

"""simple docstring"""





import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import ConditionalDetrImageProcessor







class 				SCREAMING_SNAKE_CASE					(		unittest.TestCase       ):






					"""simple docstring"""



					def __init__(       self			:				str   ,lowercase_			:				List[Any]   ,lowercase_			:				List[Any]=7   ,lowercase_			:				Tuple=3   ,lowercase_			:				List[Any]=3_0   ,lowercase_			:				Optional[Any]=4_0_0   ,lowercase_			:				str=True   ,lowercase_			:				List[Any]=None   ,lowercase_			:				Dict=True   ,lowercase_			:				Tuple=[0.5, 0.5, 0.5]   ,lowercase_			:				List[Any]=[0.5, 0.5, 0.5]   ,lowercase_			:				Any=True   ,lowercase_			:				Tuple=1 / 2_5_5   ,lowercase_			:				Optional[Any]=True   ,):
						# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
						lowerCAmelCase__	:						Any  				=	size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
						lowerCAmelCase__	:						Optional[int]  				=	parent
						lowerCAmelCase__	:						Optional[Any]  				=	batch_size
						lowerCAmelCase__	:						Any  				=	num_channels
						lowerCAmelCase__	:						Union[str, Any]  				=	min_resolution
						lowerCAmelCase__	:						Tuple  				=	max_resolution
						lowerCAmelCase__	:						int  				=	do_resize
						lowerCAmelCase__	:						Dict  				=	size
						lowerCAmelCase__	:						List[Any]  				=	do_normalize
						lowerCAmelCase__	:						List[Any]  				=	image_mean
						lowerCAmelCase__	:						str  				=	image_std
						lowerCAmelCase__	:						List[Any]  				=	do_rescale
						lowerCAmelCase__	:						Tuple  				=	rescale_factor
						lowerCAmelCase__	:						str  				=	do_pad







					def        __lowerCAmelCase (       self			:				Union[str, Any]		):
						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_normalize": self.do_normalize,
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_rescale": self.do_rescale,
						    "rescale_factor": self.rescale_factor,
						    "do_pad": self.do_pad,
						}







					def        __lowerCAmelCase (       self			:				Dict   ,lowercase_			:				str   ,lowercase_			:				List[Any]=False		):
						if not batched:
							lowerCAmelCase__	:						str  				=	image_inputs[0]
							if isinstance(lowercase_   ,Image.Image		):
								lowerCAmelCase__				,lowerCAmelCase__	:						Optional[int]  				=	image.size
							else:
								lowerCAmelCase__				,lowerCAmelCase__	:						str  				=	image.shape[1], image.shape[2]
							if w < h:
								lowerCAmelCase__	:						Optional[Any]  				=	int(self.size['''shortest_edge'''] * h / w		)
								lowerCAmelCase__	:						Optional[int]  				=	self.size['''shortest_edge''']
							elif w > h:
								lowerCAmelCase__	:						Optional[int]  				=	self.size['''shortest_edge''']
								lowerCAmelCase__	:						Tuple  				=	int(self.size['''shortest_edge'''] * w / h		)
							else:
								lowerCAmelCase__	:						Tuple  				=	self.size['''shortest_edge''']
								lowerCAmelCase__	:						Any  				=	self.size['''shortest_edge''']

						else:
							lowerCAmelCase__	:						Tuple  				=	[]
							for image in image_inputs:
								lowerCAmelCase__				,lowerCAmelCase__	:						Any  				=	self.get_expected_values([image]		)
								expected_values.append((expected_height, expected_width)		)
							lowerCAmelCase__	:						Dict  				=	max(lowercase_   ,key=lambda lowercase_		: item[0]		)[0]
							lowerCAmelCase__	:						Tuple  				=	max(lowercase_   ,key=lambda lowercase_		: item[1]		)[1]

						return expected_height, expected_width







@require_torch
@require_vision
class 				SCREAMING_SNAKE_CASE					(		a_    ,							unittest.TestCase       ):






