




import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()



def     lowerCAmelCase		(		_lowerCAmelCase				: int    ,				_lowerCAmelCase				: Tuple    ,				_lowerCAmelCase				: Any						):




 """simple docstring"""







 UpperCAmelCase__							=    {'''small''': (512, 8, 24), '''medium''': (1024, 8, 36), '''large''': (1536, 16, 48)}
 UpperCAmelCase__							=    MODELS[model_size]  # set model hyperparameters
 UpperCAmelCase__							=    ImageGPTConfig(n_embd=a_    ,				n_layer=a_    ,				n_head=a_						)
 UpperCAmelCase__							=    ImageGPTForCausalLM(a_						)

 # Load weights from numpy
 load_tf_weights_in_imagegpt(a_    ,				a_    ,				a_						)

 # Save pytorch-model
 UpperCAmelCase__							=    pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
 UpperCAmelCase__							=    pytorch_dump_folder_path + '''/''' + CONFIG_NAME
 print(F'''Save PyTorch model to {pytorch_weights_dump_path}'''						)
 torch.save(model.state_dict()    ,				a_						)
 print(F'''Save configuration file to {pytorch_config_dump_path}'''						)
 with open(a_    ,				"w"    ,				encoding="utf-8"						) as f:
  f.write(config.to_json_string()						)


if __name__ == "__main__":
  _lowerCAmelCase    : Dict		= argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--imagegpt_checkpoint_path",
      default=None,
      type=str,
      required=True,
      help="Path to the TensorFlow checkpoint path.",
  )
  parser.add_argument(
      "--model_size",
      default=None,
      type=str,
      required=True,
      help="Size of the model (can be either 'small', 'medium' or 'large').",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  )
  _lowerCAmelCase    : List[Any]		= parser.parse_args()
  convert_imagegpt_checkpoint_to_pytorch(
      args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
  )













'''simple docstring'''







import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




@skip_mps
class 					__UpperCAmelCase (		UpperCamelCase__      ,				UpperCamelCase__      ,				UpperCamelCase__      ,				unittest.TestCase	):

      '''simple docstring'''





      SCREAMING_SNAKE_CASE				:    Optional[Any]				       =					StableDiffusionModelEditingPipeline
      SCREAMING_SNAKE_CASE				:    str				       =					TEXT_TO_IMAGE_PARAMS
      SCREAMING_SNAKE_CASE				:    Union[str, Any]				       =					TEXT_TO_IMAGE_BATCH_PARAMS
      SCREAMING_SNAKE_CASE				:    Dict				       =					TEXT_TO_IMAGE_IMAGE_PARAMS
      SCREAMING_SNAKE_CASE				:    Optional[int]				       =					TEXT_TO_IMAGE_IMAGE_PARAMS




      def 							lowerCamelCase     (					self						):
             torch.manual_seed(0						)
             UpperCAmelCase__	:   List[Any]												=	UNetaDConditionModel(
                 block_out_channels=(32, 64)  ,		layers_per_block=2  ,		sample_size=32  ,		in_channels=4  ,		out_channels=4  ,		down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')  ,		up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')  ,		cross_attention_dim=32  ,		)
             UpperCAmelCase__	:   List[str]												=	DDIMScheduler()
             torch.manual_seed(0						)
             UpperCAmelCase__	:   str												=	AutoencoderKL(
                 block_out_channels=[32, 64]  ,		in_channels=3  ,		out_channels=3  ,		down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']  ,		up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']  ,		latent_channels=4  ,		)
             torch.manual_seed(0						)
             UpperCAmelCase__	:   Optional[int]												=	CLIPTextConfig(
                 bos_token_id=0  ,		eos_token_id=2  ,		hidden_size=32  ,		intermediate_size=37  ,		layer_norm_eps=1E-05  ,		num_attention_heads=4  ,		num_hidden_layers=5  ,		pad_token_id=1  ,		vocab_size=1000  ,		)
             UpperCAmelCase__	:   Optional[int]												=	CLIPTextModel(_UpperCAmelCase						)
             UpperCAmelCase__	:   Optional[Any]												=	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

