





"""simple docstring"""




import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)

def   a_   (			lowerCAmelCase_				:      Tuple     ):
   __lowerCAmelCase							     =     checkpoints.load_tax_checkpoint(lowerCAmelCase_     )
   __lowerCAmelCase							     =     flatten_dict(lowerCAmelCase_     )
   return flax_params

def   a_   (			lowerCAmelCase_				:      Tuple     ):
   __lowerCAmelCase							     =     {}

   __lowerCAmelCase							     =     {
       'token_embedder': 'embeddings',
       'encoder_norm': 'layernorm',
       'kernel': 'weight',
       '.out': '.output',
       'scale': 'weight',
       'embedders_0.pos_embedding': 'row_embedder.weight',
       'embedders_1.pos_embedding': 'column_embedder.weight',
   }

   __lowerCAmelCase							     =     {
       'query': 'attention.query',
       'key': 'attention.key',
       'value': 'attention.value',
       'output.dense': 'output',
       'encoder_decoder_attention.o': 'encoder_decoder_attention.attention.o',
       'pre_self_attention_layer_norm': 'self_attention.layer_norm',
       'pre_cross_attention_layer_norm': 'encoder_decoder_attention.layer_norm',
       'mlp.': 'mlp.DenseReluDense.',
       'pre_mlp_layer_norm': 'mlp.layer_norm',
       'self_attention.o': 'self_attention.attention.o',
       'decoder.embeddings.embedding': 'decoder.embed_tokens.weight',
       'decoder.relpos_bias.rel_embedding': 'decoder.layer.0.self_attention.attention.relative_attention_bias.weight',
       'decoder.decoder_norm.weight': 'decoder.final_layer_norm.weight',
       'decoder.logits_dense.weight': 'decoder.lm_head.weight',
   }

   for key in flax_dict.keys():
      if "target" in key:
         # remove the first prefix from the key
         __lowerCAmelCase							     =     '.'.join(key[1:]     )

         # rename the key
         for old, new in CONVERSION_MAPPING.items():
            __lowerCAmelCase							     =     new_key.replace(lowerCAmelCase_,     lowerCAmelCase_     )

         if "decoder" in new_key:
            for old, new in DECODER_CONVERSION_MAPPING.items():
               __lowerCAmelCase							     =     new_key.replace(lowerCAmelCase_,     lowerCAmelCase_     )

         if "layers" in new_key and "decoder" not in new_key:
            # use regex to replace the layer number
            __lowerCAmelCase							     =     re.sub(R'layers_(\d+)',     R'layer.\1',     lowerCAmelCase_     )
            __lowerCAmelCase							     =     new_key.replace('encoder',     'encoder.encoder'     )

         elif "layers" in new_key and "decoder" in new_key:
            # use regex to replace the layer number
            __lowerCAmelCase							     =     re.sub(R'layers_(\d+)',     R'layer.\1',     lowerCAmelCase_     )

         __lowerCAmelCase							     =     flax_dict[key]

   __lowerCAmelCase							     =     {}
   # convert converted_dict into torch format
   for key in converted_dict.keys():
      if ("embed_tokens" not in key) and ("embedder" not in key):
         __lowerCAmelCase							     =     torch.from_numpy(converted_dict[key].T     )
      else:
         __lowerCAmelCase							     =     torch.from_numpy(converted_dict[key]     )

   return converted_torch_dict

def   a_   (			lowerCAmelCase_				:      Any,     lowerCAmelCase_				:      int,     lowerCAmelCase_				:      str=False,     lowerCAmelCase_				:      Any=False     ):
   __lowerCAmelCase							     =     get_flax_param(lowerCAmelCase_     )

   if not use_large:
      __lowerCAmelCase							     =     PixaStructVisionConfig()
      __lowerCAmelCase							     =     PixaStructTextConfig()
   else:
      __lowerCAmelCase							     =     PixaStructVisionConfig(
          hidden_size=1536,     d_ff=3968,     num_attention_heads=24,     num_hidden_layers=18     )
      __lowerCAmelCase							     =     PixaStructTextConfig(hidden_size=1536,     d_ff=3968,     num_heads=24,     num_layers=18     )
   __lowerCAmelCase							     =     PixaStructConfig(
       vision_config=encoder_config.to_dict(),     text_config=decoder_config.to_dict(),     is_vqa=lowerCAmelCase_     )

   __lowerCAmelCase							     =     PixaStructForConditionalGeneration(lowerCAmelCase_     )

   __lowerCAmelCase							     =     rename_and_convert_flax_params(lowerCAmelCase_     )
   model.load_state_dict(lowerCAmelCase_     )

