
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 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__=[]    )			->   str:
       lowercase							:					Any	     =				size[0] - overlap_pixels * 2
       lowercase							:					List[str]	     =				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
       lowercase							:					Any	     =				np.ones((size_y, size_x) ,     dtype=np.uinta    ) * 255
       lowercase							:					Optional[Any]	     =				np.pad(_UpperCAmelCase ,     mode="""linear_ramp""" ,     pad_width=_UpperCAmelCase ,     end_values=0    )

       if "l" in remove_borders:
              lowercase							:					Dict	     =				mask[:, overlap_pixels : mask.shape[1]]
       if "r" in remove_borders:
              lowercase							:					Tuple	     =				mask[:, 0 : mask.shape[1] - overlap_pixels]
       if "t" in remove_borders:
              lowercase							:					Any	     =				mask[overlap_pixels : mask.shape[0], :]
       if "b" in remove_borders:
              lowercase							:					Tuple	     =				mask[0 : mask.shape[0] - overlap_pixels, :]
       return mask







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Union[str, Any]:
       return max(_UpperCAmelCase ,     min(_UpperCAmelCase ,     _UpperCAmelCase    )    )







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Optional[Any]:
       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 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Union[str, Any]:
       lowercase							:					List[Any]	     =				list(_UpperCAmelCase    )
       rect[0] -= overlap
       rect[1] -= overlap
       rect[2] += overlap
       rect[3] += overlap
       lowercase							:					Tuple	     =				clamp_rect(_UpperCAmelCase ,     [0, 0] ,     [image_size[0], image_size[1]]    )
       return rect







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   List[str]:
       lowercase							:					Union[str, Any]	     =				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(_UpperCAmelCase ,     (original_slice, 0)    )
       return result







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   List[str]:
       lowercase							:					Tuple	     =				(original_image_slice * 4, 0, tile.size[0], tile.size[1])
       lowercase							:					Dict	     =				tile.crop(_UpperCAmelCase    )
       return tile







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Tuple:
       lowercase							:					List[str]	     =				n % d
       return n - divisor

class __snake_case		(				SCREAMING_SNAKE_CASE_					):
    def __init__(  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case = 350		,):



           '''simple docstring'''







           super().__init__(
               vae=__a		,text_encoder=__a		,tokenizer=__a		,unet=__a		,low_res_scheduler=__a		,scheduler=__a		,max_noise_level=__a		,)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,snake_case		,**snake_case				):



           '''simple docstring'''







           torch.manual_seed(0				)
           lowercase							:					Dict	     =				(
               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				),
           )
           lowercase							:					Tuple	     =				add_overlap_rect(__a		,__a		,image.size				)
           lowercase							:					Optional[Any]	     =				image.crop(__a				)
           lowercase							:					Optional[Any]	     =				((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
           lowercase							:					Optional[int]	     =				translated_slice_x - (original_image_slice / 2)
           lowercase							:					Tuple	     =				max(0		,__a				)
           lowercase							:					Optional[int]	     =				squeeze_tile(__a		,__a		,__a		,__a				)
           lowercase							:					Optional[int]	     =				to_input.size
           lowercase							:					Optional[Any]	     =				to_input.resize((tile_size, tile_size)		,Image.BICUBIC				)
           lowercase							:					Union[str, Any]	     =				super(__a		,self				).__call__(image=__a		,**__a				).images[0]
           lowercase							:					List[Any]	     =				upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)		,Image.BICUBIC				)
           lowercase							:					Optional[int]	     =				unsqueeze_tile(__a		,__a				)
           lowercase							:					Optional[Any]	     =				upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)		,Image.BICUBIC				)
           lowercase							:					Dict	     =				[]
           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"""				)
           lowercase							:					Optional[Any]	     =				Image.fromarray(
               make_transparency_mask(
                   (upscaled_tile.size[0], upscaled_tile.size[1])		,tile_border * 4		,remove_borders=__a				)		,mode="""L"""		,)
           final_image.paste(
               __a		,(crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4)		,__a				)
    @torch.no_grad()
    def __call__(  self		,snake_case		,snake_case		,snake_case = 75		,snake_case = 9.0		,snake_case = 50		,snake_case = None		,snake_case = 1		,snake_case = 0.0		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = 1		,snake_case = 128		,snake_case = 32		,snake_case = 32		,):



