
'''simple docstring'''





import types
from typing import List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


A							     = logging.get_logger(__name__)  # pylint: disable=invalid-name
def 	snake_case_							(				self  :				List[str]			,       _a  :				str			,       _a  :				Optional[Any]			,       _a  :				Tuple			,       _a  :				str      ):
      snake_case_    							=				image.to(device=_a      )
      snake_case_    							=				image  # take image as image_embeddings
      snake_case_    							=				image_embeddings.unsqueeze(1      )

      # duplicate image embeddings for each generation per prompt, using mps friendly method
      snake_case_							,						snake_case_							,						snake_case_    							=				image_embeddings.shape
      snake_case_    							=				image_embeddings.repeat(1			,       _a			,       1      )
      snake_case_    							=				image_embeddings.view(bs_embed * num_images_per_prompt			,       _a			,       -1      )

      if do_classifier_free_guidance:
            snake_case_    							=				torch.zeros_like(_a      )

            # For classifier free guidance, we need to do two forward passes.
            # Here we concatenate the unconditional and text embeddings into a single batch
            # to avoid doing two forward passes
            snake_case_    							=				torch.cat([uncond_embeddings, image_embeddings]      )

      return image_embeddings




class 			lowerCAmelCase__ (							UpperCamelCase							):



  '''simple docstring'''






  def __init__(self	:			Tuple						,			lowerCAmelCase_	:			PriorTransformer						,			lowerCAmelCase_	:			CLIPTokenizer						,			lowerCAmelCase_	:			CLIPTextModelWithProjection						,			lowerCAmelCase_	:			UnCLIPScheduler						,			lowerCAmelCase_	:			Optional[dict] = None						,			) ->							Dict:
        super().__init__()

        snake_case_    							=				{"image_encoder": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

        snake_case_    							=				decoder_pipe_kwargs.get("torch_dtype"						,			lowerCAmelCase_     ) or prior.dtype

        snake_case_    							=				StableDiffusionImageVariationPipeline.from_pretrained(
            "lambdalabs/sd-image-variations-diffusers"						,			**lowerCAmelCase_     )

        # replace `_encode_image` method
        snake_case_    							=				types.MethodType(_encode_image						,			self.decoder_pipe     )

        self.register_modules(
            prior=lowerCAmelCase_						,			tokenizer=lowerCAmelCase_						,			text_encoder=lowerCAmelCase_						,			prior_scheduler=lowerCAmelCase_						,			)

  def 		_lowerCamelCase						(self	:			Optional[int]						,			lowerCAmelCase_	:			Dict						,			lowerCAmelCase_	:			Union[str, Any]						,			lowerCAmelCase_	:			Optional[int]						,			lowerCAmelCase_	:			Any						,			lowerCAmelCase_	:			Optional[Union[CLIPTextModelOutput, Tuple]] = None						,			lowerCAmelCase_	:			Optional[torch.Tensor] = None						,			) ->							Union[str, Any]:
        if text_model_output is None:
              snake_case_    							=				len(lowerCAmelCase_     ) if isinstance(lowerCAmelCase_						,			lowerCAmelCase_     ) else 1
              # get prompt text embeddings
              snake_case_    							=				self.tokenizer(
                  lowerCAmelCase_						,			padding="max_length"						,			max_length=self.tokenizer.model_max_length						,			return_tensors="pt"						,			)
              snake_case_    							=				text_inputs.input_ids
              snake_case_    							=				text_inputs.attention_mask.bool().to(lowerCAmelCase_     )

              if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                    snake_case_    							=				self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]     )
                    logger.warning(
                        "The following part of your input was truncated because CLIP can only handle sequences up to"
                        f''' {self.tokenizer.model_max_length} tokens: {removed_text}'''     )
                    snake_case_    							=				text_input_ids[:, : self.tokenizer.model_max_length]

              snake_case_    							=				self.text_encoder(text_input_ids.to(lowerCAmelCase_     )     )

              snake_case_    							=				text_encoder_output.text_embeds
              snake_case_    							=				text_encoder_output.last_hidden_state

        else:
              snake_case_    							=				text_model_output[0].shape[0]
              snake_case_							,						snake_case_    							=				text_model_output[0], text_model_output[1]
              snake_case_    							=				text_attention_mask

        snake_case_    							=				text_embeddings.repeat_interleave(lowerCAmelCase_						,			dim=0     )
        snake_case_    							=				text_encoder_hidden_states.repeat_interleave(lowerCAmelCase_						,			dim=0     )
        snake_case_    							=				text_mask.repeat_interleave(lowerCAmelCase_						,			dim=0     )