   __lowerCAmelCase							     =     AutoTokenizer.from_pretrained('ybelkada/test-pix2struct-tokenizer'     )
   __lowerCAmelCase							     =     PixaStructImageProcessor()
   __lowerCAmelCase							     =     PixaStructProcessor(image_processor=lowerCAmelCase_,     tokenizer=lowerCAmelCase_     )

   if use_large:
      __lowerCAmelCase							     =     4096

   __lowerCAmelCase							     =     True

   # mkdir if needed
   os.makedirs(lowerCAmelCase_,     exist_ok=lowerCAmelCase_     )

   model.save_pretrained(lowerCAmelCase_     )
   processor.save_pretrained(lowerCAmelCase_     )

   print('Model saved in {}'.format(lowerCAmelCase_     )     )


if __name__ == "__main__":
     _snake_case  :   List[Any]						   =  argparse.ArgumentParser()
     parser.add_argument('--t5x_checkpoint_path', default=None, type=str, help='Path to the original T5x checkpoint.')
     parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
     parser.add_argument('--use_large', action='store_true', help='Use large model.')
     parser.add_argument('--is_vqa', action='store_true', help='Use large model.')
     _snake_case  :   Any						   =  parser.parse_args()

     convert_pixastruct_original_pytorch_checkpoint_to_hf(
         args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
     )








import gc
import unittest

import numpy as np
import torch

from diffusers import (
    AudioDiffusionPipeline,
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    DiffusionPipeline,
    Mel,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()



class     _UpperCAmelCase   (   unittest.TestCase   ):

  """simple docstring"""


  def     lowercase		(    self			:		List[str]					)							->						int:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()






  @property
  def     lowercase		(    self			:		Union[str, Any]					)							->						Any:
     torch.manual_seed(0					)
     __lowerCAmelCase							     =     UNetaDModel(
         sample_size=(3_2, 6_4) ,  in_channels=1 ,  out_channels=1 ,  layers_per_block=2 ,  block_out_channels=(1_2_8, 1_2_8) ,  down_block_types=('AttnDownBlock2D', 'DownBlock2D') ,  up_block_types=('UpBlock2D', 'AttnUpBlock2D') ,  )
     return model






  @property
  def     lowercase		(    self			:		Optional[Any]					)							->						List[Any]:
     torch.manual_seed(0					)
     __lowerCAmelCase							     =     UNetaDConditionModel(
         sample_size=(6_4, 3_2) ,  in_channels=1 ,  out_channels=1 ,  layers_per_block=2 ,  block_out_channels=(1_2_8, 1_2_8) ,  down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D') ,  up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D') ,  cross_attention_dim=1_0 ,  )
     return model






  @property
  def     lowercase		(    self			:		Dict					)							->						Optional[Any]:
     torch.manual_seed(0					)
     __lowerCAmelCase							     =     AutoencoderKL(
         sample_size=(1_2_8, 6_4) ,  in_channels=1 ,  out_channels=1 ,  latent_channels=1 ,  layers_per_block=2 ,  block_out_channels=(1_2_8, 1_2_8) ,  down_block_types=('DownEncoderBlock2D', 'DownEncoderBlock2D') ,  up_block_types=('UpDecoderBlock2D', 'UpDecoderBlock2D') ,  )
     __lowerCAmelCase							     =     UNetaDModel(
         sample_size=(6_4, 3_2) ,  in_channels=1 ,  out_channels=1 ,  layers_per_block=2 ,  block_out_channels=(1_2_8, 1_2_8) ,  down_block_types=('AttnDownBlock2D', 'DownBlock2D') ,  up_block_types=('UpBlock2D', 'AttnUpBlock2D') ,  )
     return vqvae, unet






  @slow
  def     lowercase		(    self			:		Dict					)							->						Optional[Any]:
     __lowerCAmelCase							     =     'cpu'  # ensure determinism for the device-dependent torch.Generator
     __lowerCAmelCase							     =     Mel(
         x_res=self.dummy_unet.config.sample_size[1] ,  y_res=self.dummy_unet.config.sample_size[0] ,  )

     __lowerCAmelCase							     =     DDPMScheduler()
     __lowerCAmelCase							     =     AudioDiffusionPipeline(vqvae=lowerCAmelCase_ ,  unet=self.dummy_unet ,  mel=lowerCAmelCase_ ,  scheduler=lowerCAmelCase_					)
     __lowerCAmelCase							     =     pipe.to(lowerCAmelCase_					)
     pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

     __lowerCAmelCase							     =     torch.Generator(device=lowerCAmelCase_					).manual_seed(4_2					)
     __lowerCAmelCase							     =     pipe(generator=lowerCAmelCase_ ,  steps=4					)
     __lowerCAmelCase							     =     output.audios[0]
     __lowerCAmelCase							     =     output.images[0]

