
"""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  )




"""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)

