


from typing import List, Optional, Union

import torch

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,
)


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

SCREAMING_SNAKE_CASE							    =							'\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")\n        >>> pipe_prior.to("cuda")\n        >>> prompt = "red cat, 4k photo"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> zero_image_emb = out.negative_image_embeds\n        >>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")\n        >>> pipe.to("cuda")\n        >>> image = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=50,\n        ... ).images\n        >>> image[0].save("cat.png")\n        ```\n'




def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__=8   ):
   __a					=  height // scale_factor**2
   if height % scale_factor**2 != 0:
      new_height += 1
   __a					=  width // scale_factor**2
   if width % scale_factor**2 != 0:
      new_width += 1
   return new_height * scale_factor, new_width * scale_factor


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""


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

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


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A    ):
        if latents is None:
           __a					=  randn_tensor(__A  ,       generator=__A  ,       device=__A  ,       dtype=__A    )
        else:
           if latents.shape != shape:
              raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''    )
           __a					=  latents.to(__A    )

        __a					=  latents * scheduler.init_noise_sigma
        return latents


     def 	snake_case_  (	self  ,       __A=0    ):
        if is_accelerate_available():
           from accelerate import cpu_offload
        else:
           raise ImportError("""Please install accelerate via `pip install accelerate`"""    )

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

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


     def 	snake_case_  (	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."""    )

        __a					=  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)

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

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


     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def 	snake_case_  (	self    ):
        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  ,       __A  ,       __A  ,       __A = 512  ,       __A = 512  ,       __A = 100  ,       __A = 4.0  ,       __A = 1  ,       __A = None  ,       __A = None  ,       __A = "pil"  ,       __A = True  ,       ):
        __a					=  self._execution_device

        __a					=  guidance_scale > 1.0

        if isinstance(__A  ,       __A    ):
           __a					=  torch.cat(__A  ,       dim=0    )
        __a					=  image_embeds.shape[0] * num_images_per_prompt
        if isinstance(__A  ,       __A    ):
           __a					=  torch.cat(__A  ,       dim=0    )

        if do_classifier_free_guidance:
           __a					=  image_embeds.repeat_interleave(__A  ,       dim=0    )
           __a					=  negative_image_embeds.repeat_interleave(__A  ,       dim=0    )

        __a					=  torch.cat([negative_image_embeds, image_embeds]  ,       dim=0    ).to(dtype=self.unet.dtype  ,       device=__A    )

        self.scheduler.set_timesteps(__A  ,       device=__A    )
        __a					=  self.scheduler.timesteps

        __a					=  self.unet.config.in_channels

        __a	,    __a					=  downscale_height_and_width(__A  ,       __A  ,       self.movq_scale_factor    )

        # create initial latent
        __a					=  self.prepare_latents(
            (batch_size, num_channels_latents, height, width)  ,       image_embeds.dtype  ,       __A  ,       __A  ,       __A  ,       self.scheduler  ,       )

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

           __a					=  {"""image_embeds""": image_embeds}
           __a					=  self.unet(
               sample=__A  ,       timestep=__A  ,       encoder_hidden_states=__A  ,       added_cond_kwargs=__A  ,       return_dict=__A  ,       )[0]

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

           # compute the previous noisy sample x_t -> x_t-1
           __a					=  self.scheduler.step(
               __A  ,       __A  ,       __A  ,       generator=__A  ,       )[0]
        # post-processing
        __a					=  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"]:
           __a					=  image * 0.5 + 0.5
           __a					=  image.clamp(0  ,       1    )
           __a					=  image.cpu().permute(0  ,       2  ,       3  ,       1    ).float().numpy()

        if output_type == "pil":
           __a					=  self.numpy_to_pil(__A    )

        if not return_dict:
           return (image,)

        return ImagePipelineOutput(images=__A    )










