


"""simple docstring"""




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        lowercase							(		lowerCAmelCase__ :     Union[str, Any]    ,					lowerCAmelCase__ :     str    ,					lowerCAmelCase__ :     Dict=[]			) ->      List[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(lowerCAmelCase__    ,					mode='''linear_ramp'''    ,					pad_width=lowerCAmelCase__    ,					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        lowercase							(		lowerCAmelCase__ :     Tuple    ,					lowerCAmelCase__ :     Dict    ,					lowerCAmelCase__ :     Any			) ->      List[str]:
		return max(lowerCAmelCase__    ,					min(lowerCAmelCase__    ,					lowerCAmelCase__			)			)

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

def        lowercase							(		lowerCAmelCase__ :     Optional[int]    ,					lowerCAmelCase__ :     List[str]    ,					lowerCAmelCase__ :     Union[str, Any]    ,					lowerCAmelCase__ :     Any			) ->      Tuple:
		__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(lowerCAmelCase__    ,					(original_slice, 0)			)
		return result

def        lowercase							(		lowerCAmelCase__ :     Tuple    ,					lowerCAmelCase__ :     str			) ->      str:
		__a           =			(original_image_slice * 4, 0, tile.size[0], tile.size[1])
		__a           =			tile.crop(lowerCAmelCase__			)
		return tile

def        lowercase							(		lowerCAmelCase__ :     str    ,					lowerCAmelCase__ :     Any			) ->      Optional[Any]:
		__a           =			n % d
		return n - divisor
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		def __init__(  self							, _a							, _a							, _a							, _a							, _a							, _a							, _a = 350							, ):
				super().__init__(
				    vae=_a							, text_encoder=_a							, tokenizer=_a							, unet=_a							, low_res_scheduler=_a							, scheduler=_a							, max_noise_level=_a							, )

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, _a							, _a							, _a							, _a							, **_a      ):
				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(_a							, _a							, image.size      )
				__a           =			image.crop(_a      )
				__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							, _a      )
				__a           =			squeeze_tile(_a							, _a							, _a							, _a      )
				__a           =			to_input.size
				__a           =			to_input.resize((tile_size, tile_size)							, Image.BICUBIC      )
				__a           =			super(_a							, self      ).__call__(image=_a							, **_a      ).images[0]
				__a           =			upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)							, Image.BICUBIC      )
				__a           =			unsqueeze_tile(_a							, _a      )
				__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=_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							, _a							, _a							, _a = 75							, _a = 9.0							, _a = 50							, _a = None							, _a = 1							, _a = 0.0							, _a = None							, _a = None							, _a = None							, _a = 1							, _a = 128							, _a = 32							, _a = 32							, ):
				__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(_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        lowercase							(		) ->      Union[str, Any]:
		# Run a demo
		__a           =			'''stabilityai/stable-diffusion-x4-upscaler'''
		__a           =			StableDiffusionTiledUpscalePipeline.from_pretrained(lowerCAmelCase__    ,					revision='''fp16'''    ,					torch_dtype=torch.floataa			)
		__a           =			pipe.to('''cuda'''			)
		__a           =			Image.open('''../../docs/source/imgs/diffusers_library.jpg'''			)

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

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


if __name__ == "__main__":
				main()













"""simple docstring"""





import unittest

from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE__					=   "▁"

SCREAMING_SNAKE_CASE__					=   get_tests_dir("fixtures/test_sentencepiece.model")

@require_sentencepiece
@require_tokenizers
class 		lowercase					(					_UpperCAmelCase     ,  unittest.TestCase					):
      _SCREAMING_SNAKE_CASE					=      BigBirdTokenizer
      _SCREAMING_SNAKE_CASE					=      BigBirdTokenizerFast
      _SCREAMING_SNAKE_CASE					=      True
      _SCREAMING_SNAKE_CASE					=      True





      def     _snake_case			(       self )		->     List[str]:
            super().setUp()

            lowerCAmelCase  						=       self.tokenizer_class(lowercase      ,						keep_accents=lowercase )
            tokenizer.save_pretrained(self.tmpdirname )





      def     _snake_case			(       self )		->     List[Any]:
            lowerCAmelCase  						=       """<s>"""
            lowerCAmelCase  						=       1

