
"""simple docstring"""





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:
       _UpperCamelCase       :						List[str]     =   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):


      def 		_UpperCAmelCase	(       self							)	-> List[Any]:
             lowercase__      :		Optional[int]          =     tempfile.mkdtemp()

             lowercase__      :		Optional[int]          =     PixaStructImageProcessor()
             lowercase__      :		Optional[Any]          =     TaTokenizer.from_pretrained('t5-small'							)

             lowercase__      :		Any          =     PixaStructProcessor(a		,      a							)

             processor.save_pretrained(self.tmpdirname							)


      def 		_UpperCAmelCase	(       self		,      **a							)	-> Union[str, Any]:
             return AutoProcessor.from_pretrained(self.tmpdirname		,      **a							).tokenizer


      def 		_UpperCAmelCase	(       self		,      **a							)	-> Tuple:
             return AutoProcessor.from_pretrained(self.tmpdirname		,      **a							).image_processor


      def 		_UpperCAmelCase	(       self							)	-> List[Any]:
             shutil.rmtree(self.tmpdirname							)


      def 		_UpperCAmelCase	(       self							)	-> List[Any]:
             lowercase__      :		Optional[int]          =     [np.random.randint(2_5_5		,      size=(3, 3_0, 4_0_0)		,      dtype=np.uinta							)]

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

             return image_inputs


      def 		_UpperCAmelCase	(       self							)	-> Union[str, Any]:
             lowercase__      :		Tuple          =     PixaStructProcessor(tokenizer=self.get_tokenizer()		,      image_processor=self.get_image_processor()							)
             processor.save_pretrained(self.tmpdirname							)

             lowercase__      :		List[Any]          =     self.get_tokenizer(bos_token='(BOS)'		,      eos_token='(EOS)'							)
             lowercase__      :		Union[str, Any]          =     self.get_image_processor(do_normalize=a		,      padding_value=1.0							)

             lowercase__      :		Optional[int]          =     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 		_UpperCAmelCase	(       self							)	-> Union[str, Any]:
             lowercase__      :		Optional[int]          =     self.get_image_processor()
             lowercase__      :		Union[str, Any]          =     self.get_tokenizer()

             lowercase__      :		List[str]          =     PixaStructProcessor(tokenizer=a		,      image_processor=a							)

             lowercase__      :		Optional[int]          =     self.prepare_image_inputs()

             lowercase__      :		Optional[Any]          =     image_processor(a		,      return_tensors='np'							)
             lowercase__      :		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 		_UpperCAmelCase	(       self							)	-> str:
             lowercase__      :		int          =     self.get_image_processor()
             lowercase__      :		str          =     self.get_tokenizer()

             lowercase__      :		List[str]          =     PixaStructProcessor(tokenizer=a		,      image_processor=a							)

             lowercase__      :		int          =     'lower newer'

             lowercase__      :		Tuple          =     processor(text=a							)

             lowercase__      :		Union[str, Any]          =     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 		_UpperCAmelCase	(       self							)	-> int:
             lowercase__      :		str          =     self.get_image_processor()
             lowercase__      :		Optional[int]          =     self.get_tokenizer()

             lowercase__      :		List[Any]          =     PixaStructProcessor(tokenizer=a		,      image_processor=a							)

             lowercase__      :		Dict          =     'lower newer'
             lowercase__      :		Optional[Any]          =     self.prepare_image_inputs()

             lowercase__      :		int          =     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 		_UpperCAmelCase	(       self							)	-> Tuple:
             lowercase__      :		List[Any]          =     self.get_image_processor()
             lowercase__      :		Union[str, Any]          =     self.get_tokenizer()

             lowercase__      :		Union[str, Any]          =     PixaStructProcessor(tokenizer=a		,      image_processor=a							)

             lowercase__      :		Optional[int]          =     'lower newer'
             lowercase__      :		Optional[Any]          =     self.prepare_image_inputs()

             lowercase__      :		List[Any]          =     processor(text=a		,      images=a							)

             lowercase__      :		Dict          =     [5_1_2, 1_0_2_4, 2_0_4_8, 4_0_9_6]
             lowercase__      :		Tuple          =     [7_7_0, 7_7_0, 7_7_0, 7_7_0]
             # with text
             for i, max_patch in enumerate(a							):
                    lowercase__      :		int          =     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							):
                    lowercase__      :		Union[str, 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 		_UpperCAmelCase	(       self							)	-> Optional[Any]:
             lowercase__      :		int          =     self.get_image_processor()
             lowercase__      :		Any          =     self.get_tokenizer()

