

from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   def __init__(       self					:			Any     , __lowerCamelCase					:			TransformeraDModel     , __lowerCamelCase					:			AutoencoderKL     , __lowerCamelCase					:			KarrasDiffusionSchedulers     , __lowerCamelCase					:			Optional[Dict[int, str]] = None     , ):
       super().__init__()
       self.register_modules(transformer=__lowerCamelCase     , vae=__lowerCamelCase     , scheduler=__lowerCamelCase			)

       # create a imagenet -> id dictionary for easier use
       SCREAMING_SNAKE_CASE										=   {}
       if idalabel is not None:
           for key, value in idalabel.items():
               for label in value.split(","			):
                   SCREAMING_SNAKE_CASE										=   int(__lowerCamelCase			)
           SCREAMING_SNAKE_CASE										=   dict(sorted(self.labels.items()			)			)





   def      _snake_case		(       self					:			Tuple     , __lowerCamelCase					:			Union[str, List[str]]			):

       if not isinstance(__lowerCamelCase     , __lowerCamelCase			):
           SCREAMING_SNAKE_CASE										=   list(__lowerCamelCase			)

       for l in label:
           if l not in self.labels:
               raise ValueError(
                   f"{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."			)

       return [self.labels[l] for l in label]





   @torch.no_grad()
   def __call__(       self					:			str     , __lowerCamelCase					:			List[int]     , __lowerCamelCase					:			float = 4.0     , __lowerCamelCase					:			Optional[Union[torch.Generator, List[torch.Generator]]] = None     , __lowerCamelCase					:			int = 50     , __lowerCamelCase					:			Optional[str] = "pil"     , __lowerCamelCase					:			bool = True     , ):
       SCREAMING_SNAKE_CASE										=   len(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   self.transformer.config.sample_size
       SCREAMING_SNAKE_CASE										=   self.transformer.config.in_channels

       SCREAMING_SNAKE_CASE										=   randn_tensor(
           shape=(batch_size, latent_channels, latent_size, latent_size)     , generator=__lowerCamelCase     , device=self.device     , dtype=self.transformer.dtype     , )
       SCREAMING_SNAKE_CASE										=   torch.cat([latents] * 2			) if guidance_scale > 1 else latents

       SCREAMING_SNAKE_CASE										=   torch.tensor(__lowerCamelCase     , device=self.device			).reshape(-1			)
       SCREAMING_SNAKE_CASE										=   torch.tensor([1000] * batch_size     , device=self.device			)
       SCREAMING_SNAKE_CASE										=   torch.cat([class_labels, class_null]     , 0			) if guidance_scale > 1 else class_labels

       # set step values
       self.scheduler.set_timesteps(__lowerCamelCase			)

       for t in self.progress_bar(self.scheduler.timesteps			):
           if guidance_scale > 1:
               SCREAMING_SNAKE_CASE										=   latent_model_input[: len(__lowerCamelCase			) // 2]
               SCREAMING_SNAKE_CASE										=   torch.cat([half, half]     , dim=0			)
           SCREAMING_SNAKE_CASE										=   self.scheduler.scale_model_input(__lowerCamelCase     , __lowerCamelCase			)

           SCREAMING_SNAKE_CASE										=   t
           if not torch.is_tensor(__lowerCamelCase			):
               # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
               # This would be a good case for the `match` statement (Python 3.10+)
               SCREAMING_SNAKE_CASE										=   latent_model_input.device.type == "mps"
               if isinstance(__lowerCamelCase     , __lowerCamelCase			):
                   SCREAMING_SNAKE_CASE										=   torch.floataa if is_mps else torch.floataa
               else:
                   SCREAMING_SNAKE_CASE										=   torch.intaa if is_mps else torch.intaa
               SCREAMING_SNAKE_CASE										=   torch.tensor([timesteps]     , dtype=__lowerCamelCase     , device=latent_model_input.device			)
           elif len(timesteps.shape			) == 0:
               SCREAMING_SNAKE_CASE										=   timesteps[None].to(latent_model_input.device			)
           # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
           SCREAMING_SNAKE_CASE										=   timesteps.expand(latent_model_input.shape[0]			)
           # predict noise model_output
           SCREAMING_SNAKE_CASE										=   self.transformer(
               __lowerCamelCase     , timestep=__lowerCamelCase     , class_labels=__lowerCamelCase			).sample

           # perform guidance
           if guidance_scale > 1:
               SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
               SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   torch.split(__lowerCamelCase     , len(__lowerCamelCase			) // 2     , dim=0			)

               SCREAMING_SNAKE_CASE										=   uncond_eps + guidance_scale * (cond_eps - uncond_eps)
               SCREAMING_SNAKE_CASE										=   torch.cat([half_eps, half_eps]     , dim=0			)

               SCREAMING_SNAKE_CASE										=   torch.cat([eps, rest]     , dim=1			)

           # learned sigma
           if self.transformer.config.out_channels // 2 == latent_channels:
               SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   torch.split(__lowerCamelCase     , __lowerCamelCase     , dim=1			)
           else:
               SCREAMING_SNAKE_CASE										=   noise_pred

           # compute previous image: x_t -> x_t-1
           SCREAMING_SNAKE_CASE										=   self.scheduler.step(__lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase			).prev_sample

       if guidance_scale > 1:
           SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   latent_model_input.chunk(2     , dim=0			)
       else:
           SCREAMING_SNAKE_CASE										=   latent_model_input

       SCREAMING_SNAKE_CASE										=   1 / self.vae.config.scaling_factor * latents
       SCREAMING_SNAKE_CASE										=   self.vae.decode(__lowerCamelCase			).sample