     __lowerCAmelCase							     =     torch.Generator(device=lowerCAmelCase_					).manual_seed(4_2					)
     __lowerCAmelCase							     =     pipe(generator=lowerCAmelCase_ ,  steps=4 ,  return_dict=lowerCAmelCase_					)
     __lowerCAmelCase							     =     output[0][0]

     assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
     assert (
         image.height == self.dummy_unet.config.sample_size[0]
         and image.width == self.dummy_unet.config.sample_size[1]
     )
     __lowerCAmelCase							     =     np.frombuffer(image.tobytes() ,  dtype='uint8'					)[:1_0]
     __lowerCAmelCase							     =     np.frombuffer(image_from_tuple.tobytes() ,  dtype='uint8'					)[:1_0]
     __lowerCAmelCase							     =     np.array([6_9, 2_5_5, 2_5_5, 2_5_5, 0, 0, 7_7, 1_8_1, 1_2, 1_2_7]					)

     assert np.abs(image_slice.flatten() - expected_slice					).max() == 0
     assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() == 0

     __lowerCAmelCase							     =     Mel(
         x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1] ,  y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0] ,  )

     __lowerCAmelCase							     =     DDIMScheduler()
     __lowerCAmelCase							     =     self.dummy_vqvae_and_unet
     __lowerCAmelCase							     =     AudioDiffusionPipeline(
         vqvae=self.dummy_vqvae_and_unet[0] ,  unet=dummy_vqvae_and_unet[1] ,  mel=lowerCAmelCase_ ,  scheduler=lowerCAmelCase_					)
     __lowerCAmelCase							     =     pipe.to(lowerCAmelCase_					)
     pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

     np.random.seed(0					)
     __lowerCAmelCase							     =     np.random.uniform(-1 ,  1 ,  ((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)					)
     __lowerCAmelCase							     =     torch.Generator(device=lowerCAmelCase_					).manual_seed(4_2					)
     __lowerCAmelCase							     =     pipe(raw_audio=lowerCAmelCase_ ,  generator=lowerCAmelCase_ ,  start_step=5 ,  steps=1_0					)
     __lowerCAmelCase							     =     output.images[0]

     assert (
         image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
         and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
     )
     __lowerCAmelCase							     =     np.frombuffer(image.tobytes() ,  dtype='uint8'					)[:1_0]
     __lowerCAmelCase							     =     np.array([1_2_0, 1_1_7, 1_1_0, 1_0_9, 1_3_8, 1_6_7, 1_3_8, 1_4_8, 1_3_2, 1_2_1]					)

     assert np.abs(image_slice.flatten() - expected_slice					).max() == 0

     __lowerCAmelCase							     =     self.dummy_unet_condition
     __lowerCAmelCase							     =     AudioDiffusionPipeline(
         vqvae=self.dummy_vqvae_and_unet[0] ,  unet=lowerCAmelCase_ ,  mel=lowerCAmelCase_ ,  scheduler=lowerCAmelCase_					)
     __lowerCAmelCase							     =     pipe.to(lowerCAmelCase_					)
     pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

     np.random.seed(0					)
     __lowerCAmelCase							     =     torch.rand((1, 1, 1_0)					)
     __lowerCAmelCase							     =     pipe(generator=lowerCAmelCase_ ,  encoding=lowerCAmelCase_					)
     __lowerCAmelCase							     =     output.images[0]
     __lowerCAmelCase							     =     np.frombuffer(image.tobytes() ,  dtype='uint8'					)[:1_0]
     __lowerCAmelCase							     =     np.array([1_0_7, 1_0_3, 1_2_0, 1_2_7, 1_4_2, 1_2_2, 1_1_3, 1_2_2, 9_7, 1_1_1]					)

     assert np.abs(image_slice.flatten() - expected_slice					).max() == 0





@slow
@require_torch_gpu
class     _UpperCAmelCase   (   unittest.TestCase   ):

  """simple docstring"""


  def     lowercase		(    self			:		List[Any]					)							->						Optional[int]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()






  def     lowercase		(    self			:		Union[str, Any]					)							->						Optional[int]:
     __lowerCAmelCase							     =     torch_device

     __lowerCAmelCase							     =     DiffusionPipeline.from_pretrained('teticio/audio-diffusion-ddim-256'					)
     __lowerCAmelCase							     =     pipe.to(lowerCAmelCase_					)
     pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

     __lowerCAmelCase							     =     torch.Generator(device=lowerCAmelCase_					).manual_seed(4_2					)
     __lowerCAmelCase							     =     pipe(generator=lowerCAmelCase_					)
     __lowerCAmelCase							     =     output.audios[0]
     __lowerCAmelCase							     =     output.images[0]

     assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
     assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
     __lowerCAmelCase							     =     np.frombuffer(image.tobytes() ,  dtype='uint8'					)[:1_0]
     __lowerCAmelCase							     =     np.array([1_5_1, 1_6_7, 1_5_4, 1_4_4, 1_2_2, 1_3_4, 1_2_1, 1_0_5, 7_0, 2_6]					)

     assert np.abs(image_slice.flatten() - expected_slice					).max() == 0

