'''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,
)


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

a=  '''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)["depth"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline("depth-estimation")

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")


        >>> img = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/cat.png"
        ... ).resize((768, 768))

        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")

        >>> prompt = "A robot, 4k photo"
        >>> 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"

        >>> generator = torch.Generator(device="cuda").manual_seed(43)

        >>> image_emb, zero_image_emb = pipe_prior(
        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
        ... ).to_tuple()

        >>> images = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save("robot_cat.png")
        ```
'''







def  _UpperCamelCase (     _a	:	Optional[Any]				,     _a	:	List[Any]				,     _a	:	List[str]=8			):




    """simple docstring"""



    __UpperCamelCase	:	Any		    =  height // scale_factor**2
    if height % scale_factor**2 != 0:
        new_height += 1
    __UpperCamelCase	:	List[Any]		    =  width // scale_factor**2
    if width % scale_factor**2 != 0:
        new_width += 1
    return new_height * scale_factor, new_width * scale_factor






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






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

         self.register_modules(
             unet=_lowerCamelCase     ,			scheduler=_lowerCamelCase     ,			movq=_lowerCamelCase     ,			)
         __UpperCamelCase	:	Tuple		    =  2 ** (len(self.movq.config.block_out_channels    ) - 1)







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         if latents is None:
             __UpperCamelCase	:	Tuple		    =  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}"""    )
             __UpperCamelCase	:	List[str]		    =  latents.to(_lowerCamelCase    )

         __UpperCamelCase	:	Any		    =  latents * scheduler.init_noise_sigma
         return latents







     def lowerCAmelCase     (		self     ,			_lowerCamelCase=0    ):
         if is_accelerate_available():
             from accelerate import cpu_offload
         else:
             raise ImportError('Please install accelerate via `pip install accelerate`'    )

         __UpperCamelCase	:	Union[str, Any]		    =  torch.device(f"""cuda:{gpu_id}"""    )

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







     def lowerCAmelCase     (		self     ,			_lowerCamelCase=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.'    )

         __UpperCamelCase	:	Optional[int]		    =  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)

         __UpperCamelCase	:	int		    =  None
         for cpu_offloaded_model in [self.unet, self.movq]:
             __UpperCamelCase    ,					__UpperCamelCase	:	int		    =  cpu_offload_with_hook(_lowerCamelCase     ,			_lowerCamelCase     ,			prev_module_hook=_lowerCamelCase    )

         # We'll offload the last model manually.
         __UpperCamelCase	:	List[Any]		    =  hook







     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def lowerCAmelCase     (		self    ):
         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     ,			_lowerCamelCase = 5_1_2     ,			_lowerCamelCase = 5_1_2     ,			_lowerCamelCase = 1_0_0     ,			_lowerCamelCase = 4.0     ,			_lowerCamelCase = 1     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = "pil"     ,			_lowerCamelCase = True     ,			):
         __UpperCamelCase	:	Dict		    =  self._execution_device

         __UpperCamelCase	:	Optional[int]		    =  guidance_scale > 1.0

         if isinstance(_lowerCamelCase     ,			_lowerCamelCase    ):
             __UpperCamelCase	:	List[Any]		    =  torch.cat(_lowerCamelCase     ,			dim=0    )
         if isinstance(_lowerCamelCase     ,			_lowerCamelCase    ):
             __UpperCamelCase	:	Tuple		    =  torch.cat(_lowerCamelCase     ,			dim=0    )
         if isinstance(_lowerCamelCase     ,			_lowerCamelCase    ):
             __UpperCamelCase	:	Optional[int]		    =  torch.cat(_lowerCamelCase     ,			dim=0    )

         __UpperCamelCase	:	Dict		    =  image_embeds.shape[0] * num_images_per_prompt

         if do_classifier_free_guidance:
             __UpperCamelCase	:	Optional[Any]		    =  image_embeds.repeat_interleave(_lowerCamelCase     ,			dim=0    )
             __UpperCamelCase	:	Dict		    =  negative_image_embeds.repeat_interleave(_lowerCamelCase     ,			dim=0    )
             __UpperCamelCase	:	Optional[Any]		    =  hint.repeat_interleave(_lowerCamelCase     ,			dim=0    )

         __UpperCamelCase	:	Optional[Any]		    =  torch.cat([negative_image_embeds, image_embeds]     ,			dim=0    ).to(dtype=self.unet.dtype     ,			device=_lowerCamelCase    )
         __UpperCamelCase	:	Union[str, Any]		    =  torch.cat([hint, hint]     ,			dim=0    ).to(dtype=self.unet.dtype     ,			device=_lowerCamelCase    )
         self.scheduler.set_timesteps(_lowerCamelCase     ,			device=_lowerCamelCase    )
         __UpperCamelCase	:	Any		    =  self.scheduler.timesteps

