import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class UpperCAmelCase						(					__A					,       unittest.TestCase  ):


				'''simple docstring'''






				lowerCamelCase_    =					LEDTokenizer
				lowerCamelCase_    =					LEDTokenizerFast
				lowerCamelCase_    =					True
				def   lowerCAmelCase_   (						self					):



						"""simple docstring"""





						super().setUp()
						A_		:				str     =      [
						    "l",
						    "o",
						    "w",
						    "e",
						    "r",
						    "s",
						    "t",
						    "i",
						    "d",
						    "n",
						    "\u0120",
						    "\u0120l",
						    "\u0120n",
						    "\u0120lo",
						    "\u0120low",
						    "er",
						    "\u0120lowest",
						    "\u0120newer",
						    "\u0120wider",
						    "<unk>",
						]
						A_		:				Union[str, Any]     =      dict(zip(__A		,	range(len(__A					)					)					)					)
						A_		:				Any     =      ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
						A_		:				Dict     =      {"unk_token": "<unk>"}

						A_		:				Optional[int]     =      os.path.join(self.tmpdirname		,	VOCAB_FILES_NAMES['vocab_file']					)
						A_		:				Dict     =      os.path.join(self.tmpdirname		,	VOCAB_FILES_NAMES['merges_file']					)
						with open(self.vocab_file		,	'w'		,	encoding='utf-8'					) as fp:
								fp.write(json.dumps(__A					) + '\n'					)
						with open(self.merges_file		,	'w'		,	encoding='utf-8'					) as fp:
								fp.write('\n'.join(__A					)					)
				def   lowerCAmelCase_   (						self		,	**lowercase					):



						"""simple docstring"""





						kwargs.update(self.special_tokens_map					)
						return self.tokenizer_class.from_pretrained(self.tmpdirname		,	**__A					)
				def   lowerCAmelCase_   (						self		,	**lowercase					):



						"""simple docstring"""





						kwargs.update(self.special_tokens_map					)
						return self.rust_tokenizer_class.from_pretrained(self.tmpdirname		,	**__A					)
				def   lowerCAmelCase_   (						self		,	lowercase					):



						"""simple docstring"""





						return "lower newer", "lower newer"
				@cached_property
				def   lowerCAmelCase_   (						self					):



						"""simple docstring"""





						return LEDTokenizer.from_pretrained('allenai/led-base-16384'					)
				@cached_property
				def   lowerCAmelCase_   (						self					):



						"""simple docstring"""





						return LEDTokenizerFast.from_pretrained('allenai/led-base-16384'					)
				@require_torch
				def   lowerCAmelCase_   (						self					):



						"""simple docstring"""





						A_		:				List[Any]     =      ["A long paragraph for summarization.", "Another paragraph for summarization."]
						A_		:				Union[str, Any]     =      [0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								A_		:				int     =      tokenizer(__A		,	max_length=len(__A					)		,	padding=__A		,	return_tensors='pt'					)
								self.assertIsInstance(__A		,	__A					)

								self.assertEqual((2, 9)		,	batch.input_ids.shape					)
								self.assertEqual((2, 9)		,	batch.attention_mask.shape					)
								A_		:				Tuple     =      batch.input_ids.tolist()[0]
								self.assertListEqual(__A		,	__A					)
				@require_torch
				def   lowerCAmelCase_   (						self					):



						"""simple docstring"""





						A_		:				Optional[Any]     =      ["A long paragraph for summarization.", "Another paragraph for summarization."]
						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								A_		:				Union[str, Any]     =      tokenizer(__A		,	padding=__A		,	return_tensors='pt'					)
								self.assertIn('input_ids'		,	__A					)
								self.assertIn('attention_mask'		,	__A					)
								self.assertNotIn('labels'		,	__A					)
								self.assertNotIn('decoder_attention_mask'		,	__A					)
				@require_torch
				def   lowerCAmelCase_   (						self					):



						"""simple docstring"""





