
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


_UpperCAmelCase    			=					logging.get_logger(__name__)  # pylint: disable=invalid-name

_UpperCAmelCase    			=					'\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)["depth"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline("depth-estimation")\n\n        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to("cuda")\n\n        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to("cuda")\n\n        >>> img = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/cat.png"\n        ... ).resize((768, 768))\n\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")\n\n        >>> prompt = "A robot, 4k photo"\n        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"\n\n        >>> generator = torch.Generator(device="cuda").manual_seed(43)\n\n        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)\n        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)\n\n        >>> images = pipe(\n        ...     image=img,\n        ...     strength=0.5,\n        ...     image_embeds=img_emb.image_embeds,\n        ...     negative_image_embeds=negative_emb.image_embeds,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save("robot_cat.png")\n        ```\n'


def 		_lowerCamelCase       (   _a			,   _a			,   _a=8  ):
  """simple docstring"""




  _lowerCamelCase      =						height // scale_factor**2
  if height % scale_factor**2 != 0:
    new_height += 1
  _lowerCamelCase      =						width // scale_factor**2
  if width % scale_factor**2 != 0:
    new_width += 1
  return new_height * scale_factor, new_width * scale_factor


def 		_lowerCamelCase       (   _a			,   _a=5_1_2			,   _a=5_1_2  ):
  """simple docstring"""




  _lowerCamelCase      =						pil_image.resize((w, h)			,   resample=Image.BICUBIC			,   reducing_gap=1  )
  _lowerCamelCase      =						np.array(pil_image.convert('''RGB'''  )  )
  _lowerCamelCase      =						arr.astype(np.floataa  ) / 127.5 - 1
  _lowerCamelCase      =						np.transpose(lowercase_			,   [2, 0, 1]  )
  _lowerCamelCase      =						torch.from_numpy(lowercase_  ).unsqueeze(0  )
  return image






class 		__magic_name__				( UpperCAmelCase_					):


  """simple docstring"""





  def __init__(					self	,		a__	,		a__	,		a__	,		):
    super().__init__()

    self.register_modules(
        unet=__UpperCamelCase	,		scheduler=__UpperCamelCase	,		movq=__UpperCamelCase	,		)
    _lowerCamelCase      =						2 ** (len(self.movq.config.block_out_channels		) - 1)


  def     _UpperCAmelCase  (					self	,		a__	,		a__	,		a__		):
    # get the original timestep using init_timestep
    _lowerCamelCase      =						min(int(num_inference_steps * strength		)	,		__UpperCamelCase		)

    _lowerCamelCase      =						max(num_inference_steps - init_timestep	,		0		)
    _lowerCamelCase      =						self.scheduler.timesteps[t_start:]

    return timesteps, num_inference_steps - t_start


  def     _UpperCAmelCase  (					self	,		a__	,		a__	,		a__	,		a__	,		a__	,		a__	,		a__=None		):
    if not isinstance(__UpperCamelCase	,		(torch.Tensor, PIL.Image.Image, list)		):
      raise ValueError(
          f'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__UpperCamelCase		)}'''		)

    _lowerCamelCase      =						image.to(device=__UpperCamelCase	,		dtype=__UpperCamelCase		)

    _lowerCamelCase      =						batch_size * num_images_per_prompt

    if image.shape[1] == 4:
      _lowerCamelCase      =						image

    else:
      if isinstance(__UpperCamelCase	,		__UpperCamelCase		) and len(__UpperCamelCase		) != batch_size:
        raise ValueError(
            f'''You have passed a list of generators of length {len(__UpperCamelCase		)}, but requested an effective batch'''
            f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''		)

      elif isinstance(__UpperCamelCase	,		__UpperCamelCase		):
        _lowerCamelCase      =						[
            self.movq.encode(image[i : i + 1]		).latent_dist.sample(generator[i]		) for i in range(__UpperCamelCase		)
        ]
        _lowerCamelCase      =						torch.cat(__UpperCamelCase	,		dim=0		)
      else:
        _lowerCamelCase      =						self.movq.encode(__UpperCamelCase		).latent_dist.sample(__UpperCamelCase		)

      _lowerCamelCase      =						self.movq.config.scaling_factor * init_latents

    _lowerCamelCase      =						torch.cat([init_latents]	,		dim=0		)

    _lowerCamelCase      =						init_latents.shape
    _lowerCamelCase      =						randn_tensor(__UpperCamelCase	,		generator=__UpperCamelCase	,		device=__UpperCamelCase	,		dtype=__UpperCamelCase		)

    # get latents
    _lowerCamelCase      =						self.scheduler.add_noise(__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase		)