         __UpperCamelCase	:	int		    =  self.movq.config.latent_channels

         __UpperCamelCase    ,					__UpperCamelCase	:	Any		    =  downscale_height_and_width(_lowerCamelCase     ,			_lowerCamelCase     ,			self.movq_scale_factor    )

         # create initial latent
         __UpperCamelCase	:	str		    =  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
             __UpperCamelCase	:	str		    =  torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents

             __UpperCamelCase	:	str		    =  {'image_embeds': image_embeds, 'hint': hint}
             __UpperCamelCase	:	Union[str, Any]		    =  self.unet(
                 sample=_lowerCamelCase     ,			timestep=_lowerCamelCase     ,			encoder_hidden_states=_lowerCamelCase     ,			added_cond_kwargs=_lowerCamelCase     ,			return_dict=_lowerCamelCase     ,			)[0]

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

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

         if output_type == "pil":
             __UpperCamelCase	:	Union[str, Any]		    =  self.numpy_to_pil(_lowerCamelCase    )

         if not return_dict:
             return (image,)

         return ImagePipelineOutput(images=_lowerCamelCase    )




'''simple docstring'''
import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()







def  _UpperCamelCase (     _a	:	int				,     _a	:	Tuple				,     _a	:	Dict=None			):




    """simple docstring"""



    assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
    __UpperCamelCase	:	List[Any]		    =  nn.Parameter(_a			)
    if bias is not None:
        assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
        __UpperCamelCase	:	Dict		    =  nn.Parameter(_a			)







def  _UpperCamelCase (     _a	:	Tuple				,     _a	:	str				,     _a	:	List[str]			):




    """simple docstring"""



    __UpperCamelCase	:	List[Any]		    =  np.asarray(weights[0]			)
    __UpperCamelCase	:	str		    =  np.asarray(weights[1]			)
    __UpperCamelCase	:	Optional[int]		    =  np.asarray(weights[2]			)

    set_param(
        torch_layer.self_attention.query_key				,     torch.tensor(_a			).transpose(1				,     2			).contiguous().view(-1				,     _a			)				,     )
    set_param(
        torch_layer.self_attention.value				,     torch.tensor(_a			).transpose(1				,     2			).contiguous().view(-1				,     _a			)				,     )
    set_param(
        torch_layer.output.dense				,     torch.tensor(_a			).view(-1				,     _a			).contiguous().transpose(0				,     1			)				,     )







def  _UpperCamelCase (     _a	:	Union[str, Any]				,     _a	:	Any				,     _a	:	List[Any]			):




    """simple docstring"""



    __UpperCamelCase	:	Optional[int]		    =  np.asarray(weights[0]			)
    __UpperCamelCase	:	Tuple		    =  np.asarray(weights[1]			)
    __UpperCamelCase	:	Tuple		    =  np.asarray(weights[2]			)
    __UpperCamelCase	:	Optional[int]		    =  np.asarray(weights[3]			)

    set_param(
        torch_layer.self_attention.query				,     torch.tensor(_a			).transpose(1				,     2			).contiguous().view(-1				,     _a			)				,     )
    set_param(
        torch_layer.self_attention.key				,     torch.tensor(_a			).transpose(1				,     2			).contiguous().view(-1				,     _a			)				,     )
    set_param(
        torch_layer.self_attention.value				,     torch.tensor(_a			).transpose(1				,     2			).contiguous().view(-1				,     _a			)				,     )
    set_param(
        torch_layer.output.dense				,     torch.tensor(_a			).view(-1				,     _a			).contiguous().transpose(0				,     1			)				,     )







def  _UpperCamelCase (     _a	:	Dict				,     _a	:	str				,     _a	:	List[Any]			):




    """simple docstring"""



    __UpperCamelCase	:	Optional[Any]		    =  weights[0][0][0]
    __UpperCamelCase	:	Tuple		    =  np.asarray(layer_norm_a[0]			)
    __UpperCamelCase	:	List[str]		    =  np.asarray(layer_norm_a[1]			)
    set_param(
        torch_block.attention.layer_norm				,     torch.tensor(_a			)				,     torch.tensor(_a			)				,     )

    # lsh weights + output
    __UpperCamelCase	:	int		    =  weights[0][1]
    if len(_a			) < 4:
        set_layer_weights_in_torch_lsh(_a				,     torch_block.attention				,     _a			)
    else:
        set_layer_weights_in_torch_local(_a				,     torch_block.attention				,     _a			)

