

import inspect
import os
import re

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_SCREAMING_SNAKE_CASE  :						Union[str, Any]         =    '''src/transformers'''


# This is to make sure the transformers module imported is the one in the repo.
_SCREAMING_SNAKE_CASE  :						List[Any]         =    direct_transformers_import(PATH_TO_TRANSFORMERS)

_SCREAMING_SNAKE_CASE  :						Optional[int]         =    transformers.models.auto.configuration_auto.CONFIG_MAPPING

_SCREAMING_SNAKE_CASE  :						Tuple         =    {
    # used to compute the property `self.chunk_length`
    '''EncodecConfig''': ['''overlap'''],
    # used as `self.bert_model = BertModel(config, ...)`
    '''DPRConfig''': True,
    # not used in modeling files, but it's an important information
    '''FSMTConfig''': ['''langs'''],
    # used internally in the configuration class file
    '''GPTNeoConfig''': ['''attention_types'''],
    # used internally in the configuration class file
    '''EsmConfig''': ['''is_folding_model'''],
    # used during training (despite we don't have training script for these models yet)
    '''Mask2FormerConfig''': ['''ignore_value'''],
    # `ignore_value` used during training (despite we don't have training script for these models yet)
    # `norm` used in conversion script (despite not using in the modeling file)
    '''OneFormerConfig''': ['''ignore_value''', '''norm'''],
    # used during preprocessing and collation, see `collating_graphormer.py`
    '''GraphormerConfig''': ['''spatial_pos_max'''],
    # used internally in the configuration class file
    '''T5Config''': ['''feed_forward_proj'''],
    # used internally in the configuration class file
    # `tokenizer_class` get default value `T5Tokenizer` intentionally
    '''MT5Config''': ['''feed_forward_proj''', '''tokenizer_class'''],
    '''UMT5Config''': ['''feed_forward_proj''', '''tokenizer_class'''],
    # used internally in the configuration class file
    '''LongT5Config''': ['''feed_forward_proj'''],
    # used internally in the configuration class file
    '''SwitchTransformersConfig''': ['''feed_forward_proj'''],
    # having default values other than `1e-5` - we can't fix them without breaking
    '''BioGptConfig''': ['''layer_norm_eps'''],
    # having default values other than `1e-5` - we can't fix them without breaking
    '''GLPNConfig''': ['''layer_norm_eps'''],
    # having default values other than `1e-5` - we can't fix them without breaking
    '''SegformerConfig''': ['''layer_norm_eps'''],
    # having default values other than `1e-5` - we can't fix them without breaking
    '''CvtConfig''': ['''layer_norm_eps'''],
    # having default values other than `1e-5` - we can't fix them without breaking
    '''PerceiverConfig''': ['''layer_norm_eps'''],
    # used internally to calculate the feature size
    '''InformerConfig''': ['''num_static_real_features''', '''num_time_features'''],
    # used internally to calculate the feature size
    '''TimeSeriesTransformerConfig''': ['''num_static_real_features''', '''num_time_features'''],
    # used internally to calculate the feature size
    '''AutoformerConfig''': ['''num_static_real_features''', '''num_time_features'''],
    # used internally to calculate `mlp_dim`
    '''SamVisionConfig''': ['''mlp_ratio'''],
    # For (head) training, but so far not implemented
    '''ClapAudioConfig''': ['''num_classes'''],
    # Not used, but providing useful information to users
    '''SpeechT5HifiGanConfig''': ['''sampling_rate'''],
}


# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
    {
        '''CLIPSegConfig''': True,
        '''DeformableDetrConfig''': True,
        '''DetaConfig''': True,
        '''DinatConfig''': True,
        '''DonutSwinConfig''': True,
        '''EfficientFormerConfig''': True,
        '''FSMTConfig''': True,
        '''JukeboxConfig''': True,
        '''LayoutLMv2Config''': True,
        '''MaskFormerSwinConfig''': True,
        '''MT5Config''': True,
        '''NatConfig''': True,
        '''OneFormerConfig''': True,
        '''PerceiverConfig''': True,
        '''RagConfig''': True,
        '''SpeechT5Config''': True,
        '''SwinConfig''': True,
        '''Swin2SRConfig''': True,
        '''Swinv2Config''': True,
        '''SwitchTransformersConfig''': True,
        '''TableTransformerConfig''': True,
        '''TapasConfig''': True,
        '''TransfoXLConfig''': True,
        '''UniSpeechConfig''': True,
        '''UniSpeechSatConfig''': True,
        '''WavLMConfig''': True,
        '''WhisperConfig''': True,
        # TODO: @Arthur (for `alignment_head` and `alignment_layer`)
        '''JukeboxPriorConfig''': True,
        # TODO: @Younes (for `is_decoder`)
        '''Pix2StructTextConfig''': True,
    }
)
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					False
     for attribute in attributes:
          for modeling_source in source_strings:
               # check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
               if (
                   F'''config.{attribute}''' in modeling_source
                   or F'''getattr(config, "{attribute}"''' in modeling_source
                   or F'''getattr(self.config, "{attribute}"''' in modeling_source
               ):
                    SCREAMING_SNAKE_CASE__					=					True
               # Deal with multi-line cases
               elif (
                   re.search(
                       RF'''getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*"{attribute}"'''   ,	_A   ,	)
                   is not None
               ):
                    SCREAMING_SNAKE_CASE__					=					True
               # `SequenceSummary` is called with `SequenceSummary(config)`
               elif attribute in [
                   "summary_type",
                   "summary_use_proj",
                   "summary_activation",
                   "summary_last_dropout",
                   "summary_proj_to_labels",
                   "summary_first_dropout",
               ]:
                    if "SequenceSummary" in modeling_source:
                         SCREAMING_SNAKE_CASE__					=					True
               if attribute_used:
                    break
          if attribute_used:
               break

    # common and important attributes, even if they do not always appear in the modeling files
     SCREAMING_SNAKE_CASE__					=					[
         '''bos_index''',
         '''eos_index''',
         '''pad_index''',
         '''unk_index''',
         '''mask_index''',
         '''image_size''',
         '''use_cache''',
         '''out_features''',
         '''out_indices''',
     ]
     SCREAMING_SNAKE_CASE__					=					['''encoder_no_repeat_ngram_size''']

     # Special cases to be allowed
     SCREAMING_SNAKE_CASE__					=					True
     if not attribute_used:
          SCREAMING_SNAKE_CASE__					=					False
          for attribute in attributes:
               # Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
               if attribute in ["is_encoder_decoder"] and default_value is True:
                    SCREAMING_SNAKE_CASE__					=					True
               elif attribute in ["tie_word_embeddings"] and default_value is False:
                    SCREAMING_SNAKE_CASE__					=					True

               # Allow cases without checking the default value in the configuration class
               elif attribute in attributes_to_allow + attributes_used_in_generation:
                    SCREAMING_SNAKE_CASE__					=					True
               elif attribute.endswith('''_token_id'''       ):
                    SCREAMING_SNAKE_CASE__					=					True

               # configuration class specific cases
               if not case_allowed:
                    SCREAMING_SNAKE_CASE__					=					SPECIAL_CASES_TO_ALLOW.get(config_class.__name__   ,	[]       )
                    SCREAMING_SNAKE_CASE__					=					allowed_cases is True or attribute in allowed_cases

     return attribute_used or case_allowed
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					dict(inspect.signature(config_class.__init__       ).parameters       )
     SCREAMING_SNAKE_CASE__					=					[x for x in list(signature.keys()       ) if x not in ['''self''', '''kwargs''']]
     SCREAMING_SNAKE_CASE__					=					[signature[param].default for param in parameter_names]

     # If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
     # as one variant is used, the test should pass
     SCREAMING_SNAKE_CASE__					=					{}
     if len(config_class.attribute_map       ) > 0:
          SCREAMING_SNAKE_CASE__					=					{v: k for k, v in config_class.attribute_map.items()}