        if do_classifier_free_guidance:
              snake_case_    							=				[""] * batch_size

              snake_case_    							=				self.tokenizer(
                  lowerCAmelCase_						,			padding="max_length"						,			max_length=self.tokenizer.model_max_length						,			truncation=lowerCAmelCase_						,			return_tensors="pt"						,			)
              snake_case_    							=				uncond_input.attention_mask.bool().to(lowerCAmelCase_     )
              snake_case_    							=				self.text_encoder(uncond_input.input_ids.to(lowerCAmelCase_     )     )

              snake_case_    							=				uncond_embeddings_text_encoder_output.text_embeds
              snake_case_    							=				uncond_embeddings_text_encoder_output.last_hidden_state

              # duplicate unconditional embeddings for each generation per prompt, using mps friendly method

              snake_case_    							=				uncond_embeddings.shape[1]
              snake_case_    							=				uncond_embeddings.repeat(1						,			lowerCAmelCase_     )
              snake_case_    							=				uncond_embeddings.view(batch_size * num_images_per_prompt						,			lowerCAmelCase_     )

              snake_case_    							=				uncond_text_encoder_hidden_states.shape[1]
              snake_case_    							=				uncond_text_encoder_hidden_states.repeat(1						,			lowerCAmelCase_						,			1     )
              snake_case_    							=				uncond_text_encoder_hidden_states.view(
                  batch_size * num_images_per_prompt						,			lowerCAmelCase_						,			-1     )
              snake_case_    							=				uncond_text_mask.repeat_interleave(lowerCAmelCase_						,			dim=0     )

              # done duplicates

              # For classifier free guidance, we need to do two forward passes.
              # Here we concatenate the unconditional and text embeddings into a single batch
              # to avoid doing two forward passes
              snake_case_    							=				torch.cat([uncond_embeddings, text_embeddings]     )
              snake_case_    							=				torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]     )

              snake_case_    							=				torch.cat([uncond_text_mask, text_mask]     )

        return text_embeddings, text_encoder_hidden_states, text_mask

  @property
  def 		_lowerCamelCase						(self	:			Dict     ) ->							Any:
        if self.device != torch.device("meta"     ) or not hasattr(self.prior						,			"_hf_hook"     ):
              return self.device
        for module in self.prior.modules():
              if (
                  hasattr(lowerCAmelCase_						,			"_hf_hook"     )
                  and hasattr(module._hf_hook						,			"execution_device"     )
                  and module._hf_hook.execution_device is not None
              ):
                    return torch.device(module._hf_hook.execution_device     )
        return self.device

  def 		_lowerCamelCase						(self	:			List[str]						,			lowerCAmelCase_	:			Any						,			lowerCAmelCase_	:			Union[str, Any]						,			lowerCAmelCase_	:			Any						,			lowerCAmelCase_	:			int						,			lowerCAmelCase_	:			List[Any]						,			lowerCAmelCase_	:			List[str]     ) ->							str:
        if latents is None:
              snake_case_    							=				randn_tensor(lowerCAmelCase_						,			generator=lowerCAmelCase_						,			device=lowerCAmelCase_						,			dtype=lowerCAmelCase_     )
        else:
              if latents.shape != shape:
                    raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''     )
              snake_case_    							=				latents.to(lowerCAmelCase_     )

        snake_case_    							=				latents * scheduler.init_noise_sigma
        return latents

  def 		_lowerCamelCase						(self	:			Any						,			lowerCAmelCase_	:			Optional[Union[str, torch.device]] = None     ) ->							List[Any]:
        self.decoder_pipe.to(lowerCAmelCase_     )
        super().to(lowerCAmelCase_     )


  @torch.no_grad()
  def __call__(self	:			int						,			lowerCAmelCase_	:			Optional[Union[str, List[str]]] = None						,			lowerCAmelCase_	:			Optional[int] = None						,			lowerCAmelCase_	:			Optional[int] = None						,			lowerCAmelCase_	:			int = 1						,			lowerCAmelCase_	:			int = 25						,			lowerCAmelCase_	:			Optional[torch.Generator] = None						,			lowerCAmelCase_	:			Optional[torch.FloatTensor] = None						,			lowerCAmelCase_	:			Optional[Union[CLIPTextModelOutput, Tuple]] = None						,			lowerCAmelCase_	:			Optional[torch.Tensor] = None						,			lowerCAmelCase_	:			float = 4.0						,			lowerCAmelCase_	:			float = 8.0						,			lowerCAmelCase_	:			int = 50						,			lowerCAmelCase_	:			Optional[int] = 1						,			lowerCAmelCase_	:			float = 0.0						,			lowerCAmelCase_	:			Optional[str] = "pil"						,			lowerCAmelCase_	:			bool = True						,			) ->							Dict:
        if prompt is not None:
              if isinstance(lowerCAmelCase_						,			lowerCAmelCase_     ):
                    snake_case_    							=				1
              elif isinstance(lowerCAmelCase_						,			lowerCAmelCase_     ):
                    snake_case_    							=				len(lowerCAmelCase_     )
              else:
                    raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase_     )}'''     )
        else:
              snake_case_    							=				text_model_output[0].shape[0]

