
import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS


enable_full_determinism()




class 					_SCREAMING_SNAKE_CASE  (      unittest.TestCase ):
      _A						:		str    	=     StableDiffusionLDMaDPipeline
      _A						:		List[str]    	=     TEXT_TO_IMAGE_PARAMS
      _A						:		Optional[int]    	=     TEXT_TO_IMAGE_BATCH_PARAMS
      _A						:		Union[str, Any]    	=     TEXT_TO_IMAGE_IMAGE_PARAMS
      def A_			(			self ):
          torch.manual_seed(0 )
          snake_case__						=				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		,	)
          snake_case__						=				DDIMScheduler(
              beta_start=0.0_0_0_8_5		,	beta_end=0.0_1_2		,	beta_schedule="scaled_linear"		,	clip_sample=lowerCamelCase		,	set_alpha_to_one=lowerCamelCase		,	)
          torch.manual_seed(0 )
          snake_case__						=				AutoencoderKL(
              block_out_channels=[32, 64]		,	in_channels=6		,	out_channels=6		,	down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]		,	up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]		,	latent_channels=4		,	)
          torch.manual_seed(0 )
          snake_case__						=				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=10_00		,	)
          snake_case__						=				CLIPTextModel(lowerCamelCase )
          snake_case__						=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )

          snake_case__						=				{
              "unet": unet,
              "scheduler": scheduler,
              "vae": vae,
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "safety_checker": None,
              "feature_extractor": None,
          }
          return components
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase=0 ):
          if str(lowerCamelCase ).startswith("mps" ):
              snake_case__						=				torch.manual_seed(lowerCamelCase )
          else:
              snake_case__						=				torch.Generator(device=lowerCamelCase ).manual_seed(lowerCamelCase )
          snake_case__						=				{
              "prompt": "A painting of a squirrel eating a burger",
              "generator": generator,
              "num_inference_steps": 2,
              "guidance_scale": 6.0,
              "output_type": "numpy",
          }
          return inputs
      def A_			(			self ):
          snake_case__						=				"cpu"  # ensure determinism for the device-dependent torch.Generator

          snake_case__						=				self.get_dummy_components()
          snake_case__						=				StableDiffusionLDMaDPipeline(**lowerCamelCase )
          snake_case__						=				ldmad_pipe.to(lowerCamelCase )
          ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase )

          snake_case__						=				self.get_dummy_inputs(lowerCamelCase )
          snake_case__						=				ldmad_pipe(**lowerCamelCase )
          snake_case__						,					snake_case__						=				output.rgb, output.depth

          snake_case__						=				rgb[0, -3:, -3:, -1]
          snake_case__						=				depth[0, -3:, -1]

          assert rgb.shape == (1, 64, 64, 3)
          assert depth.shape == (1, 64, 64)

          snake_case__						=				np.array(
              [0.3_7_3_3_8_1_7_6, 0.7_0_2_4_7, 0.7_4_2_0_3_1_9_3, 0.5_1_6_4_3_6_0_4, 0.5_8_2_5_6_7_9_3, 0.6_0_9_3_2_1_3_6, 0.4_1_8_1_0_9_5, 0.4_8_3_5_5_8_7_7, 0.4_6_5_3_5_2_6_2] )
          snake_case__						=				np.array([1_0_3.4_6_7_2_7, 8_5.8_1_2_0_0_4, 8_7.8_4_9_2_3_6] )

          assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb ).max() < 1e-2
          assert np.abs(image_slice_depth.flatten() - expected_slice_depth ).max() < 1e-2
      def A_			(			self ):
          snake_case__						=				self.get_dummy_components()
          snake_case__						=				StableDiffusionLDMaDPipeline(**lowerCamelCase )
          snake_case__						=				ldmad_pipe.to(lowerCamelCase )
          ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase )

          snake_case__						=				self.get_dummy_inputs(lowerCamelCase )
          snake_case__						=				3 * [inputs["prompt"]]