             UpperCAmelCase__	:   List[str]												=	{
                 '''unet''': unet,
                 '''scheduler''': scheduler,
                 '''vae''': vae,
                 '''text_encoder''': text_encoder,
                 '''tokenizer''': tokenizer,
                 '''safety_checker''': None,
                 '''feature_extractor''': None,
             }
             return components




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase=0						):
             UpperCAmelCase__	:   Tuple												=	torch.manual_seed(_UpperCAmelCase						)
             UpperCAmelCase__	:   Dict												=	{
                 '''prompt''': '''A field of roses''',
                 '''generator''': generator,
                 # Setting height and width to None to prevent OOMs on CPU.
                 '''height''': None,
                 '''width''': None,
                 '''num_inference_steps''': 2,
                 '''guidance_scale''': 6.0,
                 '''output_type''': '''numpy''',
             }
             return inputs




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   List[str]												=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
             UpperCAmelCase__	:   Union[str, Any]												=	self.get_dummy_components()
             UpperCAmelCase__	:   Optional[int]												=	StableDiffusionModelEditingPipeline(**_UpperCAmelCase						)
             UpperCAmelCase__	:   Union[str, Any]												=	sd_pipe.to(_UpperCAmelCase						)
             sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase						)

             UpperCAmelCase__	:   Any												=	self.get_dummy_inputs(_UpperCAmelCase						)
             UpperCAmelCase__	:   Dict												=	sd_pipe(**_UpperCAmelCase						).images
             UpperCAmelCase__	:   Tuple												=	image[0, -3:, -3:, -1]
             assert image.shape == (1, 64, 64, 3)

             UpperCAmelCase__	:   int												=	np.array([0.47_55, 0.51_32, 0.49_76, 0.39_04, 0.35_54, 0.47_65, 0.51_39, 0.51_58, 0.48_89]						)

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




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   int												=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
             UpperCAmelCase__	:   List[str]												=	self.get_dummy_components()
             UpperCAmelCase__	:   Tuple												=	StableDiffusionModelEditingPipeline(**_UpperCAmelCase						)
             UpperCAmelCase__	:   List[Any]												=	sd_pipe.to(_UpperCAmelCase						)
             sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase						)

             UpperCAmelCase__	:   str												=	self.get_dummy_inputs(_UpperCAmelCase						)
             UpperCAmelCase__	:   List[Any]												=	'''french fries'''
             UpperCAmelCase__	:   Any												=	sd_pipe(**_UpperCAmelCase  ,		negative_prompt=_UpperCAmelCase						)
             UpperCAmelCase__	:   Dict												=	output.images
             UpperCAmelCase__	:   Tuple												=	image[0, -3:, -3:, -1]

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

             UpperCAmelCase__	:   str												=	np.array([0.49_92, 0.51_01, 0.50_04, 0.39_49, 0.36_04, 0.47_35, 0.52_16, 0.52_04, 0.49_13]						)

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




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   Dict												=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
             UpperCAmelCase__	:   List[Any]												=	self.get_dummy_components()
             UpperCAmelCase__	:   str												=	EulerAncestralDiscreteScheduler(
                 beta_start=0.0_00_85  ,		beta_end=0.0_12  ,		beta_schedule='''scaled_linear'''						)
             UpperCAmelCase__	:   Tuple												=	StableDiffusionModelEditingPipeline(**_UpperCAmelCase						)
             UpperCAmelCase__	:   Optional[int]												=	sd_pipe.to(_UpperCAmelCase						)
             sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase						)

             UpperCAmelCase__	:   Optional[Any]												=	self.get_dummy_inputs(_UpperCAmelCase						)
             UpperCAmelCase__	:   int												=	sd_pipe(**_UpperCAmelCase						).images
             UpperCAmelCase__	:   List[str]												=	image[0, -3:, -3:, -1]

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

             UpperCAmelCase__	:   Tuple												=	np.array([0.47_47, 0.53_72, 0.47_79, 0.49_82, 0.55_43, 0.48_16, 0.52_38, 0.49_04, 0.50_27]						)

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




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   Dict												=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
             UpperCAmelCase__	:   str												=	self.get_dummy_components()
             UpperCAmelCase__	:   List[Any]												=	PNDMScheduler()
             UpperCAmelCase__	:   Tuple												=	StableDiffusionModelEditingPipeline(**_UpperCAmelCase						)
             UpperCAmelCase__	:   Optional[int]												=	sd_pipe.to(_UpperCAmelCase						)
             sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase						)

