


from typing import List, Optional, Union

import torch
from transformers import (
    XLMRobertaTokenizer,
)

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


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

snake_case__			 =						"""
    Examples:
        ```py
        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained(\"kandinsky-community/Kandinsky-2-1-prior\")
        >>> pipe_prior.to(\"cuda\")

        >>> prompt = \"red cat, 4k photo\"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> negative_image_emb = out.negative_image_embeds

        >>> pipe = KandinskyPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-1\")
        >>> pipe.to(\"cuda\")

        >>> image = pipe(
        ...     prompt,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=negative_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ... ).images

        >>> image[0].save(\"cat.png\")
        ```
"""


def 				lowerCamelCase__	(      a	:   List[str]	,  a	:   Optional[Any]	,  a	:   Any=8				)    ->	Dict:





   """simple docstring"""
   a__ :Union[str, Any]						  =    h // scale_factor**2
   if h % scale_factor**2 != 0:
      new_h += 1
   a__ :Any						  =    w // scale_factor**2
   if w % scale_factor**2 != 0:
      new_w += 1
   return new_h * scale_factor, new_w * scale_factor







class 					lowerCAmelCase_ (       __snake_case):




      def __init__(	self						:   Dict   ,     __A						:   Union[str, Any]   ,     __A						:   Optional[Any]   ,     __A						:   str   ,     __A						:   List[Any]   ,     __A						:   int   ,     )      ->Optional[Any]:






         """simple docstring"""






         super().__init__()

         self.register_modules(
             text_encoder=A_   ,     tokenizer=A_   ,     unet=A_   ,     scheduler=A_   ,     movq=A_   ,     )
         a__ :List[Any]						  =    2 ** (len(self.movq.config.block_out_channels						) - 1)




      def  _snake_case					(	self						:   Union[str, Any]   ,     __A						:   Union[str, Any]   ,     __A						:   List[str]   ,     __A						:   int   ,     __A						:   Any   ,     __A						:   Tuple   ,     __A						:   Optional[int]						)      ->Union[str, Any]:






         """simple docstring"""






         if latents is None:
            a__ :int						  =    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__ :Optional[Any]						  =    latents.to(A_						)

         a__ :Dict						  =    latents * scheduler.init_noise_sigma
         return latents




      def  _snake_case					(	self						:   Optional[Any]   ,     __A						:   List[str]   ,     __A						:   Union[str, Any]   ,     __A						:   Tuple   ,     __A						:   str   ,     __A						:   List[Any]=None   ,     )      ->List[str]:






         """simple docstring"""






         a__ :int						  =    len(A_						) if isinstance(A_   ,     A_						) else 1
         # get prompt text embeddings
         a__ :Any						  =    self.tokenizer(
             A_   ,     padding="max_length"   ,     truncation=A_   ,     max_length=77   ,     return_attention_mask=A_   ,     add_special_tokens=A_   ,     return_tensors="pt"   ,     )

         a__ :List[Any]						  =    text_inputs.input_ids
         a__ :List[Any]						  =    self.tokenizer(A_   ,     padding="longest"   ,     return_tensors="pt"						).input_ids

         if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(A_   ,     A_						):
            a__ :Union[str, Any]						  =    self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]						)
            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}'''						)

         a__ :str						  =    text_input_ids.to(A_						)
         a__ :int						  =    text_inputs.attention_mask.to(A_						)

         a__ :Optional[int]						  =    self.text_encoder(
             input_ids=A_   ,     attention_mask=A_						)

         a__ :Optional[Any]						  =    prompt_embeds.repeat_interleave(A_   ,     dim=0						)
         a__ :Any						  =    text_encoder_hidden_states.repeat_interleave(A_   ,     dim=0						)
         a__ :int						  =    text_mask.repeat_interleave(A_   ,     dim=0						)

         if do_classifier_free_guidance:
            a__ :List[str]
            if negative_prompt is None:
               a__ :Optional[Any]						  =    [""] * batch_size
            elif type(A_						) is not type(A_						):
               raise TypeError(
                   F'''`negative_prompt` should be the same type to `prompt`, but got {type(A_						)} !='''
                   F''' {type(A_						)}.'''						)
            elif isinstance(A_   ,     A_						):
               a__ :Union[str, Any]						  =    [negative_prompt]
            elif batch_size != len(A_						):
               raise ValueError(
                   F'''`negative_prompt`: {negative_prompt} has batch size {len(A_						)}, but `prompt`:'''
                   F''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
                   " the batch size of `prompt`."						)
            else:
               a__ :Optional[int]						  =    negative_prompt

            a__ :Tuple						  =    self.tokenizer(
                A_   ,     padding="max_length"   ,     max_length=77   ,     truncation=A_   ,     return_attention_mask=A_   ,     add_special_tokens=A_   ,     return_tensors="pt"   ,     )
            a__ :Optional[Any]						  =    uncond_input.input_ids.to(A_						)
            a__ :Tuple						  =    uncond_input.attention_mask.to(A_						)

            a__ :Optional[int]						  =    self.text_encoder(
                input_ids=A_   ,     attention_mask=A_						)