        snake_case_    							=				self._execution_device

        snake_case_    							=				batch_size * num_images_per_prompt

        snake_case_    							=				prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

        snake_case_							,						snake_case_							,						snake_case_    							=				self._encode_prompt(
            lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_     )

        # prior

        self.prior_scheduler.set_timesteps(lowerCAmelCase_						,			device=lowerCAmelCase_     )
        snake_case_    							=				self.prior_scheduler.timesteps

        snake_case_    							=				self.prior.config.embedding_dim

        snake_case_    							=				self.prepare_latents(
            (batch_size, embedding_dim)						,			text_embeddings.dtype						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			self.prior_scheduler						,			)

        for i, t in enumerate(self.progress_bar(lowerCAmelCase_     )     ):
              # expand the latents if we are doing classifier free guidance
              snake_case_    							=				torch.cat([prior_latents] * 2     ) if do_classifier_free_guidance else prior_latents

              snake_case_    							=				self.prior(
                  lowerCAmelCase_						,			timestep=lowerCAmelCase_						,			proj_embedding=lowerCAmelCase_						,			encoder_hidden_states=lowerCAmelCase_						,			attention_mask=lowerCAmelCase_						,			).predicted_image_embedding

              if do_classifier_free_guidance:
                    snake_case_							,						snake_case_    							=				predicted_image_embedding.chunk(2     )
                    snake_case_    							=				predicted_image_embedding_uncond + prior_guidance_scale * (
                        predicted_image_embedding_text - predicted_image_embedding_uncond
                    )

              if i + 1 == prior_timesteps_tensor.shape[0]:
                    snake_case_    							=				None
              else:
                    snake_case_    							=				prior_timesteps_tensor[i + 1]

              snake_case_    							=				self.prior_scheduler.step(
                  lowerCAmelCase_						,			timestep=lowerCAmelCase_						,			sample=lowerCAmelCase_						,			generator=lowerCAmelCase_						,			prev_timestep=lowerCAmelCase_						,			).prev_sample

        snake_case_    							=				self.prior.post_process_latents(lowerCAmelCase_     )

        snake_case_    							=				prior_latents

        snake_case_    							=				self.decoder_pipe(
            image=lowerCAmelCase_						,			height=lowerCAmelCase_						,			width=lowerCAmelCase_						,			num_inference_steps=lowerCAmelCase_						,			guidance_scale=lowerCAmelCase_						,			generator=lowerCAmelCase_						,			output_type=lowerCAmelCase_						,			return_dict=lowerCAmelCase_						,			num_images_per_prompt=lowerCAmelCase_						,			eta=lowerCAmelCase_						,			)
        return output



'''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:
     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 			lowerCAmelCase__ (							unittest.TestCase							):



  '''simple docstring'''






  def 		_lowerCamelCase						(self	:			Tuple     ) ->							Tuple:
        snake_case_    							=				tempfile.mkdtemp()

        snake_case_    							=				PixaStructImageProcessor()
        snake_case_    							=				TaTokenizer.from_pretrained("t5-small"     )

        snake_case_    							=				PixaStructProcessor(lowerCAmelCase_						,			lowerCAmelCase_     )

        processor.save_pretrained(self.tmpdirname     )

  def 		_lowerCamelCase						(self	:			List[str]						,			**lowerCAmelCase_	:			int     ) ->							Optional[Any]:
        return AutoProcessor.from_pretrained(self.tmpdirname						,			**lowerCAmelCase_     ).tokenizer

  def 		_lowerCamelCase						(self	:			Union[str, Any]						,			**lowerCAmelCase_	:			List[str]     ) ->							List[Any]:
        return AutoProcessor.from_pretrained(self.tmpdirname						,			**lowerCAmelCase_     ).image_processor

  def 		_lowerCamelCase						(self	:			int     ) ->							List[str]:
        shutil.rmtree(self.tmpdirname     )

  def 		_lowerCamelCase						(self	:			Any     ) ->							Optional[Any]:
        snake_case_    							=				[np.random.randint(2_55						,			size=(3, 30, 4_00)						,			dtype=np.uinta     )]