             UpperCAmelCase__	:   Tuple												=	self.get_dummy_inputs(_UpperCAmelCase						)
             # the pipeline does not expect pndm so test if it raises error.
             with self.assertRaises(_UpperCAmelCase						):
                    UpperCAmelCase__	:   List[Any]												=	sd_pipe(**_UpperCAmelCase						).images




      def 							lowerCamelCase     (					self						):
             super().test_inference_batch_single_identical(expected_max_diff=5E-3						)




      def 							lowerCamelCase     (					self						):
             super().test_attention_slicing_forward_pass(expected_max_diff=5E-3						)




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

      '''simple docstring'''





      def 							lowerCamelCase     (					self						):
             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase=0						):
             UpperCAmelCase__	:   Dict												=	torch.manual_seed(_UpperCAmelCase						)
             UpperCAmelCase__	:   Union[str, Any]												=	{
                 '''prompt''': '''A field of roses''',
                 '''generator''': generator,
                 '''num_inference_steps''': 3,
                 '''guidance_scale''': 7.5,
                 '''output_type''': '''numpy''',
             }
             return inputs




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   List[str]												=	'''CompVis/stable-diffusion-v1-4'''
             UpperCAmelCase__	:   List[str]												=	StableDiffusionModelEditingPipeline.from_pretrained(_UpperCAmelCase  ,		safety_checker=_UpperCAmelCase						)
             pipe.to(_UpperCAmelCase						)
             pipe.set_progress_bar_config(disable=_UpperCAmelCase						)
             pipe.enable_attention_slicing()

             UpperCAmelCase__	:   Union[str, Any]												=	self.get_inputs()
             UpperCAmelCase__	:   Union[str, Any]												=	pipe(**_UpperCAmelCase						).images
             UpperCAmelCase__	:   Optional[int]												=	image[0, -3:, -3:, -1].flatten()

             assert image.shape == (1, 512, 512, 3)

             UpperCAmelCase__	:   List[str]												=	np.array(
                 [0.6_74_94_96, 0.6_38_64_53, 0.51_44_32_67, 0.66_09_49_05, 0.61_92_12_15, 0.5_49_13_32, 0.5_74_44_17, 0.58_07_51_06, 0.5_17_46_58]						)

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

             # make sure image changes after editing
             pipe.edit_model('''A pack of roses'''  ,		'''A pack of blue roses'''						)

             UpperCAmelCase__	:   Optional[int]												=	pipe(**_UpperCAmelCase						).images
             UpperCAmelCase__	:   List[str]												=	image[0, -3:, -3:, -1].flatten()

             assert image.shape == (1, 512, 512, 3)

             assert np.abs(expected_slice - image_slice						).max() > 1E-1




      def 							lowerCamelCase     (					self						):
             torch.cuda.empty_cache()
             torch.cuda.reset_max_memory_allocated()
             torch.cuda.reset_peak_memory_stats()

             UpperCAmelCase__	:   Dict												=	'''CompVis/stable-diffusion-v1-4'''
             UpperCAmelCase__	:   int												=	DDIMScheduler.from_pretrained(_UpperCAmelCase  ,		subfolder='''scheduler'''						)
             UpperCAmelCase__	:   int												=	StableDiffusionModelEditingPipeline.from_pretrained(
                 _UpperCAmelCase  ,		scheduler=_UpperCAmelCase  ,		safety_checker=_UpperCAmelCase						)
             UpperCAmelCase__	:   Tuple												=	pipe.to(_UpperCAmelCase						)
             pipe.set_progress_bar_config(disable=_UpperCAmelCase						)
             pipe.enable_attention_slicing(1						)
             pipe.enable_sequential_cpu_offload()

             UpperCAmelCase__	:   Any												=	self.get_inputs()
             UpperCAmelCase__	:   Any												=	pipe(**_UpperCAmelCase						)

             UpperCAmelCase__	:   Any												=	torch.cuda.max_memory_allocated()
             # make sure that less than 4.4 GB is allocated
             assert mem_bytes < 4.4 * 10**9