            self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase )      ,						lowercase )
            self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase )      ,						lowercase )





      def     _snake_case			(       self )		->     List[str]:
            lowerCAmelCase  						=       list(self.get_tokenizer().get_vocab().keys() )

            self.assertEqual(vocab_keys[0]      ,						"""<unk>""" )
            self.assertEqual(vocab_keys[1]      ,						"""<s>""" )
            self.assertEqual(vocab_keys[-1]      ,						"""[MASK]""" )
            self.assertEqual(len(lowercase )      ,						1_004 )





      def     _snake_case			(       self )		->     Union[str, Any]:
            self.assertEqual(self.get_tokenizer().vocab_size      ,						1_000 )





      def     _snake_case			(       self )		->     List[str]:
            if not self.test_rust_tokenizer:
                  return

            lowerCAmelCase  						=       self.get_tokenizer()
            lowerCAmelCase  						=       self.get_rust_tokenizer()

            lowerCAmelCase  						=       """I was born in 92000, and this is falsé."""

            lowerCAmelCase  						=       tokenizer.tokenize(lowercase )
            lowerCAmelCase  						=       rust_tokenizer.tokenize(lowercase )
            self.assertListEqual(lowercase      ,						lowercase )

            lowerCAmelCase  						=       tokenizer.encode(lowercase      ,						add_special_tokens=lowercase )
            lowerCAmelCase  						=       rust_tokenizer.encode(lowercase      ,						add_special_tokens=lowercase )
            self.assertListEqual(lowercase      ,						lowercase )

            lowerCAmelCase  						=       self.get_rust_tokenizer()
            lowerCAmelCase  						=       tokenizer.encode(lowercase )
            lowerCAmelCase  						=       rust_tokenizer.encode(lowercase )
            self.assertListEqual(lowercase      ,						lowercase )





      def     _snake_case			(       self )		->     int:
            lowerCAmelCase  						=       BigBirdTokenizer(lowercase      ,						keep_accents=lowercase )

            lowerCAmelCase  						=       tokenizer.tokenize("""This is a test""" )
            self.assertListEqual(lowercase      ,						["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )

            self.assertListEqual(
                tokenizer.convert_tokens_to_ids(lowercase )      ,						[285, 46, 10, 170, 382]      ,						)

            lowerCAmelCase  						=       tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
            self.assertListEqual(
                lowercase      ,						[
                    SPIECE_UNDERLINE + """I""",
                    SPIECE_UNDERLINE + """was""",
                    SPIECE_UNDERLINE + """b""",
                    """or""",
                    """n""",
                    SPIECE_UNDERLINE + """in""",
                    SPIECE_UNDERLINE + """""",
                    """9""",
                    """2""",
                    """0""",
                    """0""",
                    """0""",
                    """,""",
                    SPIECE_UNDERLINE + """and""",
                    SPIECE_UNDERLINE + """this""",
                    SPIECE_UNDERLINE + """is""",
                    SPIECE_UNDERLINE + """f""",
                    """al""",
                    """s""",
                    """é""",
                    """.""",
                ]      ,						)
            lowerCAmelCase  						=       tokenizer.convert_tokens_to_ids(lowercase )
            self.assertListEqual(
                lowercase      ,						[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]      ,						)

            lowerCAmelCase  						=       tokenizer.convert_ids_to_tokens(lowercase )
            self.assertListEqual(
                lowercase      ,						[
                    SPIECE_UNDERLINE + """I""",
                    SPIECE_UNDERLINE + """was""",
                    SPIECE_UNDERLINE + """b""",
                    """or""",
                    """n""",
                    SPIECE_UNDERLINE + """in""",
                    SPIECE_UNDERLINE + """""",
                    """<unk>""",
                    """2""",
                    """0""",
                    """0""",
                    """0""",
                    """,""",
                    SPIECE_UNDERLINE + """and""",
                    SPIECE_UNDERLINE + """this""",
                    SPIECE_UNDERLINE + """is""",
                    SPIECE_UNDERLINE + """f""",
                    """al""",
                    """s""",
                    """<unk>""",
                    """.""",
                ]      ,						)





      @cached_property
      def     _snake_case			(       self )		->     Tuple:
            return BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base""" )