					"""simple docstring"""



					lowercase__				 =       ConditionalDetrImageProcessor if is_vision_available() else None







					def        __lowerCAmelCase (       self			:				List[Any]		):
						lowerCAmelCase__	:						Union[str, Any]  				=	ConditionalDetrImageProcessingTester(self		)







					@property
					def        __lowerCAmelCase (       self			:				str		):
						return self.image_processor_tester.prepare_image_processor_dict()







					def        __lowerCAmelCase (       self			:				List[Any]		):
						lowerCAmelCase__	:						Optional[Any]  				=	self.image_processing_class(**self.image_processor_dict		)
						self.assertTrue(hasattr(lowercase_   ,'''image_mean'''		)		)
						self.assertTrue(hasattr(lowercase_   ,'''image_std'''		)		)
						self.assertTrue(hasattr(lowercase_   ,'''do_normalize'''		)		)
						self.assertTrue(hasattr(lowercase_   ,'''do_resize'''		)		)
						self.assertTrue(hasattr(lowercase_   ,'''size'''		)		)







					def        __lowerCAmelCase (       self			:				Dict		):
						lowerCAmelCase__	:						List[str]  				=	self.image_processing_class.from_dict(self.image_processor_dict		)
						self.assertEqual(image_processor.size   ,{'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}		)
						self.assertEqual(image_processor.do_pad   ,lowercase_		)

						lowerCAmelCase__	:						Optional[int]  				=	self.image_processing_class.from_dict(
						    self.image_processor_dict   ,size=4_2   ,max_size=8_4   ,pad_and_return_pixel_mask=lowercase_		)
						self.assertEqual(image_processor.size   ,{'''shortest_edge''': 4_2, '''longest_edge''': 8_4}		)
						self.assertEqual(image_processor.do_pad   ,lowercase_		)







					def        __lowerCAmelCase (       self			:				Optional[int]		):
						pass







					def        __lowerCAmelCase (       self			:				Union[str, Any]		):
						# Initialize image_processing
						lowerCAmelCase__	:						Optional[Any]  				=	self.image_processing_class(**self.image_processor_dict		)
						# create random PIL images
						lowerCAmelCase__	:						Dict  				=	prepare_image_inputs(self.image_processor_tester   ,equal_resolution=lowercase_		)
						for image in image_inputs:
							self.assertIsInstance(lowercase_   ,Image.Image		)

						# Test not batched input
						lowerCAmelCase__	:						Tuple  				=	image_processing(image_inputs[0]   ,return_tensors='''pt'''		).pixel_values

						lowerCAmelCase__				,lowerCAmelCase__	:						Tuple  				=	self.image_processor_tester.get_expected_values(lowercase_		)

						self.assertEqual(
						    encoded_images.shape   ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,)

						# Test batched
						lowerCAmelCase__				,lowerCAmelCase__	:						Optional[int]  				=	self.image_processor_tester.get_expected_values(lowercase_   ,batched=lowercase_		)

						lowerCAmelCase__	:						List[Any]  				=	image_processing(lowercase_   ,return_tensors='''pt'''		).pixel_values
						self.assertEqual(
						    encoded_images.shape   ,(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )   ,)







					def        __lowerCAmelCase (       self			:				List[Any]		):
						# Initialize image_processing
						lowerCAmelCase__	:						Union[str, Any]  				=	self.image_processing_class(**self.image_processor_dict		)
						# create random numpy tensors
						lowerCAmelCase__	:						List[Any]  				=	prepare_image_inputs(self.image_processor_tester   ,equal_resolution=lowercase_   ,numpify=lowercase_		)
						for image in image_inputs:
							self.assertIsInstance(lowercase_   ,np.ndarray		)

						# Test not batched input
						lowerCAmelCase__	:						int  				=	image_processing(image_inputs[0]   ,return_tensors='''pt'''		).pixel_values

						lowerCAmelCase__				,lowerCAmelCase__	:						Dict  				=	self.image_processor_tester.get_expected_values(lowercase_		)

						self.assertEqual(
						    encoded_images.shape   ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,)

