




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,
)


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

snake_case_ : Tuple    		=       "\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						(__A   :    Any   ,				__A   :    List[str]   ,				__A   :    Optional[int]=8       )   ->      str:







     """simple docstring"""


     UpperCAmelCase_          =							height // scale_factor**2
     if height % scale_factor**2 != 0:
          new_height += 1
     UpperCAmelCase_          =							width // scale_factor**2
     if width % scale_factor**2 != 0:
          new_width += 1
     return new_height * scale_factor, new_width * scale_factor







class __snake_case			(			a		):







   def __init__(      self		:						List[str]					,						_snake_case		:						UNetaDConditionModel					,						_snake_case		:						DDPMScheduler					,						_snake_case		:						VQModel					,						):

        """simple docstring"""


        super().__init__()

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







   def lowerCamelCase						(      self		:						int					,						_snake_case		:						List[str]					,						_snake_case		:						Any					,						_snake_case		:						int					,						_snake_case		:						Optional[Any]					,						_snake_case		:						str					,						_snake_case		:						Optional[Any]):

        """simple docstring"""


        if latents is None:
             UpperCAmelCase_          =							randn_tensor(_snake_case					,						generator=_snake_case					,						device=_snake_case					,						dtype=_snake_case)
        else:
             if latents.shape != shape:
                  raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}""")
             UpperCAmelCase_          =							latents.to(_snake_case)

        UpperCAmelCase_          =							latents * scheduler.init_noise_sigma
        return latents







   def lowerCamelCase						(      self		:						Any					,						_snake_case		:						Union[str, Any]=0):

        """simple docstring"""


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

        UpperCAmelCase_          =							torch.device(F"""cuda:{gpu_id}""")

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







   def lowerCamelCase						(      self		:						List[str]					,						_snake_case		:						Optional[int]=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.''')

        UpperCAmelCase_          =							torch.device(F"""cuda:{gpu_id}""")

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

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

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







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

        """simple docstring"""


        if not hasattr(self.unet					,						'''_hf_hook'''):
             return self.device
        for module in self.unet.modules():
             if (
                 hasattr(_snake_case					,						'''_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(_snake_case)
   def __call__(      self		:						List[Any]					,						_snake_case		:						Union[torch.FloatTensor, List[torch.FloatTensor]]					,						_snake_case		:						Union[torch.FloatTensor, List[torch.FloatTensor]]					,						_snake_case		:						int = 512					,						_snake_case		:						int = 512					,						_snake_case		:						int = 100					,						_snake_case		:						float = 4.0					,						_snake_case		:						int = 1					,						_snake_case		:						Optional[Union[torch.Generator, List[torch.Generator]]] = None					,						_snake_case		:						Optional[torch.FloatTensor] = None					,						_snake_case		:						Optional[str] = "pil"					,						_snake_case		:						bool = True					,						):

        """simple docstring"""


        UpperCAmelCase_          =							self._execution_device

        UpperCAmelCase_          =							guidance_scale > 1.0

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

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

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

        self.scheduler.set_timesteps(_snake_case					,						device=_snake_case)
        UpperCAmelCase_          =							self.scheduler.timesteps

        UpperCAmelCase_          =							self.unet.config.in_channels

        UpperCAmelCase_     ,       UpperCAmelCase_          =							downscale_height_and_width(_snake_case					,						_snake_case					,						self.movq_scale_factor)

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

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

             UpperCAmelCase_          =							{'''image_embeds''': image_embeds}
             UpperCAmelCase_          =							self.unet(
                 sample=_snake_case					,						timestep=_snake_case					,						encoder_hidden_states=_snake_case					,						added_cond_kwargs=_snake_case					,						return_dict=_snake_case					,						)[0]

