code_in_diffusers('''schedulers.scheduling_ddpm.DDPMSchedulerOutput'''   )
														self.assertEqual(lowerCAmelCase_				,     lowerCAmelCase_   )







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


														"""simple docstring"""


														self.check_copy_consistency(
														    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'''				,     '''DDPMSchedulerOutput'''				,     REFERENCE_CODE + '''\n'''				,     )

														# With no empty line at the end
														self.check_copy_consistency(
														    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'''				,     '''DDPMSchedulerOutput'''				,     lowerCAmelCase_				,     )

														# Copy consistency with rename
														self.check_copy_consistency(
														    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'''				,     '''TestSchedulerOutput'''				,     re.sub('''DDPM'''				,     '''Test'''				,     lowerCAmelCase_   )				,     )

														# Copy consistency with a really long name
														_a      	=							'''TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'''
														self.check_copy_consistency(
														    F'# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}'				,     F'{long_class_name}SchedulerOutput'				,     re.sub('''Bert'''				,     lowerCAmelCase_				,     lowerCAmelCase_   )				,     )

														# Copy consistency with overwrite
														self.check_copy_consistency(
														    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'''				,     '''TestSchedulerOutput'''				,     lowerCAmelCase_				,     overwrite_result=re.sub('''DDPM'''				,     '''Test'''				,     lowerCAmelCase_   )				,     )