'''simple docstring'''

import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
   import PIL.Image

   from .features import FeatureType


lowercase__		:     Optional[List[str]]			=							None
lowercase__		:     List[Any]			=							'<' if sys.byteorder == 'little' else '>'
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
lowercase__		:     int			=							[
    np.dtype('|b1'),
    np.dtype('|u1'),
    np.dtype('<u2'),
    np.dtype('>u2'),
    np.dtype('<i2'),
    np.dtype('>i2'),
    np.dtype('<u4'),
    np.dtype('>u4'),
    np.dtype('<i4'),
    np.dtype('>i4'),
    np.dtype('<f4'),
    np.dtype('>f4'),
    np.dtype('<f8'),
    np.dtype('>f8'),
]
@dataclass
class 							__lowerCAmelCase	:






       """simple docstring"""
       _snake_case      :	bool									=       True
       _snake_case      :	Optional[str]									=       None
       # Automatically constructed
       _snake_case      :	ClassVar[str]									=       "PIL.Image.Image"
       _snake_case      :	ClassVar[Any]									=       pa.struct({'bytes': pa.binary(), 'path': pa.string()}					)
       _snake_case      :	str									=       field(default='Image'		,		init=__magic_name__		,		repr=__magic_name__					)





       def __call__(				self  :						Any	)       ->	str:

            '''simple docstring'''




            return self.pa_type





       def snake_case__		(				self  :						Dict			,					lowerCAmelCase__  :						Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"]	)       ->	dict:

            '''simple docstring'''




            if config.PIL_AVAILABLE:
                 import PIL.Image
            else:
                 raise ImportError('''To support encoding images, please install \'Pillow\'.'''	)

            if isinstance(lowerCAmelCase__			,					lowerCAmelCase__	):
                 _UpperCamelCase							       =			np.array(lowerCAmelCase__	)

            if isinstance(lowerCAmelCase__			,					lowerCAmelCase__	):
                 return {"path": value, "bytes": None}
            elif isinstance(lowerCAmelCase__			,					lowerCAmelCase__	):
                 return {"path": None, "bytes": value}
            elif isinstance(lowerCAmelCase__			,					np.ndarray	):
                 # convert the image array to PNG/TIFF bytes
                 return encode_np_array(lowerCAmelCase__	)
            elif isinstance(lowerCAmelCase__			,					PIL.Image.Image	):
                 # convert the PIL image to bytes (default format is PNG/TIFF)
                 return encode_pil_image(lowerCAmelCase__	)
            elif value.get('''path'''	) is not None and os.path.isfile(value['''path''']	):
                 # we set "bytes": None to not duplicate the data if they're already available locally
                 return {"bytes": None, "path": value.get('''path'''	)}
            elif value.get('''bytes'''	) is not None or value.get('''path'''	) is not None:
                 # store the image bytes, and path is used to infer the image format using the file extension
                 return {"bytes": value.get('''bytes'''	), "path": value.get('''path'''	)}
            else:
                 raise ValueError(
                     f"""An image sample should have one of 'path' or 'bytes' but they are missing or None in {value}."""	)





       def snake_case__		(				self  :						Tuple			,					lowerCAmelCase__  :						dict			,					lowerCAmelCase__  :						Dict=None	)       ->	"PIL.Image.Image":

            '''simple docstring'''




            if not self.decode:
                 raise RuntimeError('''Decoding is disabled for this feature. Please use Image(decode=True) instead.'''	)

            if config.PIL_AVAILABLE:
                 import PIL.Image
            else:
                 raise ImportError('''To support decoding images, please install \'Pillow\'.'''	)

            if token_per_repo_id is None:
                 _UpperCamelCase							       =			{}

            _UpperCamelCase       ,							_UpperCamelCase							       =			value['''path'''], value['''bytes''']
            if bytes_ is None:
                 if path is None:
                      raise ValueError(f"""An image should have one of 'path' or 'bytes' but both are None in {value}."""	)
                 else:
                      if is_local_path(lowerCAmelCase__	):
                           _UpperCamelCase							       =			PIL.Image.open(lowerCAmelCase__	)
                      else:
                           _UpperCamelCase							       =			path.split('''::'''	)[-1]
                           try:
                                _UpperCamelCase							       =			string_to_dict(lowerCAmelCase__			,					config.HUB_DATASETS_URL	)['''repo_id''']
                                _UpperCamelCase							       =			token_per_repo_id.get(lowerCAmelCase__	)
                           except ValueError:
                                _UpperCamelCase							       =			None
                           with xopen(lowerCAmelCase__			,					'''rb'''			,					use_auth_token=lowerCAmelCase__	) as f:
                                _UpperCamelCase							       =			BytesIO(f.read()	)
                           _UpperCamelCase							       =			PIL.Image.open(bytes_	)
            else:
                 _UpperCamelCase							       =			PIL.Image.open(BytesIO(bytes_	)	)
            image.load()  # to avoid "Too many open files" errors
            return image