    # intermediate weighs
    __UpperCamelCase	:	int		    =  weights[2][0][1][2]

    # Chunked Feed Forward
    if len(_a			) == 4:
        __UpperCamelCase	:	Optional[Any]		    =  intermediate_weights[2]

    # layernorm 2
    __UpperCamelCase	:	int		    =  np.asarray(intermediate_weights[0][0]			)
    __UpperCamelCase	:	Tuple		    =  np.asarray(intermediate_weights[0][1]			)
    set_param(
        torch_block.feed_forward.layer_norm				,     torch.tensor(_a			)				,     torch.tensor(_a			)				,     )

    # intermediate dense
    __UpperCamelCase	:	Optional[Any]		    =  np.asarray(intermediate_weights[1][0]			)
    __UpperCamelCase	:	int		    =  np.asarray(intermediate_weights[1][1]			)
    set_param(
        torch_block.feed_forward.dense.dense				,     torch.tensor(_a			).transpose(0				,     1			).contiguous()				,     torch.tensor(_a			)				,     )

    # intermediate out
    __UpperCamelCase	:	Dict		    =  np.asarray(intermediate_weights[4][0]			)
    __UpperCamelCase	:	List[str]		    =  np.asarray(intermediate_weights[4][1]			)
    set_param(
        torch_block.feed_forward.output.dense				,     torch.tensor(_a			).transpose(0				,     1			).contiguous()				,     torch.tensor(_a			)				,     )







def  _UpperCamelCase (     _a	:	int				,     _a	:	str				,     _a	:	Union[str, Any]			):




    """simple docstring"""



    __UpperCamelCase	:	Union[str, Any]		    =  torch_model.reformer

    # word embeds
    __UpperCamelCase	:	Tuple		    =  np.asarray(weights[1]			)
    set_param(
        torch_model_reformer.embeddings.word_embeddings				,     torch.tensor(_a			)				,     )

    if isinstance(weights[3]				,     _a			):
        __UpperCamelCase	:	Dict		    =  torch_model_reformer.embeddings.position_embeddings
        for emb_idx in range(len(position_embeddings.weights			)			):
            __UpperCamelCase	:	str		    =  np.asarray(weights[3][emb_idx][0]			)
            assert (
                position_embeddings.weights[emb_idx].shape == emb_weights.shape
            ), f"""{position_embeddings[emb_idx]} emb does not match"""
            __UpperCamelCase	:	Tuple		    =  nn.Parameter(torch.tensor(_a			)			)

    __UpperCamelCase	:	int		    =  weights[5]
    assert len(torch_model_reformer.encoder.layers			) * 4 == len(
        _a			), "HF and trax model do not have the same number of layers"
    for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers			):
        __UpperCamelCase	:	Any		    =  trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
        set_block_weights_in_torch(_a				,     _a				,     _a			)

    # output layer norm
    __UpperCamelCase	:	Optional[Any]		    =  np.asarray(weights[7][0]			)
    __UpperCamelCase	:	Any		    =  np.asarray(weights[7][1]			)
    set_param(
        torch_model_reformer.encoder.layer_norm				,     torch.tensor(_a			)				,     torch.tensor(_a			)				,     )

    # output embeddings
    __UpperCamelCase	:	List[Any]		    =  np.asarray(weights[9][0]			)
    __UpperCamelCase	:	Union[str, Any]		    =  np.asarray(weights[9][1]			)
    set_param(
        torch_model.lm_head.decoder				,     torch.tensor(_a			).transpose(0				,     1			).contiguous()				,     torch.tensor(_a			)				,     )







def  _UpperCamelCase (     _a	:	Optional[int]				,     _a	:	List[Any]				,     _a	:	Optional[Any]			):




    """simple docstring"""



    __UpperCamelCase	:	str		    =  ReformerConfig.from_json_file(_a			)
    print(f"""Building PyTorch model from configuration: {config}"""			)
    __UpperCamelCase	:	List[str]		    =  ReformerModelWithLMHead(_a			)

    with open(_a				,     'rb'			) as f:
        __UpperCamelCase	:	Tuple		    =  pickle.load(_a			)['weights']

    set_model_weights_in_torch(_a				,     _a				,     config.hidden_size			)

    # Save pytorch-model
    print(f"""Save PyTorch model to {pytorch_dump_path}"""			)
    torch.save(model.state_dict()				,     _a			)


if __name__ == "__main__":
  a=  argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--trax_model_pkl_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
  )
  parser.add_argument(
      '''--config_file''',
      default=None,
      type=str,
      required=True,
      help=(
          '''The config json file corresponding to the pre-trained Reformer model. \n'''
          '''This specifies the model architecture.'''
      ),
  )
  parser.add_argument(
      '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
  )
  a=  parser.parse_args()
  convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)