           '''simple docstring'''







           lowercase							:					str	     =				Image.new("""RGB"""		,(image.size[0] * 4, image.size[1] * 4)				)
           lowercase							:					List[str]	     =				math.ceil(image.size[0] / tile_size				)
           lowercase							:					List[str]	     =				math.ceil(image.size[1] / tile_size				)
           lowercase							:					int	     =				tcx * tcy
           lowercase							:					int	     =				0
           for y in range(__a				):
                  for x in range(__a				):
                         self._process_tile(
                             __a		,__a		,__a		,__a		,__a		,__a		,__a		,prompt=__a		,num_inference_steps=__a		,guidance_scale=__a		,noise_level=__a		,negative_prompt=__a		,num_images_per_prompt=__a		,eta=__a		,generator=__a		,latents=__a		,)
                         current_count += 1
                         if callback is not None:
                                callback({"""progress""": current_count / total_tile_count, """image""": final_image}				)
           return final_image







def 						_snake_case(  )			->   int:
       # Run a demo
       lowercase							:					Optional[Any]	     =				'stabilityai/stable-diffusion-x4-upscaler'
       lowercase							:					str	     =				StableDiffusionTiledUpscalePipeline.from_pretrained(_UpperCAmelCase ,     revision="""fp16""" ,     torch_dtype=torch.floataa    )
       lowercase							:					Tuple	     =				pipe.to("""cuda"""    )
       lowercase							:					Any	     =				Image.open("""../../docs/source/imgs/diffusers_library.jpg"""    )

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

       lowercase							:					Tuple	     =				pipe(image=_UpperCAmelCase ,     prompt="""Black font, white background, vector""" ,     noise_level=40 ,     callback=_UpperCAmelCase    )
       final_image.save("""diffusers_library.jpg"""    )


if __name__ == "__main__":
  main()



from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


lowercase  : List[str]						   =							logging.get_logger(__name__)

lowercase  : Tuple						   =							{
    """google/umt5-small""": """https://huggingface.co/google/umt5-small/resolve/main/config.json""",
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}

class __snake_case		(				lowerCAmelCase					):
    _a		:						List[str]=    "umt5"
    _a		:						Optional[Any]=    ["past_key_values"]
    def __init__(  self		,snake_case=250112		,snake_case=512		,snake_case=64		,snake_case=1024		,snake_case=8		,snake_case=None		,snake_case=6		,snake_case=32		,snake_case=128		,snake_case=0.1		,snake_case=1e-6		,snake_case=1.0		,snake_case="gated-gelu"		,snake_case=True		,snake_case=True		,snake_case="T5Tokenizer"		,snake_case=True		,snake_case=0		,snake_case=1		,snake_case=0		,**snake_case		,):



           '''simple docstring'''







           super().__init__(
               is_encoder_decoder=snake_case		,tokenizer_class=snake_case		,tie_word_embeddings=snake_case		,pad_token_id=snake_case		,eos_token_id=snake_case		,decoder_start_token_id=snake_case		,**snake_case		,)
           lowercase							:					List[str]	     =				vocab_size
           lowercase							:					Optional[Any]	     =				d_model
           lowercase							:					int	     =				d_kv
           lowercase							:					List[Any]	     =				d_ff
           lowercase							:					Dict	     =				num_layers
           lowercase							:					int	     =				(
               num_decoder_layers if num_decoder_layers is not None else self.num_layers
           )  # default = symmetry
           lowercase							:					List[Any]	     =				num_heads
           lowercase							:					Optional[Any]	     =				relative_attention_num_buckets
           lowercase							:					Dict	     =				relative_attention_max_distance
           lowercase							:					Dict	     =				dropout_rate
           lowercase							:					Any	     =				layer_norm_epsilon
           lowercase							:					Any	     =				initializer_factor
           lowercase							:					Union[str, Any]	     =				feed_forward_proj
           lowercase							:					Optional[Any]	     =				use_cache

           lowercase							:					Dict	     =				self.feed_forward_proj.split("""-"""				)
           lowercase							:					List[Any]	     =				act_info[-1]
           lowercase							:					Any	     =				act_info[0] == """gated"""

           if len(snake_case				) > 1 and act_info[0] != "gated" or len(snake_case				) > 2:
                  raise ValueError(
                      f"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
                      """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
                      """'gated-gelu' or 'relu'"""				)

           if feed_forward_proj == "gated-gelu":
                  lowercase							:					int	     =				"""gelu_new"""
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return self.d_model
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return self.num_heads
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return self.num_layers






class __snake_case		(				lowerCAmelCase					):
    @property
    # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					int	     =				{
               """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
               """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
           }
           if self.use_past:
                  lowercase							:					List[Any]	     =				"""past_encoder_sequence + sequence"""
                  lowercase							:					Dict	     =				{0: """batch"""}
                  lowercase							:					Union[str, Any]	     =				{0: """batch""", 1: """past_decoder_sequence + sequence"""}
           else:
                  lowercase							:					Optional[Any]	     =				{0: """batch""", 1: """decoder_sequence"""}
                  lowercase							:					List[Any]	     =				{0: """batch""", 1: """decoder_sequence"""}

           if self.use_past:
                  self.fill_with_past_key_values_(snake_case		,direction="""inputs"""				)

           return common_inputs
    @property
    # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return 13
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return 5e-4