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

            a__ :int						  =    negative_prompt_embeds.shape[1]
            a__ :Any						  =    negative_prompt_embeds.repeat(1   ,     A_						)
            a__ :List[Any]						  =    negative_prompt_embeds.view(batch_size * num_images_per_prompt   ,     A_						)

            a__ :List[Any]						  =    uncond_text_encoder_hidden_states.shape[1]
            a__ :int						  =    uncond_text_encoder_hidden_states.repeat(1   ,     A_   ,     1						)
            a__ :Union[str, Any]						  =    uncond_text_encoder_hidden_states.view(
                batch_size * num_images_per_prompt   ,     A_   ,     -1						)
            a__ :Optional[int]						  =    uncond_text_mask.repeat_interleave(A_   ,     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
            a__ :List[Any]						  =    torch.cat([negative_prompt_embeds, prompt_embeds]						)
            a__ :Optional[int]						  =    torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]						)

            a__ :Tuple						  =    torch.cat([uncond_text_mask, text_mask]						)

         return prompt_embeds, text_encoder_hidden_states, text_mask




      def  _snake_case					(	self						:   List[str]   ,     __A						:   Optional[int]=0						)      ->Union[str, Any]:






         """simple docstring"""






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

         a__ :Dict						  =    torch.device(F'''cuda:{gpu_id}'''						)

         a__ :Optional[int]						  =    [
             self.unet,
             self.text_encoder,
             self.movq,
         ]
         for cpu_offloaded_model in models:
            if cpu_offloaded_model is not None:
               cpu_offload(A_   ,     A_						)




      def  _snake_case					(	self						:   Optional[Any]   ,     __A						:   Dict=0						)      ->Tuple:






         """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."						)

         a__ :Union[str, Any]						  =    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__ :Tuple						  =    None
         for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
            a__ :Optional[int]						  =    cpu_offload_with_hook(A_   ,     A_   ,     prev_module_hook=A_						)

         if self.safety_checker is not None:
            a__ :Any						  =    cpu_offload_with_hook(self.safety_checker   ,     A_   ,     prev_module_hook=A_						)

         # We'll offload the last model manually.
         a__ :str						  =    hook




      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def  _snake_case					(	self						:   Any						)      ->List[Any]:






         """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						:   Tuple   ,     __A						:   List[str]   ,     __A						:   Tuple   ,     __A						:   Dict   ,     __A						:   Optional[Any] = None   ,     __A						:   List[Any] = 512   ,     __A						:   List[Any] = 512   ,     __A						:   List[Any] = 100   ,     __A						:   Union[str, Any] = 4.0   ,     __A						:   Union[str, Any] = 1   ,     __A						:   Any = None   ,     __A						:   List[Any] = None   ,     __A						:   str = "pil"   ,     __A						:   Union[str, Any] = True   ,     )      ->Union[str, Any]:






         """simple docstring"""






         if isinstance(A_   ,     A_						):
            a__ :Optional[Any]						  =    1
         elif isinstance(A_   ,     A_						):
            a__ :List[Any]						  =    len(A_						)
         else:
            raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(A_						)}'''						)

         a__ :str						  =    self._execution_device

         a__ :Dict						  =    batch_size * num_images_per_prompt
         a__ :Optional[int]						  =    guidance_scale > 1.0

         a__ :Union[str, Any]						  =    self._encode_prompt(
             A_   ,     A_   ,     A_   ,     A_   ,     A_						)

         if isinstance(A_   ,     A_						):
            a__ :Any						  =    torch.cat(A_   ,     dim=0						)
         if isinstance(A_   ,     A_						):
            a__ :int						  =    torch.cat(A_   ,     dim=0						)

         if do_classifier_free_guidance:
            a__ :int						  =    image_embeds.repeat_interleave(A_   ,     dim=0						)
            a__ :Optional[Any]						  =    negative_image_embeds.repeat_interleave(A_   ,     dim=0						)

         a__ :List[Any]						  =    torch.cat([negative_image_embeds, image_embeds]   ,     dim=0						).to(
             dtype=prompt_embeds.dtype   ,     device=A_						)

         self.scheduler.set_timesteps(A_   ,     device=A_						)
         a__ :Any						  =    self.scheduler.timesteps

         a__ :Tuple						  =    self.unet.config.in_channels

         a__ :Union[str, Any]						  =    get_new_h_w(A_   ,     A_   ,     self.movq_scale_factor						)

         # create initial latent
         a__ :Tuple						  =    self.prepare_latents(
             (batch_size, num_channels_latents, height, width)   ,     text_encoder_hidden_states.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__ :Union[str, Any]						  =    torch.cat([latents] * 2						) if do_classifier_free_guidance else latents

            a__ :Dict						  =    {"text_embeds": prompt_embeds, "image_embeds": image_embeds}
            a__ :int						  =    self.unet(
                sample=A_   ,     timestep=A_   ,     encoder_hidden_states=A_   ,     added_cond_kwargs=A_   ,     return_dict=A_   ,     )[0]

