
'''simple docstring'''

from dataclasses import dataclass, field
from typing import Optional


@dataclass
class      a_     :
       __lowerCAmelCase			: Optional[str]			=					field(
           default="""codeparrot/codeparrot"""							,	metadata={"""help""": """Model name or path of model to be trained."""}			)
       __lowerCAmelCase			: Optional[str]			=					field(
           default="""./"""							,	metadata={"""help""": """Save dir where model repo is cloned and models updates are saved to."""}			)
       __lowerCAmelCase			: Optional[str]			=					field(
           default="""codeparrot/codeparrot-clean-train"""							,	metadata={"""help""": """Name or path of training dataset."""}			)
       __lowerCAmelCase			: Optional[str]			=					field(
           default="""codeparrot/codeparrot-clean-valid"""							,	metadata={"""help""": """Name or path of validation dataset."""}			)
       __lowerCAmelCase			: Optional[int]			=					field(default=2							,	metadata={"""help""": """Batch size for training."""}			)
       __lowerCAmelCase			: Optional[int]			=					field(default=2							,	metadata={"""help""": """Batch size for evaluation."""}			)
       __lowerCAmelCase			: Optional[float]			=					field(default=0.1							,	metadata={"""help""": """Value of weight decay."""}			)
       __lowerCAmelCase			: Optional[int]			=					field(
           default=1_0_0_0_0							,	metadata={"""help""": """Size of buffer used to shuffle streaming dataset."""}			)
       __lowerCAmelCase			: Optional[float]			=					field(default=2E-4							,	metadata={"""help""": """Learning rate fo training."""}			)
       __lowerCAmelCase			: Optional[str]			=					field(default="""cosine"""							,	metadata={"""help""": """Learning rate."""}			)
       __lowerCAmelCase			: Optional[int]			=					field(
           default=7_5_0							,	metadata={"""help""": """Number of warmup steps in the learning rate schedule."""}			)
       __lowerCAmelCase			: Optional[int]			=					field(
           default=1_6							,	metadata={"""help""": """Number of gradient accumulation steps."""}			)
       __lowerCAmelCase			: Optional[bool]			=					field(
           default=__lowerCamelCase							,	metadata={"""help""": """Use gradient checkpointing to reduce memory footprint."""}			)
       __lowerCAmelCase			: Optional[int]			=					field(default=5_0_0_0_0							,	metadata={"""help""": """Maximum number of training steps."""}			)
       __lowerCAmelCase			: Optional[int]			=					field(
           default=-1							,	metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""}			)
       __lowerCAmelCase			: Optional[int]			=					field(default=1_0_2_4							,	metadata={"""help""": """Sequence lengths used for training."""}			)
       __lowerCAmelCase			: Optional[int]			=					field(default=1							,	metadata={"""help""": """Training seed."""}			)
       __lowerCAmelCase			: Optional[int]			=					field(
           default=1_0_2_4							,	metadata={"""help""": """Interval to save checkpoints. Measured as number of forward passes not training steps."""}							,	)
       __lowerCAmelCase			: Optional[str]			=					field(
           default=__lowerCamelCase							,	metadata={"""help""": """States path if the training should continue from a checkpoint folder."""}			)
       __lowerCAmelCase			: Optional[bool]			=					field(default=__lowerCamelCase							,	metadata={"""help""": """If True the data is pretokenized."""}			)



@dataclass
class      a_     :
       __lowerCAmelCase			: Optional[str]			=					field(
           default="""codeparrot/codeparrot"""							,	metadata={"""help""": """Model name or path of model to be evaluated."""}			)
       __lowerCAmelCase			: Optional[str]			=					field(
           default="""codeparrot/codeparrot-clean-valid"""							,	metadata={"""help""": """Name or path of validation dataset."""}			)
       __lowerCAmelCase			: Optional[int]			=					field(default=2							,	metadata={"""help""": """Batch size used for evaluation."""}			)
       __lowerCAmelCase			: Optional[int]			=					field(
           default=-1							,	metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""}			)
       __lowerCAmelCase			: Optional[int]			=					field(default=1_0_2_4							,	metadata={"""help""": """Length of sequences to be evaluated."""}			)
       __lowerCAmelCase			: Optional[int]			=					field(default=1							,	metadata={"""help""": """Random seed used for evaluation."""}			)



