code: 
  """simple docstring"""
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  import torch
  from PIL import Image
  
  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 KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
          >>> 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 = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
          ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
          ... )
          >>> pipe_prior = pipe_prior.to(\"cuda\")
  
          >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.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)
  
          >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
          >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)
  
          >>> images = pipe(
          ...     image=img,
          ...     strength=0.5,
          ...     image_embeds=img_emb.image_embeds,
          ...     negative_image_embeds=negative_emb.image_embeds,
          ...     hint=hint,
          ...     num_inference_steps=50,
          ...     generator=generator,
          ...     height=768,
          ...     width=768,
          ... ).images
  
          >>> images[0].save(\"robot_cat.png\")
          ```
  """
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase=8						)  ->   Union[str, Any]:
        UpperCAmelCase__ :       Optional[Any]						=						height // scale_factor**2
        if height % scale_factor**2 != 0:
              new_height += 1
        UpperCAmelCase__ :       Optional[int]						=						width // scale_factor**2
        if width % scale_factor**2 != 0:
              new_width += 1
        return new_height * scale_factor, new_width * scale_factor
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase=5_12 ,  lowerCAmelCase=5_12						)  ->   Optional[Any]:
        UpperCAmelCase__ :       Union[str, Any]						=						pil_image.resize((w, h) ,  resample=Image.BICUBIC ,  reducing_gap=1						)
        UpperCAmelCase__ :       List[Any]						=						np.array(pil_image.convert("""RGB"""						)						)
        UpperCAmelCase__ :       Optional[int]						=						arr.astype(np.floataa						) / 127.5 - 1
        UpperCAmelCase__ :       Optional[int]						=						np.transpose(lowerCAmelCase ,  [2, 0, 1]						)
        UpperCAmelCase__ :       List[str]						=						torch.from_numpy(lowerCAmelCase						).unsqueeze(0						)
        return image
  
  
  
  
  
  class 			lowerCamelCase  (					lowerCAmelCase__      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def __init__(self       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					):
  
  
          """simple docstring"""
  
          super().__init__()
  
          self.register_modules(
              unet=_lowerCamelCase       ,					scheduler=_lowerCamelCase       ,					movq=_lowerCamelCase       ,					)
          UpperCAmelCase__ :       List[str]						=						2 ** (len(self.movq.config.block_out_channels  ) - 1)
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Tuple						=						min(int(num_inference_steps * strength  )       ,					_lowerCamelCase  )
  
          UpperCAmelCase__ :       str						=						max(num_inference_steps - init_timestep       ,					0  )
          UpperCAmelCase__ :       str						=						self.scheduler.timesteps[t_start:]
  
          return timesteps, num_inference_steps - t_start
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase=None  ):
  
  
          """simple docstring"""
  
          if not isinstance(_lowerCamelCase       ,					(torch.Tensor, PIL.Image.Image, list)  ):
                raise ValueError(
                    F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_lowerCamelCase  )}"""  )
  
          UpperCAmelCase__ :       Any						=						image.to(device=_lowerCamelCase       ,					dtype=_lowerCamelCase  )
  
          UpperCAmelCase__ :       Any						=						batch_size * num_images_per_prompt
  
          if image.shape[1] == 4:
                UpperCAmelCase__ :       Any						=						image
  
          else:
                if isinstance(_lowerCamelCase       ,					_lowerCamelCase  ) and len(_lowerCamelCase  ) != batch_size:
                      raise ValueError(
                          F"""You have passed a list of generators of length {len(_lowerCamelCase  )}, but requested an effective batch"""
                          F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""  )
  
                elif isinstance(_lowerCamelCase       ,					_lowerCamelCase  ):
                      UpperCAmelCase__ :       int						=						[
                          self.movq.encode(image[i : i + 1]  ).latent_dist.sample(generator[i]  ) for i in range(_lowerCamelCase  )
                      ]
                      UpperCAmelCase__ :       List[str]						=						torch.cat(_lowerCamelCase       ,					dim=0  )
                else:
                      UpperCAmelCase__ :       Union[str, Any]						=						self.movq.encode(_lowerCamelCase  ).latent_dist.sample(_lowerCamelCase  )
  
                UpperCAmelCase__ :       Optional[int]						=						self.movq.config.scaling_factor * init_latents
  
          UpperCAmelCase__ :       List[str]						=						torch.cat([init_latents]       ,					dim=0  )
  
          UpperCAmelCase__ :       List[str]						=						init_latents.shape
          UpperCAmelCase__ :       Tuple						=						randn_tensor(_lowerCamelCase       ,					generator=_lowerCamelCase       ,					device=_lowerCamelCase       ,					dtype=_lowerCamelCase  )
  