    _lowerCamelCase      =						init_latents

    return latents


  def     _UpperCAmelCase  (					self	,		a__=0		):
    if is_accelerate_available():
      from accelerate import cpu_offload
    else:
      raise ImportError('''Please install accelerate via `pip install accelerate`'''		)

    _lowerCamelCase      =						torch.device(f'''cuda:{gpu_id}'''		)

    _lowerCamelCase      =						[
        self.unet,
        self.movq,
    ]
    for cpu_offloaded_model in models:
      if cpu_offloaded_model is not None:
        cpu_offload(__UpperCamelCase	,		__UpperCamelCase		)


  def     _UpperCAmelCase  (					self	,		a__=0		):
    if is_accelerate_available() and is_accelerate_version('''>='''	,		'''0.17.0.dev0'''		):
      from accelerate import cpu_offload_with_hook
    else:
      raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''		)

    _lowerCamelCase      =						torch.device(f'''cuda:{gpu_id}'''		)

    if self.device.type != "cpu":
      self.to('''cpu'''	,		silence_dtype_warnings=__UpperCamelCase		)
      torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

    _lowerCamelCase      =						None
    for cpu_offloaded_model in [self.unet, self.movq]:
      _lowerCamelCase    ,    _lowerCamelCase      =						cpu_offload_with_hook(__UpperCamelCase	,		__UpperCamelCase	,		prev_module_hook=__UpperCamelCase		)

    # We'll offload the last model manually.
    _lowerCamelCase      =						hook


  @property
  # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
  def     _UpperCAmelCase  (					self		):
    if not hasattr(self.unet	,		'''_hf_hook'''		):
      return self.device
    for module in self.unet.modules():
      if (
          hasattr(__UpperCamelCase	,		'''_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


  @torch.no_grad()
  @replace_example_docstring(__UpperCamelCase		)
  def __call__(					self	,		a__	,		a__	,		a__	,		a__	,		a__ = 5_12	,		a__ = 5_12	,		a__ = 1_00	,		a__ = 4.0	,		a__ = 0.3	,		a__ = 1	,		a__ = None	,		a__ = "pil"	,		a__ = True	,		):
    _lowerCamelCase      =						self._execution_device

    _lowerCamelCase      =						guidance_scale > 1.0

    if isinstance(__UpperCamelCase	,		__UpperCamelCase		):
      _lowerCamelCase      =						torch.cat(__UpperCamelCase	,		dim=0		)
    if isinstance(__UpperCamelCase	,		__UpperCamelCase		):
      _lowerCamelCase      =						torch.cat(__UpperCamelCase	,		dim=0		)
    if isinstance(__UpperCamelCase	,		__UpperCamelCase		):
      _lowerCamelCase      =						torch.cat(__UpperCamelCase	,		dim=0		)

    _lowerCamelCase      =						image_embeds.shape[0]

    if do_classifier_free_guidance:
      _lowerCamelCase      =						image_embeds.repeat_interleave(__UpperCamelCase	,		dim=0		)
      _lowerCamelCase      =						negative_image_embeds.repeat_interleave(__UpperCamelCase	,		dim=0		)
      _lowerCamelCase      =						hint.repeat_interleave(__UpperCamelCase	,		dim=0		)

    _lowerCamelCase      =						torch.cat([negative_image_embeds, image_embeds]	,		dim=0		).to(dtype=self.unet.dtype	,		device=__UpperCamelCase		)
    _lowerCamelCase      =						torch.cat([hint, hint]	,		dim=0		).to(dtype=self.unet.dtype	,		device=__UpperCamelCase		)

    if not isinstance(__UpperCamelCase	,		__UpperCamelCase		):
      _lowerCamelCase      =						[image]
    if not all(isinstance(__UpperCamelCase	,		(PIL.Image.Image, torch.Tensor)		) for i in image		):
      raise ValueError(
          f'''Input is in incorrect format: {[type(__UpperCamelCase		) for i in image]}. Currently, we only support  PIL image and pytorch tensor'''		)

    _lowerCamelCase      =						torch.cat([prepare_image(__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase		) for i in image]	,		dim=0		)
    _lowerCamelCase      =						image.to(dtype=image_embeds.dtype	,		device=__UpperCamelCase		)

    _lowerCamelCase      =						self.movq.encode(__UpperCamelCase		)['''latents''']
    _lowerCamelCase      =						latents.repeat_interleave(__UpperCamelCase	,		dim=0		)
    self.scheduler.set_timesteps(__UpperCamelCase	,		device=__UpperCamelCase		)
    _lowerCamelCase    ,    _lowerCamelCase      =						self.get_timesteps(__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase		)
    _lowerCamelCase      =						timesteps[:1].repeat(batch_size * num_images_per_prompt		)
    _lowerCamelCase    ,    _lowerCamelCase      =						downscale_height_and_width(__UpperCamelCase	,		__UpperCamelCase	,		self.movq_scale_factor		)
    _lowerCamelCase      =						self.prepare_latents(
        __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		image_embeds.dtype	,		__UpperCamelCase	,		__UpperCamelCase		)
    for i, t in enumerate(self.progress_bar(__UpperCamelCase		)		):
      # expand the latents if we are doing classifier free guidance
      _lowerCamelCase      =						torch.cat([latents] * 2		) if do_classifier_free_guidance else latents