						# Test batched
						lowerCAmelCase__	:						Optional[int]  				=	image_processing(lowercase_   ,return_tensors='''pt'''		).pixel_values

						lowerCAmelCase__				,lowerCAmelCase__	:						int  				=	self.image_processor_tester.get_expected_values(lowercase_   ,batched=lowercase_		)

						self.assertEqual(
						    encoded_images.shape   ,(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )   ,)







					def        __lowerCAmelCase (       self			:				Dict		):
						# Initialize image_processing
						lowerCAmelCase__	:						List[str]  				=	self.image_processing_class(**self.image_processor_dict		)
						# create random PyTorch tensors
						lowerCAmelCase__	:						Tuple  				=	prepare_image_inputs(self.image_processor_tester   ,equal_resolution=lowercase_   ,torchify=lowercase_		)
						for image in image_inputs:
							self.assertIsInstance(lowercase_   ,torch.Tensor		)

						# Test not batched input
						lowerCAmelCase__	:						str  				=	image_processing(image_inputs[0]   ,return_tensors='''pt'''		).pixel_values

						lowerCAmelCase__				,lowerCAmelCase__	:						Dict  				=	self.image_processor_tester.get_expected_values(lowercase_		)

						self.assertEqual(
						    encoded_images.shape   ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,)

						# Test batched
						lowerCAmelCase__	:						Dict  				=	image_processing(lowercase_   ,return_tensors='''pt'''		).pixel_values

						lowerCAmelCase__				,lowerCAmelCase__	:						List[Any]  				=	self.image_processor_tester.get_expected_values(lowercase_   ,batched=lowercase_		)

						self.assertEqual(
						    encoded_images.shape   ,(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )   ,)







					@slow
					def        __lowerCAmelCase (       self			:				Optional[Any]		):
						# prepare image and target
						lowerCAmelCase__	:						str  				=	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
						with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''   ,'''r'''		) as f:
							lowerCAmelCase__	:						List[str]  				=	json.loads(f.read()		)

						lowerCAmelCase__	:						Optional[Any]  				=	{'''image_id''': 3_9_7_6_9, '''annotations''': target}

						# encode them
						lowerCAmelCase__	:						Any  				=	ConditionalDetrImageProcessor.from_pretrained('''microsoft/conditional-detr-resnet-50'''		)
						lowerCAmelCase__	:						str  				=	image_processing(images=lowercase_   ,annotations=lowercase_   ,return_tensors='''pt'''		)

						# verify pixel values
						lowerCAmelCase__	:						List[Any]  				=	torch.Size([1, 3, 8_0_0, 1_0_6_6]		)
						self.assertEqual(encoding['''pixel_values'''].shape   ,lowercase_		)

						lowerCAmelCase__	:						Tuple  				=	torch.tensor([0.2796, 0.3138, 0.3481]		)
						self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]   ,lowercase_   ,atol=1E-4		)		)

						# verify area
						lowerCAmelCase__	:						str  				=	torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']   ,lowercase_		)		)
						# verify boxes
						lowerCAmelCase__	:						Any  				=	torch.Size([6, 4]		)
						self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape   ,lowercase_		)
						lowerCAmelCase__	:						Union[str, Any]  				=	torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]   ,lowercase_   ,atol=1E-3		)		)
						# verify image_id
						lowerCAmelCase__	:						int  				=	torch.tensor([3_9_7_6_9]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']   ,lowercase_		)		)
						# verify is_crowd
						lowerCAmelCase__	:						Optional[int]  				=	torch.tensor([0, 0, 0, 0, 0, 0]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']   ,lowercase_		)		)
						# verify class_labels
						lowerCAmelCase__	:						List[str]  				=	torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']   ,lowercase_		)		)
						# verify orig_size
						lowerCAmelCase__	:						str  				=	torch.tensor([4_8_0, 6_4_0]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']   ,lowercase_		)		)
						# verify size
						lowerCAmelCase__	:						Union[str, Any]  				=	torch.tensor([8_0_0, 1_0_6_6]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']   ,lowercase_		)		)