        snake_case_    							=				[Image.fromarray(np.moveaxis(lowerCAmelCase_						,			0						,			-1     )     ) for x in image_inputs]

        return image_inputs

  def 		_lowerCamelCase						(self	:			Union[str, Any]     ) ->							Dict:
        snake_case_    							=				PixaStructProcessor(tokenizer=self.get_tokenizer()						,			image_processor=self.get_image_processor()     )
        processor.save_pretrained(self.tmpdirname     )

        snake_case_    							=				self.get_tokenizer(bos_token="(BOS)"						,			eos_token="(EOS)"     )
        snake_case_    							=				self.get_image_processor(do_normalize=lowerCAmelCase_						,			padding_value=1.0     )

        snake_case_    							=				PixaStructProcessor.from_pretrained(
            self.tmpdirname						,			bos_token="(BOS)"						,			eos_token="(EOS)"						,			do_normalize=lowerCAmelCase_						,			padding_value=1.0     )

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

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

  def 		_lowerCamelCase						(self	:			Dict     ) ->							Optional[int]:
        snake_case_    							=				self.get_image_processor()
        snake_case_    							=				self.get_tokenizer()

        snake_case_    							=				PixaStructProcessor(tokenizer=lowerCAmelCase_						,			image_processor=lowerCAmelCase_     )

        snake_case_    							=				self.prepare_image_inputs()

        snake_case_    							=				image_processor(lowerCAmelCase_						,			return_tensors="np"     )
        snake_case_    							=				processor(images=lowerCAmelCase_						,			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 		_lowerCamelCase						(self	:			Tuple     ) ->							List[str]:
        snake_case_    							=				self.get_image_processor()
        snake_case_    							=				self.get_tokenizer()

        snake_case_    							=				PixaStructProcessor(tokenizer=lowerCAmelCase_						,			image_processor=lowerCAmelCase_     )

        snake_case_    							=				"lower newer"

        snake_case_    							=				processor(text=lowerCAmelCase_     )

        snake_case_    							=				tokenizer(lowerCAmelCase_						,			return_token_type_ids=lowerCAmelCase_						,			add_special_tokens=lowerCAmelCase_     )

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

  def 		_lowerCamelCase						(self	:			str     ) ->							List[str]:
        snake_case_    							=				self.get_image_processor()
        snake_case_    							=				self.get_tokenizer()

        snake_case_    							=				PixaStructProcessor(tokenizer=lowerCAmelCase_						,			image_processor=lowerCAmelCase_     )

        snake_case_    							=				"lower newer"
        snake_case_    							=				self.prepare_image_inputs()

        snake_case_    							=				processor(text=lowerCAmelCase_						,			images=lowerCAmelCase_     )

        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(lowerCAmelCase_     ):
              processor()

  def 		_lowerCamelCase						(self	:			Optional[Any]     ) ->							Any:
        snake_case_    							=				self.get_image_processor()
        snake_case_    							=				self.get_tokenizer()

        snake_case_    							=				PixaStructProcessor(tokenizer=lowerCAmelCase_						,			image_processor=lowerCAmelCase_     )

        snake_case_    							=				"lower newer"
        snake_case_    							=				self.prepare_image_inputs()

        snake_case_    							=				processor(text=lowerCAmelCase_						,			images=lowerCAmelCase_     )

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

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

  def 		_lowerCamelCase						(self	:			int     ) ->							Any:
        snake_case_    							=				self.get_image_processor()
        snake_case_    							=				self.get_tokenizer()

        snake_case_    							=				PixaStructProcessor(tokenizer=lowerCAmelCase_						,			image_processor=lowerCAmelCase_     )

        snake_case_    							=				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        snake_case_    							=				processor.batch_decode(lowerCAmelCase_     )
        snake_case_    							=				tokenizer.batch_decode(lowerCAmelCase_     )

        self.assertListEqual(lowerCAmelCase_						,			lowerCAmelCase_     )


  def 		_lowerCamelCase						(self	:			Optional[int]     ) ->							str:
        snake_case_    							=				self.get_image_processor()
        snake_case_    							=				self.get_tokenizer()

        snake_case_    							=				PixaStructProcessor(tokenizer=lowerCAmelCase_						,			image_processor=lowerCAmelCase_     )

        snake_case_    							=				"lower newer"
        snake_case_    							=				self.prepare_image_inputs()

        snake_case_    							=				processor(text=lowerCAmelCase_						,			images=lowerCAmelCase_     )

        # 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"]     )

        snake_case_    							=				processor(text=lowerCAmelCase_     )

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

