

import inspect
from typing import Optional, Union

import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
    PIL_INTERPOLATION,
    randn_tensor,
)





def      A__					(			__A  :						int     ,				__A  :						str     ,				__A  :						List[str]			)					->List[Any]:
      if isinstance(__A     ,				torch.Tensor			):
            return image
      elif isinstance(__A     ,				PIL.Image.Image			):
            __A    =[image]

      if isinstance(image[0]     ,				PIL.Image.Image			):
            __A    =[np.array(i.resize((w, h)     ,				resample=PIL_INTERPOLATION['''lanczos''']			)			)[None, :] for i in image]
            __A    =np.concatenate(__A     ,				axis=0			)
            __A    =np.array(__A			).astype(np.floataa			) / 255.0
            __A    =image.transpose(0     ,				3     ,				1     ,				2			)
            __A    =2.0 * image - 1.0
            __A    =torch.from_numpy(__A			)
      elif isinstance(image[0]     ,				torch.Tensor			):
            __A    =torch.cat(__A     ,				dim=0			)
      return image





def      A__					(			__A  :						Tuple     ,				__A  :						int     ,				__A  :						Optional[Any]     ,				__A  :						Any=0.9995			)					->List[Any]:
      if not isinstance(__A     ,				np.ndarray			):
            __A    =True
            __A    =va.device
            __A    =va.cpu().numpy()
            __A    =va.cpu().numpy()

      __A    =np.sum(va * va / (np.linalg.norm(__A			) * np.linalg.norm(__A			))			)
      if np.abs(__A			) > DOT_THRESHOLD:
            __A    =(1 - t) * va + t * va
      else:
            __A    =np.arccos(__A			)
            __A    =np.sin(__A			)
            __A    =theta_a * t
            __A    =np.sin(__A			)
            __A    =np.sin(theta_a - theta_t			) / sin_theta_a
            __A    =sin_theta_t / sin_theta_a
            __A    =sa * va + sa * va

      if inputs_are_torch:
            __A    =torch.from_numpy(__A			).to(__A			)

      return va





def      A__					(			__A  :						List[Any]     ,				__A  :						Tuple			)					->Tuple:
      __A    =F.normalize(__A     ,				dim=-1			)
      __A    =F.normalize(__A     ,				dim=-1			)
      return (x - y).norm(dim=-1			).div(2			).arcsin().pow(2			).mul(2			)





def      A__					(			__A  :						Any     ,				__A  :						Optional[int]			)					->int:
      for param in model.parameters():
            __A    =value





class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       def __init__(							self ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__=None ,   lowercase__=None ,   lowercase__=None ,   ):







             '''simple docstring'''



             super().__init__()
             self.register_modules(
                 vae=lowercase__ ,   text_encoder=lowercase__ ,   clip_model=lowercase__ ,   tokenizer=lowercase__ ,   unet=lowercase__ ,   scheduler=lowercase__ ,   feature_extractor=lowercase__ ,   coca_model=lowercase__ ,   coca_tokenizer=lowercase__ ,   coca_transform=lowercase__ ,   )
             __A    =(
                 feature_extractor.size
                 if isinstance(feature_extractor.size ,   lowercase__     )
                 else feature_extractor.size['''shortest_edge''']
             )
             __A    =transforms.Normalize(mean=feature_extractor.image_mean ,   std=feature_extractor.image_std     )
             set_requires_grad(self.text_encoder ,   lowercase__     )
             set_requires_grad(self.clip_model ,   lowercase__     )

       def     __UpperCamelCase     (							self ,   lowercase__ = "auto"     ):







             '''simple docstring'''



             if slice_size == "auto":
                   # half the attention head size is usually a good trade-off between
                   # speed and memory
                   __A    =self.unet.config.attention_head_dim // 2
             self.unet.set_attention_slice(lowercase__     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             self.enable_attention_slicing(lowercase__     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             set_requires_grad(self.vae ,   lowercase__     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             set_requires_grad(self.vae ,   lowercase__     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             set_requires_grad(self.unet ,   lowercase__     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             set_requires_grad(self.unet ,   lowercase__     )

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ ,   lowercase__     ):







             '''simple docstring'''