					@slow
					def        __lowerCAmelCase (       self			:				Dict		):
						# prepare image, target and masks_path
						lowerCAmelCase__	:						Tuple  				=	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
						with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''   ,'''r'''		) as f:
							lowerCAmelCase__	:						List[str]  				=	json.loads(f.read()		)

						lowerCAmelCase__	:						int  				=	{'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}

						lowerCAmelCase__	:						Union[str, Any]  				=	pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''		)

						# encode them
						lowerCAmelCase__	:						Any  				=	ConditionalDetrImageProcessor(format='''coco_panoptic'''		)
						lowerCAmelCase__	:						str  				=	image_processing(images=lowercase_   ,annotations=lowercase_   ,masks_path=lowercase_   ,return_tensors='''pt'''		)

						# verify pixel values
						lowerCAmelCase__	:						str  				=	torch.Size([1, 3, 8_0_0, 1_0_6_6]		)
						self.assertEqual(encoding['''pixel_values'''].shape   ,lowercase_		)

						lowerCAmelCase__	:						Union[str, Any]  				=	torch.tensor([0.2796, 0.3138, 0.3481]		)
						self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]   ,lowercase_   ,atol=1E-4		)		)

						# verify area
						lowerCAmelCase__	:						int  				=	torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']   ,lowercase_		)		)
						# verify boxes
						lowerCAmelCase__	:						int  				=	torch.Size([6, 4]		)
						self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape   ,lowercase_		)
						lowerCAmelCase__	:						int  				=	torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]   ,lowercase_   ,atol=1E-3		)		)
						# verify image_id
						lowerCAmelCase__	:						Optional[Any]  				=	torch.tensor([3_9_7_6_9]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']   ,lowercase_		)		)
						# verify is_crowd
						lowerCAmelCase__	:						int  				=	torch.tensor([0, 0, 0, 0, 0, 0]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']   ,lowercase_		)		)
						# verify class_labels
						lowerCAmelCase__	:						Union[str, Any]  				=	torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']   ,lowercase_		)		)
						# verify masks
						lowerCAmelCase__	:						List[str]  				=	8_2_2_8_7_3
						self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()   ,lowercase_		)
						# verify orig_size
						lowerCAmelCase__	:						List[str]  				=	torch.tensor([4_8_0, 6_4_0]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']   ,lowercase_		)		)
						# verify size
						lowerCAmelCase__	:						List[str]  				=	torch.tensor([8_0_0, 1_0_6_6]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']   ,lowercase_		)		)









'''simple docstring'''





import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

_lowerCAmelCase      						=	[
    # (stable-diffusion, HF Diffusers)
    ('''time_embed.0.weight''', '''time_embedding.linear_1.weight'''),
    ('''time_embed.0.bias''', '''time_embedding.linear_1.bias'''),
    ('''time_embed.2.weight''', '''time_embedding.linear_2.weight'''),
    ('''time_embed.2.bias''', '''time_embedding.linear_2.bias'''),
    ('''input_blocks.0.0.weight''', '''conv_in.weight'''),
    ('''input_blocks.0.0.bias''', '''conv_in.bias'''),
    ('''out.0.weight''', '''conv_norm_out.weight'''),
    ('''out.0.bias''', '''conv_norm_out.bias'''),
    ('''out.2.weight''', '''conv_out.weight'''),
    ('''out.2.bias''', '''conv_out.bias'''),
]

_lowerCAmelCase      						=	[
    # (stable-diffusion, HF Diffusers)
    ('''in_layers.0''', '''norm1'''),
    ('''in_layers.2''', '''conv1'''),
    ('''out_layers.0''', '''norm2'''),
    ('''out_layers.3''', '''conv2'''),
    ('''emb_layers.1''', '''time_emb_proj'''),
    ('''skip_connection''', '''conv_shortcut'''),
]