						A_		:				Optional[Any]     =      [
						    "Summary of the text.",
						    "Another summary.",
						]
						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								A_		:				str     =      tokenizer(text_target=__A		,	max_length=3_2		,	padding='max_length'		,	return_tensors='pt'					)
								self.assertEqual(3_2		,	targets['input_ids'].shape[1]					)
				@require_torch
				def   lowerCAmelCase_   (						self					):



						"""simple docstring"""





						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								A_		:				Tuple     =      tokenizer(
								    ['I am a small frog' * 1_0_2_4, 'I am a small frog']		,	padding=__A		,	truncation=__A		,	return_tensors='pt'					)
								self.assertIsInstance(__A		,	__A					)
								self.assertEqual(batch.input_ids.shape		,	(2, 5_1_2_2)					)
				@require_torch
				def   lowerCAmelCase_   (						self					):



						"""simple docstring"""





						A_		:				Optional[Any]     =      ["A long paragraph for summarization."]
						A_		:				str     =      [
						    "Summary of the text.",
						]
						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								A_		:				Union[str, Any]     =      tokenizer(__A		,	return_tensors='pt'					)
								A_		:				List[str]     =      tokenizer(text_target=__A		,	return_tensors='pt'					)
								A_		:				Tuple     =      inputs["input_ids"]
								A_		:				List[Any]     =      targets["input_ids"]
								self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()					)
								self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()					)
								self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()					)
								self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()					)
				@require_torch
				def   lowerCAmelCase_   (						self					):



						"""simple docstring"""





						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								A_		:				Dict     =      ["Summary of the text.", "Another summary."]
								A_		:				Union[str, Any]     =      [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

								A_		:				Tuple     =      tokenizer(__A		,	padding=__A					)
								A_		:				Union[str, Any]     =      [[0] * len(__A					) for x in encoded_output["input_ids"]]
								A_		:				Union[str, Any]     =      tokenizer.pad(__A					)
								self.assertSequenceEqual(outputs['global_attention_mask']		,	__A					)
				def   lowerCAmelCase_   (						self					):



						"""simple docstring"""





						pass







				def   lowerCAmelCase_   (						self					):



						"""simple docstring"""





						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
								with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''					):
										A_		:				Dict     =      self.rust_tokenizer_class.from_pretrained(__A		,	**__A					)
										A_		:				Union[str, Any]     =      self.tokenizer_class.from_pretrained(__A		,	**__A					)
										A_		:				Optional[int]     =      "A, <mask> AllenNLP sentence."
										A_		:				List[Any]     =      tokenizer_r.encode_plus(__A		,	add_special_tokens=__A		,	return_token_type_ids=__A					)
										A_		:				int     =      tokenizer_p.encode_plus(__A		,	add_special_tokens=__A		,	return_token_type_ids=__A					)
										self.assertEqual(sum(tokens_r['token_type_ids']					)		,	sum(tokens_p['token_type_ids']					)					)
										self.assertEqual(
										    sum(tokens_r['attention_mask']					) / len(tokens_r['attention_mask']					)		,	sum(tokens_p['attention_mask']					) / len(tokens_p['attention_mask']					)		,	)

										A_		:				Optional[int]     =      tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']					)
										A_		:				Optional[Any]     =      tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']					)
										self.assertSequenceEqual(tokens_p['input_ids']		,	[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]					)
										self.assertSequenceEqual(tokens_r['input_ids']		,	[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]					)

										self.assertSequenceEqual(
										    __A		,	['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']					)
										self.assertSequenceEqual(
										    __A		,	['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']					)










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_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_=[]	):


  '''simple docstring'''

  lowerCamelCase :					Optional[Any]    =				size[0] - overlap_pixels * 2
  lowerCamelCase :					int    =				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
  lowerCamelCase :					Tuple    =				np.ones((size_y, size_x)      ,						dtype=np.uinta	) * 255
  lowerCamelCase :					List[Any]    =				np.pad(SCREAMING_SNAKE_CASE_      ,						mode="linear_ramp"      ,						pad_width=SCREAMING_SNAKE_CASE_      ,						end_values=0	)

