
from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):
	lowerCAmelCase__         = ['vqvae']



	def __init__(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  )				->       Dict:
					super().__init__()
					self.register_modules(unet=lowercase						,  scheduler=lowercase						,  mel=lowercase						,  vqvae=lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       int:
					return 50 if isinstance(self.scheduler						,  lowercase ) else 1000



	@torch.no_grad()
	def __call__(    self						,  lowercase = 1						,  lowercase = None						,  lowercase = None						,  lowercase = 0						,  lowercase = 0						,  lowercase = None						,  lowercase = None						,  lowercase = 0						,  lowercase = 0						,  lowercase = None						,  lowercase = 0						,  lowercase = None						,  lowercase = None						,  lowercase=True						,  )				->       Union[
	    Union[AudioPipelineOutput, ImagePipelineOutput],
	    Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
	]:
					lowerCamelCase_						=			steps or self.get_default_steps()
					self.scheduler.set_timesteps(lowercase )
					lowerCamelCase_						=			step_generator or generator
					# For backwards compatibility
					if type(self.unet.config.sample_size ) == int:
									lowerCamelCase_						=			(self.unet.config.sample_size, self.unet.config.sample_size)
					if noise is None:
									lowerCamelCase_						=			randn_tensor(
									    (
									        batch_size,
									        self.unet.config.in_channels,
									        self.unet.config.sample_size[0],
									        self.unet.config.sample_size[1],
									    )						,  generator=lowercase						,  device=self.device						,  )
					lowerCamelCase_						=			noise
					lowerCamelCase_						=			None

					if audio_file is not None or raw_audio is not None:
									self.mel.load_audio(lowercase						,  lowercase )
									lowerCamelCase_						=			self.mel.audio_slice_to_image(lowercase )
									lowerCamelCase_						=			np.frombuffer(input_image.tobytes()						,  dtype="uint8" ).reshape(
									    (input_image.height, input_image.width) )
									lowerCamelCase_						=			(input_image / 255) * 2 - 1
									lowerCamelCase_						=			torch.tensor(input_image[np.newaxis, :, :]						,  dtype=torch.float ).to(self.device )

									if self.vqvae is not None:
													lowerCamelCase_						=			self.vqvae.encode(torch.unsqueeze(lowercase						,  0 ) ).latent_dist.sample(
													    generator=lowercase )[0]
													lowerCamelCase_						=			self.vqvae.config.scaling_factor * input_images

									if start_step > 0:
													lowerCamelCase_						=			self.scheduler.add_noise(lowercase						,  lowercase						,  self.scheduler.timesteps[start_step - 1] )

									lowerCamelCase_						=			(
									    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
									)
									lowerCamelCase_						=			int(mask_start_secs * pixels_per_second )
									lowerCamelCase_						=			int(mask_end_secs * pixels_per_second )
									lowerCamelCase_						=			self.scheduler.add_noise(lowercase						,  lowercase						,  torch.tensor(self.scheduler.timesteps[start_step:] ) )

					for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:] ) ):
									if isinstance(self.unet						,  lowercase ):
													lowerCamelCase_						=			self.unet(lowercase						,  lowercase						,  lowercase )["sample"]
									else:
													lowerCamelCase_						=			self.unet(lowercase						,  lowercase )["sample"]

									if isinstance(self.scheduler						,  lowercase ):
													lowerCamelCase_						=			self.scheduler.step(
													    model_output=lowercase						,  timestep=lowercase						,  sample=lowercase						,  eta=lowercase						,  generator=lowercase						,  )["prev_sample"]
									else:
													lowerCamelCase_						=			self.scheduler.step(
													    model_output=lowercase						,  timestep=lowercase						,  sample=lowercase						,  generator=lowercase						,  )["prev_sample"]

									if mask is not None:
													if mask_start > 0:
																	lowerCamelCase_						=			mask[:, step, :, :mask_start]
													if mask_end > 0:
																	lowerCamelCase_						=			mask[:, step, :, -mask_end:]

					if self.vqvae is not None:
									# 0.18215 was scaling factor used in training to ensure unit variance
									lowerCamelCase_						=			1 / self.vqvae.config.scaling_factor * images
									lowerCamelCase_						=			self.vqvae.decode(lowercase )["sample"]

					lowerCamelCase_						=			(images / 2 + 0.5).clamp(0						,  1 )
					lowerCamelCase_						=			images.cpu().permute(0						,  2						,  3						,  1 ).numpy()
					lowerCamelCase_						=			(images * 255).round().astype("uint8" )
					lowerCamelCase_						=			list(
					    (Image.fromarray(_[:, :, 0] ) for _ in images)
					    if images.shape[3] == 1
					    else (Image.fromarray(lowercase						,  mode="RGB" ).convert("L" ) for _ in images) )

					lowerCamelCase_						=			[self.mel.image_to_audio(lowercase ) for _ in images]
					if not return_dict:
									return images, (self.mel.get_sample_rate(), audios)

					return BaseOutput(**AudioPipelineOutput(np.array(lowercase )[:, np.newaxis, :] )						,  **ImagePipelineOutput(lowercase ) )



