





"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()




class 							snake_case							(			__UpperCAmelCase      ,  unittest.TestCase ):

      '''simple docstring'''



      _A     :       str         =       KandinskyVaaControlnetPipeline
      _A     :       Union[str, Any]         =       ['image_embeds', 'negative_image_embeds', 'hint']
      _A     :       Dict         =       ['image_embeds', 'negative_image_embeds', 'hint']
      _A     :       Tuple         =       [
          'generator',
          'height',
          'width',
          'latents',
          'guidance_scale',
          'num_inference_steps',
          'return_dict',
          'guidance_scale',
          'num_images_per_prompt',
          'output_type',
          'return_dict',
      ]
      _A     :       str         =       False

      @property
      def       A_					(							self       :      Dict ):


        '''simple docstring'''




        return 32

      @property
      def       A_					(							self       :      Tuple ):


        '''simple docstring'''




        return 32

      @property
      def       A_					(							self       :      Dict ):


        '''simple docstring'''




        return self.time_input_dim

      @property
      def       A_					(							self       :      Tuple ):


        '''simple docstring'''




        return self.time_input_dim * 4

      @property
      def       A_					(							self       :      int ):


        '''simple docstring'''




        return 100

      @property
      def       A_					(							self       :      Optional[int] ):


        '''simple docstring'''




        torch.manual_seed(0 )

        __UpperCAmelCase    :						List[str]       =						{
            '''in_channels''': 8,
            # Out channels is double in channels because predicts mean and variance
            '''out_channels''': 8,
            '''addition_embed_type''': '''image_hint''',
            '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
            '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
            '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
            '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
            '''layers_per_block''': 1,
            '''encoder_hid_dim''': self.text_embedder_hidden_size,
            '''encoder_hid_dim_type''': '''image_proj''',
            '''cross_attention_dim''': self.cross_attention_dim,
            '''attention_head_dim''': 4,
            '''resnet_time_scale_shift''': '''scale_shift''',
            '''class_embed_type''': None,
        }

        __UpperCAmelCase    :						List[Any]       =						UNetaDConditionModel(**__lowercase )
        return model

      @property
      def       A_					(							self       :      List[Any] ):


        '''simple docstring'''




        return {
            "block_out_channels": [32, 32, 64, 64],
            "down_block_types": [
                "DownEncoderBlock2D",
                "DownEncoderBlock2D",
                "DownEncoderBlock2D",
                "AttnDownEncoderBlock2D",
            ],
            "in_channels": 3,
            "latent_channels": 4,
            "layers_per_block": 1,
            "norm_num_groups": 8,
            "norm_type": "spatial",
            "num_vq_embeddings": 12,
            "out_channels": 3,
            "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
            "vq_embed_dim": 4,
        }

      @property
      def       A_					(							self       :      Tuple ):


        '''simple docstring'''




        torch.manual_seed(0 )
        __UpperCAmelCase    :						Tuple       =						VQModel(**self.dummy_movq_kwargs )
        return model

      def       A_					(							self       :      Optional[Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Union[str, Any]       =						self.dummy_unet
        __UpperCAmelCase    :						Dict       =						self.dummy_movq

        __UpperCAmelCase    :						Optional[Any]       =						DDIMScheduler(
            num_train_timesteps=1_000						,				beta_schedule='''linear'''						,				beta_start=0.0_0_0_8_5						,				beta_end=0.0_1_2						,				clip_sample=__lowercase						,				set_alpha_to_one=__lowercase						,				steps_offset=1						,				prediction_type='''epsilon'''						,				thresholding=__lowercase						,				)

        __UpperCAmelCase    :						Tuple       =						{
            '''unet''': unet,
            '''scheduler''': scheduler,
            '''movq''': movq,
        }
        return components

      def       A_					(							self       :      List[str]						,				__lowercase       :      Dict						,				__lowercase       :      Dict=0 ):


        '''simple docstring'''




        __UpperCAmelCase    :						str       =						floats_tensor((1, self.text_embedder_hidden_size)						,				rng=random.Random(__lowercase ) ).to(__lowercase )
        __UpperCAmelCase    :						Any       =						floats_tensor((1, self.text_embedder_hidden_size)						,				rng=random.Random(seed + 1 ) ).to(
            __lowercase )