       def snake_case__		(				self  :						List[str]	)       ->	Union["FeatureType", Dict[str, "FeatureType"]]:

            '''simple docstring'''




            from .features import Value

            return (
                self
                if self.decode
                else {
                    "bytes": Value('''binary'''	),
                    "path": Value('''string'''	),
                }
            )





       def snake_case__		(				self  :						int			,					lowerCAmelCase__  :						Union[pa.StringArray, pa.StructArray, pa.ListArray]	)       ->	pa.StructArray:

            '''simple docstring'''




            if pa.types.is_string(storage.type	):
                 _UpperCamelCase							       =			pa.array([None] * len(lowerCAmelCase__	)			,					type=pa.binary()	)
                 _UpperCamelCase							       =			pa.StructArray.from_arrays([bytes_array, storage]			,					['''bytes''', '''path''']			,					mask=storage.is_null()	)
            elif pa.types.is_binary(storage.type	):
                 _UpperCamelCase							       =			pa.array([None] * len(lowerCAmelCase__	)			,					type=pa.string()	)
                 _UpperCamelCase							       =			pa.StructArray.from_arrays([storage, path_array]			,					['''bytes''', '''path''']			,					mask=storage.is_null()	)
            elif pa.types.is_struct(storage.type	):
                 if storage.type.get_field_index('''bytes'''	) >= 0:
                      _UpperCamelCase							       =			storage.field('''bytes'''	)
                 else:
                      _UpperCamelCase							       =			pa.array([None] * len(lowerCAmelCase__	)			,					type=pa.binary()	)
                 if storage.type.get_field_index('''path'''	) >= 0:
                      _UpperCamelCase							       =			storage.field('''path'''	)
                 else:
                      _UpperCamelCase							       =			pa.array([None] * len(lowerCAmelCase__	)			,					type=pa.string()	)
                 _UpperCamelCase							       =			pa.StructArray.from_arrays([bytes_array, path_array]			,					['''bytes''', '''path''']			,					mask=storage.is_null()	)
            elif pa.types.is_list(storage.type	):
                 _UpperCamelCase							       =			pa.array(
                     [encode_np_array(np.array(lowerCAmelCase__	)	)['''bytes'''] if arr is not None else None for arr in storage.to_pylist()]			,					type=pa.binary()			,					)
                 _UpperCamelCase							       =			pa.array([None] * len(lowerCAmelCase__	)			,					type=pa.string()	)
                 _UpperCamelCase							       =			pa.StructArray.from_arrays(
                     [bytes_array, path_array]			,					['''bytes''', '''path''']			,					mask=bytes_array.is_null()	)
            return array_cast(lowerCAmelCase__			,					self.pa_type	)







       def snake_case__		(				self  :						Union[str, Any]			,					lowerCAmelCase__  :						pa.StructArray	)       ->	pa.StructArray:

            '''simple docstring'''




            @no_op_if_value_is_null
            def path_to_bytes(lowerCAmelCase__  :						Optional[Any]	):
                 with xopen(lowerCAmelCase__			,					'''rb'''	) as f:
                      _UpperCamelCase							       =			f.read()
                 return bytes_

            _UpperCamelCase							       =			pa.array(
                [
                    (path_to_bytes(x['''path''']	) if x['''bytes'''] is None else x['''bytes''']) if x is not None else None
                    for x in storage.to_pylist()
                ]			,					type=pa.binary()			,					)
            _UpperCamelCase							       =			pa.array(
                [os.path.basename(lowerCAmelCase__	) if path is not None else None for path in storage.field('''path'''	).to_pylist()]			,					type=pa.string()			,					)
            _UpperCamelCase							       =			pa.StructArray.from_arrays([bytes_array, path_array]			,					['''bytes''', '''path''']			,					mask=bytes_array.is_null()	)
            return array_cast(lowerCAmelCase__			,					self.pa_type	)







def    a__							(							)						->						List[str]:





