




"""simple docstring"""

import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()






def 						_snake_case					( _snake_case							: Optional[Any]    ,  _snake_case							: int    ,  _snake_case							: int							)   -> Union[str, Any]:






      '''simple docstring'''


      return params[F'''{prefix}/{prefix}/relpos_bias/rel_embedding'''][:, i, :]






def 						_snake_case					( _snake_case							: int    ,  _snake_case							: int    ,  _snake_case							: int    ,  _snake_case							: Optional[int]="attention"							)   -> List[Any]:






      '''simple docstring'''


      _A        =       _A        =       np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/key/kernel'''][:, i, :, :]							)
      _A        =       k_tmp.reshape(k_tmp.shape[0]    ,  k_tmp.shape[1] * k_tmp.shape[2]							)
      _A        =       np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/out/kernel'''][:, i, :, :]							)
      _A        =       o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]    ,  o_tmp.shape[2]							)
      _A        =       np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/query/kernel'''][:, i, :, :]							)
      _A        =       q_tmp.reshape(q_tmp.shape[0]    ,  q_tmp.shape[1] * q_tmp.shape[2]							)
      _A        =       np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/value/kernel'''][:, i, :, :]							)
      _A        =       v_tmp.reshape(v_tmp.shape[0]    ,  v_tmp.shape[1] * v_tmp.shape[2]							)
      return k, o, q, v






def 						_snake_case					( _snake_case							: List[Any]    ,  _snake_case							: Any    ,  _snake_case							: Union[str, Any]    ,  _snake_case							: Any=False							)   -> List[Any]:






      '''simple docstring'''


      if split_mlp_wi:
            _A        =       params[F'''{prefix}/{prefix}/mlp/wi_0/kernel'''][:, i, :]
            _A        =       params[F'''{prefix}/{prefix}/mlp/wi_1/kernel'''][:, i, :]
            _A        =       (wi_a, wi_a)
      else:
            _A        =       params[F'''{prefix}/{prefix}/mlp/wi/kernel'''][:, i, :]

      _A        =       params[F'''{prefix}/{prefix}/mlp/wo/kernel'''][:, i, :]
      return wi, wo






def 						_snake_case					( _snake_case							: int    ,  _snake_case							: Optional[Any]    ,  _snake_case							: Union[str, Any]    ,  _snake_case							: Dict							)   -> Union[str, Any]:






      '''simple docstring'''


      return params[F'''{prefix}/{prefix}/{layer_name}/scale'''][:, i]






def 						_snake_case					( _snake_case							: dict    ,  *, _snake_case							: int    ,  _snake_case							: bool    ,  _snake_case							: bool = False							)   -> Tuple:






      '''simple docstring'''


      _A        =       traverse_util.flatten_dict(variables['target']							)
      _A        =       {'/'.join(SCREAMING_SNAKE_CASE_							): v for k, v in old.items()}

      # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
      _A        =       'encoder/encoder/mlp/wi_0/kernel' in old
      print('Split MLP:'    ,  SCREAMING_SNAKE_CASE_							)

      _A        =       collections.OrderedDict()

      # Shared embeddings.
      _A        =       old['token_embedder/embedding']

      # Encoder.
      for i in range(SCREAMING_SNAKE_CASE_							):
            # Block i, layer 0 (Self Attention).
            _A        =       tax_layer_norm_lookup(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  'encoder'    ,  'pre_attention_layer_norm'							)
            _A       ,   _A       ,   _A       ,   _A        =       tax_attention_lookup(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  'encoder'    ,  'attention'							)
            _A        =       layer_norm
            _A        =       k.T
            _A        =       o.T
            _A        =       q.T
            _A        =       v.T

            # Block i, layer 1 (MLP).
            _A        =       tax_layer_norm_lookup(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  'encoder'    ,  'pre_mlp_layer_norm'							)
            _A       ,   _A        =       tax_mlp_lookup(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  'encoder'    ,  SCREAMING_SNAKE_CASE_							)
            _A        =       layer_norm
            if split_mlp_wi:
                  _A        =       wi[0].T
                  _A        =       wi[1].T
            else:
                  _A        =       wi.T
            _A        =       wo.T
            if scalable_attention:
                  # convert the rel_embedding of each layer
                  _A        =       tax_relpos_bias_lookup(
                      SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  'encoder'							).T