          # forward
          snake_case__						=				ldmad_pipe(**lowerCamelCase )
          snake_case__						,					snake_case__						=				output.rgb, output.depth
          snake_case__						=				rgb_slice_a[0, -3:, -3:, -1]
          snake_case__						=				depth_slice_a[0, -3:, -1]

          snake_case__						=				self.get_dummy_inputs(lowerCamelCase )
          snake_case__						=				3 * [inputs.pop("prompt" )]

          snake_case__						=				ldmad_pipe.tokenizer(
              lowerCamelCase		,	padding="max_length"		,	max_length=ldmad_pipe.tokenizer.model_max_length		,	truncation=lowerCamelCase		,	return_tensors="pt"		,	)
          snake_case__						=				text_inputs["input_ids"].to(lowerCamelCase )

          snake_case__						=				ldmad_pipe.text_encoder(lowerCamelCase )[0]

          snake_case__						=				prompt_embeds

          # forward
          snake_case__						=				ldmad_pipe(**lowerCamelCase )
          snake_case__						,					snake_case__						=				output.rgb, output.depth
          snake_case__						=				rgb_slice_a[0, -3:, -3:, -1]
          snake_case__						=				depth_slice_a[0, -3:, -1]

          assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten() ).max() < 1e-4
          assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten() ).max() < 1e-4



      def A_			(			self ):
          snake_case__						=				"cpu"  # ensure determinism for the device-dependent torch.Generator
          snake_case__						=				self.get_dummy_components()
          snake_case__						=				PNDMScheduler(skip_prk_steps=lowerCamelCase )
          snake_case__						=				StableDiffusionLDMaDPipeline(**lowerCamelCase )
          snake_case__						=				ldmad_pipe.to(lowerCamelCase )
          ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase )

          snake_case__						=				self.get_dummy_inputs(lowerCamelCase )
          snake_case__						=				"french fries"
          snake_case__						=				ldmad_pipe(**lowerCamelCase		,	negative_prompt=lowerCamelCase )

          snake_case__						,					snake_case__						=				output.rgb, output.depth
          snake_case__						=				rgb[0, -3:, -3:, -1]
          snake_case__						=				depth[0, -3:, -1]

          assert rgb.shape == (1, 64, 64, 3)
          assert depth.shape == (1, 64, 64)

          snake_case__						=				np.array(
              [0.3_7_0_4_4, 0.7_1_8_1_1_5_0_3, 0.7_2_2_3_2_5_1, 0.4_8_6_0_3_6_7_5, 0.5_6_3_8_3_9_1, 0.6_3_6_4_9_4_8, 0.4_2_8_3_3_7_0_4, 0.4_9_0_1_3_1_5, 0.4_7_9_2_6_2_1_7] )
          snake_case__						=				np.array([1_0_7.8_4_7_3_8, 8_4.6_2_8_0_2, 8_9.9_6_2_1_3_5] )
          assert np.abs(rgb_slice.flatten() - expected_slice_rgb ).max() < 1e-2
          assert np.abs(depth_slice.flatten() - expected_slice_depth ).max() < 1e-2





@slow
@require_torch_gpu
class 					_SCREAMING_SNAKE_CASE  (      unittest.TestCase ):
      def A_			(			self ):
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase="cpu"		,	lowerCamelCase=torch.floataa		,	lowerCamelCase=0 ):
          snake_case__						=				torch.Generator(device=lowerCamelCase ).manual_seed(lowerCamelCase )
          snake_case__						=				np.random.RandomState(lowerCamelCase ).standard_normal((1, 4, 64, 64) )
          snake_case__						=				torch.from_numpy(lowerCamelCase ).to(device=lowerCamelCase		,	dtype=lowerCamelCase )
          snake_case__						=				{
              "prompt": "a photograph of an astronaut riding a horse",
              "latents": latents,
              "generator": generator,
              "num_inference_steps": 3,
              "guidance_scale": 7.5,
              "output_type": "numpy",
          }
          return inputs



      def A_			(			self ):
          snake_case__						=				StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d" )
          snake_case__						=				ldmad_pipe.to(lowerCamelCase )
          ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase )

          snake_case__						=				self.get_inputs(lowerCamelCase )
          snake_case__						=				ldmad_pipe(**lowerCamelCase )
          snake_case__						,					snake_case__						=				output.rgb, output.depth
          snake_case__						=				rgb[0, -3:, -3:, -1].flatten()
          snake_case__						=				rgb[0, -3:, -1].flatten()

          assert rgb.shape == (1, 5_12, 5_12, 3)
          assert depth.shape == (1, 5_12, 5_12)

          snake_case__						=				np.array(
              [0.5_3_8_0_5_4_6_5, 0.5_6_7_0_7_3_0_5, 0.5_4_8_6_5_1_5, 0.5_7_0_1_2_2_3_6, 0.5_8_1_4_5_1_1, 0.5_6_2_5_3_4_8_7, 0.5_4_8_4_3_0_1_4, 0.5_5_0_9_2_2_6_3, 0.6_4_5_9_7_0_6] )
          snake_case__						=				np.array(
              [0.9_2_6_3_7_8_1, 0.6_6_7_8_6_7_2, 0.5_4_8_6_5_1_5, 0.9_2_2_0_2_1_4_5, 0.6_7_8_3_1_1_3_5, 0.5_6_2_5_3_4_8_7, 0.9_2_4_1_6_9_4, 0.7_5_5_1_4_7_8, 0.6_4_5_9_7_0_6] )
          assert np.abs(rgb_slice - expected_slice_rgb ).max() < 3e-3
          assert np.abs(depth_slice - expected_slice_depth ).max() < 3e-3





