



from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)

class 			__snake_case   ( SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							def __init__(				self	,	a_	,	a_	,	a_	,	a_	,	a_	,	a_ = 0.93	,	a_ = True	,	):
											a__					   =		mixing_coeff
											a__					   =		leapfrog_steps

											super().__init__()
											self.register_modules(
											    vae=a_	,	text_encoder=a_	,	tokenizer=a_	,	unet=a_	,	scheduler=a_	,	)

											a__					   =		2 ** (len(self.vae.config.block_out_channels		) - 1)
											a__					   =		VaeImageProcessor(vae_scale_factor=self.vae_scale_factor		)


							def _a   (				self	,	a_	,	a_ = None	,	a_ = False		):
											a__					   =		self.tokenizer(
											    a_	,	padding="""max_length"""	,	max_length=self.tokenizer.model_max_length	,	truncation=a_	,	return_tensors="""pt"""	,	)

											a__					   =		self.text_encoder(text_inputs.input_ids.to(self.device		)		).last_hidden_state

											a__					   =		prompt_embeds.to(dtype=self.text_encoder.dtype	,	device=self.device		)

											if do_classifier_free_guidance:
															a__					   =		"""""" if negative_prompt is None else negative_prompt

															a__					   =		self.tokenizer(
															    a_	,	padding="""max_length"""	,	max_length=self.tokenizer.model_max_length	,	truncation=a_	,	return_tensors="""pt"""	,	)

															a__					   =		self.text_encoder(uncond_input.input_ids.to(self.device		)		).last_hidden_state

															a__					   =		torch.cat([negative_prompt_embeds, prompt_embeds]		)

											return prompt_embeds


							def _a   (				self	,	a_	,	a_		):
											a__					   =		self.mixing_coeff * x + (1 - self.mixing_coeff) * y
											a__					   =		self.mixing_coeff * y + (1 - self.mixing_coeff) * x

											return [x, y]


							def _a   (				self	,	a_	,	a_		):
											a__					   =		(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
											a__					   =		(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

											return [x, y]


							def _a   (				self	,	a_		):
											# as self.alphas_cumprod is always in cpu
											a__					   =		int(a_		)

											a__					   =		self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

											return alpha_prod, 1 - alpha_prod


							def _a   (				self	,	a_	,	a_	,	a_	,	a_	,	):
											a__					   =		timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

											a__   ,      a__					   =		self._get_alpha_and_beta(a_		)
											a__   ,      a__					   =		self._get_alpha_and_beta(a_		)

											a__					   =		(alpha_prod_t_prev / alpha_prod_t) ** 0.5
											a__					   =		-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

											a__					   =		(base - b_t * model_output) / a_t

											return model_input, next_model_input.to(base.dtype		)


							def _a   (				self	,	a_	,	a_	,	a_	,	a_	,	):
											a__					   =		timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

											a__   ,      a__					   =		self._get_alpha_and_beta(a_		)
											a__   ,      a__					   =		self._get_alpha_and_beta(a_		)

											a__					   =		(alpha_prod_t_prev / alpha_prod_t) ** 0.5
											a__					   =		-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
											a__					   =		a_t * base + b_t * model_output

											return model_input, next_model_input.to(base.dtype		)


							@torch.no_grad()
							def _a   (				self	,	a_		):
											a__					   =		1 / self.vae.config.scaling_factor * latents
											a__					   =		self.vae.decode(a_		).sample
											a__					   =		(image / 2 + 0.5).clamp(0	,	1		)
											return image


							@torch.no_grad()
							def _a   (				self	,	a_	,	a_	,	a_	,	a_	,	a_ = None	,	):
											a__					   =		guidance_scale > 1.0

											a__					   =		image.to(device=self.device	,	dtype=text_embeds.dtype		)
											a__					   =		self.vae.encode(a_		).latent_dist.sample(a_		)

											a__					   =		self.vae.config.scaling_factor * latent

											a__					   =		[latent.clone(), latent.clone()]

											for i, t in tqdm(enumerate(a_		)	,	total=len(a_		)		):
															a__					   =		self.noise_mixing_layer(x=coupled_latents[0]	,	y=coupled_latents[1]		)

