




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  snake_case (							A						,       A						,       A=8      )							->							Dict:




 '''simple docstring'''


 lowerCamelCase_							:		List[Any]							       =							height // scale_factor**2
 if height % scale_factor**2 != 0:
  new_height += 1
 lowerCamelCase_							:		List[str]							       =							width // scale_factor**2
 if width % scale_factor**2 != 0:
  new_width += 1
 return new_height * scale_factor, new_width * scale_factor






def  snake_case (							A						,       A=512						,       A=512      )							->							Dict:




 '''simple docstring'''


 lowerCamelCase_							:		List[Any]							       =							pil_image.resize((w, h)						,       resample=Image.BICUBIC						,       reducing_gap=1      )
 lowerCamelCase_							:		Dict							       =							np.array(pil_image.convert('RGB'      )      )
 lowerCamelCase_							:		Optional[Any]							       =							arr.astype(np.floataa      ) / 127.5 - 1
 lowerCamelCase_							:		int							       =							np.transpose(A						,       [2, 0, 1]      )
 lowerCamelCase_							:		Dict							       =							torch.from_numpy(A      ).unsqueeze(0      )
 return image



class     __SCREAMING_SNAKE_CASE							(		lowerCAmelCase__  ):

     def __init__(    self			,	__lowercase			,	__lowercase			,	__lowercase			,	):







      '''simple docstring'''




      super().__init__()

      self.register_modules(
          unet=_A			,	scheduler=_A			,	movq=_A			,	)
      lowerCamelCase_							:		int							       =							2 ** (len(self.movq.config.block_out_channels ) - 1)

     def      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase ):







      '''simple docstring'''




      lowerCamelCase_							:		List[str]							       =							min(int(num_inference_steps * strength )			,	_A )

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

      return timesteps, num_inference_steps - t_start

     def      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase=None ):







      '''simple docstring'''




      if not isinstance(_A			,	(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(_A )}" )

      lowerCamelCase_							:		Optional[int]							       =							image.to(device=_A			,	dtype=_A )

      lowerCamelCase_							:		List[Any]							       =							batch_size * num_images_per_prompt

      if image.shape[1] == 4:
       lowerCamelCase_							:		Optional[Any]							       =							image

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

       elif isinstance(_A			,	_A ):
        lowerCamelCase_							:		str							       =							[
            self.movq.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(_A )
        ]
        lowerCamelCase_							:		int							       =							torch.cat(_A			,	dim=0 )
       else:
        lowerCamelCase_							:		Union[str, Any]							       =							self.movq.encode(_A ).latent_dist.sample(_A )

       lowerCamelCase_							:		Optional[int]							       =							self.movq.config.scaling_factor * init_latents

      lowerCamelCase_							:		Optional[Any]							       =							torch.cat([init_latents]			,	dim=0 )

      lowerCamelCase_							:		List[str]							       =							init_latents.shape
      lowerCamelCase_							:		Union[str, Any]							       =							randn_tensor(_A			,	generator=_A			,	device=_A			,	dtype=_A )

      # get latents
      lowerCamelCase_							:		Union[str, Any]							       =							self.scheduler.add_noise(_A			,	_A			,	_A )

      lowerCamelCase_							:		Optional[int]							       =							init_latents

      return latents

     def      lowercase_							(    self			,	__lowercase=0 ):







      '''simple docstring'''




      if is_accelerate_available():
       from accelerate import cpu_offload
      else:
       raise ImportError('Please install accelerate via `pip install accelerate`' )

      lowerCamelCase_							:		Union[str, Any]							       =							torch.device(f"cuda:{gpu_id}" )

      lowerCamelCase_							:		Union[str, Any]							       =							[
          self.unet,
          self.movq,
      ]
      for cpu_offloaded_model in models:
       if cpu_offloaded_model is not None:
        cpu_offload(_A			,	_A )

     def      lowercase_							(    self			,	__lowercase=0 ):







      '''simple docstring'''