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

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

        if output_type == "pil":
             UpperCAmelCase_          =							self.numpy_to_pil(_snake_case)

        if not return_dict:
             return (image,)

        return ImagePipelineOutput(images=_snake_case)












import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
snake_case_ : List[Any]    		=       data_utils.TransfoXLTokenizer
snake_case_ : int    		=       data_utils.TransfoXLCorpus
snake_case_ : List[Any]    		=       data_utils
snake_case_ : int    		=       data_utils


def    A						(__A   :    Dict   ,				__A   :    List[Any]   ,				__A   :    Union[str, Any]   ,				__A   :    Tuple       )   ->      Union[str, Any]:







     """simple docstring"""


     if transfo_xl_dataset_file:
          # Convert a pre-processed corpus (see original TensorFlow repo)
          with open(__A   ,				'''rb'''       ) as fp:
               UpperCAmelCase_          =							pickle.load(__A   ,				encoding='''latin1'''       )
          # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
          UpperCAmelCase_          =							pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''pretrained_vocab_file''']
          print(F"""Save vocabulary to {pytorch_vocab_dump_path}"""       )
          UpperCAmelCase_          =							corpus.vocab.__dict__
          torch.save(__A   ,				__A       )

          UpperCAmelCase_          =							corpus.__dict__
          corpus_dict_no_vocab.pop('''vocab'''   ,				__A       )
          UpperCAmelCase_          =							pytorch_dump_folder_path + '''/''' + CORPUS_NAME
          print(F"""Save dataset to {pytorch_dataset_dump_path}"""       )
          torch.save(__A   ,				__A       )

     if tf_checkpoint_path:
          # Convert a pre-trained TensorFlow model
          UpperCAmelCase_          =							os.path.abspath(__A       )
          UpperCAmelCase_          =							os.path.abspath(__A       )

          print(F"""Converting Transformer XL checkpoint from {tf_path} with config at {config_path}."""       )
          # Initialise PyTorch model
          if transfo_xl_config_file == "":
               UpperCAmelCase_          =							TransfoXLConfig()
          else:
               UpperCAmelCase_          =							TransfoXLConfig.from_json_file(__A       )
          print(F"""Building PyTorch model from configuration: {config}"""       )
          UpperCAmelCase_          =							TransfoXLLMHeadModel(__A       )

          UpperCAmelCase_          =							load_tf_weights_in_transfo_xl(__A   ,				__A   ,				__A       )
          # Save pytorch-model
          UpperCAmelCase_          =							os.path.join(__A   ,				__A       )
          UpperCAmelCase_          =							os.path.join(__A   ,				__A       )
          print(F"""Save PyTorch model to {os.path.abspath(__A       )}"""       )
          torch.save(model.state_dict()   ,				__A       )
          print(F"""Save configuration file to {os.path.abspath(__A       )}"""       )
          with open(__A   ,				'''w'''   ,				encoding='''utf-8'''       ) as f:
               f.write(config.to_json_string()       )


if __name__ == "__main__":
    snake_case_ : List[str]    		=       argparse.ArgumentParser()
    parser.add_argument(
        "--pytorch_dump_folder_path",
        default=None,
        type=str,
        required=True,
        help="Path to the folder to store the PyTorch model or dataset/vocab.",
    )
    parser.add_argument(
        "--tf_checkpoint_path",
        default="",
        type=str,
        help="An optional path to a TensorFlow checkpoint path to be converted.",
    )
    parser.add_argument(
        "--transfo_xl_config_file",
        default="",
        type=str,
        help=(
            "An optional config json file corresponding to the pre-trained BERT model. \n"
            "This specifies the model architecture."
        ),
    )
    parser.add_argument(
        "--transfo_xl_dataset_file",
        default="",
        type=str,
        help="An optional dataset file to be converted in a vocabulary.",
    )
    snake_case_ : int    		=       parser.parse_args()
    convert_transfo_xl_checkpoint_to_pytorch(
        args.tf_checkpoint_path,
        args.transfo_xl_config_file,
        args.pytorch_dump_folder_path,
        args.transfo_xl_dataset_file,
    )