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

            # compute the previous noisy sample x_t -> x_t-1
            a__ :Optional[int]						  =    self.scheduler.step(
                A_   ,     A_   ,     A_   ,     generator=A_   ,     ).prev_sample
         # post-processing
         a__ :Tuple						  =    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__ :Tuple						  =    image * 0.5 + 0.5
            a__ :Union[str, Any]						  =    image.clamp(0   ,     1						)
            a__ :str						  =    image.cpu().permute(0   ,     2   ,     3   ,     1						).float().numpy()

         if output_type == "pil":
            a__ :Tuple						  =    self.numpy_to_pil(A_						)

         if not return_dict:
            return (image,)

         return ImagePipelineOutput(images=A_						)






def 				lowerCamelCase__	(      a	:   list[list]				)    ->	list[list]:





			"""simple docstring"""
			a__ :Any						  =    current_set.copy()
			for row_index, row in enumerate(a				):
						a__ :List[str]						  =    row[0]
						for column_index, column in enumerate(a				):
									if magnitude == 0:
												a__ :Tuple						  =    column
												continue
									a__ :Tuple						  =    column / magnitude
    # Subtract to cancel term
			a__ :Any						  =    current_set[0]
			a__ :Dict						  =    [first_row]
			a__ :Dict						  =    current_set[1::]
			for row in current_set:
						a__ :int						  =    []
						# If first term is 0, it is already in form we want, so we preserve it
						if row[0] == 0:
									final_set.append(a				)
									continue
						for column_index in range(len(a				)				):
									temp_row.append(first_row[column_index] - row[column_index]				)
						final_set.append(a				)
			# Create next recursion iteration set
			if len(final_set[0]				) != 3:
						a__ :List[str]						  =    final_set[0]
						a__ :Optional[Any]						  =    []
						a__ :List[Any]						  =    []
						for row in final_set[1::]:
									current_first_column.append(row[0]				)
									next_iteration.append(row[1::]				)
						a__ :Dict						  =    simplify(a				)
						for i in range(len(a				)				):
									resultant[i].insert(0	,  current_first_column[i]				)
						resultant.insert(0	,  a				)
						a__ :Optional[int]						  =    resultant
			return final_set


def 				lowerCamelCase__	(      a	:   list[list]				)    ->	list:





			"""simple docstring"""
			if len(a				) == 0:
						raise IndexError("solve_simultaneous() requires n lists of length n+1"				)
			a__ :Any						  =    len(a				) + 1
			if any(len(a				) != _length for item in equations				):
						raise IndexError("solve_simultaneous() requires n lists of length n+1"				)
			for row in equations:
						if any(not isinstance(a	,  (int, float)				) for column in row				):
									raise ValueError("solve_simultaneous() requires lists of integers"				)
			if len(a				) == 1:
						return [equations[0][-1] / equations[0][0]]
			a__ :Union[str, Any]						  =    equations.copy()
			if any(0 in row for row in data_set				):
						a__ :int						  =    data_set.copy()
						a__ :Dict						  =    []
						for row_index, row in enumerate(a				):
									if 0 not in row:
												a__ :Union[str, Any]						  =    data_set.pop(a				)
												break
						if not full_row:
									raise ValueError("solve_simultaneous() requires at least 1 full equation"				)
						data_set.insert(0	,  a				)
			a__ :Optional[int]						  =    data_set.copy()
			a__ :Any						  =    simplify(a				)
			a__ :str						  =    simplified[::-1]
			a__ :list						  =    []
			for row in simplified:
						a__ :Optional[int]						  =    row[-1]
						if not solutions:
									if row[-2] == 0:
												solutions.append(0				)
												continue
									solutions.append(current_solution / row[-2]				)
									continue
						a__ :Dict						  =    row.copy()[: len(a				) - 1 :]
						while temp_row[0] == 0:
									temp_row.pop(0				)
						if len(a				) == 0:
									solutions.append(0				)
									continue
						a__ :List[Any]						  =    temp_row[1::]
						a__ :Union[str, Any]						  =    temp_row[::-1]
						for column_index, column in enumerate(a				):
									current_solution -= column * solutions[column_index]
						solutions.append(a				)
			a__ :List[str]						  =    []
			for item in solutions:
						final.append(float(round(a	,  5				)				)				)
			return final[::-1]


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		snake_case__			 =						[
		    [2, 1, 1, 1, 1, 4],
		    [1, 2, 1, 1, 1, 5],
		    [1, 1, 2, 1, 1, 6],
		    [1, 1, 1, 2, 1, 7],
		    [1, 1, 1, 1, 2, 8],
		]
		print(solve_simultaneous(eq))
		print(solve_simultaneous([[4, 2]]))