      _lowerCamelCase      =						{'''image_embeds''': image_embeds, '''hint''': hint}
      _lowerCamelCase      =						self.unet(
          sample=__UpperCamelCase	,		timestep=__UpperCamelCase	,		encoder_hidden_states=__UpperCamelCase	,		added_cond_kwargs=__UpperCamelCase	,		return_dict=__UpperCamelCase	,		)[0]

      if do_classifier_free_guidance:
        _lowerCamelCase    ,    _lowerCamelCase      =						noise_pred.split(latents.shape[1]	,		dim=1		)
        _lowerCamelCase    ,    _lowerCamelCase      =						noise_pred.chunk(2		)
        _lowerCamelCase    ,    _lowerCamelCase      =						variance_pred.chunk(2		)
        _lowerCamelCase      =						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
        _lowerCamelCase      =						torch.cat([noise_pred, variance_pred_text]	,		dim=1		)

      if not (
          hasattr(self.scheduler.config	,		'''variance_type'''		)
          and self.scheduler.config.variance_type in ["learned", "learned_range"]
      ):
        _lowerCamelCase    ,    _lowerCamelCase      =						noise_pred.split(latents.shape[1]	,		dim=1		)

      # compute the previous noisy sample x_t -> x_t-1

      _lowerCamelCase      =						self.scheduler.step(
          __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		generator=__UpperCamelCase	,		)[0]

    # post-processing
    _lowerCamelCase      =						self.movq.decode(__UpperCamelCase	,		force_not_quantize=__UpperCamelCase		)['''sample''']

    if output_type not in ["pt", "np", "pil"]:
      raise ValueError(f'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''		)

    if output_type in ["np", "pil"]:
      _lowerCamelCase      =						image * 0.5 + 0.5
      _lowerCamelCase      =						image.clamp(0	,		1		)
      _lowerCamelCase      =						image.cpu().permute(0	,		2	,		3	,		1		).float().numpy()

    if output_type == "pil":
      _lowerCamelCase      =						self.numpy_to_pil(__UpperCamelCase		)

    if not return_dict:
      return (image,)

    return ImagePipelineOutput(images=__UpperCamelCase		)










'''simple docstring'''







import concurrent.futures
import json
import os
import shutil
import tempfile
import unittest

from transformers import AutoTokenizer, PreTrainedTokenizerFast
from transformers.testing_utils import require_tokenizers

from ..test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 	lowerCamelCase_				(			UpperCAmelCase_,			unittest.TestCase				):




					'''simple docstring'''






					lowercase__       =					PreTrainedTokenizerFast
					lowercase__       =					False
					lowercase__       =					True
					lowercase__       =					"""tokenizer_file"""






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			False  # because we don't have pretrained_vocab_files_map
							super().setUp()
							_snake_case      			=			True

							_snake_case      			=			["robot-test/dummy-tokenizer-fast", "robot-test/dummy-tokenizer-wordlevel"]
							_snake_case      			=			"SaulLu/dummy-tokenizer-bytelevel-bpe"

							# Inclusion of 2 tokenizers to test different types of models (Unigram and WordLevel for the moment)
							_snake_case      			=			[(PreTrainedTokenizerFast, model_path, {}) for model_path in model_paths]

							_snake_case      			=			PreTrainedTokenizerFast.from_pretrained(model_paths[0]					)
							tokenizer.save_pretrained(self.tmpdirname					)






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
							# model
							pass






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
							# model
							pass






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
							# model
							pass






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							# PreTrainedTokenizerFast doesn't have tokenizer_file in its signature
							pass






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			self.tmpdirname
							# Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''					):
											try:
													_snake_case      			=			tempfile.mkdtemp()
													_snake_case      			=			self.rust_tokenizer_class.from_pretrained(__UpperCamelCase,    **__UpperCamelCase					)

													tokenizer.save_pretrained(self.tmpdirname					)
													super().test_training_new_tokenizer()
											finally:
													# Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
													# is restored
													shutil.rmtree(self.tmpdirname					)
													_snake_case      			=			tmpdirname_orig