     # Get the path to modeling source files
     SCREAMING_SNAKE_CASE__					=					inspect.getsourcefile(_A       )
     SCREAMING_SNAKE_CASE__					=					os.path.dirname(_A       )
     # Let's check against all frameworks: as long as one framework uses an attribute, we are good.
     SCREAMING_SNAKE_CASE__					=					[os.path.join(_A   ,	_A       ) for fn in os.listdir(_A       ) if fn.startswith('''modeling_'''       )]

     # Get the source code strings
     SCREAMING_SNAKE_CASE__					=					[]
     for path in modeling_paths:
          if os.path.isfile(_A       ):
               with open(_A       ) as fp:
                    modeling_sources.append(fp.read()       )

     SCREAMING_SNAKE_CASE__					=					[]
     for config_param, default_value in zip(_A   ,	_A       ):
          # `attributes` here is all the variant names for `config_param`
          SCREAMING_SNAKE_CASE__					=					[config_param]
          # some configuration classes have non-empty `attribute_map`, and both names could be used in the
          # corresponding modeling files. As long as one of them appears, it is fine.
          if config_param in reversed_attribute_map:
               attributes.append(reversed_attribute_map[config_param]       )

          if not check_attribute_being_used(_A   ,	_A   ,	_A   ,	_A       ):
               unused_attributes.append(attributes[0]       )

     return sorted(_A       )
def        UpperCAmelCase_     (		):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					{}
     for _config_class in list(CONFIG_MAPPING.values()       ):
          # Skip deprecated models
          if "models.deprecated" in _config_class.__module__:
               continue
          # Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
          SCREAMING_SNAKE_CASE__					=					[
              cls
              for name, cls in inspect.getmembers(
                  inspect.getmodule(_config_class       )   ,	lambda _A       : inspect.isclass(_A       )
                  and issubclass(_A   ,	_A       )
                  and inspect.getmodule(_A       ) == inspect.getmodule(_config_class       )   ,	)
          ]
          for config_class in config_classes_in_module:
               SCREAMING_SNAKE_CASE__					=					check_config_attributes_being_used(_A       )
               if len(_A       ) > 0:
                    SCREAMING_SNAKE_CASE__					=					unused_attributes

     if len(_A       ) > 0:
          SCREAMING_SNAKE_CASE__					=					'''The following configuration classes contain unused attributes in the corresponding modeling files:\n'''
          for name, attributes in configs_with_unused_attributes.items():
               error += F'''{name}: {attributes}\n'''

          raise ValueError(_A       )


if __name__ == "__main__":
       check_config_attributes()





import math
from typing import Callable, List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
from diffusers.schedulers import DDIMScheduler, DDPMScheduler, LMSDiscreteScheduler, PNDMScheduler
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A=[]       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					size[0] - overlap_pixels * 2
     SCREAMING_SNAKE_CASE__					=					size[1] - overlap_pixels * 2
     for letter in ["l", "r"]:
          if letter in remove_borders:
               size_x += overlap_pixels
     for letter in ["t", "b"]:
          if letter in remove_borders:
               size_y += overlap_pixels
     SCREAMING_SNAKE_CASE__					=					np.ones((size_y, size_x)   ,	dtype=np.uinta       ) * 2_55
     SCREAMING_SNAKE_CASE__					=					np.pad(_A   ,	mode='''linear_ramp'''   ,	pad_width=_A   ,	end_values=0       )

