

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         =    False






class  UpperCAmelCase__	( unittest.TestCase ):






  """simple docstring"""


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






  @property
  def    lowercase_	(							self		:	Dict      )	->							Optional[Any]:
       return 12






  @property
  def    lowercase_	(							self		:	List[str]      )	->							List[str]:
       return 12






  @property
  def    lowercase_	(							self		:	Dict      )	->							str:
       return 32






  @property
  def    lowercase_	(							self		:	Optional[Any]      )	->							Union[str, Any]:
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__					=					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    lowercase_	(							self		:	List[str]      )	->							Optional[int]:
       SCREAMING_SNAKE_CASE__					=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )
       return tokenizer






  @property
  def    lowercase_	(							self		:	List[Any]      )	->							List[str]:
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__					=					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(__lowerCamelCase      )






  @property
  def    lowercase_	(							self		:	List[Any]      )	->							Optional[Any]:
       torch.manual_seed(0      )

       SCREAMING_SNAKE_CASE__					=					12
       SCREAMING_SNAKE_CASE__					=					12

       SCREAMING_SNAKE_CASE__					=					{
           '''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''',
       }

       SCREAMING_SNAKE_CASE__					=					TransformeraDModel(**__lowerCamelCase      )
       return model






  def    lowercase_	(							self		:	int      )	->							Dict:
       SCREAMING_SNAKE_CASE__					=					'''cpu'''

       SCREAMING_SNAKE_CASE__					=					self.dummy_vqvae
       SCREAMING_SNAKE_CASE__					=					self.dummy_text_encoder
       SCREAMING_SNAKE_CASE__					=					self.dummy_tokenizer
       SCREAMING_SNAKE_CASE__					=					self.dummy_transformer
       SCREAMING_SNAKE_CASE__					=					VQDiffusionScheduler(self.num_embed      )
       SCREAMING_SNAKE_CASE__					=					LearnedClassifierFreeSamplingEmbeddings(learnable=__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					VQDiffusionPipeline(
           vqvae=__lowerCamelCase			,     text_encoder=__lowerCamelCase			,     tokenizer=__lowerCamelCase			,     transformer=__lowerCamelCase			,     scheduler=__lowerCamelCase			,     learned_classifier_free_sampling_embeddings=__lowerCamelCase			,     )
       SCREAMING_SNAKE_CASE__					=					pipe.to(__lowerCamelCase      )
       pipe.set_progress_bar_config(disable=__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					'''teddy bear playing in the pool'''

       SCREAMING_SNAKE_CASE__					=					torch.Generator(device=__lowerCamelCase      ).manual_seed(0      )
       SCREAMING_SNAKE_CASE__					=					pipe([prompt]			,     generator=__lowerCamelCase			,     num_inference_steps=2			,     output_type='''np'''      )
       SCREAMING_SNAKE_CASE__					=					output.images

       SCREAMING_SNAKE_CASE__					=					torch.Generator(device=__lowerCamelCase      ).manual_seed(0      )
       SCREAMING_SNAKE_CASE__					=					pipe(
           [prompt]			,     generator=__lowerCamelCase			,     output_type='''np'''			,     return_dict=__lowerCamelCase			,     num_inference_steps=2      )[0]

       SCREAMING_SNAKE_CASE__					=					image[0, -3:, -3:, -1]
       SCREAMING_SNAKE_CASE__					=					image_from_tuple[0, -3:, -3:, -1]

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

       SCREAMING_SNAKE_CASE__					=					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    lowercase_	(							self		:	Optional[int]      )	->							List[str]:
       SCREAMING_SNAKE_CASE__					=					'''cpu'''

       SCREAMING_SNAKE_CASE__					=					self.dummy_vqvae
       SCREAMING_SNAKE_CASE__					=					self.dummy_text_encoder
       SCREAMING_SNAKE_CASE__					=					self.dummy_tokenizer
       SCREAMING_SNAKE_CASE__					=					self.dummy_transformer
       SCREAMING_SNAKE_CASE__					=					VQDiffusionScheduler(self.num_embed      )
       SCREAMING_SNAKE_CASE__					=					LearnedClassifierFreeSamplingEmbeddings(
           learnable=__lowerCamelCase			,     hidden_size=self.text_embedder_hidden_size			,     length=tokenizer.model_max_length      )