             lowercase__      :		int          =     PixaStructProcessor(tokenizer=a		,      image_processor=a							)

             lowercase__      :		List[Any]          =     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

             lowercase__      :		Optional[Any]          =     processor.batch_decode(a							)
             lowercase__      :		Optional[Any]          =     tokenizer.batch_decode(a							)

             self.assertListEqual(a		,      a							)


      def 		_UpperCAmelCase	(       self							)	-> List[str]:
             lowercase__      :		List[Any]          =     self.get_image_processor()
             lowercase__      :		Optional[Any]          =     self.get_tokenizer()

             lowercase__      :		Union[str, Any]          =     PixaStructProcessor(tokenizer=a		,      image_processor=a							)

             lowercase__      :		Dict          =     'lower newer'
             lowercase__      :		int          =     self.prepare_image_inputs()

             lowercase__      :		Tuple          =     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']							)

             lowercase__      :		List[Any]          =     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']							)


"""simple docstring"""





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 					UpperCAmelCase_     (   _a):


      def __init__(       self		,      a		,      a		,      a		,      a		,      a		,      a = 0.93		,      a = True		,      )	-> Any:
             lowercase__      :		Union[str, Any]          =     mixing_coeff
             lowercase__      :		Any          =     leapfrog_steps

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

             lowercase__      :		List[str]          =     2 ** (len(self.vae.config.block_out_channels							) - 1)
             lowercase__      :		Union[str, Any]          =     VaeImageProcessor(vae_scale_factor=self.vae_scale_factor							)


      def 		_UpperCAmelCase	(       self		,      a		,      a = None		,      a = False							)	-> Optional[int]:
             lowercase__      :		str          =     self.tokenizer(
                 a		,      padding='max_length'		,      max_length=self.tokenizer.model_max_length		,      truncation=a		,      return_tensors='pt'		,      )

             lowercase__      :		str          =     self.text_encoder(text_inputs.input_ids.to(self.device							)							).last_hidden_state

             lowercase__      :		Any          =     prompt_embeds.to(dtype=self.text_encoder.dtype		,      device=self.device							)

             if do_classifier_free_guidance:
                    lowercase__      :		Tuple          =     '' if negative_prompt is None else negative_prompt

                    lowercase__      :		int          =     self.tokenizer(
                        a		,      padding='max_length'		,      max_length=self.tokenizer.model_max_length		,      truncation=a		,      return_tensors='pt'		,      )

                    lowercase__      :		str          =     self.text_encoder(uncond_input.input_ids.to(self.device							)							).last_hidden_state

                    lowercase__      :		str          =     torch.cat([negative_prompt_embeds, prompt_embeds]							)

             return prompt_embeds


      def 		_UpperCAmelCase	(       self		,      a		,      a							)	-> Optional[Any]:
             lowercase__      :		int          =     self.mixing_coeff * x + (1 - self.mixing_coeff) * y
             lowercase__      :		Tuple          =     self.mixing_coeff * y + (1 - self.mixing_coeff) * x

             return [x, y]


      def 		_UpperCAmelCase	(       self		,      a		,      a							)	-> List[str]:
             lowercase__      :		str          =     (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
             lowercase__      :		Union[str, Any]          =     (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

             return [x, y]


      def 		_UpperCAmelCase	(       self		,      a							)	-> List[str]:
             # as self.alphas_cumprod is always in cpu
             lowercase__      :		Any          =     int(a							)

             lowercase__      :		Any          =     self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

             return alpha_prod, 1 - alpha_prod


      def 		_UpperCAmelCase	(       self		,      a		,      a		,      a		,      a		,      )	-> int:
             lowercase__      :		List[Any]          =     timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

             lowercase__   ,				lowercase__      :		Optional[Any]          =     self._get_alpha_and_beta(a							)
             lowercase__   ,				lowercase__      :		str          =     self._get_alpha_and_beta(a							)

             lowercase__      :		Tuple          =     (alpha_prod_t_prev / alpha_prod_t) ** 0.5
             lowercase__      :		Optional[int]          =     -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

             lowercase__      :		int          =     (base - b_t * model_output) / a_t

             return model_input, next_model_input.to(base.dtype							)