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






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  return max(SCREAMING_SNAKE_CASE_      ,						min(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)	)






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''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 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					Optional[Any]    =				list(SCREAMING_SNAKE_CASE_	)
  rect[0] -= overlap
  rect[1] -= overlap
  rect[2] += overlap
  rect[3] += overlap
  lowerCamelCase :					Any    =				clamp_rect(SCREAMING_SNAKE_CASE_      ,						[0, 0]      ,						[image_size[0], image_size[1]]	)
  return rect






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					Dict    =				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(SCREAMING_SNAKE_CASE_      ,						(original_slice, 0)	)
  return result






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					Union[str, Any]    =				(original_image_slice * 4, 0, tile.size[0], tile.size[1])
  lowerCamelCase :					int    =				tile.crop(SCREAMING_SNAKE_CASE_	)
  return tile






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					int    =				n % d
  return n - divisor
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   def __init__(		self						,				__A						,				__A						,				__A						,				__A						,				__A						,				__A						,				__A = 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 _snake_case    (		self						,				__A						,				__A						,				__A						,				__A						,				__A						,				__A						,				__A						,				**__A ):






     """simple docstring"""





     torch.manual_seed(0 )
     lowerCamelCase :					Tuple    =				(
         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 ),
     )
     lowerCamelCase :					Union[str, Any]    =				add_overlap_rect(__A						,				__A						,				image.size )
     lowerCamelCase :					List[str]    =				image.crop(__A )
     lowerCamelCase :					Optional[int]    =				((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
     lowerCamelCase :					int    =				translated_slice_x - (original_image_slice / 2)
     lowerCamelCase :					Optional[Any]    =				max(0						,				__A )
     lowerCamelCase :					Tuple    =				squeeze_tile(__A						,				__A						,				__A						,				__A )
     lowerCamelCase :					Dict    =				to_input.size
     lowerCamelCase :					Optional[int]    =				to_input.resize((tile_size, tile_size)						,				Image.BICUBIC )
     lowerCamelCase :					Dict    =				super(__A						,				self ).__call__(image=__A						,				**__A ).images[0]
     lowerCamelCase :					Tuple    =				upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)						,				Image.BICUBIC )
     lowerCamelCase :					Optional[Any]    =				unsqueeze_tile(__A						,				__A )
     lowerCamelCase :					Optional[Any]    =				upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)						,				Image.BICUBIC )
     lowerCamelCase :					int    =				[]
     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" )
     lowerCamelCase :					int    =				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						,				):






     """simple docstring"""





     lowerCamelCase :					Dict    =				Image.new("RGB"						,				(image.size[0] * 4, image.size[1] * 4) )
     lowerCamelCase :					Union[str, Any]    =				math.ceil(image.size[0] / tile_size )
     lowerCamelCase :					Dict    =				math.ceil(image.size[1] / tile_size )
     lowerCamelCase :					str    =				tcx * tcy
     lowerCamelCase :					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 		lowercase_(  ):


  '''simple docstring'''

  lowerCamelCase :					Dict    =				"stabilityai/stable-diffusion-x4-upscaler"
  lowerCamelCase :					Union[str, Any]    =				StableDiffusionTiledUpscalePipeline.from_pretrained(SCREAMING_SNAKE_CASE_      ,						revision="fp16"      ,						torch_dtype=torch.floataa	)
  lowerCamelCase :					Optional[Any]    =				pipe.to("cuda"	)
  lowerCamelCase :					List[str]    =				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"	)

  lowerCamelCase :					int    =				pipe(image=SCREAMING_SNAKE_CASE_      ,						prompt="Black font, white background, vector"      ,						noise_level=40      ,						callback=SCREAMING_SNAKE_CASE_	)
  final_image.save("diffusers_library.jpg"	)


if __name__ == "__main__":
   main()