	@torch.no_grad()
	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase = 50 )				->       np.ndarray:
					assert isinstance(self.scheduler						,  lowercase )
					self.scheduler.set_timesteps(lowercase )
					lowerCamelCase_						=			np.array(
					    [np.frombuffer(image.tobytes()						,  dtype="uint8" ).reshape((1, image.height, image.width) ) for image in images] )
					lowerCamelCase_						=			(sample / 255) * 2 - 1
					lowerCamelCase_						=			torch.Tensor(lowercase ).to(self.device )

					for t in self.progress_bar(torch.flip(self.scheduler.timesteps						,  (0,) ) ):
									lowerCamelCase_						=			t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
									lowerCamelCase_						=			self.scheduler.alphas_cumprod[t]
									lowerCamelCase_						=			(
									    self.scheduler.alphas_cumprod[prev_timestep]
									    if prev_timestep >= 0
									    else self.scheduler.final_alpha_cumprod
									)
									lowerCamelCase_						=			1 - alpha_prod_t
									lowerCamelCase_						=			self.unet(lowercase						,  lowercase )["sample"]
									lowerCamelCase_						=			(1 - alpha_prod_t_prev) ** 0.5 * model_output
									lowerCamelCase_						=			(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
									lowerCamelCase_						=			sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

					return sample



	@staticmethod
	def     SCREAMING_SNAKE_CASE_(    lowercase						,  lowercase						,  lowercase )				->       torch.Tensor:
					lowerCamelCase_						=			acos(torch.dot(torch.flatten(lowercase )						,  torch.flatten(lowercase ) ) / torch.norm(lowercase ) / torch.norm(lowercase ) )
					return sin((1 - alpha) * theta ) * xa / sin(lowercase ) + sin(alpha * theta ) * xa / sin(lowercase )




"""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 					_UpperCAmelCase      ( __lowerCamelCase   :       Tuple		)     ->					Optional[int]:
  _snake_case					     =       checkpoints.load_tax_checkpoint(__lowerCamelCase		)
  _snake_case					     =       flatten_dict(__lowerCamelCase		)
  return flax_params
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Dict		)     ->					Optional[int]:
  _snake_case					     =       {}

  _snake_case					     =       {
      '''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''',
  }

  _snake_case					     =       {
      '''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
      _snake_case					     =       '''.'''.join(key[1:]		)

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

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

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

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

      _snake_case					     =       flax_dict[key]

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

  return converted_torch_dict
def 					_UpperCAmelCase      ( __lowerCamelCase   :       List[str] ,					__lowerCamelCase   :       Dict ,					__lowerCamelCase   :       Any=False ,					__lowerCamelCase   :       Optional[int]=False		)     ->					int:
  _snake_case					     =       get_flax_param(__lowerCamelCase		)

  if not use_large:
    _snake_case					     =       PixaStructVisionConfig()
    _snake_case					     =       PixaStructTextConfig()
  else:
    _snake_case					     =       PixaStructVisionConfig(
        hidden_size=15_36 ,					d_ff=39_68 ,					num_attention_heads=24 ,					num_hidden_layers=18		)
    _snake_case					     =       PixaStructTextConfig(hidden_size=15_36 ,					d_ff=39_68 ,					num_heads=24 ,					num_layers=18		)
  _snake_case					     =       PixaStructConfig(
      vision_config=encoder_config.to_dict() ,					text_config=decoder_config.to_dict() ,					is_vqa=__lowerCamelCase		)

  _snake_case					     =       PixaStructForConditionalGeneration(__lowerCamelCase		)

  _snake_case					     =       rename_and_convert_flax_params(__lowerCamelCase		)
  model.load_state_dict(__lowerCamelCase		)

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

  if use_large:
    _snake_case					     =       40_96

  _snake_case					     =       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__":
 UpperCAmelCase__          = 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.')
 UpperCAmelCase__          = parser.parse_args()

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