      def 		_UpperCAmelCase	(       self		,      a		,      a		,      a		,      a		,      )	-> Tuple:
             lowercase__      :		Optional[int]          =     timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

             lowercase__   ,				lowercase__      :		Optional[int]          =     self._get_alpha_and_beta(a							)
             lowercase__   ,				lowercase__      :		List[Any]          =     self._get_alpha_and_beta(a							)

             lowercase__      :		Union[str, Any]          =     (alpha_prod_t_prev / alpha_prod_t) ** 0.5
             lowercase__      :		Optional[Any]          =     -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
             lowercase__      :		List[str]          =     a_t * base + b_t * model_output

             return model_input, next_model_input.to(base.dtype							)


      @torch.no_grad()
      def 		_UpperCAmelCase	(       self		,      a							)	-> Optional[Any]:
             lowercase__      :		Union[str, Any]          =     1 / self.vae.config.scaling_factor * latents
             lowercase__      :		Union[str, Any]          =     self.vae.decode(a							).sample
             lowercase__      :		int          =     (image / 2 + 0.5).clamp(0		,      1							)
             return image


      @torch.no_grad()
      def 		_UpperCAmelCase	(       self		,      a		,      a		,      a		,      a		,      a = None		,      )	-> Dict:
             lowercase__      :		List[str]          =     guidance_scale > 1.0

             lowercase__      :		Union[str, Any]          =     image.to(device=self.device		,      dtype=text_embeds.dtype							)
             lowercase__      :		Tuple          =     self.vae.encode(a							).latent_dist.sample(a							)

             lowercase__      :		Optional[Any]          =     self.vae.config.scaling_factor * latent

             lowercase__      :		Tuple          =     [latent.clone(), latent.clone()]

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

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

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

                           lowercase__      :		Union[str, Any]          =     coupled_latents[j]
                           lowercase__      :		Tuple          =     coupled_latents[k]

                           lowercase__      :		Dict          =     torch.cat([model_input] * 2							) if do_classifier_free_guidance else model_input

                           lowercase__      :		Union[str, Any]          =     self.unet(a		,      a		,      encoder_hidden_states=a							).sample

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

                           lowercase__   ,				lowercase__      :		Optional[Any]          =     self.noise_step(
                               base=a		,      model_input=a		,      model_output=a		,      timestep=a		,      )

                           lowercase__      :		str          =     model_input

             return coupled_latents


      @torch.no_grad()
      def __call__(       self		,      a		,      a		,      a		,      a = 3.0		,      a = 5_0		,      a = 0.8		,      a = None		,      a = None		,      a = "pil"		,      )	-> Dict:
             lowercase__      :		List[Any]          =     guidance_scale > 1.0

             lowercase__      :		List[str]          =     self.image_processor.preprocess(a							)

             lowercase__      :		Union[str, Any]          =     self._encode_prompt(a		,      a		,      a							)
             lowercase__      :		int          =     self._encode_prompt(a		,      a		,      a							)

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

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

             lowercase__      :		int          =     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							):
                           lowercase__      :		Optional[Any]          =     k ^ 1

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

                           lowercase__      :		Optional[int]          =     coupled_latents[j]
                           lowercase__      :		int          =     coupled_latents[k]

                           lowercase__      :		List[Any]          =     torch.cat([model_input] * 2							) if do_classifier_free_guidance else model_input

                           lowercase__      :		List[str]          =     self.unet(a		,      a		,      encoder_hidden_states=a							).sample

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

                           lowercase__   ,				lowercase__      :		List[str]          =     self.denoise_step(
                               base=a		,      model_input=a		,      model_output=a		,      timestep=a		,      )

                           lowercase__      :		List[str]          =     model_input

                    lowercase__      :		Union[str, Any]          =     self.denoise_mixing_layer(x=coupled_latents[0]		,      y=coupled_latents[1]							)

             # either one is fine
             lowercase__      :		Optional[Any]          =     coupled_latents[0]

             if output_type not in ["latent", "pt", "np", "pil"]:
                    lowercase__      :		Any          =     (
                        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							)
                    lowercase__      :		str          =     'np'

             if output_type == "latent":
                    lowercase__      :		List[str]          =     final_latent
             else:
                    lowercase__      :		Optional[int]          =     self.decode_latents(a							)
                    lowercase__      :		Optional[int]          =     self.image_processor.postprocess(a		,      output_type=a							)

             return image