      _A        =       old['encoder/encoder_norm/scale']

      if not scalable_attention:
            _A        =       tax_relpos_bias_lookup(
                SCREAMING_SNAKE_CASE_    ,  0    ,  'encoder'							).T
            _A        =       tax_relpos_bias_lookup(
                SCREAMING_SNAKE_CASE_    ,  0    ,  'decoder'							).T

      if not is_encoder_only:
            # Decoder.
            for i in range(SCREAMING_SNAKE_CASE_							):
                  # Block i, layer 0 (Self Attention).
                  _A        =       tax_layer_norm_lookup(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  'decoder'    ,  'pre_self_attention_layer_norm'							)
                  _A       ,   _A       ,   _A       ,   _A        =       tax_attention_lookup(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  'decoder'    ,  'self_attention'							)
                  _A        =       layer_norm
                  _A        =       k.T
                  _A        =       o.T
                  _A        =       q.T
                  _A        =       v.T

                  # Block i, layer 1 (Cross Attention).
                  _A        =       tax_layer_norm_lookup(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  'decoder'    ,  'pre_cross_attention_layer_norm'							)
                  _A       ,   _A       ,   _A       ,   _A        =       tax_attention_lookup(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  'decoder'    ,  'encoder_decoder_attention'							)
                  _A        =       layer_norm
                  _A        =       k.T
                  _A        =       o.T
                  _A        =       q.T
                  _A        =       v.T

                  # Block i, layer 2 (MLP).
                  _A        =       tax_layer_norm_lookup(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  'decoder'    ,  'pre_mlp_layer_norm'							)
                  _A       ,   _A        =       tax_mlp_lookup(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  'decoder'    ,  SCREAMING_SNAKE_CASE_							)
                  _A        =       layer_norm
                  if split_mlp_wi:
                        _A        =       wi[0].T
                        _A        =       wi[1].T
                  else:
                        _A        =       wi.T
                  _A        =       wo.T

                  if scalable_attention:
                        # convert the rel_embedding of each layer
                        _A        =       tax_relpos_bias_lookup(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  'decoder'							).T

            _A        =       old['decoder/decoder_norm/scale']

            # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
            if "decoder/logits_dense/kernel" in old:
                  _A        =       old['decoder/logits_dense/kernel'].T

      return new






def 						_snake_case					( _snake_case							: Union[str, Any]    ,  _snake_case							: bool							)   -> int:






      '''simple docstring'''


      _A        =       collections.OrderedDict([(k, torch.from_numpy(v.copy()							)) for (k, v) in converted_params.items()]							)

      # Add what is missing.
      if "encoder.embed_tokens.weight" not in state_dict:
            _A        =       state_dict['shared.weight']

      if not is_encoder_only:
            if "decoder.embed_tokens.weight" not in state_dict:
                  _A        =       state_dict['shared.weight']

            if "lm_head.weight" not in state_dict:  # For old 1.0 models.
                  print('Using shared word embeddings as lm_head.'							)
                  _A        =       state_dict['shared.weight']

      return state_dict






def 						_snake_case					( _snake_case							: Optional[Any]    ,  _snake_case							: List[str]    ,  _snake_case							: Optional[int]    ,  _snake_case							: List[Any]    ,  _snake_case							: Optional[Any]							)   -> int:






      '''simple docstring'''


      _A        =       checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE_							)
      _A        =       convert_tax_to_pytorch(
          SCREAMING_SNAKE_CASE_    ,  num_layers=config.num_layers    ,  is_encoder_only=SCREAMING_SNAKE_CASE_    ,  scalable_attention=SCREAMING_SNAKE_CASE_							)
      _A        =       make_state_dict(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_							)
      model.load_state_dict(SCREAMING_SNAKE_CASE_    ,  strict=SCREAMING_SNAKE_CASE_							)






def 						_snake_case					( _snake_case							: List[str]    ,  _snake_case							: Any    ,  _snake_case							: List[Any]    ,  _snake_case							: bool = False    ,  _snake_case							: bool = False    ,  )   -> Any:






      '''simple docstring'''


      _A        =       MTaConfig.from_json_file(SCREAMING_SNAKE_CASE_							)
      print(F'''Building PyTorch model from configuration: {config}'''							)
      # Non-v1.1 checkpoints could also use T5Model, but this works for all.
      # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
      if is_encoder_only:
            _A        =       UMTaEncoderModel(SCREAMING_SNAKE_CASE_							)
      else:
            _A        =       UMTaForConditionalGeneration(SCREAMING_SNAKE_CASE_							)

      # Load weights from tf checkpoint
      load_tax_weights_in_ta(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_							)

      # Save pytorch-model
      print(F'''Save PyTorch model to {pytorch_dump_path}'''							)
      model.save_pretrained(SCREAMING_SNAKE_CASE_							)