             __A    =min(int(num_inference_steps * strength     ) ,   lowercase__     )

             __A    =max(num_inference_steps - init_timestep ,   0     )
             __A    =self.scheduler.timesteps[t_start:]

             return timesteps, num_inference_steps - t_start

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__=None     ):







             '''simple docstring'''



             if not isinstance(lowercase__ ,   torch.Tensor     ):
                   raise ValueError(f'''`image` has to be of type `torch.Tensor` but is {type(lowercase__     )}'''     )

             __A    =image.to(device=lowercase__ ,   dtype=lowercase__     )

             if isinstance(lowercase__ ,   lowercase__     ):
                   __A    =[
                       self.vae.encode(image[i : i + 1]     ).latent_dist.sample(generator[i]     ) for i in range(lowercase__     )
                   ]
                   __A    =torch.cat(lowercase__ ,   dim=0     )
             else:
                   __A    =self.vae.encode(lowercase__     ).latent_dist.sample(lowercase__     )

             # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
             __A    =0.1_8215 * init_latents
             __A    =init_latents.repeat_interleave(lowercase__ ,   dim=0     )

             __A    =randn_tensor(init_latents.shape ,   generator=lowercase__ ,   device=lowercase__ ,   dtype=lowercase__     )

             # get latents
             __A    =self.scheduler.add_noise(lowercase__ ,   lowercase__ ,   lowercase__     )
             __A    =init_latents

             return latents

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             __A    =self.coca_transform(lowercase__     ).unsqueeze(0     )
             with torch.no_grad(), torch.cuda.amp.autocast():
                   __A    =self.coca_model.generate(transformed_image.to(device=self.device ,   dtype=self.coca_model.dtype     )     )
             __A    =self.coca_tokenizer.decode(generated[0].cpu().numpy()     )
             return generated.split('''<end_of_text>'''     )[0].replace('''<start_of_text>''' ,   ''''''     ).rstrip(''' .,'''     )

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__     ):







             '''simple docstring'''



             __A    =self.feature_extractor.preprocess(lowercase__     )
             __A    =torch.from_numpy(clip_image_input['''pixel_values'''][0]     ).unsqueeze(0     ).to(self.device     ).half()
             __A    =self.clip_model.get_image_features(lowercase__     )
             __A    =image_embeddings_clip / image_embeddings_clip.norm(p=2 ,   dim=-1 ,   keepdim=lowercase__     )
             __A    =image_embeddings_clip.repeat_interleave(lowercase__ ,   dim=0     )
             return image_embeddings_clip

       @torch.enable_grad()
       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   ):







             '''simple docstring'''



             __A    =latents.detach().requires_grad_()

             __A    =self.scheduler.scale_model_input(lowercase__ ,   lowercase__     )

             # predict the noise residual
             __A    =self.unet(lowercase__ ,   lowercase__ ,   encoder_hidden_states=lowercase__     ).sample

             if isinstance(self.scheduler ,   (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)     ):
                   __A    =self.scheduler.alphas_cumprod[timestep]
                   __A    =1 - alpha_prod_t
                   # compute predicted original sample from predicted noise also called
                   # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
                   __A    =(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

                   __A    =torch.sqrt(lowercase__     )
                   __A    =pred_original_sample * (fac) + latents * (1 - fac)
             elif isinstance(self.scheduler ,   lowercase__     ):
                   __A    =self.scheduler.sigmas[index]
                   __A    =latents - sigma * noise_pred
             else:
                   raise ValueError(f'''scheduler type {type(self.scheduler     )} not supported'''     )

             # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
             __A    =1 / 0.1_8215 * sample
             __A    =self.vae.decode(lowercase__     ).sample
             __A    =(image / 2 + 0.5).clamp(0 ,   1     )

             __A    =transforms.Resize(self.feature_extractor_size     )(lowercase__     )
             __A    =self.normalize(lowercase__     ).to(latents.dtype     )

             __A    =self.clip_model.get_image_features(lowercase__     )
             __A    =image_embeddings_clip / image_embeddings_clip.norm(p=2 ,   dim=-1 ,   keepdim=lowercase__     )