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			self.tmpdirname
							# Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''					):
											try:
													_snake_case      			=			tempfile.mkdtemp()
													_snake_case      			=			self.rust_tokenizer_class.from_pretrained(__UpperCamelCase,    **__UpperCamelCase					)

													tokenizer.save_pretrained(self.tmpdirname					)
													super().test_training_new_tokenizer_with_special_tokens_change()
											finally:
													# Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
													# is restored
													shutil.rmtree(self.tmpdirname					)
													_snake_case      			=			tmpdirname_orig






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name					)

							_snake_case      			=			("a" for _ in range(1_000					))
							_snake_case      			=			tokenizer.train_new_from_iterator(text_iterator=__UpperCamelCase,    length=1_000,    vocab_size=50					)

							_snake_case      			=			new_tokenizer.encode("a🤗"					)
							self.assertEqual(__UpperCamelCase,    [64, 172, 253, 97, 245]					)





@require_tokenizers
class 	lowerCamelCase_				(			unittest.TestCase				):




					'''simple docstring'''






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			AutoTokenizer.from_pretrained("bert-base-cased"					)
							_snake_case      			=			json.loads(tokenizer._tokenizer.to_str()					)
							_snake_case      			=			len(__UpperCamelCase					)

							with tempfile.TemporaryDirectory() as tmp_dir:
									# Hack to save this in the tokenizer_config.json
									_snake_case      			=			["tokenizer.4.0.0.json"]
									tokenizer.save_pretrained(__UpperCamelCase					)
									json.dump(__UpperCamelCase,    open(os.path.join(__UpperCamelCase,    "tokenizer.4.0.0.json"					),    "w"					)					)

									# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
									_snake_case      			=			AutoTokenizer.from_pretrained(__UpperCamelCase					)
									self.assertEqual(len(__UpperCamelCase					),    len(__UpperCamelCase					) + 1					)
									_snake_case      			=			json.loads(new_tokenizer._tokenizer.to_str()					)
									self.assertIn("huggingface",    json_tokenizer["model"]["vocab"]					)

									# Will need to be adjusted if we reach v42 and this test is still here.
									# Should pick the old tokenizer file as the version of Transformers is < 4.0.0
									shutil.move(os.path.join(__UpperCamelCase,    "tokenizer.4.0.0.json"					),    os.path.join(__UpperCamelCase,    "tokenizer.42.0.0.json"					)					)
									_snake_case      			=			["tokenizer.42.0.0.json"]
									tokenizer.save_pretrained(__UpperCamelCase					)
									_snake_case      			=			AutoTokenizer.from_pretrained(__UpperCamelCase					)
									self.assertEqual(len(__UpperCamelCase					),    len(__UpperCamelCase					)					)
									_snake_case      			=			json.loads(new_tokenizer._tokenizer.to_str()					)
									self.assertNotIn("huggingface",    json_tokenizer["model"]["vocab"]					)






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							# This repo has two tokenizer files, one for v4.0.0 and above with an added token, one for versions lower.
							_snake_case      			=			"hf-internal-testing/test-two-tokenizers"

							# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
							_snake_case      			=			AutoTokenizer.from_pretrained(__UpperCamelCase					)
							self.assertEqual(len(__UpperCamelCase					),    28_997					)
							_snake_case      			=			json.loads(tokenizer._tokenizer.to_str()					)
							self.assertIn("huggingface",    json_tokenizer["model"]["vocab"]					)

							# Testing an older version by monkey-patching the version in the module it's used.
							import transformers as old_transformers

							_snake_case      			=			"3.0.0"
							_snake_case      			=			old_transformers.models.auto.AutoTokenizer.from_pretrained(__UpperCamelCase					)
							self.assertEqual(len(__UpperCamelCase					),    28_996					)
							_snake_case      			=			json.loads(old_tokenizer._tokenizer.to_str()					)
							self.assertNotIn("huggingface",    json_tokenizer["model"]["vocab"]					)





@require_tokenizers
class 	lowerCamelCase_				(			unittest.TestCase				):




					'''simple docstring'''






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							# See https://github.com/huggingface/transformers/pull/12550
							# and https://github.com/huggingface/tokenizers/issues/537
							_snake_case      			=			PreTrainedTokenizerFast.from_pretrained("robot-test/dummy-tokenizer-wordlevel"					)
							_snake_case      			=			"The Matrix is a 1999 science fiction action film."

							with concurrent.futures.ThreadPoolExecutor() as executor:
									_snake_case      			=			[executor.submit(self.fetch,    __UpperCamelCase,    __UpperCamelCase					) for i in range(10					)]
									_snake_case      			=			[future.result() for future in futures]
									self.assertEqual(__UpperCamelCase,    [[1, 10, 0, 8, 0, 18, 0, 0, 0, 2] for i in range(10					)]					)






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase,    __UpperCamelCase					):
							return tokenizer.encode(__UpperCamelCase,    truncation="longest_first",    padding="longest"					)