     """simple docstring"""

     if config.PIL_AVAILABLE:
          import PIL.Image
     else:
          raise ImportError('''To support encoding images, please install \'Pillow\'.''' )

     global _IMAGE_COMPRESSION_FORMATS
     if _IMAGE_COMPRESSION_FORMATS is None:
          PIL.Image.init()
          _UpperCamelCase							       =			list(set(PIL.Image.OPEN.keys() ) & set(PIL.Image.SAVE.keys() ) )
     return _IMAGE_COMPRESSION_FORMATS







def    a__							(							lowercase   :	"PIL.Image.Image" )						->						bytes:





     """simple docstring"""

     _UpperCamelCase							       =			BytesIO()
     if image.format in list_image_compression_formats():
          _UpperCamelCase							       =			image.format
     else:
          _UpperCamelCase							       =			'''PNG''' if image.mode in ['''1''', '''L''', '''LA''', '''RGB''', '''RGBA'''] else '''TIFF'''
     image.save(lowercase,   format=lowercase )
     return buffer.getvalue()







def    a__							(							lowercase   :	"PIL.Image.Image" )						->						dict:





     """simple docstring"""

     if hasattr(lowercase,   '''filename''' ) and image.filename != "":
          return {"path": image.filename, "bytes": None}
     else:
          return {"path": None, "bytes": image_to_bytes(lowercase )}







def    a__							(							lowercase   :	np.ndarray )						->						dict:





     """simple docstring"""

     if config.PIL_AVAILABLE:
          import PIL.Image
     else:
          raise ImportError('''To support encoding images, please install \'Pillow\'.''' )

     _UpperCamelCase							       =			array.dtype
     _UpperCamelCase							       =			dtype.byteorder if dtype.byteorder != '''=''' else _NATIVE_BYTEORDER
     _UpperCamelCase							       =			dtype.kind
     _UpperCamelCase							       =			dtype.itemsize

     _UpperCamelCase							       =			None

     # Multi-channel array case (only np.dtype("|u1") is allowed)
     if array.shape[2:]:
          _UpperCamelCase							       =			np.dtype('''|u1''' )
          if dtype_kind not in ["u", "i"]:
               raise TypeError(
                   F"""Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.""" )
          if dtype is not dest_dtype:
               warnings.warn(F"""Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'""" )
    # Exact match
     elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
          _UpperCamelCase							       =			dtype
     else:  # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
          while dtype_itemsize >= 1:
               _UpperCamelCase							       =			dtype_byteorder + dtype_kind + str(lowercase )
               _UpperCamelCase							       =			np.dtype(lowercase )
               if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
                    warnings.warn(F"""Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'""" )
                    break
               else:
                    dtype_itemsize //= 2

     if dest_dtype is None:
          raise TypeError(
              F"""Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}""" )
     _UpperCamelCase							       =			PIL.Image.fromarray(array.astype(lowercase ) )
     return {"path": None, "bytes": image_to_bytes(lowercase )}







def    a__							(							lowercase   :	Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]] )						->						List[dict]:





     """simple docstring"""

     if config.PIL_AVAILABLE:
          import PIL.Image
     else:
          raise ImportError('''To support encoding images, please install \'Pillow\'.''' )

     if objs:
          _UpperCamelCase       ,							_UpperCamelCase							       =			first_non_null_value(lowercase )
          if isinstance(lowercase,   lowercase ):
               return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
          if isinstance(lowercase,   np.ndarray ):
               _UpperCamelCase							       =			no_op_if_value_is_null(lowercase )
               return [obj_to_image_dict_func(lowercase ) for obj in objs]
          elif isinstance(lowercase,   PIL.Image.Image ):
               _UpperCamelCase							       =			no_op_if_value_is_null(lowercase )
               return [obj_to_image_dict_func(lowercase ) for obj in objs]
          else:
               return objs
     else:
          return objs



