


import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


SCREAMING_SNAKE_CASE       :		Any         =				False
SCREAMING_SNAKE_CASE       :		Optional[int]         =				True
SCREAMING_SNAKE_CASE       :		List[str]         =				False


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE       :		Dict         =				argparse.ArgumentParser()

   parser.add_argument(
       "--repo_path",
       default=None,
       type=str,
       required=True,
       help="The config json file corresponding to the architecture.",
   )

   parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")

   SCREAMING_SNAKE_CASE       :		Dict         =				parser.parse_args()

   SCREAMING_SNAKE_CASE       :		List[Any]         =				{
       "image_size": "sample_size",
       "num_res_blocks": "layers_per_block",
       "block_channels": "block_out_channels",
       "down_blocks": "down_block_types",
       "up_blocks": "up_block_types",
       "downscale_freq_shift": "freq_shift",
       "resnet_num_groups": "norm_num_groups",
       "resnet_act_fn": "act_fn",
       "resnet_eps": "norm_eps",
       "num_head_channels": "attention_head_dim",
   }

   SCREAMING_SNAKE_CASE       :		Optional[int]         =				{
       "time_steps": "time_proj",
       "mid": "mid_block",
       "downsample_blocks": "down_blocks",
       "upsample_blocks": "up_blocks",
   }

   SCREAMING_SNAKE_CASE       :		Union[str, Any]         =				"" if has_file(args.repo_path, "config.json") else "unet"

   with open(os.path.join(args.repo_path, subfolder, "config.json"), "r", encoding="utf-8") as reader:
      SCREAMING_SNAKE_CASE       :		int         =				reader.read()
      SCREAMING_SNAKE_CASE       :		Tuple         =				json.loads(text)

   if do_only_config:
      for key in config_parameters_to_change.keys():
         config.pop(key, None)

   if has_file(args.repo_path, "config.json"):
      SCREAMING_SNAKE_CASE       :		Dict         =				UNetaDModel(**config)
   else:
      SCREAMING_SNAKE_CASE       :		List[Any]         =				UNetaDConditionModel if "ldm-text2im-large-256" in args.repo_path else UNetaDModel
      SCREAMING_SNAKE_CASE       :		int         =				class_name(**config)

   if do_only_config:
      model.save_config(os.path.join(args.repo_path, subfolder))

   SCREAMING_SNAKE_CASE       :		Union[str, Any]         =				dict(model.config)

   if do_only_renaming:
      for key, value in config_parameters_to_change.items():
         if key in config:
            SCREAMING_SNAKE_CASE       :		Dict         =				config[key]
            del config[key]

      SCREAMING_SNAKE_CASE       :		Optional[int]         =				[k.replace("UNetRes", "") for k in config["down_block_types"]]
      SCREAMING_SNAKE_CASE       :		List[Any]         =				[k.replace("UNetRes", "") for k in config["up_block_types"]]

   if do_only_weights:
      SCREAMING_SNAKE_CASE       :		List[Any]         =				torch.load(os.path.join(args.repo_path, subfolder, "diffusion_pytorch_model.bin"))

      SCREAMING_SNAKE_CASE       :		Dict         =				{}
      for param_key, param_value in state_dict.items():
         if param_key.endswith(".op.bias") or param_key.endswith(".op.weight"):
            continue
         SCREAMING_SNAKE_CASE       :		Union[str, Any]         =				False
         for key, new_key in key_parameters_to_change.items():
            if not has_changed and param_key.split(".")[0] == key:
               SCREAMING_SNAKE_CASE       :		Any         =				param_value
               SCREAMING_SNAKE_CASE       :		Union[str, Any]         =				True
         if not has_changed:
            SCREAMING_SNAKE_CASE       :		Dict         =				param_value

      model.load_state_dict(new_state_dict)
      model.save_pretrained(os.path.join(args.repo_path, subfolder))





import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


SCREAMING_SNAKE_CASE       :		int         =				False






class      UpperCamelCase					(		unittest.TestCase     ):