             __A    =spherical_dist_loss(lowercase__ ,   lowercase__     ).mean() * clip_guidance_scale

             __A    =-torch.autograd.grad(lowercase__ ,   lowercase__     )[0]

             if isinstance(self.scheduler ,   lowercase__     ):
                   __A    =latents.detach() + grads * (sigma**2)
                   __A    =noise_pred_original
             else:
                   __A    =noise_pred_original - torch.sqrt(lowercase__     ) * grads
             return noise_pred, latents


       @torch.no_grad()
       def __call__(							self ,   lowercase__ ,   lowercase__ ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = 5_1_2 ,   lowercase__ = 5_1_2 ,   lowercase__ = 0.6 ,   lowercase__ = 5_0 ,   lowercase__ = 7.5 ,   lowercase__ = 1 ,   lowercase__ = 0.0 ,   lowercase__ = 1_0_0 ,   lowercase__ = None ,   lowercase__ = "pil" ,   lowercase__ = True ,   lowercase__ = 0.8 ,   lowercase__ = 0.1 ,   lowercase__ = 0.1 ,   ):







             '''simple docstring'''



             if isinstance(lowercase__ ,   lowercase__     ) and len(lowercase__     ) != batch_size:
                   raise ValueError(f'''You have passed {batch_size} batch_size, but only {len(lowercase__     )} generators.'''     )

             if height % 8 != 0 or width % 8 != 0:
                   raise ValueError(f'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''     )

             if isinstance(lowercase__ ,   torch.Generator     ) and batch_size > 1:
                   __A    =[generator] + [None] * (batch_size - 1)

             __A    =[
                 ('''model''', self.coca_model is None),
                 ('''tokenizer''', self.coca_tokenizer is None),
                 ('''transform''', self.coca_transform is None),
             ]
             __A    =[x[0] for x in coca_is_none if x[1]]
             __A    =''', '''.join(lowercase__     )
             # generate prompts with coca model if prompt is None
             if content_prompt is None:
                   if len(lowercase__     ):
                         raise ValueError(
                             f'''Content prompt is None and CoCa [{coca_is_none_str}] is None.'''
                             f'''Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'''     )
                   __A    =self.get_image_description(lowercase__     )
             if style_prompt is None:
                   if len(lowercase__     ):
                         raise ValueError(
                             f'''Style prompt is None and CoCa [{coca_is_none_str}] is None.'''
                             f''' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'''     )
                   __A    =self.get_image_description(lowercase__     )

             # get prompt text embeddings for content and style
             __A    =self.tokenizer(
                 lowercase__ ,   padding='''max_length''' ,   max_length=self.tokenizer.model_max_length ,   truncation=lowercase__ ,   return_tensors='''pt''' ,   )
             __A    =self.text_encoder(content_text_input.input_ids.to(self.device     )     )[0]

             __A    =self.tokenizer(
                 lowercase__ ,   padding='''max_length''' ,   max_length=self.tokenizer.model_max_length ,   truncation=lowercase__ ,   return_tensors='''pt''' ,   )
             __A    =self.text_encoder(style_text_input.input_ids.to(self.device     )     )[0]

             __A    =slerp(lowercase__ ,   lowercase__ ,   lowercase__     )

             # duplicate text embeddings for each generation per prompt
             __A    =text_embeddings.repeat_interleave(lowercase__ ,   dim=0     )

             # set timesteps
             __A    ='''offset''' in set(inspect.signature(self.scheduler.set_timesteps     ).parameters.keys()     )
             __A    ={}
             if accepts_offset:
                   __A    =1

             self.scheduler.set_timesteps(lowercase__ ,   **lowercase__     )
             # Some schedulers like PNDM have timesteps as arrays
             # It's more optimized to move all timesteps to correct device beforehand
             self.scheduler.timesteps.to(self.device     )

             __A    ,		__A    =self.get_timesteps(lowercase__ ,   lowercase__ ,   self.device     )
             __A    =timesteps[:1].repeat(lowercase__     )

             # Preprocess image
             __A    =preprocess(lowercase__ ,   lowercase__ ,   lowercase__     )
             __A    =self.prepare_latents(
                 lowercase__ ,   lowercase__ ,   lowercase__ ,   text_embeddings.dtype ,   self.device ,   lowercase__     )