       SCREAMING_SNAKE_CASE										=   (samples / 2 + 0.5).clamp(0     , 1			)

       # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
       SCREAMING_SNAKE_CASE										=   samples.cpu().permute(0     , 2     , 3     , 1			).float().numpy()

       if output_type == "pil":
           SCREAMING_SNAKE_CASE										=   self.numpy_to_pil(__lowerCamelCase			)

       if not return_dict:
           return (samples,)

       return ImagePipelineOutput(images=__lowerCamelCase			)


'''simple docstring'''







import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__="attention"					)      ->      Dict:
       A_						       =   params[F'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
       A_						       =   params[F'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
       A_						       =   params[F'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
       A_						       =   params[F'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
       return k, o, q, v
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__=False					)      ->      Optional[Any]:
       if split_mlp_wi:
              A_						       =   params[F'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
              A_						       =   params[F'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
              A_						       =   (wi_a, wi_a)
       else:
              A_						       =   params[F'''{prefix}/layers_{i}/mlp/wi/kernel''']

       A_						       =   params[F'''{prefix}/layers_{i}/mlp/wo/kernel''']
       return wi, wo
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)      ->      Any:
       return params[F'''{prefix}/layers_{i}/{layer_name}/scale''']
def    UpperCAmelCase__     (   UpperCAmelCase__,							*, UpperCAmelCase__,							UpperCAmelCase__					)      ->      Union[str, Any]:
       A_						       =   traverse_util.flatten_dict(variables["""target"""]					)
       A_						       =   {"""/""".join(UpperCAmelCase__					): 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/layers_0/mlp/wi_0/kernel""" in old
       print("""Split MLP:""",							UpperCAmelCase__					)

       A_						       =   collections.OrderedDict()

       # Shared embeddings.
       A_						       =   old["""token_embedder/embedding"""]

       # Encoder.
       for i in range(UpperCAmelCase__					):
              # Block i, layer 0 (Self Attention).
              A_						       =   tax_layer_norm_lookup(UpperCAmelCase__,							UpperCAmelCase__,							"""encoder""",							"""pre_attention_layer_norm"""					)
              A_       ,      A_       ,      A_       ,      A_						       =   tax_attention_lookup(UpperCAmelCase__,							UpperCAmelCase__,							"""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(UpperCAmelCase__,							UpperCAmelCase__,							"""encoder""",							"""pre_mlp_layer_norm"""					)
              A_       ,      A_						       =   tax_mlp_lookup(UpperCAmelCase__,							UpperCAmelCase__,							"""encoder""",							UpperCAmelCase__					)
              A_						       =   layer_norm
              if split_mlp_wi:
                     A_						       =   wi[0].T
                     A_						       =   wi[1].T
              else:
                     A_						       =   wi.T
              A_						       =   wo.T

       A_						       =   old[
           """encoder/relpos_bias/rel_embedding"""
       ].T
       A_						       =   old["""encoder/encoder_norm/scale"""]

       if not is_encoder_only:
              # Decoder.
              for i in range(UpperCAmelCase__					):
                     # Block i, layer 0 (Self Attention).
                     A_						       =   tax_layer_norm_lookup(UpperCAmelCase__,							UpperCAmelCase__,							"""decoder""",							"""pre_self_attention_layer_norm"""					)
                     A_       ,      A_       ,      A_       ,      A_						       =   tax_attention_lookup(UpperCAmelCase__,							UpperCAmelCase__,							"""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(UpperCAmelCase__,							UpperCAmelCase__,							"""decoder""",							"""pre_cross_attention_layer_norm"""					)
                     A_       ,      A_       ,      A_       ,      A_						       =   tax_attention_lookup(UpperCAmelCase__,							UpperCAmelCase__,							"""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(UpperCAmelCase__,							UpperCAmelCase__,							"""decoder""",							"""pre_mlp_layer_norm"""					)
                     A_       ,      A_						       =   tax_mlp_lookup(UpperCAmelCase__,							UpperCAmelCase__,							"""decoder""",							UpperCAmelCase__					)
                     A_						       =   layer_norm
                     if split_mlp_wi:
                            A_						       =   wi[0].T
                            A_						       =   wi[1].T
                     else:
                            A_						       =   wi.T
                     A_						       =   wo.T

              A_						       =   old["""decoder/decoder_norm/scale"""]
              A_						       =   old[
                  """decoder/relpos_bias/rel_embedding"""
              ].T

              # 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    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__					)      ->      List[Any]:
       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    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)      ->      Union[str, Any]:
       A_						       =   checkpoints.load_tax_checkpoint(UpperCAmelCase__					)
       A_						       =   convert_tax_to_pytorch(UpperCAmelCase__,							num_layers=config.num_layers,							is_encoder_only=UpperCAmelCase__					)
       A_						       =   make_state_dict(UpperCAmelCase__,							UpperCAmelCase__					)
       model.load_state_dict(UpperCAmelCase__,							strict=UpperCAmelCase__					)
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__ = False					)      ->      List[Any]:
       A_						       =   TaConfig.from_json_file(UpperCAmelCase__					)
       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_						       =   TaEncoderModel(UpperCAmelCase__					)
       else:
              A_						       =   TaForConditionalGeneration(UpperCAmelCase__					)

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

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

       # Verify that we can load the checkpoint.
       model.from_pretrained(UpperCAmelCase__					)
       print("""Done"""					)


if __name__ == "__main__":
      __lowerCamelCase									=     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
      )
      __lowerCamelCase									=     parser.parse_args()
      convert_tax_checkpoint_to_pytorch(
          args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
      )