   '''simple docstring'''







   def 		UpperCamelCase  (     self		):
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()
   @property
   def 		UpperCamelCase  (     self		):
        return 12
   @property
   def 		UpperCamelCase  (     self		):
        return 12
   @property
   def 		UpperCamelCase  (     self		):
        return 32
   @property
   def 		UpperCamelCase  (     self		):
        torch.manual_seed(0		)
        lowercase_       :List[Any]     =       VQModel(
            block_out_channels=[32, 64]   ,							in_channels=3   ,							out_channels=3   ,							down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']   ,							up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']   ,							latent_channels=3   ,							num_vq_embeddings=self.num_embed   ,							vq_embed_dim=3   ,							)
        return model
   @property
   def 		UpperCamelCase  (     self		):
        lowercase_       :Optional[Any]     =       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)
        return tokenizer
   @property
   def 		UpperCamelCase  (     self		):
        torch.manual_seed(0		)
        lowercase_       :int     =       CLIPTextConfig(
            bos_token_id=0   ,							eos_token_id=2   ,							hidden_size=self.text_embedder_hidden_size   ,							intermediate_size=37   ,							layer_norm_eps=1E-05   ,							num_attention_heads=4   ,							num_hidden_layers=5   ,							pad_token_id=1   ,							vocab_size=1000   ,							)
        return CLIPTextModel(UpperCamelCase_		)
   @property
   def 		UpperCamelCase  (     self		):
        torch.manual_seed(0		)

        lowercase_       :int     =       12
        lowercase_       :List[Any]     =       12

        lowercase_       :Dict     =       {
            '''attention_bias''': True,
            '''cross_attention_dim''': 32,
            '''attention_head_dim''': height * width,
            '''num_attention_heads''': 1,
            '''num_vector_embeds''': self.num_embed,
            '''num_embeds_ada_norm''': self.num_embeds_ada_norm,
            '''norm_num_groups''': 32,
            '''sample_size''': width,
            '''activation_fn''': '''geglu-approximate''',
        }

        lowercase_       :int     =       TransformeraDModel(**UpperCamelCase_		)
        return model
   def 		UpperCamelCase  (     self		):
        lowercase_       :List[str]     =       '''cpu'''

        lowercase_       :int     =       self.dummy_vqvae
        lowercase_       :int     =       self.dummy_text_encoder
        lowercase_       :Any     =       self.dummy_tokenizer
        lowercase_       :Optional[int]     =       self.dummy_transformer
        lowercase_       :List[str]     =       VQDiffusionScheduler(self.num_embed		)
        lowercase_       :int     =       LearnedClassifierFreeSamplingEmbeddings(learnable=UpperCamelCase_		)

        lowercase_       :List[Any]     =       VQDiffusionPipeline(
            vqvae=UpperCamelCase_   ,							text_encoder=UpperCamelCase_   ,							tokenizer=UpperCamelCase_   ,							transformer=UpperCamelCase_   ,							scheduler=UpperCamelCase_   ,							learned_classifier_free_sampling_embeddings=UpperCamelCase_   ,							)
        lowercase_       :Union[str, Any]     =       pipe.to(UpperCamelCase_		)
        pipe.set_progress_bar_config(disable=UpperCamelCase_		)

        lowercase_       :Dict     =       '''teddy bear playing in the pool'''

        lowercase_       :Dict     =       torch.Generator(device=UpperCamelCase_		).manual_seed(0		)
        lowercase_       :Optional[int]     =       pipe([prompt]   ,							generator=UpperCamelCase_   ,							num_inference_steps=2   ,							output_type='''np'''		)
        lowercase_       :Any     =       output.images

        lowercase_       :Tuple     =       torch.Generator(device=UpperCamelCase_		).manual_seed(0		)
        lowercase_       :str     =       pipe(
            [prompt]   ,							generator=UpperCamelCase_   ,							output_type='''np'''   ,							return_dict=UpperCamelCase_   ,							num_inference_steps=2		)[0]