             __A    =preprocess(lowercase__ ,   lowercase__ ,   lowercase__     )
             __A    =self.prepare_latents(
                 lowercase__ ,   lowercase__ ,   lowercase__ ,   text_embeddings.dtype ,   self.device ,   lowercase__     )

             __A    =slerp(lowercase__ ,   lowercase__ ,   lowercase__     )

             if clip_guidance_scale > 0:
                   __A    =self.get_clip_image_embeddings(lowercase__ ,   lowercase__     )
                   __A    =self.get_clip_image_embeddings(lowercase__ ,   lowercase__     )
                   __A    =slerp(
                       lowercase__ ,   lowercase__ ,   lowercase__     )

             # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
             # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
             # corresponds to doing no classifier free guidance.
             __A    =guidance_scale > 1.0
             # get unconditional embeddings for classifier free guidance
             if do_classifier_free_guidance:
                   __A    =content_text_input.input_ids.shape[-1]
                   __A    =self.tokenizer([''''''] ,   padding='''max_length''' ,   max_length=lowercase__ ,   return_tensors='''pt'''     )
                   __A    =self.text_encoder(uncond_input.input_ids.to(self.device     )     )[0]
                   # duplicate unconditional embeddings for each generation per prompt
                   __A    =uncond_embeddings.repeat_interleave(lowercase__ ,   dim=0     )

                   # 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    =torch.cat([uncond_embeddings, text_embeddings]     )

             # get the initial random noise unless the user supplied it

             # Unlike in other pipelines, latents need to be generated in the target device
             # for 1-to-1 results reproducibility with the CompVis implementation.
             # However this currently doesn't work in `mps`.
             __A    =(batch_size, self.unet.config.in_channels, height // 8, width // 8)
             __A    =text_embeddings.dtype
             if latents is None:
                   if self.device.type == "mps":
                         # randn does not work reproducibly on mps
                         __A    =torch.randn(lowercase__ ,   generator=lowercase__ ,   device='''cpu''' ,   dtype=lowercase__     ).to(
                             self.device     )
                   else:
                         __A    =torch.randn(lowercase__ ,   generator=lowercase__ ,   device=self.device ,   dtype=lowercase__     )
             else:
                   if latents.shape != latents_shape:
                         raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''     )
                   __A    =latents.to(self.device     )

             # scale the initial noise by the standard deviation required by the scheduler
             __A    =latents * self.scheduler.init_noise_sigma

             # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
             # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
             # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
             # and should be between [0, 1]
             __A    ='''eta''' in set(inspect.signature(self.scheduler.step     ).parameters.keys()     )
             __A    ={}
             if accepts_eta:
                   __A    =eta

             # check if the scheduler accepts generator
             __A    ='''generator''' in set(inspect.signature(self.scheduler.step     ).parameters.keys()     )
             if accepts_generator:
                   __A    =generator

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

                         # predict the noise residual
                         __A    =self.unet(lowercase__ ,   lowercase__ ,   encoder_hidden_states=lowercase__     ).sample

                         # perform classifier free guidance
                         if do_classifier_free_guidance:
                               __A    ,		__A    =noise_pred.chunk(2     )
                               __A    =noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                         # perform clip guidance
                         if clip_guidance_scale > 0:
                               __A    =(
                                   text_embeddings.chunk(2     )[1] if do_classifier_free_guidance else text_embeddings
                               )
                               __A    ,		__A    =self.cond_fn(
                                   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   )

                         # compute the previous noisy sample x_t -> x_t-1
                         __A    =self.scheduler.step(lowercase__ ,   lowercase__ ,   lowercase__ ,   **lowercase__     ).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
             __A    =1 / 0.1_8215 * latents
             __A    =self.vae.decode(lowercase__     ).sample

             __A    =(image / 2 + 0.5).clamp(0 ,   1     )
             __A    =image.cpu().permute(0 ,   2 ,   3 ,   1     ).numpy()

             if output_type == "pil":
                   __A    =self.numpy_to_pil(lowercase__     )

             if not return_dict:
                   return (image, None)

             return StableDiffusionPipelineOutput(images=lowercase__ ,   nsfw_content_detected=lowercase__     )