          # get latents
          UpperCAmelCase__ :       List[str]						=						self.scheduler.add_noise(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  )
  
          UpperCAmelCase__ :       List[Any]						=						init_latents
  
          return latents
  
  
  
    def    _a			(self       ,					_lowerCamelCase=0  ):
  
  
          """simple docstring"""
  
          if is_accelerate_available():
                from accelerate import cpu_offload
          else:
                raise ImportError("""Please install accelerate via `pip install accelerate`"""  )
  
          UpperCAmelCase__ :       Optional[Any]						=						torch.device(F"""cuda:{gpu_id}"""  )
  
          UpperCAmelCase__ :       Union[str, Any]						=						[
              self.unet,
              self.movq,
          ]
          for cpu_offloaded_model in models:
                if cpu_offloaded_model is not None:
                      cpu_offload(_lowerCamelCase       ,					_lowerCamelCase  )
  
  
  
    def    _a			(self       ,					_lowerCamelCase=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__ :       str						=						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__ :       Union[str, Any]						=						None
          for cpu_offloaded_model in [self.unet, self.movq]:
                UpperCAmelCase__ :       Union[str, Any]						=						cpu_offload_with_hook(_lowerCamelCase       ,					_lowerCamelCase       ,					prev_module_hook=_lowerCamelCase  )
  
          # We'll offload the last model manually.
          UpperCAmelCase__ :       List[str]						=						hook
  
  
  
    @property
    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          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       ,					_lowerCamelCase = 512       ,					_lowerCamelCase = 512       ,					_lowerCamelCase = 100       ,					_lowerCamelCase = 4.0       ,					_lowerCamelCase = 0.3       ,					_lowerCamelCase = 1       ,					_lowerCamelCase = None       ,					_lowerCamelCase = "pil"       ,					_lowerCamelCase = True       ,					):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Any						=						self._execution_device
  
          UpperCAmelCase__ :       Dict						=						guidance_scale > 1.0
  
          if isinstance(_lowerCamelCase       ,					_lowerCamelCase  ):
                UpperCAmelCase__ :       Optional[int]						=						torch.cat(_lowerCamelCase       ,					dim=0  )
          if isinstance(_lowerCamelCase       ,					_lowerCamelCase  ):
                UpperCAmelCase__ :       Union[str, Any]						=						torch.cat(_lowerCamelCase       ,					dim=0  )
          if isinstance(_lowerCamelCase       ,					_lowerCamelCase  ):
                UpperCAmelCase__ :       Any						=						torch.cat(_lowerCamelCase       ,					dim=0  )
  
          UpperCAmelCase__ :       Tuple						=						image_embeds.shape[0]
  
          if do_classifier_free_guidance:
                UpperCAmelCase__ :       Union[str, Any]						=						image_embeds.repeat_interleave(_lowerCamelCase       ,					dim=0  )
                UpperCAmelCase__ :       int						=						negative_image_embeds.repeat_interleave(_lowerCamelCase       ,					dim=0  )
                UpperCAmelCase__ :       str						=						hint.repeat_interleave(_lowerCamelCase       ,					dim=0  )
  
          UpperCAmelCase__ :       Union[str, Any]						=						torch.cat([negative_image_embeds, image_embeds]       ,					dim=0  ).to(dtype=self.unet.dtype       ,					device=_lowerCamelCase  )
          UpperCAmelCase__ :       Optional[int]						=						torch.cat([hint, hint]       ,					dim=0  ).to(dtype=self.unet.dtype       ,					device=_lowerCamelCase  )
  
          if not isinstance(_lowerCamelCase       ,					_lowerCamelCase  ):
                UpperCAmelCase__ :       Optional[int]						=						[image]
          if not all(isinstance(_lowerCamelCase       ,					(PIL.Image.Image, torch.Tensor)  ) for i in image  ):
                raise ValueError(
                    F"""Input is in incorrect format: {[type(_lowerCamelCase  ) for i in image]}. Currently, we only support  PIL image and pytorch tensor"""  )
  
          UpperCAmelCase__ :       Any						=						torch.cat([prepare_image(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  ) for i in image]       ,					dim=0  )
          UpperCAmelCase__ :       Tuple						=						image.to(dtype=image_embeds.dtype       ,					device=_lowerCamelCase  )
  