        lowercase_       :Optional[Any]     =       image[0, -3:, -3:, -1]
        lowercase_       :Tuple     =       image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 24, 24, 3)

        lowercase_       :str     =       np.array([0.6551, 0.6168, 0.5008, 0.5676, 0.5659, 0.4295, 0.6073, 0.5599, 0.4992]		)

        assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1E-2




   def 		UpperCamelCase  (     self		):
        lowercase_       :int     =       '''cpu'''

        lowercase_       :Dict     =       self.dummy_vqvae
        lowercase_       :str     =       self.dummy_text_encoder
        lowercase_       :List[Any]     =       self.dummy_tokenizer
        lowercase_       :Any     =       self.dummy_transformer
        lowercase_       :Optional[Any]     =       VQDiffusionScheduler(self.num_embed		)
        lowercase_       :List[str]     =       LearnedClassifierFreeSamplingEmbeddings(
            learnable=UpperCamelCase_   ,							hidden_size=self.text_embedder_hidden_size   ,							length=tokenizer.model_max_length		)

        lowercase_       :Optional[int]     =       VQDiffusionPipeline(
            vqvae=UpperCamelCase_   ,							text_encoder=UpperCamelCase_   ,							tokenizer=UpperCamelCase_   ,							transformer=UpperCamelCase_   ,							scheduler=UpperCamelCase_   ,							learned_classifier_free_sampling_embeddings=UpperCamelCase_   ,							)
        lowercase_       :Dict     =       pipe.to(UpperCamelCase_		)
        pipe.set_progress_bar_config(disable=UpperCamelCase_		)

        lowercase_       :int     =       '''teddy bear playing in the pool'''

        lowercase_       :Tuple     =       torch.Generator(device=UpperCamelCase_		).manual_seed(0		)
        lowercase_       :Optional[int]     =       pipe([prompt]   ,							generator=UpperCamelCase_   ,							num_inference_steps=2   ,							output_type='''np'''		)
        lowercase_       :Optional[Any]     =       output.images

        lowercase_       :Optional[int]     =       torch.Generator(device=UpperCamelCase_		).manual_seed(0		)
        lowercase_       :Dict     =       pipe(
            [prompt]   ,							generator=UpperCamelCase_   ,							output_type='''np'''   ,							return_dict=UpperCamelCase_   ,							num_inference_steps=2		)[0]

        lowercase_       :List[str]     =       image[0, -3:, -3:, -1]
        lowercase_       :Optional[int]     =       image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 24, 24, 3)

        lowercase_       :Dict     =       np.array([0.6693, 0.6075, 0.4959, 0.5701, 0.5583, 0.4333, 0.6171, 0.5684, 0.4988]		)

        assert np.abs(image_slice.flatten() - expected_slice		).max() < 2.0
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1E-2






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


   '''simple docstring'''







   def 		UpperCamelCase  (     self		):
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()




   def 		UpperCamelCase  (     self		):
        lowercase_       :List[str]     =       load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy'''		)

        lowercase_       :int     =       VQDiffusionPipeline.from_pretrained('''microsoft/vq-diffusion-ithq'''		)
        lowercase_       :Tuple     =       pipeline.to(UpperCamelCase_		)
        pipeline.set_progress_bar_config(disable=UpperCamelCase_		)

        # requires GPU generator for gumbel softmax
        # don't use GPU generator in tests though
        lowercase_       :Dict     =       torch.Generator(device=UpperCamelCase_		).manual_seed(0		)
        lowercase_       :int     =       pipeline(
            '''teddy bear playing in the pool'''   ,							num_images_per_prompt=1   ,							generator=UpperCamelCase_   ,							output_type='''np'''   ,							)

        lowercase_       :List[str]     =       output.images[0]

        assert image.shape == (256, 256, 3)
        assert np.abs(expected_image - image		).max() < 2.0