@dataclass
class      a_     :
       __lowerCAmelCase			: Optional[str]			=					field(
           default="""codeparrot/codeparrot"""							,	metadata={"""help""": """Model name or path of model to be evaluated."""}			)
       __lowerCAmelCase			: Optional[int]			=					field(default=__lowerCamelCase							,	metadata={"""help""": """Number of workers used for code evaluation."""}			)
       __lowerCAmelCase			: Optional[int]			=					field(
           default=__lowerCamelCase							,	metadata={"""help""": """The number of human-eval tasks to run. If not included all tasks are evaluated."""}							,	)
       __lowerCAmelCase			: Optional[bool]			=					field(
           default=__lowerCamelCase							,	metadata={"""help""": """Sample from the language model\'s output distribution."""}			)
       __lowerCAmelCase			: Optional[float]			=					field(default=0.2							,	metadata={"""help""": """Sampling temperature used for generation."""}			)
       __lowerCAmelCase			: Optional[int]			=					field(default=2_5_6							,	metadata={"""help""": """Maximum number of newly generated tokens."""}			)
       __lowerCAmelCase			: Optional[int]			=					field(default=0							,	metadata={"""help""": """Top-k parameter used for generation."""}			)
       __lowerCAmelCase			: Optional[float]			=					field(default=0.95							,	metadata={"""help""": """Top-p parameter used for nucleus sampling."""}			)
       __lowerCAmelCase			: Optional[int]			=					field(default=1_0							,	metadata={"""help""": """Number of generations to run in parallel."""}			)
       __lowerCAmelCase			: Optional[int]			=					field(
           default=2_0_0							,	metadata={"""help""": """Number of completions to generate for each sample."""}			)
       __lowerCAmelCase			: Optional[int]			=					field(default=1							,	metadata={"""help""": """Random seed used for evaluation."""}			)
       __lowerCAmelCase			: Optional[str]			=					field(
           default="""eval_results.json"""							,	metadata={"""help""": """Random seed used for evaluation."""}			)
       __lowerCAmelCase			: Optional[str]			=					field(
           default="""0"""							,	metadata={"""help""": """Allow `code_eval` to execute Python code on machine"""}			)
       __lowerCAmelCase			: Optional[int]			=					field(
           default=-1							,	metadata={
               """help""": (
                   """Determine which device to run the `text-generation` Pipeline on. -1 is CPU and any zero or positive"""
                   """ number corresponds to which GPU device id to run on."""
               )
           }							,	)



@dataclass
class      a_     :
       __lowerCAmelCase			: Optional[int]			=					field(
           default=__lowerCamelCase							,	metadata={
               """help""": """The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."""
           }							,	)
       __lowerCAmelCase			: Optional[str]			=					field(
           default="""transformersbook/codeparrot"""							,	metadata={"""help""": """Folder or name of dataset to process."""}			)
       __lowerCAmelCase			: Optional[str]			=					field(
           default="""codeparrot-clean"""							,	metadata={"""help""": """Folder to save processed processed dataset."""}			)
       __lowerCAmelCase			: Optional[int]			=					field(
           default=1_0_0_0_0_0							,	metadata={"""help""": """Number of files to save per JSON output file."""}			)
       __lowerCAmelCase			: Optional[str]			=					field(default="""content"""							,	metadata={"""help""": """Column containing text data to process."""}			)
       __lowerCAmelCase			: Optional[float]			=					field(
           default=1_0_0_0							,	metadata={"""help""": """Maximum line length in file, otherwise file is filtered."""}			)
       __lowerCAmelCase			: Optional[float]			=					field(
           default=1_0_0							,	metadata={"""help""": """Maximum mean line length in file, otherwise file is filtered."""}			)
       __lowerCAmelCase			: Optional[float]			=					field(
           default=0.25							,	metadata={"""help""": """Maximum fraction of non-alphanumeric characters, otherwise file is filtered."""}			)
       __lowerCAmelCase			: Optional[float]			=					field(
           default=1.5							,	metadata={"""help""": """Minimum character token ratio for the file, otherwise file is filtered."""}			)
       __lowerCAmelCase			: Optional[float]			=					field(
           default=0.7							,	metadata={"""help""": """Probability for filtering config, test and uncommon files."""}			)
       __lowerCAmelCase			: Optional[str]			=					field(
           default="""codeparrot/codeparrot"""							,	metadata={"""help""": """Name or path to the tokenizer."""}							,	)
       __lowerCAmelCase			: Optional[bool]			=					field(
           default=__lowerCamelCase							,	metadata={"""help""": """If True, near-duplicate samples are removed."""}			)
       __lowerCAmelCase			: Optional[float]			=					field(
           default=0.85							,	metadata={"""help""": """Jaccard threshold for near-duplicate samples."""}			)



@dataclass
class      a_     :
       __lowerCAmelCase			: Optional[str]			=					field(
           default="""gpt2"""							,	metadata={"""help""": """Base tokenizer to build new tokenizer from."""}			)
       __lowerCAmelCase			: Optional[str]			=					field(
           default="""transformersbook/codeparrot-train"""							,	metadata={"""help""": """Dataset to train tokenizer on."""}			)
       __lowerCAmelCase			: Optional[str]			=					field(default="""content"""							,	metadata={"""help""": """Column containing text data to process."""}			)
       __lowerCAmelCase			: Optional[int]			=					field(default=2_0_0_0_0_0							,	metadata={"""help""": """Number of examples to train tokenizer on."""}			)
       __lowerCAmelCase			: Optional[int]			=					field(
           default=3_2_7_6_8							,	metadata={"""help""": """Number of examples to train the tokenizer on."""}			)
       __lowerCAmelCase			: Optional[str]			=					field(default="""codeparrot"""							,	metadata={"""help""": """Name of new tokenizer."""}			)
       __lowerCAmelCase			: Optional[bool]			=					field(default=__lowerCamelCase							,	metadata={"""help""": """Push saved tokenizer to the hub."""}			)