															# j - model_input index, k - base index
															for j in range(2		):
																			a__					   =		j ^ 1

																			if self.leapfrog_steps:
																							if i % 2 == 0:
																											a__   ,      a__					   =		j, k

																			a__					   =		coupled_latents[j]
																			a__					   =		coupled_latents[k]

																			a__					   =		torch.cat([model_input] * 2		) if do_classifier_free_guidance else model_input

																			a__					   =		self.unet(a_	,	a_	,	encoder_hidden_states=a_		).sample

																			if do_classifier_free_guidance:
																							a__   ,      a__					   =		noise_pred.chunk(2		)
																							a__					   =		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																			a__   ,      a__					   =		self.noise_step(
																			    base=a_	,	model_input=a_	,	model_output=a_	,	timestep=a_	,	)

																			a__					   =		model_input

											return coupled_latents







							@torch.no_grad()
							def __call__(				self	,	a_	,	a_	,	a_	,	a_ = 3.0	,	a_ = 50	,	a_ = 0.8	,	a_ = None	,	a_ = None	,	a_ = "pil"	,	):
											a__					   =		guidance_scale > 1.0

											a__					   =		self.image_processor.preprocess(a_		)

											a__					   =		self._encode_prompt(a_	,	a_	,	a_		)
											a__					   =		self._encode_prompt(a_	,	a_	,	a_		)

											self.scheduler.set_timesteps(a_	,	self.device		)

											a__					   =		num_inference_steps - int(num_inference_steps * strength		)
											a__					   =		self.scheduler.timesteps[t_limit:]
											a__					   =		fwd_timesteps.flip(0		)

											a__					   =		self.prepare_latents(a_	,	a_	,	a_	,	a_	,	a_		)

											for i, t in tqdm(enumerate(a_		)	,	total=len(a_		)		):
															# j - model_input index, k - base index
															for k in range(2		):
																			a__					   =		k ^ 1

																			if self.leapfrog_steps:
																							if i % 2 == 1:
																											a__   ,      a__					   =		j, k

																			a__					   =		coupled_latents[j]
																			a__					   =		coupled_latents[k]

																			a__					   =		torch.cat([model_input] * 2		) if do_classifier_free_guidance else model_input

																			a__					   =		self.unet(a_	,	a_	,	encoder_hidden_states=a_		).sample

																			if do_classifier_free_guidance:
																							a__   ,      a__					   =		noise_pred.chunk(2		)
																							a__					   =		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																			a__   ,      a__					   =		self.denoise_step(
																			    base=a_	,	model_input=a_	,	model_output=a_	,	timestep=a_	,	)

																			a__					   =		model_input

															a__					   =		self.denoise_mixing_layer(x=coupled_latents[0]	,	y=coupled_latents[1]		)

											# either one is fine
											a__					   =		coupled_latents[0]

											if output_type not in ["latent", "pt", "np", "pil"]:
															a__					   =		(
															    F'''the output_type {output_type} is outdated. Please make sure to set it to one of these instead: '''
															    """`pil`, `np`, `pt`, `latent`"""
															)
															deprecate("""Unsupported output_type"""	,	"""1.0.0"""	,	a_	,	standard_warn=a_		)
															a__					   =		"""np"""

											if output_type == "latent":
															a__					   =		final_latent
											else:
															a__					   =		self.decode_latents(a_		)
															a__					   =		self.image_processor.postprocess(a_	,	output_type=a_		)

											return image






import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
				from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
				__A       =False

if is_vision_available():
				from PIL import Image

				from transformers import (
				    AutoProcessor,
				    PixaStructImageProcessor,
				    PixaStructProcessor,
				    PreTrainedTokenizerFast,
				    TaTokenizer,
				)

@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11		,reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""		,)
@require_vision
@require_torch
class 						UpperCAmelCase__     (     unittest.TestCase  ):




			'''simple docstring'''






			def 					snake_case__	(  self				:					List[str]					):
					'''simple docstring'''





					__UpperCAmelCase       :	Optional[Any]						=      tempfile.mkdtemp()

					__UpperCAmelCase       :	Any						=      PixaStructImageProcessor()
					__UpperCAmelCase       :	Any						=      TaTokenizer.from_pretrained('''t5-small'''					)