      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_							:		int							       =							torch.device(f"cuda:{gpu_id}" )

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

      lowerCamelCase_							:		List[Any]							       =							None
      for cpu_offloaded_model in [self.unet, self.movq]:
       lowerCamelCase_							:		Union[str, Any]							       =							cpu_offload_with_hook(_A			,	_A			,	prev_module_hook=_A )

      # We'll offload the last model manually.
      lowerCamelCase_							:		int							       =							hook

     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def      lowercase_							(    self ):







      '''simple docstring'''




      if not hasattr(self.unet			,	'_hf_hook' ):
       return self.device
      for module in self.unet.modules():
       if (
           hasattr(_A			,	'_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(_A )
     def __call__(    self			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase = 512			,	__lowercase = 512			,	__lowercase = 100			,	__lowercase = 4.0			,	__lowercase = 0.3			,	__lowercase = 1			,	__lowercase = None			,	__lowercase = "pil"			,	__lowercase = True			,	):







      '''simple docstring'''




      lowerCamelCase_							:		Dict							       =							self._execution_device

      lowerCamelCase_							:		Optional[Any]							       =							guidance_scale > 1.0

      if isinstance(_A			,	_A ):
       lowerCamelCase_							:		Any							       =							torch.cat(_A			,	dim=0 )
      if isinstance(_A			,	_A ):
       lowerCamelCase_							:		Union[str, Any]							       =							torch.cat(_A			,	dim=0 )
      if isinstance(_A			,	_A ):
       lowerCamelCase_							:		str							       =							torch.cat(_A			,	dim=0 )

      lowerCamelCase_							:		str							       =							image_embeds.shape[0]

      if do_classifier_free_guidance:
       lowerCamelCase_							:		Tuple							       =							image_embeds.repeat_interleave(_A			,	dim=0 )
       lowerCamelCase_							:		int							       =							negative_image_embeds.repeat_interleave(_A			,	dim=0 )
       lowerCamelCase_							:		int							       =							hint.repeat_interleave(_A			,	dim=0 )

      lowerCamelCase_							:		List[str]							       =							torch.cat([negative_image_embeds, image_embeds]			,	dim=0 ).to(dtype=self.unet.dtype			,	device=_A )
      lowerCamelCase_							:		Union[str, Any]							       =							torch.cat([hint, hint]			,	dim=0 ).to(dtype=self.unet.dtype			,	device=_A )

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

      lowerCamelCase_							:		Any							       =							torch.cat([prepare_image(_A			,	_A			,	_A ) for i in image]			,	dim=0 )
      lowerCamelCase_							:		Dict							       =							image.to(dtype=image_embeds.dtype			,	device=_A )

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

       lowerCamelCase_							:		List[str]							       =							{'''image_embeds''': image_embeds, '''hint''': hint}
       lowerCamelCase_							:		Optional[Any]							       =							self.unet(
           sample=_A			,	timestep=_A			,	encoder_hidden_states=_A			,	added_cond_kwargs=_A			,	return_dict=_A			,	)[0]

       if do_classifier_free_guidance:
        lowerCamelCase_							:		List[str]							       =							noise_pred.split(latents.shape[1]			,	dim=1 )
        lowerCamelCase_							:		List[str]							       =							noise_pred.chunk(2 )
        lowerCamelCase_							:		Dict							       =							variance_pred.chunk(2 )
        lowerCamelCase_							:		Union[str, Any]							       =							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
        lowerCamelCase_							:		Optional[Any]							       =							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_							:		Any							       =							noise_pred.split(latents.shape[1]			,	dim=1 )

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

       lowerCamelCase_							:		int							       =							self.scheduler.step(
           _A			,	_A			,	_A			,	generator=_A			,	)[0]