      # Verify that we can load the checkpoint.
      model.from_pretrained(SCREAMING_SNAKE_CASE_							)
      print('Done'							)


if __name__ == "__main__":
       a         =      argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
       # Required parameters
       parser.add_argument(
           '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
       )
       parser.add_argument(
           '''--config_file''',
           default=None,
           type=str,
           required=True,
           help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
       )
       parser.add_argument(
           '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
       )
       parser.add_argument(
           '''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
       )
       parser.add_argument(
           '''--scalable_attention''',
           action='''store_true''',
           help='''Whether the model uses scaled attention (umt5 model)''',
           default=False,
       )
       a         =      parser.parse_args()
       convert_tax_checkpoint_to_pytorch(
           args.tax_checkpoint_path,
           args.config_file,
           args.pytorch_dump_path,
           args.is_encoder_only,
           args.scalable_attention,
       )







"""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
        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")
        >>> pipe_prior.to("cuda")
        >>> prompt = "red cat, 4k photo"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> zero_image_emb = out.negative_image_embeds
        >>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")
        >>> pipe.to("cuda")
        >>> image = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=50,
        ... ).images
        >>> image[0].save("cat.png")
        ```
'''






def 						_snake_case					( _snake_case							: Dict    ,  _snake_case							: List[Any]    ,  _snake_case							: List[Any]=8							)   -> Optional[int]:






      '''simple docstring'''


      _A        =       height // scale_factor**2
      if height % scale_factor**2 != 0:
            new_height += 1
      _A        =       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	:	Dict   ,							_UpperCAmelCase	:	UNetaDConditionModel   ,							_UpperCAmelCase	:	DDPMScheduler   ,							_UpperCAmelCase	:	VQModel   ,							):
       super().__init__()

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

 def       lowerCAmelCase_							(			self	:	Any   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	Tuple   ,							_UpperCAmelCase	:	Optional[Any]   ,							_UpperCAmelCase	:	Dict   ,							_UpperCAmelCase	:	List[str]   ,							_UpperCAmelCase	:	int		):
       if latents is None:
             _A        =       randn_tensor(_UpperCAmelCase   ,							generator=_UpperCAmelCase   ,							device=_UpperCAmelCase   ,							dtype=_UpperCAmelCase		)
       else:
             if latents.shape != shape:
                   raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''		)
             _A        =       latents.to(_UpperCAmelCase		)

       _A        =       latents * scheduler.init_noise_sigma
       return latents

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

       _A        =       torch.device(F'''cuda:{gpu_id}'''		)

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

 def       lowerCAmelCase_							(			self	:	Union[str, Any]   ,							_UpperCAmelCase	:	Union[str, Any]=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.'		)

       _A        =       torch.device(F'''cuda:{gpu_id}'''		)

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

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

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

 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def       lowerCAmelCase_							(			self	:	int		):
       if not hasattr(self.unet   ,							'_hf_hook'		):
             return self.device
       for module in self.unet.modules():
             if (
                 hasattr(_UpperCAmelCase   ,							'_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(_UpperCAmelCase		)
 def __call__(			self	:	Union[str, Any]   ,							_UpperCAmelCase	:	Union[torch.FloatTensor, List[torch.FloatTensor]]   ,							_UpperCAmelCase	:	Union[torch.FloatTensor, List[torch.FloatTensor]]   ,							_UpperCAmelCase	:	int = 512   ,							_UpperCAmelCase	:	int = 512   ,							_UpperCAmelCase	:	int = 100   ,							_UpperCAmelCase	:	float = 4.0   ,							_UpperCAmelCase	:	int = 1   ,							_UpperCAmelCase	:	Optional[Union[torch.Generator, List[torch.Generator]]] = None   ,							_UpperCAmelCase	:	Optional[torch.FloatTensor] = None   ,							_UpperCAmelCase	:	Optional[str] = "pil"   ,							_UpperCAmelCase	:	bool = True   ,							):
       _A        =       self._execution_device

       _A        =       guidance_scale > 1.0

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

       if do_classifier_free_guidance:
             _A        =       image_embeds.repeat_interleave(_UpperCAmelCase   ,							dim=0		)
             _A        =       negative_image_embeds.repeat_interleave(_UpperCAmelCase   ,							dim=0		)

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

       self.scheduler.set_timesteps(_UpperCAmelCase   ,							device=_UpperCAmelCase		)
       _A        =       self.scheduler.timesteps

       _A        =       self.unet.config.in_channels

       _A       ,   _A        =       downscale_height_and_width(_UpperCAmelCase   ,							_UpperCAmelCase   ,							self.movq_scale_factor		)

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

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

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

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

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

       if output_type == "pil":
             _A        =       self.numpy_to_pil(_UpperCAmelCase		)

       if not return_dict:
             return (image,)

       return ImagePipelineOutput(images=_UpperCAmelCase		)