					__UpperCAmelCase       :	Tuple						=      PixaStructProcessor(a_			,			a_					)

					processor.save_pretrained(self.tmpdirname					)




			def 					snake_case__	(  self				:					Union[str, Any]			,			**a_				:					Any					):
					'''simple docstring'''





					return AutoProcessor.from_pretrained(self.tmpdirname			,			**a_					).tokenizer




			def 					snake_case__	(  self				:					int			,			**a_				:					Any					):
					'''simple docstring'''





					return AutoProcessor.from_pretrained(self.tmpdirname			,			**a_					).image_processor




			def 					snake_case__	(  self				:					List[Any]					):
					'''simple docstring'''





					shutil.rmtree(self.tmpdirname					)




			def 					snake_case__	(  self				:					List[str]					):
					'''simple docstring'''





					__UpperCAmelCase       :	Optional[Any]						=      [np.random.randint(2_55			,			size=(3, 30, 4_00)			,			dtype=np.uinta					)]

					__UpperCAmelCase       :	Any						=      [Image.fromarray(np.moveaxis(a_			,			0			,			-1					)					) for x in image_inputs]

					return image_inputs




			def 					snake_case__	(  self				:					Optional[int]					):
					'''simple docstring'''





					__UpperCAmelCase       :	str						=      PixaStructProcessor(tokenizer=self.get_tokenizer()			,			image_processor=self.get_image_processor()					)
					processor.save_pretrained(self.tmpdirname					)

					__UpperCAmelCase       :	Tuple						=      self.get_tokenizer(bos_token='''(BOS)'''			,			eos_token='''(EOS)'''					)
					__UpperCAmelCase       :	Any						=      self.get_image_processor(do_normalize=a_			,			padding_value=1.0					)

					__UpperCAmelCase       :	Dict						=      PixaStructProcessor.from_pretrained(
					    self.tmpdirname			,			bos_token='''(BOS)'''			,			eos_token='''(EOS)'''			,			do_normalize=a_			,			padding_value=1.0					)

					self.assertEqual(processor.tokenizer.get_vocab()			,			tokenizer_add_kwargs.get_vocab()					)
					self.assertIsInstance(processor.tokenizer			,			a_					)

					self.assertEqual(processor.image_processor.to_json_string()			,			image_processor_add_kwargs.to_json_string()					)
					self.assertIsInstance(processor.image_processor			,			a_					)




			def 					snake_case__	(  self				:					List[Any]					):
					'''simple docstring'''





					__UpperCAmelCase       :	Tuple						=      self.get_image_processor()
					__UpperCAmelCase       :	Tuple						=      self.get_tokenizer()

					__UpperCAmelCase       :	Any						=      PixaStructProcessor(tokenizer=a_			,			image_processor=a_					)

					__UpperCAmelCase       :	Tuple						=      self.prepare_image_inputs()

					__UpperCAmelCase       :	List[Any]						=      image_processor(a_			,			return_tensors='''np'''					)
					__UpperCAmelCase       :	Union[str, Any]						=      processor(images=a_			,			return_tensors='''np'''					)

					for key in input_feat_extract.keys():
							self.assertAlmostEqual(input_feat_extract[key].sum()			,			input_processor[key].sum()			,			delta=1e-2					)




			def 					snake_case__	(  self				:					Optional[int]					):
					'''simple docstring'''





					__UpperCAmelCase       :	Any						=      self.get_image_processor()
					__UpperCAmelCase       :	str						=      self.get_tokenizer()

					__UpperCAmelCase       :	str						=      PixaStructProcessor(tokenizer=a_			,			image_processor=a_					)

					__UpperCAmelCase       :	List[str]						=      '''lower newer'''

					__UpperCAmelCase       :	Any						=      processor(text=a_					)

					__UpperCAmelCase       :	Dict						=      tokenizer(a_			,			return_token_type_ids=a_			,			add_special_tokens=a_					)

					for key in encoded_tok.keys():
							self.assertListEqual(encoded_tok[key]			,			encoded_processor[key]					)




			def 					snake_case__	(  self				:					str					):
					'''simple docstring'''