          UpperCAmelCase__ :       Dict						=						self.movq.encode(_lowerCamelCase  )["""latents"""]
          UpperCAmelCase__ :       List[str]						=						latents.repeat_interleave(_lowerCamelCase       ,					dim=0  )
          self.scheduler.set_timesteps(_lowerCamelCase       ,					device=_lowerCamelCase  )
          UpperCAmelCase__ :       Dict						=						self.get_timesteps(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  )
          UpperCAmelCase__ :       Optional[Any]						=						timesteps[:1].repeat(batch_size * num_images_per_prompt  )
          UpperCAmelCase__ :       List[str]						=						downscale_height_and_width(_lowerCamelCase       ,					_lowerCamelCase       ,					self.movq_scale_factor  )
          UpperCAmelCase__ :       List[Any]						=						self.prepare_latents(
              _lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					image_embeds.dtype       ,					_lowerCamelCase       ,					_lowerCamelCase  )
          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__ :       List[Any]						=						{"""image_embeds""": image_embeds, """hint""": hint}
                UpperCAmelCase__ :       Optional[int]						=						self.unet(
                    sample=_lowerCamelCase       ,					timestep=_lowerCamelCase       ,					encoder_hidden_states=_lowerCamelCase       ,					added_cond_kwargs=_lowerCamelCase       ,					return_dict=_lowerCamelCase       ,					)[0]
  
                if do_classifier_free_guidance:
                      UpperCAmelCase__ :       List[str]						=						noise_pred.split(latents.shape[1]       ,					dim=1  )
                      UpperCAmelCase__ :       int						=						noise_pred.chunk(2  )
                      UpperCAmelCase__ :       Union[str, Any]						=						variance_pred.chunk(2  )
                      UpperCAmelCase__ :       Dict						=						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                      UpperCAmelCase__ :       int						=						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__ :       Optional[Any]						=						noise_pred.split(latents.shape[1]       ,					dim=1  )
  
                # compute the previous noisy sample x_t -> x_t-1
  
                UpperCAmelCase__ :       Dict						=						self.scheduler.step(
                    _lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=_lowerCamelCase       ,					)[0]
  
          # post-processing
          UpperCAmelCase__ :       Dict						=						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__ :       int						=						image * 0.5 + 0.5
                UpperCAmelCase__ :       List[str]						=						image.clamp(0       ,					1  )
                UpperCAmelCase__ :       Dict						=						image.cpu().permute(0       ,					2       ,					3       ,					1  ).float().numpy()
  
          if output_type == "pil":
                UpperCAmelCase__ :       int						=						self.numpy_to_pil(_lowerCamelCase  )
  
          if not return_dict:
                return (image,)
  
          return ImagePipelineOutput(images=_lowerCamelCase  )
  
  
style_context: 
  
  """simple docstring"""
  import argparse
  
  from tax import checkpoints
  
  from transformers import FlaxTaForConditionalGeneration, TaConfig
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)  ->   Tuple:
        UpperCAmelCase__ :       int						=						TaConfig.from_pretrained(lowerCAmelCase						)
        UpperCAmelCase__ :       Optional[int]						=						FlaxTaForConditionalGeneration(config=lowerCAmelCase						)
        UpperCAmelCase__ :       int						=						checkpoints.load_tax_checkpoint(lowerCAmelCase						)
  
        UpperCAmelCase__ :       Optional[Any]						=						"""wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]
  
        # Encoder
        for layer_index in range(config.num_layers						):
              UpperCAmelCase__ :       Union[str, Any]						=						F"""layers_{str(lowerCAmelCase						)}"""
  
              # Self-Attention
              UpperCAmelCase__ :       Dict						=						tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
              UpperCAmelCase__ :       Optional[Any]						=						tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
              UpperCAmelCase__ :       Dict						=						tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
              UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""value"""]["""kernel"""]
  
              # Layer Normalization
              UpperCAmelCase__ :       str						=						tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]
  
              if split_mlp_wi:
                    UpperCAmelCase__ :       int						=						tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
                    UpperCAmelCase__ :       Any						=						tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
              else:
                    UpperCAmelCase__ :       Any						=						tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]
  
              UpperCAmelCase__ :       Tuple						=						tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]
  
              # Layer Normalization
              UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]
  
              # Assigning
              UpperCAmelCase__ :       str						=						tax_attention_key
              UpperCAmelCase__ :       Union[str, Any]						=						tax_attention_out
              UpperCAmelCase__ :       Union[str, Any]						=						tax_attention_query
              UpperCAmelCase__ :       List[Any]						=						tax_attention_value
  
              UpperCAmelCase__ :       Any						=						tax_attention_layer_norm
  
              if split_mlp_wi:
                    UpperCAmelCase__ :       Any						=						tax_mlp_wi_a
                    UpperCAmelCase__ :       List[Any]						=						tax_mlp_wi_a
              else:
                    UpperCAmelCase__ :       Union[str, Any]						=						tax_mlp_wi
  