@nightly
@require_torch_gpu
class 					_SCREAMING_SNAKE_CASE  (      unittest.TestCase ):
      def A_			(			self ):
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase="cpu"		,	lowerCamelCase=torch.floataa		,	lowerCamelCase=0 ):
          snake_case__						=				torch.Generator(device=lowerCamelCase ).manual_seed(lowerCamelCase )
          snake_case__						=				np.random.RandomState(lowerCamelCase ).standard_normal((1, 4, 64, 64) )
          snake_case__						=				torch.from_numpy(lowerCamelCase ).to(device=lowerCamelCase		,	dtype=lowerCamelCase )
          snake_case__						=				{
              "prompt": "a photograph of an astronaut riding a horse",
              "latents": latents,
              "generator": generator,
              "num_inference_steps": 50,
              "guidance_scale": 7.5,
              "output_type": "numpy",
          }
          return inputs
      def A_			(			self ):
          snake_case__						=				StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d" ).to(lowerCamelCase )
          ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase )

          snake_case__						=				self.get_inputs(lowerCamelCase )
          snake_case__						=				ldmad_pipe(**lowerCamelCase )
          snake_case__						,					snake_case__						=				output.rgb, output.depth

          snake_case__						=				0.4_9_5_5_8_6
          snake_case__						=				0.3_3_7_9_5_5_1_5
          snake_case__						=				1_1_2.4_8_5_1_8
          snake_case__						=				9_8.4_8_9_7_4_6
          assert np.abs(expected_rgb_mean - rgb.mean() ) < 1e-3
          assert np.abs(expected_rgb_std - rgb.std() ) < 1e-3
          assert np.abs(expected_depth_mean - depth.mean() ) < 1e-3
          assert np.abs(expected_depth_std - depth.std() ) < 1e-3



      def A_			(			self ):
          snake_case__						=				StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d-4c" ).to(lowerCamelCase )
          ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase )

          snake_case__						=				self.get_inputs(lowerCamelCase )
          snake_case__						=				ldmad_pipe(**lowerCamelCase )
          snake_case__						,					snake_case__						=				output.rgb, output.depth

          snake_case__						=				0.4_1_9_4_1_2_7
          snake_case__						=				0.3_5_3_7_5_5_8_6
          snake_case__						=				0.5_6_3_8_5_0_2
          snake_case__						=				0.3_4_6_8_6_1_0_3

          assert rgb.shape == (1, 5_12, 5_12, 3)
          assert depth.shape == (1, 5_12, 5_12, 1)
          assert np.abs(expected_rgb_mean - rgb.mean() ) < 1e-3
          assert np.abs(expected_rgb_std - rgb.std() ) < 1e-3
          assert np.abs(expected_depth_mean - depth.mean() ) < 1e-3
          assert np.abs(expected_depth_std - depth.std() ) < 1e-3





import argparse
import json

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

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    snake_case__						=				SwinConfig()
    snake_case__						=				swin_name.split("_"							)

    snake_case__						=				name_split[1]
    snake_case__						=				int(name_split[4]							)
    snake_case__						=				int(name_split[3][-1]							)

    if model_size == "tiny":
        snake_case__						=				96
        snake_case__						=				(2, 2, 6, 2)
        snake_case__						=				(3, 6, 12, 24)
    elif model_size == "small":
        snake_case__						=				96
        snake_case__						=				(2, 2, 18, 2)
        snake_case__						=				(3, 6, 12, 24)
    elif model_size == "base":
        snake_case__						=				128
        snake_case__						=				(2, 2, 18, 2)
        snake_case__						=				(4, 8, 16, 32)
    else:
        snake_case__						=				192
        snake_case__						=				(2, 2, 18, 2)
        snake_case__						=				(6, 12, 24, 48)