        # create hint
        __UpperCAmelCase    :						List[str]       =						floats_tensor((1, 3, 64, 64)						,				rng=random.Random(__lowercase ) ).to(__lowercase )

        if str(__lowercase ).startswith('''mps''' ):
          __UpperCAmelCase    :						Any       =						torch.manual_seed(__lowercase )
        else:
          __UpperCAmelCase    :						Union[str, Any]       =						torch.Generator(device=__lowercase ).manual_seed(__lowercase )
        __UpperCAmelCase    :						Optional[int]       =						{
            '''image_embeds''': image_embeds,
            '''negative_image_embeds''': negative_image_embeds,
            '''hint''': hint,
            '''generator''': generator,
            '''height''': 64,
            '''width''': 64,
            '''guidance_scale''': 4.0,
            '''num_inference_steps''': 2,
            '''output_type''': '''np''',
        }
        return inputs







      def       A_					(							self       :      str ):


        '''simple docstring'''




        __UpperCAmelCase    :						Union[str, Any]       =						'''cpu'''

        __UpperCAmelCase    :						Dict       =						self.get_dummy_components()

        __UpperCAmelCase    :						Union[str, Any]       =						self.pipeline_class(**__lowercase )
        __UpperCAmelCase    :						int       =						pipe.to(__lowercase )

        pipe.set_progress_bar_config(disable=__lowercase )

        __UpperCAmelCase    :						Optional[Any]       =						pipe(**self.get_dummy_inputs(__lowercase ) )
        __UpperCAmelCase    :						str       =						output.images

        __UpperCAmelCase    :						List[Any]       =						pipe(
            **self.get_dummy_inputs(__lowercase )						,				return_dict=__lowercase						,				)[0]

        __UpperCAmelCase    :						List[Any]       =						image[0, -3:, -3:, -1]
        __UpperCAmelCase    :						Union[str, Any]       =						image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)

        __UpperCAmelCase    :						List[str]       =						np.array(
            [0.6_9_5_9_8_2_6, 0.8_6_8_2_7_9, 0.7_5_5_8_0_9_2, 0.6_8_7_6_9_4_6_7, 0.8_5_8_0_5_8_0_4, 0.6_5_9_7_7_4_9_6, 0.4_4_8_8_5_3_0_2, 0.5_9_5_9_1_1_1, 0.4_2_5_1_5_9_5] )

        assert (
            np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
        ), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''

        assert (
            np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
        ), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''




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

      '''simple docstring'''



      def       A_					(							self       :      Dict ):


        '''simple docstring'''




        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







      def       A_					(							self       :      Optional[Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Any       =						load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy''' )

        __UpperCAmelCase    :						Union[str, Any]       =						load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/kandinskyv22/hint_image_cat.png''' )
        __UpperCAmelCase    :						Optional[Any]       =						torch.from_numpy(np.array(__lowercase ) ).float() / 2_5_5.0
        __UpperCAmelCase    :						Union[str, Any]       =						hint.permute(2						,				0						,				1 ).unsqueeze(0 )

        __UpperCAmelCase    :						Optional[int]       =						KandinskyVaaPriorPipeline.from_pretrained(
            '''kandinsky-community/kandinsky-2-2-prior'''						,				torch_dtype=torch.floataa )
        pipe_prior.to(__lowercase )

        __UpperCAmelCase    :						List[str]       =						KandinskyVaaControlnetPipeline.from_pretrained(
            '''kandinsky-community/kandinsky-2-2-controlnet-depth'''						,				torch_dtype=torch.floataa )
        __UpperCAmelCase    :						str       =						pipeline.to(__lowercase )
        pipeline.set_progress_bar_config(disable=__lowercase )

        __UpperCAmelCase    :						Tuple       =						'''A robot, 4k photo'''

        __UpperCAmelCase    :						List[Any]       =						torch.Generator(device='''cuda''' ).manual_seed(0 )
        __UpperCAmelCase       ,     __UpperCAmelCase    :						Optional[int]       =						pipe_prior(
            __lowercase						,				generator=__lowercase						,				num_inference_steps=5						,				negative_prompt=''''''						,				).to_tuple()