@dataclass
class      a_     :
       __lowerCAmelCase			: Optional[str]			=					field(
           default="""codeparrot/codeparrot"""							,	metadata={"""help""": """Name or path to the tokenizer."""}			)
       __lowerCAmelCase			: Optional[str]			=					field(
           default="""codeparrot/codeparrot-clean-train"""							,	metadata={"""help""": """Name or path to the dataset to pretokenize."""}			)
       __lowerCAmelCase			: Optional[str]			=					field(
           default="""tokenized-codeparrot-train"""							,	metadata={"""help""": """Repo name of the pretokenized data."""}			)
       __lowerCAmelCase			: Optional[int]			=					field(default=__lowerCamelCase							,	metadata={"""help""": """Number of workers used for code evaluation."""}			)



@dataclass
class      a_     :
       __lowerCAmelCase			: Optional[str]			=					field(
           default="""gpt2-large"""							,	metadata={"""help""": """Configuration to use for model initialization."""}			)
       __lowerCAmelCase			: Optional[str]			=					field(
           default="""codeparrot/codeparrot"""							,	metadata={"""help""": """Tokenizer attached to model."""}			)
       __lowerCAmelCase			: Optional[str]			=					field(default="""codeparrot"""							,	metadata={"""help""": """Name of the created model."""}			)
       __lowerCAmelCase			: Optional[bool]			=					field(default=__lowerCamelCase							,	metadata={"""help""": """Push saved tokenizer to the hub."""}			)








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,
)


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 KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\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 = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to("cuda")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to("cuda")\n\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        >>> 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        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\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__       :					Optional[int]  ,			A__       :					Dict  ,			A__       :					Optional[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__(					__lowerCamelCase):
      def __init__(				self: List[Any]    ,       UpperCamelCase_: UNetaDConditionModel    ,       UpperCamelCase_: DDPMScheduler    ,       UpperCamelCase_: VQModel    ,       ):
             super().__init__()

             self.register_modules(
                 unet=UpperCamelCase_    ,       scheduler=UpperCamelCase_    ,       movq=UpperCamelCase_    ,       )
             __lowerCamelCase   =						2 ** (len(self.movq.config.block_out_channels   ) - 1)
      def 			lowerCAmelCase__						(				self: int    ,       UpperCamelCase_: Any    ,       UpperCamelCase_: Tuple    ,       UpperCamelCase_: int    ,       UpperCamelCase_: Dict    ,       UpperCamelCase_: Dict    ,       UpperCamelCase_: int   ):
             if latents is None:
                    __lowerCamelCase   =						randn_tensor(UpperCamelCase_    ,       generator=UpperCamelCase_    ,       device=UpperCamelCase_    ,       dtype=UpperCamelCase_   )
             else:
                    if latents.shape != shape:
                           raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {shape}'   )
                    __lowerCamelCase   =						latents.to(UpperCamelCase_   )

             __lowerCamelCase   =						latents * scheduler.init_noise_sigma
             return latents
      def 			lowerCAmelCase__						(				self: Dict    ,       UpperCamelCase_: str=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 			lowerCAmelCase__						(				self: List[str]    ,       UpperCamelCase_: Optional[int]=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 			lowerCAmelCase__						(				self: int   ):
             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: Tuple    ,       UpperCamelCase_: Union[torch.FloatTensor, List[torch.FloatTensor]]    ,       UpperCamelCase_: Union[torch.FloatTensor, List[torch.FloatTensor]]    ,       UpperCamelCase_: torch.FloatTensor    ,       UpperCamelCase_: int = 5_12    ,       UpperCamelCase_: int = 5_12    ,       UpperCamelCase_: int = 1_00    ,       UpperCamelCase_: float = 4.0    ,       UpperCamelCase_: int = 1    ,       UpperCamelCase_: Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,       UpperCamelCase_: Optional[torch.FloatTensor] = None    ,       UpperCamelCase_: Optional[str] = "pil"    ,       UpperCamelCase_: bool = 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] * num_images_per_prompt

             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_   )
             self.scheduler.set_timesteps(UpperCamelCase_    ,       device=UpperCamelCase_   )
             __lowerCamelCase   =						self.scheduler.timesteps

             __lowerCamelCase   =						self.movq.config.latent_channels

             __lowerCamelCase,   __lowerCamelCase   =						downscale_height_and_width(UpperCamelCase_    ,       UpperCamelCase_    ,       self.movq_scale_factor   )

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

             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_   )