              UpperCAmelCase__ :       int						=						tax_mlp_wo
              UpperCAmelCase__ :       List[Any]						=						tax_mlp_layer_norm
  
        # Only for layer 0:
        UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
        UpperCAmelCase__ :       str						=						tax_encoder_rel_embedding
  
        # Assigning
        UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
        UpperCAmelCase__ :       Optional[Any]						=						tax_encoder_norm
  
        # Decoder
        for layer_index in range(config.num_decoder_layers						):
              UpperCAmelCase__ :       str						=						F"""layers_{str(lowerCAmelCase						)}"""
  
              # Self-Attention
              UpperCAmelCase__ :       List[Any]						=						tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""key"""]["""kernel"""]
              UpperCAmelCase__ :       str						=						tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""out"""]["""kernel"""]
              UpperCAmelCase__ :       Any						=						tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""query"""]["""kernel"""]
              UpperCAmelCase__ :       Union[str, Any]						=						tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""value"""]["""kernel"""]
  
              # Layer Normalization
              UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
                  """scale"""
              ]
  
              # Encoder-Decoder-Attention
              UpperCAmelCase__ :       str						=						tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""key"""][
                  """kernel"""
              ]
              UpperCAmelCase__ :       Union[str, Any]						=						tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""out"""][
                  """kernel"""
              ]
              UpperCAmelCase__ :       Optional[Any]						=						tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""query"""][
                  """kernel"""
              ]
              UpperCAmelCase__ :       Dict						=						tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""value"""][
                  """kernel"""
              ]
  
              # Layer Normalization
              UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]
  
              # MLP
              if split_mlp_wi:
                    UpperCAmelCase__ :       Union[str, Any]						=						tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
                    UpperCAmelCase__ :       int						=						tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
              else:
                    UpperCAmelCase__ :       Union[str, Any]						=						tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]
  
              UpperCAmelCase__ :       Optional[int]						=						tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]
  
              # Layer Normalization
              UpperCAmelCase__ :       Any						=						tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]
  
              # Assigning
              UpperCAmelCase__ :       Union[str, Any]						=						tax_attention_key
              UpperCAmelCase__ :       str						=						tax_attention_out
              UpperCAmelCase__ :       Tuple						=						tax_attention_query
              UpperCAmelCase__ :       int						=						tax_attention_value
  
              UpperCAmelCase__ :       List[Any]						=						tax_pre_attention_layer_norm
  
              UpperCAmelCase__ :       Union[str, Any]						=						tax_enc_dec_attention_key
              UpperCAmelCase__ :       Optional[int]						=						tax_enc_dec_attention_out
              UpperCAmelCase__ :       List[Any]						=						tax_enc_dec_attention_query
              UpperCAmelCase__ :       Any						=						tax_enc_dec_attention_value
  
              UpperCAmelCase__ :       List[Any]						=						tax_cross_layer_norm
  
              if split_mlp_wi:
                    UpperCAmelCase__ :       Tuple						=						tax_mlp_wi_a
                    UpperCAmelCase__ :       Dict						=						tax_mlp_wi_a
              else:
                    UpperCAmelCase__ :       str						=						tax_mlp_wi
  
              UpperCAmelCase__ :       Tuple						=						tax_mlp_wo
  
              UpperCAmelCase__ :       Any						=						txa_mlp_layer_norm
  
        # Decoder Normalization
        UpperCAmelCase__ :       int						=						tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
        UpperCAmelCase__ :       Union[str, Any]						=						txa_decoder_norm
  
        # Only for layer 0:
        UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""decoder"""]["""relpos_bias"""]["""rel_embedding"""].T
        UpperCAmelCase__ :       Optional[int]						=						tax_decoder_rel_embedding
  
        # Token Embeddings
        UpperCAmelCase__ :       Tuple						=						tax_model["""target"""]["""token_embedder"""]["""embedding"""]
        UpperCAmelCase__ :       int						=						txa_token_embeddings
  
        # LM Head (only in v1.1 checkpoints)
        if "logits_dense" in tax_model["target"]["decoder"]:
              UpperCAmelCase__ :       str						=						tax_model["""target"""]["""decoder"""]["""logits_dense"""]["""kernel"""]
  
        flax_model.save_pretrained(lowerCAmelCase						)
        print("""T5X Model was sucessfully converted!"""						)
  
  
  if __name__ == "__main__":
      _A						=     argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path the TX5 checkpoint."""
      )
      parser.add_argument("""--config_name""", default=None, type=str, required=True, help="""Config name of T5 model.""")
      parser.add_argument(
          """--flax_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output FLAX model."""
      )
      _A						=     parser.parse_args()
      convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)
  
  