       SCREAMING_SNAKE_CASE__					=					VQDiffusionPipeline(
           vqvae=__lowerCamelCase			,     text_encoder=__lowerCamelCase			,     tokenizer=__lowerCamelCase			,     transformer=__lowerCamelCase			,     scheduler=__lowerCamelCase			,     learned_classifier_free_sampling_embeddings=__lowerCamelCase			,     )
       SCREAMING_SNAKE_CASE__					=					pipe.to(__lowerCamelCase      )
       pipe.set_progress_bar_config(disable=__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					'''teddy bear playing in the pool'''

       SCREAMING_SNAKE_CASE__					=					torch.Generator(device=__lowerCamelCase      ).manual_seed(0      )
       SCREAMING_SNAKE_CASE__					=					pipe([prompt]			,     generator=__lowerCamelCase			,     num_inference_steps=2			,     output_type='''np'''      )
       SCREAMING_SNAKE_CASE__					=					output.images

       SCREAMING_SNAKE_CASE__					=					torch.Generator(device=__lowerCamelCase      ).manual_seed(0      )
       SCREAMING_SNAKE_CASE__					=					pipe(
           [prompt]			,     generator=__lowerCamelCase			,     output_type='''np'''			,     return_dict=__lowerCamelCase			,     num_inference_steps=2      )[0]

       SCREAMING_SNAKE_CASE__					=					image[0, -3:, -3:, -1]
       SCREAMING_SNAKE_CASE__					=					image_from_tuple[0, -3:, -3:, -1]

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

       SCREAMING_SNAKE_CASE__					=					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    lowercase_	(							self		:	Any      )	->							Any:
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()







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

       SCREAMING_SNAKE_CASE__					=					VQDiffusionPipeline.from_pretrained('''microsoft/vq-diffusion-ithq'''      )
       SCREAMING_SNAKE_CASE__					=					pipeline.to(__lowerCamelCase      )
       pipeline.set_progress_bar_config(disable=__lowerCamelCase      )

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

       SCREAMING_SNAKE_CASE__					=					output.images[0]

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





import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE  :						Dict         =    logging.get_logger(__name__)
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					original_name.split('''.'''       )[0]
     SCREAMING_SNAKE_CASE__					=					key.split('''.'''       )
     SCREAMING_SNAKE_CASE__					=					int(key_list[key_list.index(_A       ) - 2]       )
     SCREAMING_SNAKE_CASE__					=					int(key_list[key_list.index(_A       ) - 1]       )
     SCREAMING_SNAKE_CASE__					=					orig_block_num - offset