      # post-processing
      lowerCamelCase_							:		Dict							       =							self.movq.decode(_A			,	force_not_quantize=_A )['''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_							:		Dict							       =							image * 0.5 + 0.5
       lowerCamelCase_							:		int							       =							image.clamp(0			,	1 )
       lowerCamelCase_							:		Optional[int]							       =							image.cpu().permute(0			,	2			,	3			,	1 ).float().numpy()

      if output_type == "pil":
       lowerCamelCase_							:		str							       =							self.numpy_to_pil(_A )

      if not return_dict:
       return (image,)

      return ImagePipelineOutput(images=_A )






from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


UpperCamelCase__		=						logging.get_logger(__name__)

UpperCamelCase__		=						{
    'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json',
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}


class     __SCREAMING_SNAKE_CASE							(		lowerCamelCase  ):
					_lowerCamelCase		  =			"""m2m_100"""
					_lowerCamelCase		  =			["""past_key_values"""]
					_lowerCamelCase		  =			{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}

					def __init__(    self			,	__lowercase=128_112			,	__lowercase=1_024			,	__lowercase=12			,	__lowercase=4_096			,	__lowercase=16			,	__lowercase=12			,	__lowercase=4_096			,	__lowercase=16			,	__lowercase=0.05			,	__lowercase=0.05			,	__lowercase=True			,	__lowercase=True			,	__lowercase="relu"			,	__lowercase=1_024			,	__lowercase=0.1			,	__lowercase=0.1			,	__lowercase=0.0			,	__lowercase=0.02			,	__lowercase=2			,	__lowercase=True			,	__lowercase=1			,	__lowercase=0			,	__lowercase=2			,	**__lowercase			,	):







						'''simple docstring'''




						lowerCamelCase_							:		int							       =							vocab_size
						lowerCamelCase_							:		List[str]							       =							max_position_embeddings
						lowerCamelCase_							:		Tuple							       =							d_model
						lowerCamelCase_							:		Optional[int]							       =							encoder_ffn_dim
						lowerCamelCase_							:		Union[str, Any]							       =							encoder_layers
						lowerCamelCase_							:		Dict							       =							encoder_attention_heads
						lowerCamelCase_							:		Union[str, Any]							       =							decoder_ffn_dim
						lowerCamelCase_							:		List[str]							       =							decoder_layers
						lowerCamelCase_							:		Union[str, Any]							       =							decoder_attention_heads
						lowerCamelCase_							:		Optional[int]							       =							dropout
						lowerCamelCase_							:		str							       =							attention_dropout
						lowerCamelCase_							:		Optional[Any]							       =							activation_dropout
						lowerCamelCase_							:		str							       =							activation_function
						lowerCamelCase_							:		int							       =							init_std
						lowerCamelCase_							:		Optional[int]							       =							encoder_layerdrop
						lowerCamelCase_							:		Tuple							       =							decoder_layerdrop
						lowerCamelCase_							:		Tuple							       =							use_cache
						lowerCamelCase_							:		Any							       =							encoder_layers
						lowerCamelCase_							:		Any							       =							scale_embedding  # scale factor will be sqrt(d_model) if True

						super().__init__(
						    pad_token_id=__lowercase			,	bos_token_id=__lowercase			,	eos_token_id=__lowercase			,	is_encoder_decoder=__lowercase			,	decoder_start_token_id=__lowercase			,	**__lowercase			,	)


class     __SCREAMING_SNAKE_CASE							(		lowerCamelCase  ):

					@property
					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Any							       =							OrderedDict(
						    [
						        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
						        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
						    ] )

						if self.use_past:
							lowerCamelCase_							:		Union[str, Any]							       =							{0: 'batch'}
							lowerCamelCase_							:		Optional[Any]							       =							{0: 'batch', 1: 'past_decoder_sequence + sequence'}
						else:
							lowerCamelCase_							:		Any							       =							{0: 'batch', 1: 'decoder_sequence'}
							lowerCamelCase_							:		Union[str, Any]							       =							{0: 'batch', 1: 'decoder_sequence'}

						if self.use_past:
							self.fill_with_past_key_values_(__lowercase			,	direction='inputs' )
						return common_inputs

					def      lowercase_							(    self			,	__lowercase			,	__lowercase = -1			,	__lowercase = -1			,	__lowercase = False			,	__lowercase = None			,	):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[int]							       =							compute_effective_axis_dimension(
						    __lowercase			,	fixed_dimension=OnnxConfig.default_fixed_batch			,	num_token_to_add=0 )

						# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
						lowerCamelCase_							:		str							       =							tokenizer.num_special_tokens_to_add(__lowercase )
						lowerCamelCase_							:		Dict							       =							compute_effective_axis_dimension(
						    __lowercase			,	fixed_dimension=OnnxConfig.default_fixed_sequence			,	num_token_to_add=__lowercase )

						# Generate dummy inputs according to compute batch and sequence
						lowerCamelCase_							:		Optional[int]							       =							[' '.join([tokenizer.unk_token] ) * seq_length] * batch_size
						lowerCamelCase_							:		Optional[Any]							       =							dict(tokenizer(__lowercase			,	return_tensors=__lowercase ) )
						return common_inputs

					def      lowercase_							(    self			,	__lowercase			,	__lowercase = -1			,	__lowercase = -1			,	__lowercase = False			,	__lowercase = None			,	):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[Any]							       =							self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    __lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase )

						# Generate decoder inputs
						lowerCamelCase_							:		Dict							       =							seq_length if not self.use_past else 1
						lowerCamelCase_							:		List[str]							       =							self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    __lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase )
						lowerCamelCase_							:		Any							       =							{f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
						lowerCamelCase_							:		str							       =							dict(**__lowercase			,	**__lowercase )

						if self.use_past:
							if not is_torch_available():
								raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.' )
							else:
								import torch
							lowerCamelCase_       ,      lowerCamelCase_							:		int							       =							common_inputs['input_ids'].shape
							lowerCamelCase_							:		List[Any]							       =							common_inputs['decoder_input_ids'].shape[1]
							lowerCamelCase_       ,      lowerCamelCase_							:		int							       =							self.num_attention_heads
							lowerCamelCase_							:		Any							       =							(
							    batch,
							    num_encoder_attention_heads,
							    encoder_seq_length,
							    self._config.hidden_size // num_encoder_attention_heads,
							)
							lowerCamelCase_							:		Tuple							       =							decoder_seq_length + 3
							lowerCamelCase_							:		Any							       =							(
							    batch,
							    num_decoder_attention_heads,
							    decoder_past_length,
							    self._config.hidden_size // num_decoder_attention_heads,
							)

							lowerCamelCase_							:		List[Any]							       =							torch.cat(
							    [common_inputs['decoder_attention_mask'], torch.ones(__lowercase			,	__lowercase )]			,	dim=1 )

							lowerCamelCase_							:		Tuple							       =							[]
							# If the number of encoder and decoder layers are present in the model configuration, both are considered
							lowerCamelCase_       ,      lowerCamelCase_							:		int							       =							self.num_layers
							lowerCamelCase_							:		Optional[int]							       =							min(__lowercase			,	__lowercase )
							lowerCamelCase_							:		int							       =							max(__lowercase			,	__lowercase ) - min_num_layers
							lowerCamelCase_							:		Union[str, Any]							       =							'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

							for _ in range(__lowercase ):
								common_inputs["past_key_values"].append(
								    (
								        torch.zeros(__lowercase ),
								        torch.zeros(__lowercase ),
								        torch.zeros(__lowercase ),
								        torch.zeros(__lowercase ),
								    ) )
							# TODO: test this.
							lowerCamelCase_							:		Optional[int]							       =							encoder_shape if remaining_side_name == 'encoder' else decoder_shape
							for _ in range(__lowercase			,	__lowercase ):
								common_inputs["past_key_values"].append((torch.zeros(__lowercase ), torch.zeros(__lowercase )) )
						return common_inputs





					_lowerCamelCase		  =			_generate_dummy_inputs_for_default_and_seqaseq_lm