      @slow
      def     _snake_case			(       self )		->     Tuple:
            lowerCAmelCase  						=       """Hello World!"""
            lowerCAmelCase  						=       [65, 18_536, 2_260, 101, 66]

            self.assertListEqual(lowercase      ,						self.big_tokenizer.encode(lowercase ) )





      @slow
      def     _snake_case			(       self )		->     int:
            lowerCAmelCase  						=       (
                """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
                """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
            )
            # fmt: off
            lowerCAmelCase  						=       [65, 871, 419, 358, 946, 991, 2_521, 452, 358, 1_357, 387, 7_751, 3_536, 112, 985, 456, 126, 865, 938, 5_400, 5_734, 458, 1_368, 467, 786, 2_462, 5_246, 1_159, 633, 865, 4_519, 457, 582, 852, 2_557, 427, 916, 508, 405, 34_324, 497, 391, 408, 11_342, 1_244, 385, 100, 938, 985, 456, 574, 362, 12_597, 3_200, 3_129, 1_172, 66]  # noqa: E231
            # fmt: on
            self.assertListEqual(lowercase      ,						self.big_tokenizer.encode(lowercase ) )





      @require_torch
      @slow
      def     _snake_case			(       self )		->     Tuple:
            import torch

            from transformers import BigBirdConfig, BigBirdModel

            # Build sequence
            lowerCAmelCase  						=       list(self.big_tokenizer.get_vocab().keys() )[:10]
            lowerCAmelCase  						=       """ """.join(lowercase )
            lowerCAmelCase  						=       self.big_tokenizer.encode_plus(lowercase      ,						return_tensors="""pt"""      ,						return_token_type_ids=lowercase )
            lowerCAmelCase  						=       self.big_tokenizer.batch_encode_plus(
                [sequence + """ """ + sequence]      ,						return_tensors="""pt"""      ,						return_token_type_ids=lowercase )

            lowerCAmelCase  						=       BigBirdConfig(attention_type="""original_full""" )
            lowerCAmelCase  						=       BigBirdModel(lowercase )

            assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

            with torch.no_grad():
                  model(**lowercase )
                  model(**lowercase )





      @slow
      def     _snake_case			(       self )		->     List[str]:
            lowerCAmelCase  						=       BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base""" )
            lowerCAmelCase  						=       tokenizer.decode(tokenizer("""Paris is the [MASK].""" ).input_ids )

            self.assertTrue(decoded_text == """[CLS] Paris is the[MASK].[SEP]""" )





      @slow
      def     _snake_case			(       self )		->     Optional[int]:
            # fmt: off
            lowerCAmelCase  						=       {"""input_ids""": [[65, 39_286, 458, 36_335, 2_001, 456, 13_073, 13_266, 455, 113, 7_746, 1_741, 11_157, 391, 13_073, 13_266, 455, 113, 3_967, 35_412, 113, 4_936, 109, 3_870, 2_377, 113, 30_084, 45_720, 458, 134, 17_496, 112, 503, 11_672, 113, 118, 112, 5_665, 13_347, 38_687, 112, 1_496, 31_389, 112, 3_268, 47_264, 134, 962, 112, 16_377, 8_035, 23_130, 430, 12_169, 15_518, 28_592, 458, 146, 41_697, 109, 391, 12_169, 15_518, 16_689, 458, 146, 41_358, 109, 452, 726, 4_034, 111, 763, 35_412, 5_082, 388, 1_903, 111, 9_051, 391, 2_870, 48_918, 1_900, 1_123, 550, 998, 112, 9_586, 15_985, 455, 391, 410, 22_955, 37_636, 114, 66], [65, 448, 17_496, 419, 3_663, 385, 763, 113, 27_533, 2_870, 3_283, 13_043, 1_639, 24_713, 523, 656, 24_013, 18_550, 2_521, 517, 27_014, 21_244, 420, 1_212, 1_465, 391, 927, 4_833, 388, 578, 11_786, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 484, 2_169, 7_687, 21_932, 18_146, 726, 363, 17_032, 3_391, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
            # fmt: on

            self.tokenizer_integration_test_util(
                expected_encoding=lowercase      ,						model_name="""google/bigbird-roberta-base"""      ,						revision="""215c99f1600e06f83acce68422f2035b2b5c3510"""      ,						)