        __UpperCAmelCase    :						int       =						torch.Generator(device='''cuda''' ).manual_seed(0 )
        __UpperCAmelCase    :						List[str]       =						pipeline(
            image_embeds=__lowercase						,				negative_image_embeds=__lowercase						,				hint=__lowercase						,				generator=__lowercase						,				num_inference_steps=100						,				output_type='''np'''						,				)

        __UpperCAmelCase    :						Union[str, Any]       =						output.images[0]

        assert image.shape == (512, 512, 3)

        assert_mean_pixel_difference(__lowercase						,				__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       lowerCamelCase_       (   UpperCAmelCase_  )						->Dict:





  """simple docstring"""


  __UpperCAmelCase    :						Optional[Any]       =						checkpoints.load_tax_checkpoint(UpperCAmelCase_  )
  __UpperCAmelCase    :						Any       =						flatten_dict(UpperCAmelCase_  )
  return flax_params


def       lowerCamelCase_       (   UpperCAmelCase_  )						->List[str]:





  """simple docstring"""


  __UpperCAmelCase    :						Optional[int]       =						{}

  __UpperCAmelCase    :						Any       =						{
      '''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''',
  }

  __UpperCAmelCase    :						Optional[int]       =						{
      '''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
      __UpperCAmelCase    :						Optional[Any]       =						'''.'''.join(key[1:]  )

      # rename the key
      for old, new in CONVERSION_MAPPING.items():
        __UpperCAmelCase    :						List[str]       =						new_key.replace(UpperCAmelCase_  ,			UpperCAmelCase_  )

      if "decoder" in new_key:
        for old, new in DECODER_CONVERSION_MAPPING.items():
          __UpperCAmelCase    :						Dict       =						new_key.replace(UpperCAmelCase_  ,			UpperCAmelCase_  )

      if "layers" in new_key and "decoder" not in new_key:
        # use regex to replace the layer number
        __UpperCAmelCase    :						Dict       =						re.sub(R'''layers_(\d+)'''  ,			R'''layer.\1'''  ,			UpperCAmelCase_  )
        __UpperCAmelCase    :						Union[str, Any]       =						new_key.replace('''encoder'''  ,			'''encoder.encoder'''  )

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

      __UpperCAmelCase    :						Dict       =						flax_dict[key]

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

  return converted_torch_dict


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_=False  ,			UpperCAmelCase_=False  )						->Dict:





  """simple docstring"""


  __UpperCAmelCase    :						List[Any]       =						get_flax_param(UpperCAmelCase_  )

  if not use_large:
    __UpperCAmelCase    :						List[str]       =						PixaStructVisionConfig()
    __UpperCAmelCase    :						Optional[Any]       =						PixaStructTextConfig()
  else:
    __UpperCAmelCase    :						Optional[Any]       =						PixaStructVisionConfig(
        hidden_size=15_36  ,			d_ff=39_68  ,			num_attention_heads=24  ,			num_hidden_layers=18  )
    __UpperCAmelCase    :						Optional[Any]       =						PixaStructTextConfig(hidden_size=15_36  ,			d_ff=39_68  ,			num_heads=24  ,			num_layers=18  )
  __UpperCAmelCase    :						Dict       =						PixaStructConfig(
      vision_config=encoder_config.to_dict()  ,			text_config=decoder_config.to_dict()  ,			is_vqa=UpperCAmelCase_  )

  __UpperCAmelCase    :						Optional[Any]       =						PixaStructForConditionalGeneration(UpperCAmelCase_  )

  __UpperCAmelCase    :						Any       =						rename_and_convert_flax_params(UpperCAmelCase_  )
  model.load_state_dict(UpperCAmelCase_  )

  __UpperCAmelCase    :						List[str]       =						AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer'''  )
  __UpperCAmelCase    :						Union[str, Any]       =						PixaStructImageProcessor()
  __UpperCAmelCase    :						Optional[int]       =						PixaStructProcessor(image_processor=UpperCAmelCase_  ,			tokenizer=UpperCAmelCase_  )

  if use_large:
    __UpperCAmelCase    :						str       =						40_96

  __UpperCAmelCase    :						str       =						True

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

  model.save_pretrained(UpperCAmelCase_  )
  processor.save_pretrained(UpperCAmelCase_  )

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


if __name__ == "__main__":
  lowercase__				:List[str]				   =       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.')
  lowercase__				:Optional[Any]				   =       parser.parse_args()

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