     SCREAMING_SNAKE_CASE__					=					key.replace(F'''{orig_block_num}.{layer_num}.{original_name}'''   ,	F'''block.{new_block_num}.{layer_num}.{new_name}'''       )
     return key
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					OrderedDict()
     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					0, 0
     for key, value in state_dict.items():
          if key.startswith('''network'''       ):
               SCREAMING_SNAKE_CASE__					=					key.replace('''network'''   ,	'''poolformer.encoder'''       )
          if "proj" in key:
               # Works for the first embedding as well as the internal embedding layers
               if key.endswith('''bias'''       ) and "patch_embed" not in key:
                    patch_emb_offset += 1
               SCREAMING_SNAKE_CASE__					=					key[: key.find('''proj'''       )]
               SCREAMING_SNAKE_CASE__					=					key.replace(_A   ,	F'''patch_embeddings.{total_embed_found}.'''       )
               SCREAMING_SNAKE_CASE__					=					key.replace('''proj'''   ,	'''projection'''       )
               if key.endswith('''bias'''       ):
                    total_embed_found += 1
          if "patch_embeddings" in key:
               SCREAMING_SNAKE_CASE__					=					'''poolformer.encoder.''' + key
          if "mlp.fc1" in key:
               SCREAMING_SNAKE_CASE__					=					replace_key_with_offset(_A   ,	_A   ,	'''mlp.fc1'''   ,	'''output.conv1'''       )
          if "mlp.fc2" in key:
               SCREAMING_SNAKE_CASE__					=					replace_key_with_offset(_A   ,	_A   ,	'''mlp.fc2'''   ,	'''output.conv2'''       )
          if "norm1" in key:
               SCREAMING_SNAKE_CASE__					=					replace_key_with_offset(_A   ,	_A   ,	'''norm1'''   ,	'''before_norm'''       )
          if "norm2" in key:
               SCREAMING_SNAKE_CASE__					=					replace_key_with_offset(_A   ,	_A   ,	'''norm2'''   ,	'''after_norm'''       )
          if "layer_scale_1" in key:
               SCREAMING_SNAKE_CASE__					=					replace_key_with_offset(_A   ,	_A   ,	'''layer_scale_1'''   ,	'''layer_scale_1'''       )
          if "layer_scale_2" in key:
               SCREAMING_SNAKE_CASE__					=					replace_key_with_offset(_A   ,	_A   ,	'''layer_scale_2'''   ,	'''layer_scale_2'''       )
          if "head" in key:
               SCREAMING_SNAKE_CASE__					=					key.replace('''head'''   ,	'''classifier'''       )
          SCREAMING_SNAKE_CASE__					=					value
     return new_state_dict
def        UpperCAmelCase_     (		):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
     SCREAMING_SNAKE_CASE__					=					Image.open(requests.get(_A   ,	stream=_A       ).raw       )

     return image
@torch.no_grad()
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					PoolFormerConfig()

     # set attributes based on model_name
     SCREAMING_SNAKE_CASE__					=					'''huggingface/label-files'''
     SCREAMING_SNAKE_CASE__					=					model_name[-3:]
     SCREAMING_SNAKE_CASE__					=					10_00
     SCREAMING_SNAKE_CASE__					=					'''imagenet-1k-id2label.json'''
     SCREAMING_SNAKE_CASE__					=					(1, 10_00)

     # set config attributes
     SCREAMING_SNAKE_CASE__					=					json.load(open(hf_hub_download(_A   ,	_A   ,	repo_type='''dataset'''       )   ,	'''r'''       )       )
     SCREAMING_SNAKE_CASE__					=					{int(_A       ): v for k, v in idalabel.items()}
     SCREAMING_SNAKE_CASE__					=					idalabel
     SCREAMING_SNAKE_CASE__					=					{v: k for k, v in idalabel.items()}
     if size == "s12":
          SCREAMING_SNAKE_CASE__					=					[2, 2, 6, 2]
          SCREAMING_SNAKE_CASE__					=					[64, 1_28, 3_20, 5_12]
          SCREAMING_SNAKE_CASE__					=					4.0
          SCREAMING_SNAKE_CASE__					=					0.9
     elif size == "s24":
          SCREAMING_SNAKE_CASE__					=					[4, 4, 12, 4]
          SCREAMING_SNAKE_CASE__					=					[64, 1_28, 3_20, 5_12]
          SCREAMING_SNAKE_CASE__					=					4.0
          SCREAMING_SNAKE_CASE__					=					0.9
     elif size == "s36":
          SCREAMING_SNAKE_CASE__					=					[6, 6, 18, 6]
          SCREAMING_SNAKE_CASE__					=					[64, 1_28, 3_20, 5_12]
          SCREAMING_SNAKE_CASE__					=					4.0
          SCREAMING_SNAKE_CASE__					=					1e-6
          SCREAMING_SNAKE_CASE__					=					0.9
     elif size == "m36":
          SCREAMING_SNAKE_CASE__					=					[6, 6, 18, 6]
          SCREAMING_SNAKE_CASE__					=					[96, 1_92, 3_84, 7_68]
          SCREAMING_SNAKE_CASE__					=					4.0
          SCREAMING_SNAKE_CASE__					=					1e-6
          SCREAMING_SNAKE_CASE__					=					0.9_5
     elif size == "m48":
          SCREAMING_SNAKE_CASE__					=					[8, 8, 24, 8]
          SCREAMING_SNAKE_CASE__					=					[96, 1_92, 3_84, 7_68]
          SCREAMING_SNAKE_CASE__					=					4.0
          SCREAMING_SNAKE_CASE__					=					1e-6
          SCREAMING_SNAKE_CASE__					=					0.9_5
     else:
          raise ValueError(F'''Size {size} not supported'''       )