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      A__					(			__A  :						Any     ,				__A  :						Dict     ,				__A  :						Optional[int]=None			)					->Tuple:
      # set parameter of one layer
      assert torch_layer.weight.shape == weight.shape, F'''{torch_layer} layer.weight does not match'''
      __A    =nn.Parameter(__A			)
      if bias is not None:
            assert torch_layer.bias.shape == bias.shape, F'''{torch_layer} layer.bias does not match'''
            __A    =nn.Parameter(__A			)





def      A__					(			__A  :						List[Any]     ,				__A  :						Tuple     ,				__A  :						List[Any]			)					->Dict:
      # set torch weights for 1-to-1 comparison
      __A    =np.asarray(weights[0]			)
      __A    =np.asarray(weights[1]			)
      __A    =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      A__					(			__A  :						Optional[Any]     ,				__A  :						Tuple     ,				__A  :						Optional[Any]			)					->int:
      # set torch weights for 1-to-1 comparison
      __A    =np.asarray(weights[0]			)
      __A    =np.asarray(weights[1]			)
      __A    =np.asarray(weights[2]			)
      __A    =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      A__					(			__A  :						int     ,				__A  :						List[str]     ,				__A  :						Optional[Any]			)					->Any:
      # layernorm 1
      __A    =weights[0][0][0]
      __A    =np.asarray(layer_norm_a[0]			)
      __A    =np.asarray(layer_norm_a[1]			)
      set_param(
          torch_block.attention.layer_norm     ,				torch.tensor(__A			)     ,				torch.tensor(__A			)     ,				)

      # lsh weights + output
      __A    =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
      __A    =weights[2][0][1][2]

      # Chunked Feed Forward
      if len(__A			) == 4:
            __A    =intermediate_weights[2]

      # layernorm 2
      __A    =np.asarray(intermediate_weights[0][0]			)
      __A    =np.asarray(intermediate_weights[0][1]			)
      set_param(
          torch_block.feed_forward.layer_norm     ,				torch.tensor(__A			)     ,				torch.tensor(__A			)     ,				)

      # intermediate dense
      __A    =np.asarray(intermediate_weights[1][0]			)
      __A    =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
      __A    =np.asarray(intermediate_weights[4][0]			)
      __A    =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      A__					(			__A  :						Tuple     ,				__A  :						List[Any]     ,				__A  :						Optional[int]			)					->List[Any]:
      # reformer model
      __A    =torch_model.reformer

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

      if isinstance(weights[3]     ,				__A			):
            __A    =torch_model_reformer.embeddings.position_embeddings
            for emb_idx in range(len(position_embeddings.weights			)			):
                  __A    =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'''
                  __A    =nn.Parameter(torch.tensor(__A			)			)

      __A    =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			):
            __A    =trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
            set_block_weights_in_torch(__A     ,				__A     ,				__A			)

      # output layer norm
      __A    =np.asarray(weights[7][0]			)
      __A    =np.asarray(weights[7][1]			)
      set_param(
          torch_model_reformer.encoder.layer_norm     ,				torch.tensor(__A			)     ,				torch.tensor(__A			)     ,				)

      # output embeddings
      __A    =np.asarray(weights[9][0]			)
      __A    =np.asarray(weights[9][1]			)
      set_param(
          torch_model.lm_head.decoder     ,				torch.tensor(__A			).transpose(0     ,				1			).contiguous()     ,				torch.tensor(__A			)     ,				)





def      A__					(			__A  :						int     ,				__A  :						Any     ,				__A  :						Tuple			)					->Union[str, Any]:
      # Initialise PyTorch model
      __A    =ReformerConfig.from_json_file(__A			)
      print(F'''Building PyTorch model from configuration: {config}'''			)
      __A    =ReformerModelWithLMHead(__A			)

      with open(__A     ,				'''rb'''			) as f:
            __A    =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__":
   _lowerCamelCase      :					Union[str, Any]					    =      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.'''
   )
   _lowerCamelCase      :					Tuple					    =      parser.parse_args()
   convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)


