
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     __lowerCAmelCase			(			a__						,    a__						,    a__=[]  )       ->  Any:
				__a										=    size[0] - overlap_pixels * 2
				__a										=    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
				__a										=    np.ones((size_y, size_x)						,    dtype=np.uinta  ) * 255
				__a										=    np.pad(a__						,    mode='''linear_ramp'''						,    pad_width=a__						,    end_values=0  )

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


def     __lowerCAmelCase			(			a__						,    a__						,    a__  )       ->  str:
				return max(a__						,    min(a__						,    a__  )  )


def     __lowerCAmelCase			(			a__						,    a__						,    a__  )       ->  int:
				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     __lowerCAmelCase			(			a__						,    a__						,    a__  )       ->  str:
				__a										=    list(a__  )
				rect[0] -= overlap
				rect[1] -= overlap
				rect[2] += overlap
				rect[3] += overlap
				__a										=    clamp_rect(a__						,    [0, 0]						,    [image_size[0], image_size[1]]  )
				return rect


def     __lowerCAmelCase			(			a__						,    a__						,    a__						,    a__  )       ->  List[str]:
				__a										=    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     __lowerCAmelCase			(			a__						,    a__  )       ->  List[str]:
				__a										=    (original_image_slice * 4, 0, tile.size[0], tile.size[1])
				__a										=    tile.crop(a__  )
				return tile


def     __lowerCAmelCase			(			a__						,    a__  )       ->  Any:
				__a										=    n % d
				return n - divisor







class 			__A( a			):




				def __init__(     self		,     _snake_case		,     _snake_case		,     _snake_case		,     _snake_case		,     _snake_case		,     _snake_case		,     _snake_case = 350		,     )    ->      int:


								'''simple docstring'''




								super().__init__(
								    vae=_snake_case		,     text_encoder=_snake_case		,     tokenizer=_snake_case		,     unet=_snake_case		,     low_res_scheduler=_snake_case		,     scheduler=_snake_case		,     max_noise_level=_snake_case		,     )




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case		,     _snake_case		,     _snake_case		,     _snake_case		,     _snake_case		,     **_snake_case				)    ->      Tuple:


								'''simple docstring'''




								torch.manual_seed(0				)
								__a										=    (
								    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				),
								)
								__a										=    add_overlap_rect(_snake_case		,     _snake_case		,     image.size				)
								__a										=    image.crop(_snake_case				)
								__a										=    ((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
								__a										=    translated_slice_x - (original_image_slice / 2)
								__a										=    max(0		,     _snake_case				)
								__a										=    squeeze_tile(_snake_case		,     _snake_case		,     _snake_case		,     _snake_case				)
								__a										=    to_input.size
								__a										=    to_input.resize((tile_size, tile_size)		,     Image.BICUBIC				)
								__a										=    super(_snake_case		,     self				).__call__(image=_snake_case		,     **_snake_case				).images[0]
								__a										=    upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)		,     Image.BICUBIC				)
								__a										=    unsqueeze_tile(_snake_case		,     _snake_case				)
								__a										=    upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)		,     Image.BICUBIC				)
								__a										=    []
								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'''				)
								__a										=    Image.fromarray(
								    make_transparency_mask(
								        (upscaled_tile.size[0], upscaled_tile.size[1])		,     tile_border * 4		,     remove_borders=_snake_case				)		,     mode='''L'''		,     )
								final_image.paste(
								    _snake_case		,     (crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4)		,     _snake_case				)




				@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		,     )    ->      Optional[int]:


								'''simple docstring'''




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



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

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

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


if __name__ == "__main__":
					main()

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A     :	Dict    = {
    'configuration_xlm_roberta': [
        'XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XLMRobertaConfig',
        'XLMRobertaOnnxConfig',
    ],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	Union[str, Any]    = ['XLMRobertaTokenizer']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	int    = ['XLMRobertaTokenizerFast']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	List[Any]    = [
					    'XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'XLMRobertaForCausalLM',
					    'XLMRobertaForMaskedLM',
					    'XLMRobertaForMultipleChoice',
					    'XLMRobertaForQuestionAnswering',
					    'XLMRobertaForSequenceClassification',
					    'XLMRobertaForTokenClassification',
					    'XLMRobertaModel',
					    'XLMRobertaPreTrainedModel',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	int    = [
					    'TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFXLMRobertaForCausalLM',
					    'TFXLMRobertaForMaskedLM',
					    'TFXLMRobertaForMultipleChoice',
					    'TFXLMRobertaForQuestionAnswering',
					    'TFXLMRobertaForSequenceClassification',
					    'TFXLMRobertaForTokenClassification',
					    'TFXLMRobertaModel',
					    'TFXLMRobertaPreTrainedModel',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	Tuple    = [
					    'FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'FlaxXLMRobertaForMaskedLM',
					    'FlaxXLMRobertaForCausalLM',
					    'FlaxXLMRobertaForMultipleChoice',
					    'FlaxXLMRobertaForQuestionAnswering',
					    'FlaxXLMRobertaForSequenceClassification',
					    'FlaxXLMRobertaForTokenClassification',
					    'FlaxXLMRobertaModel',
					    'FlaxXLMRobertaPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_xlm_roberta import (
					    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    XLMRobertaConfig,
					    XLMRobertaOnnxConfig,
					)

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_xlm_roberta import XLMRobertaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_xlm_roberta import (
										    XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    XLMRobertaForCausalLM,
										    XLMRobertaForMaskedLM,
										    XLMRobertaForMultipleChoice,
										    XLMRobertaForQuestionAnswering,
										    XLMRobertaForSequenceClassification,
										    XLMRobertaForTokenClassification,
										    XLMRobertaModel,
										    XLMRobertaPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_xlm_roberta import (
										    TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFXLMRobertaForCausalLM,
										    TFXLMRobertaForMaskedLM,
										    TFXLMRobertaForMultipleChoice,
										    TFXLMRobertaForQuestionAnswering,
										    TFXLMRobertaForSequenceClassification,
										    TFXLMRobertaForTokenClassification,
										    TFXLMRobertaModel,
										    TFXLMRobertaPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_xlm_roberta import (
										    FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FlaxXLMRobertaForCausalLM,
										    FlaxXLMRobertaForMaskedLM,
										    FlaxXLMRobertaForMultipleChoice,
										    FlaxXLMRobertaForQuestionAnswering,
										    FlaxXLMRobertaForSequenceClassification,
										    FlaxXLMRobertaForTokenClassification,
										    FlaxXLMRobertaModel,
										    FlaxXLMRobertaPreTrainedModel,
										)

else:
					import sys

					A     :	Tuple    = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)