    if "in22k" in swin_name:
        snake_case__						=				21_841
    else:
        snake_case__						=				1_000
        snake_case__						=				"huggingface/label-files"
        snake_case__						=				"imagenet-1k-id2label.json"
        snake_case__						=				json.load(open(hf_hub_download(__lowerCAmelCase    ,		__lowerCAmelCase    ,		repo_type="dataset"							)    ,		"r"							)							)
        snake_case__						=				{int(__lowerCAmelCase							): v for k, v in idalabel.items()}
        snake_case__						=				idalabel
        snake_case__						=				{v: k for k, v in idalabel.items()}

    snake_case__						=				img_size
    snake_case__						=				num_classes
    snake_case__						=				embed_dim
    snake_case__						=				depths
    snake_case__						=				num_heads
    snake_case__						=				window_size

    return config





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    if "patch_embed.proj" in name:
        snake_case__						=				name.replace("patch_embed.proj"    ,		"embeddings.patch_embeddings.projection"							)
    if "patch_embed.norm" in name:
        snake_case__						=				name.replace("patch_embed.norm"    ,		"embeddings.norm"							)
    if "layers" in name:
        snake_case__						=				"encoder." + name
    if "attn.proj" in name:
        snake_case__						=				name.replace("attn.proj"    ,		"attention.output.dense"							)
    if "attn" in name:
        snake_case__						=				name.replace("attn"    ,		"attention.self"							)
    if "norm1" in name:
        snake_case__						=				name.replace("norm1"    ,		"layernorm_before"							)
    if "norm2" in name:
        snake_case__						=				name.replace("norm2"    ,		"layernorm_after"							)
    if "mlp.fc1" in name:
        snake_case__						=				name.replace("mlp.fc1"    ,		"intermediate.dense"							)
    if "mlp.fc2" in name:
        snake_case__						=				name.replace("mlp.fc2"    ,		"output.dense"							)

    if name == "norm.weight":
        snake_case__						=				"layernorm.weight"
    if name == "norm.bias":
        snake_case__						=				"layernorm.bias"

    if "head" in name:
        snake_case__						=				name.replace("head"    ,		"classifier"							)
    else:
        snake_case__						=				"swin." + name

    return name





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    for key in orig_state_dict.copy().keys():
        snake_case__						=				orig_state_dict.pop(__lowerCAmelCase							)

        if "mask" in key:
            continue
        elif "qkv" in key:
            snake_case__						=				key.split("."							)
            snake_case__						=				int(key_split[1]							)
            snake_case__						=				int(key_split[3]							)
            snake_case__						=				model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

            if "weight" in key:
                snake_case__						=				val[:dim, :]
                snake_case__						=				val[
                    dim : dim * 2, :
                ]
                snake_case__						=				val[-dim:, :]
            else:
                snake_case__						=				val[
                    :dim
                ]
                snake_case__						=				val[
                    dim : dim * 2
                ]
                snake_case__						=				val[
                    -dim:
                ]
        else:
            snake_case__						=				val

    return orig_state_dict





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    snake_case__						=				timm.create_model(__lowerCAmelCase    ,		pretrained=__lowerCAmelCase							)
    timm_model.eval()

    snake_case__						=				get_swin_config(__lowerCAmelCase							)
    snake_case__						=				SwinForImageClassification(__lowerCAmelCase							)
    model.eval()

    snake_case__						=				convert_state_dict(timm_model.state_dict()    ,		__lowerCAmelCase							)
    model.load_state_dict(__lowerCAmelCase							)

    snake_case__						=				"http://images.cocodataset.org/val2017/000000039769.jpg"

    snake_case__						=				AutoImageProcessor.from_pretrained("microsoft/{}".format(swin_name.replace("_"    ,		"-"							)							)							)
    snake_case__						=				Image.open(requests.get(__lowerCAmelCase    ,		stream=__lowerCAmelCase							).raw							)
    snake_case__						=				image_processor(images=__lowerCAmelCase    ,		return_tensors="pt"							)

    snake_case__						=				timm_model(inputs["pixel_values"]							)
    snake_case__						=				model(**__lowerCAmelCase							).logits

    assert torch.allclose(__lowerCAmelCase    ,		__lowerCAmelCase    ,		atol=1E-3							)

    print(F"""Saving model {swin_name} to {pytorch_dump_folder_path}"""							)
    model.save_pretrained(__lowerCAmelCase							)

    print(F"""Saving image processor to {pytorch_dump_folder_path}"""							)
    image_processor.save_pretrained(__lowerCAmelCase							)


if __name__ == "__main__":
     __magic_name__            =  argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--swin_name''',
         default='''swin_tiny_patch4_window7_224''',
         type=str,
         help='''Name of the Swin timm model you\'d like to convert.''',
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
     )

     __magic_name__            =  parser.parse_args()
     convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)