     if "l" in remove_borders:
          SCREAMING_SNAKE_CASE__					=					mask[:, overlap_pixels : mask.shape[1]]
     if "r" in remove_borders:
          SCREAMING_SNAKE_CASE__					=					mask[:, 0 : mask.shape[1] - overlap_pixels]
     if "t" in remove_borders:
          SCREAMING_SNAKE_CASE__					=					mask[overlap_pixels : mask.shape[0], :]
     if "b" in remove_borders:
          SCREAMING_SNAKE_CASE__					=					mask[0 : mask.shape[0] - overlap_pixels, :]
     return mask
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     return max(_A   ,	min(_A   ,	_A       )       )
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     return (
         clamp(rect[0]   ,	min[0]   ,	max[0]       ),
         clamp(rect[1]   ,	min[1]   ,	max[1]       ),
         clamp(rect[2]   ,	min[0]   ,	max[0]       ),
         clamp(rect[3]   ,	min[1]   ,	max[1]       ),
     )
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					list(_A       )
     rect[0] -= overlap
     rect[1] -= overlap
     rect[2] += overlap
     rect[3] += overlap
     SCREAMING_SNAKE_CASE__					=					clamp_rect(_A   ,	[0, 0]   ,	[image_size[0], image_size[1]]       )
     return rect
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					Image.new('''RGB'''   ,	(tile.size[0] + original_slice, tile.size[1])       )
     result.paste(
         original_image.resize((tile.size[0], tile.size[1])   ,	Image.BICUBIC       ).crop(
             (slice_x, 0, slice_x + original_slice, tile.size[1])       )   ,	(0, 0)   ,	)
     result.paste(_A   ,	(original_slice, 0)       )
     return result
def        UpperCAmelCase_     (		_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					(original_image_slice * 4, 0, tile.size[0], tile.size[1])
     SCREAMING_SNAKE_CASE__					=					tile.crop(_A       )
     return tile
def        UpperCAmelCase_     (		_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					n % d
     return n - divisor






class  UpperCAmelCase__	( A__ ):






  """simple docstring"""


  def __init__(							self		:	int			,     __lowerCamelCase		:	AutoencoderKL			,     __lowerCamelCase		:	CLIPTextModel			,     __lowerCamelCase		:	CLIPTokenizer			,     __lowerCamelCase		:	UNetaDConditionModel			,     __lowerCamelCase		:	DDPMScheduler			,     __lowerCamelCase		:	Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]			,     __lowerCamelCase		:	int = 350			,     )	->							int:
       super().__init__(
           vae=__lowerCamelCase			,     text_encoder=__lowerCamelCase			,     tokenizer=__lowerCamelCase			,     unet=__lowerCamelCase			,     low_res_scheduler=__lowerCamelCase			,     scheduler=__lowerCamelCase			,     max_noise_level=__lowerCamelCase			,     )






  def    lowercase_	(							self		:	Tuple			,     __lowerCamelCase		:	Optional[int]			,     __lowerCamelCase		:	Tuple			,     __lowerCamelCase		:	Any			,     __lowerCamelCase		:	List[Any]			,     __lowerCamelCase		:	str			,     __lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	str			,     **__lowerCamelCase		:	Tuple      )	->							Dict:
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__					=					(
           min(image.size[0] - (tile_size + original_image_slice)			,     x * tile_size      ),
           min(image.size[1] - (tile_size + original_image_slice)			,     y * tile_size      ),
           min(image.size[0]			,     (x + 1) * tile_size      ),
           min(image.size[1]			,     (y + 1) * tile_size      ),
       )
       SCREAMING_SNAKE_CASE__					=					add_overlap_rect(__lowerCamelCase			,     __lowerCamelCase			,     image.size      )
       SCREAMING_SNAKE_CASE__					=					image.crop(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
       SCREAMING_SNAKE_CASE__					=					translated_slice_x - (original_image_slice / 2)
       SCREAMING_SNAKE_CASE__					=					max(0			,     __lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					squeeze_tile(__lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					to_input.size
       SCREAMING_SNAKE_CASE__					=					to_input.resize((tile_size, tile_size)			,     Image.BICUBIC      )
       SCREAMING_SNAKE_CASE__					=					super(__lowerCamelCase			,     self      ).__call__(image=__lowerCamelCase			,     **__lowerCamelCase      ).images[0]
       SCREAMING_SNAKE_CASE__					=					upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)			,     Image.BICUBIC      )
       SCREAMING_SNAKE_CASE__					=					unsqueeze_tile(__lowerCamelCase			,     __lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)			,     Image.BICUBIC      )
       SCREAMING_SNAKE_CASE__					=					[]
       if x == 0:
            remove_borders.append('''l'''      )
       elif crop_rect[2] == image.size[0]:
            remove_borders.append('''r'''      )
       if y == 0:
            remove_borders.append('''t'''      )
       elif crop_rect[3] == image.size[1]:
            remove_borders.append('''b'''      )
       SCREAMING_SNAKE_CASE__					=					Image.fromarray(
           make_transparency_mask(
               (upscaled_tile.size[0], upscaled_tile.size[1])			,     tile_border * 4			,     remove_borders=__lowerCamelCase      )			,     mode='''L'''			,     )
       final_image.paste(
           __lowerCamelCase			,     (crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4)			,     __lowerCamelCase      )







