





'''simple docstring'''


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(SCREAMING_SNAKE_CASE_	:       int		,							SCREAMING_SNAKE_CASE_	:       str		,							SCREAMING_SNAKE_CASE_	:       int=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





class  lowerCAmelCase_		(		__magic_name__		):




 def __init__(						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  )     ->				List[Any]:
     super().__init__()

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




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Tuple:
     if latents is None:
         _lowerCAmelCase						      =   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}'''		)
         _lowerCAmelCase						      =   latents.to(_lowerCAmelCase		)

     _lowerCAmelCase						      =   latents * scheduler.init_noise_sigma
     return latents




 def 				_snake_case (						self      ,  _lowerCAmelCase=0		)     ->				Optional[int]:
     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(_lowerCAmelCase      ,  _lowerCAmelCase		)




 def 				_snake_case (						self      ,  _lowerCAmelCase=0		)     ->				List[Any]:
     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=_lowerCAmelCase		)
         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(_lowerCAmelCase      ,  _lowerCAmelCase      ,  prev_module_hook=_lowerCAmelCase		)

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




 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def 				_snake_case (						self		)     ->				int:
     if not hasattr(self.unet      ,  "_hf_hook"		):
         return self.device
     for module in self.unet.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





 @torch.no_grad()
 @replace_example_docstring(_lowerCAmelCase		)
 def __call__(						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase = 512      ,  _lowerCAmelCase = 512      ,  _lowerCAmelCase = 100      ,  _lowerCAmelCase = 4.0      ,  _lowerCAmelCase = 1      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = "pil"      ,  _lowerCAmelCase = True      ,  )     ->				List[str]:
     _lowerCAmelCase						      =   self._execution_device

     _lowerCAmelCase						      =   guidance_scale > 1.0

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

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

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

     self.scheduler.set_timesteps(_lowerCAmelCase      ,  device=_lowerCAmelCase		)
     _lowerCAmelCase						      =   self.scheduler.timesteps

     _lowerCAmelCase						      =   self.unet.config.in_channels

     _lowerCAmelCase      ,      _lowerCAmelCase						      =   downscale_height_and_width(_lowerCAmelCase      ,  _lowerCAmelCase      ,  self.movq_scale_factor		)

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

     for i, t in enumerate(self.progress_bar(_lowerCAmelCase		)		):
         # 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}
         _lowerCAmelCase						      =   self.unet(
             sample=_lowerCAmelCase      ,  timestep=_lowerCAmelCase      ,  encoder_hidden_states=_lowerCAmelCase      ,  added_cond_kwargs=_lowerCAmelCase      ,  return_dict=_lowerCAmelCase      ,  )[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(
             _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  generator=_lowerCAmelCase      ,  )[0]
     # post-processing
     _lowerCAmelCase						      =   self.movq.decode(_lowerCAmelCase      ,  force_not_quantize=_lowerCAmelCase		)["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(_lowerCAmelCase		)

     if not return_dict:
         return (image,)

     return ImagePipelineOutput(images=_lowerCAmelCase		)









'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE									=				{
    "bigcode/gpt_bigcode-santacoder": "https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json",
}

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				str								=       "gpt_bigcode"
 __lowerCamelCase    :				Optional[int]								=       ["past_key_values"]
 __lowerCamelCase    :				List[str]								=       {
     "hidden_size": "n_embd",
     "max_position_embeddings": "n_positions",
     "num_attention_heads": "n_head",
     "num_hidden_layers": "n_layer",
 }




 def __init__(						self      ,  _lowerCAmelCase=50257      ,  _lowerCAmelCase=1024      ,  _lowerCAmelCase=768      ,  _lowerCAmelCase=12      ,  _lowerCAmelCase=12      ,  _lowerCAmelCase=None      ,  _lowerCAmelCase="gelu_pytorch_tanh"      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=1E-5      ,  _lowerCAmelCase=0.02      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=50256      ,  _lowerCAmelCase=50256      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=True      ,  **_lowerCAmelCase      ,  )     ->				List[Any]:
     _lowerCAmelCase						      =   vocab_size
     _lowerCAmelCase						      =   n_positions
     _lowerCAmelCase						      =   n_embd
     _lowerCAmelCase						      =   n_layer
     _lowerCAmelCase						      =   n_head
     _lowerCAmelCase						      =   n_inner
     _lowerCAmelCase						      =   activation_function
     _lowerCAmelCase						      =   resid_pdrop
     _lowerCAmelCase						      =   embd_pdrop
     _lowerCAmelCase						      =   attn_pdrop
     _lowerCAmelCase						      =   layer_norm_epsilon
     _lowerCAmelCase						      =   initializer_range
     _lowerCAmelCase						      =   scale_attn_weights
     _lowerCAmelCase						      =   use_cache
     _lowerCAmelCase						      =   attention_softmax_in_fpaa
     _lowerCAmelCase						      =   scale_attention_softmax_in_fpaa
     _lowerCAmelCase						      =   multi_query

     _lowerCAmelCase						      =   bos_token_id
     _lowerCAmelCase						      =   eos_token_id

     super().__init__(bos_token_id=_lowerCAmelCase      ,  eos_token_id=_lowerCAmelCase      ,  **_lowerCAmelCase		)