					__UpperCAmelCase       :	str						=      self.get_image_processor()
					__UpperCAmelCase       :	List[Any]						=      self.get_tokenizer()

					__UpperCAmelCase       :	Tuple						=      PixaStructProcessor(tokenizer=a_			,			image_processor=a_					)

					__UpperCAmelCase       :	List[str]						=      '''lower newer'''
					__UpperCAmelCase       :	Tuple						=      self.prepare_image_inputs()

					__UpperCAmelCase       :	Dict						=      processor(text=a_			,			images=a_					)

					self.assertListEqual(
					    list(inputs.keys()					)			,			['''flattened_patches''', '''attention_mask''', '''decoder_attention_mask''', '''decoder_input_ids''']					)

					# test if it raises when no input is passed
					with pytest.raises(a_					):
							processor()




			def 					snake_case__	(  self				:					Any					):
					'''simple docstring'''





					__UpperCAmelCase       :	str						=      self.get_image_processor()
					__UpperCAmelCase       :	Tuple						=      self.get_tokenizer()

					__UpperCAmelCase       :	List[Any]						=      PixaStructProcessor(tokenizer=a_			,			image_processor=a_					)

					__UpperCAmelCase       :	int						=      '''lower newer'''
					__UpperCAmelCase       :	List[Any]						=      self.prepare_image_inputs()

					__UpperCAmelCase       :	int						=      processor(text=a_			,			images=a_					)

					__UpperCAmelCase       :	Tuple						=      [5_12, 10_24, 20_48, 40_96]
					__UpperCAmelCase       :	Tuple						=      [7_70, 7_70, 7_70, 7_70]
					# with text
					for i, max_patch in enumerate(a_					):
							__UpperCAmelCase       :	Dict						=      processor(text=a_			,			images=a_			,			max_patches=a_					)
							self.assertEqual(inputs['''flattened_patches'''][0].shape[0]			,			a_					)
							self.assertEqual(inputs['''flattened_patches'''][0].shape[1]			,			expected_hidden_size[i]					)

					# without text input
					for i, max_patch in enumerate(a_					):
							__UpperCAmelCase       :	Any						=      processor(images=a_			,			max_patches=a_					)
							self.assertEqual(inputs['''flattened_patches'''][0].shape[0]			,			a_					)
							self.assertEqual(inputs['''flattened_patches'''][0].shape[1]			,			expected_hidden_size[i]					)




			def 					snake_case__	(  self				:					Optional[Any]					):
					'''simple docstring'''





					__UpperCAmelCase       :	List[Any]						=      self.get_image_processor()
					__UpperCAmelCase       :	str						=      self.get_tokenizer()

					__UpperCAmelCase       :	List[Any]						=      PixaStructProcessor(tokenizer=a_			,			image_processor=a_					)

					__UpperCAmelCase       :	Union[str, Any]						=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					__UpperCAmelCase       :	List[Any]						=      processor.batch_decode(a_					)
					__UpperCAmelCase       :	Any						=      tokenizer.batch_decode(a_					)

					self.assertListEqual(a_			,			a_					)




			def 					snake_case__	(  self				:					Union[str, Any]					):
					'''simple docstring'''





					__UpperCAmelCase       :	Optional[int]						=      self.get_image_processor()
					__UpperCAmelCase       :	Optional[Any]						=      self.get_tokenizer()

					__UpperCAmelCase       :	Any						=      PixaStructProcessor(tokenizer=a_			,			image_processor=a_					)

					__UpperCAmelCase       :	Dict						=      '''lower newer'''
					__UpperCAmelCase       :	List[str]						=      self.prepare_image_inputs()

					__UpperCAmelCase       :	Optional[Any]						=      processor(text=a_			,			images=a_					)

					# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
					self.assertListEqual(
					    list(inputs.keys()					)			,			['''flattened_patches''', '''attention_mask''', '''decoder_attention_mask''', '''decoder_input_ids''']					)

					__UpperCAmelCase       :	Optional[int]						=      processor(text=a_					)

					# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
					self.assertListEqual(list(inputs.keys()					)			,			['''input_ids''', '''attention_mask''']					)