  @torch.no_grad()
  def __call__(							self		:	List[str]			,     __lowerCamelCase		:	Union[str, List[str]]			,     __lowerCamelCase		:	Union[PIL.Image.Image, List[PIL.Image.Image]]			,     __lowerCamelCase		:	int = 75			,     __lowerCamelCase		:	float = 9.0			,     __lowerCamelCase		:	int = 50			,     __lowerCamelCase		:	Optional[Union[str, List[str]]] = None			,     __lowerCamelCase		:	Optional[int] = 1			,     __lowerCamelCase		:	float = 0.0			,     __lowerCamelCase		:	Optional[torch.Generator] = None			,     __lowerCamelCase		:	Optional[torch.FloatTensor] = None			,     __lowerCamelCase		:	Optional[Callable[[int, int, torch.FloatTensor], None]] = None			,     __lowerCamelCase		:	int = 1			,     __lowerCamelCase		:	int = 128			,     __lowerCamelCase		:	int = 32			,     __lowerCamelCase		:	int = 32			,     )	->							List[Any]:
       SCREAMING_SNAKE_CASE__					=					Image.new('''RGB'''			,     (image.size[0] * 4, image.size[1] * 4)      )
       SCREAMING_SNAKE_CASE__					=					math.ceil(image.size[0] / tile_size      )
       SCREAMING_SNAKE_CASE__					=					math.ceil(image.size[1] / tile_size      )
       SCREAMING_SNAKE_CASE__					=					tcx * tcy
       SCREAMING_SNAKE_CASE__					=					0
       for y in range(__lowerCamelCase      ):
            for x in range(__lowerCamelCase      ):
                 self._process_tile(
                     __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase			,     prompt=__lowerCamelCase			,     num_inference_steps=__lowerCamelCase			,     guidance_scale=__lowerCamelCase			,     noise_level=__lowerCamelCase			,     negative_prompt=__lowerCamelCase			,     num_images_per_prompt=__lowerCamelCase			,     eta=__lowerCamelCase			,     generator=__lowerCamelCase			,     latents=__lowerCamelCase			,     )
                 current_count += 1
                 if callback is not None:
                      callback({'''progress''': current_count / total_tile_count, '''image''': final_image}      )
       return final_image



def        UpperCAmelCase_     (		):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					'''stabilityai/stable-diffusion-x4-upscaler'''
     SCREAMING_SNAKE_CASE__					=					StableDiffusionTiledUpscalePipeline.from_pretrained(_A   ,	revision='''fp16'''   ,	torch_dtype=torch.floataa       )
     SCREAMING_SNAKE_CASE__					=					pipe.to('''cuda'''       )
     SCREAMING_SNAKE_CASE__					=					Image.open('''../../docs/source/imgs/diffusers_library.jpg'''       )

     def callback(_A       ):
          print(F'''progress: {obj["progress"]:.4f}'''       )
          obj["image"].save('''diffusers_library_progress.jpg'''       )

     SCREAMING_SNAKE_CASE__					=					pipe(image=_A   ,	prompt='''Black font, white background, vector'''   ,	noise_level=40   ,	callback=_A       )
     final_image.save('''diffusers_library.jpg'''       )


if __name__ == "__main__":
       main()