     # load image processor
     SCREAMING_SNAKE_CASE__					=					PoolFormerImageProcessor(crop_pct=_A       )

     # Prepare image
     SCREAMING_SNAKE_CASE__					=					prepare_img()
     SCREAMING_SNAKE_CASE__					=					image_processor(images=_A   ,	return_tensors='''pt'''       ).pixel_values

     logger.info(F'''Converting model {model_name}...'''       )

     # load original state dict
     SCREAMING_SNAKE_CASE__					=					torch.load(_A   ,	map_location=torch.device('''cpu'''       )       )

     # rename keys
     SCREAMING_SNAKE_CASE__					=					rename_keys(_A       )

     # create HuggingFace model and load state dict
     SCREAMING_SNAKE_CASE__					=					PoolFormerForImageClassification(_A       )
     model.load_state_dict(_A       )
     model.eval()

     # Define image processor
     SCREAMING_SNAKE_CASE__					=					PoolFormerImageProcessor(crop_pct=_A       )
     SCREAMING_SNAKE_CASE__					=					image_processor(images=prepare_img()   ,	return_tensors='''pt'''       ).pixel_values

     # forward pass
     SCREAMING_SNAKE_CASE__					=					model(_A       )
     SCREAMING_SNAKE_CASE__					=					outputs.logits

     # define expected logit slices for different models
     if size == "s12":
          SCREAMING_SNAKE_CASE__					=					torch.tensor([-0.3_0_4_5, -0.6_7_5_8, -0.4_8_6_9]       )
     elif size == "s24":
          SCREAMING_SNAKE_CASE__					=					torch.tensor([0.4_4_0_2, -0.1_3_7_4, -0.8_0_4_5]       )
     elif size == "s36":
          SCREAMING_SNAKE_CASE__					=					torch.tensor([-0.6_0_8_0, -0.5_1_3_3, -0.5_8_9_8]       )
     elif size == "m36":
          SCREAMING_SNAKE_CASE__					=					torch.tensor([0.3_9_5_2, 0.2_2_6_3, -1.2_6_6_8]       )
     elif size == "m48":
          SCREAMING_SNAKE_CASE__					=					torch.tensor([0.1_1_6_7, -0.0_6_5_6, -0.3_4_2_3]       )
     else:
          raise ValueError(F'''Size {size} not supported'''       )

     # verify logits
     assert logits.shape == expected_shape
     assert torch.allclose(logits[0, :3]   ,	_A   ,	atol=1e-2       )

     # finally, save model and image processor
     logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''       )
     Path(_A       ).mkdir(exist_ok=_A       )
     model.save_pretrained(_A       )
     print(F'''Saving image processor to {pytorch_dump_folder_path}'''       )
     image_processor.save_pretrained(_A       )


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE  :						Union[str, Any]         =    argparse.ArgumentParser()

       parser.add_argument(
           '''--model_name''',
           default='''poolformer_s12''',
           type=str,
           help='''Name of the model you\'d like to convert.''',
       )
       parser.add_argument(
           '''--checkpoint_path''', default=None, type=str, help='''Path to the original PyTorch checkpoint (.pth file).'''
       )
       parser.add_argument(
           '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
       )
       _SCREAMING_SNAKE_CASE  :						Tuple         =    parser.parse_args()
       convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)