_lowerCAmelCase      						=	[]
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
 # loop over downblocks/upblocks

 for j in range(2):
  # loop over resnets/attentions for downblocks
  _lowerCAmelCase      						=	F"""down_blocks.{i}.resnets.{j}."""
  _lowerCAmelCase      						=	F"""input_blocks.{3*i + j + 1}.0."""
  unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

  if i < 3:
   # no attention layers in down_blocks.3
   _lowerCAmelCase      						=	F"""down_blocks.{i}.attentions.{j}."""
   _lowerCAmelCase      						=	F"""input_blocks.{3*i + j + 1}.1."""
   unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

 for j in range(3):
  # loop over resnets/attentions for upblocks
  _lowerCAmelCase      						=	F"""up_blocks.{i}.resnets.{j}."""
  _lowerCAmelCase      						=	F"""output_blocks.{3*i + j}.0."""
  unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

  if i > 0:
   # no attention layers in up_blocks.0
   _lowerCAmelCase      						=	F"""up_blocks.{i}.attentions.{j}."""
   _lowerCAmelCase      						=	F"""output_blocks.{3*i + j}.1."""
   unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

 if i < 3:
  # no downsample in down_blocks.3
  _lowerCAmelCase      						=	F"""down_blocks.{i}.downsamplers.0.conv."""
  _lowerCAmelCase      						=	F"""input_blocks.{3*(i+1)}.0.op."""
  unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

  # no upsample in up_blocks.3
  _lowerCAmelCase      						=	F"""up_blocks.{i}.upsamplers.0."""
  _lowerCAmelCase      						=	F"""output_blocks.{3*i + 2}.{1 if i == 0 else 2}."""
  unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

_lowerCAmelCase      						=	'''mid_block.attentions.0.'''
_lowerCAmelCase      						=	'''middle_block.1.'''
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
 _lowerCAmelCase      						=	F"""mid_block.resnets.{j}."""
 _lowerCAmelCase      						=	F"""middle_block.{2*j}."""
 unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  Any							      =					{k: k for k in unet_state_dict.keys()}
    for sd_name, hf_name in unet_conversion_map:
        lowerCAmelCase__ :  Optional[int]							      =					sd_name
    for k, v in mapping.items():
        if "resnets" in k:
            for sd_part, hf_part in unet_conversion_map_resnet:
                lowerCAmelCase__ :  Any							      =					v.replace(UpperCamelCase							,    UpperCamelCase			)
            lowerCAmelCase__ :  List[Any]							      =					v
    for k, v in mapping.items():
        for sd_part, hf_part in unet_conversion_map_layer:
            lowerCAmelCase__ :  List[Any]							      =					v.replace(UpperCamelCase							,    UpperCamelCase			)
        lowerCAmelCase__ :  Optional[Any]							      =					v
    lowerCAmelCase__ :  Tuple							      =					{v: unet_state_dict[k] for k, v in mapping.items()}
    return new_state_dict


# ================#
# VAE Conversion #
# ================#

_lowerCAmelCase      						=	[
    # (stable-diffusion, HF Diffusers)
    ('''nin_shortcut''', '''conv_shortcut'''),
    ('''norm_out''', '''conv_norm_out'''),
    ('''mid.attn_1.''', '''mid_block.attentions.0.'''),
]

for i in range(4):
 # down_blocks have two resnets
 for j in range(2):
  _lowerCAmelCase      						=	F"""encoder.down_blocks.{i}.resnets.{j}."""
  _lowerCAmelCase      						=	F"""encoder.down.{i}.block.{j}."""
  vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

 if i < 3:
  _lowerCAmelCase      						=	F"""down_blocks.{i}.downsamplers.0."""
  _lowerCAmelCase      						=	F"""down.{i}.downsample."""
  vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

  _lowerCAmelCase      						=	F"""up_blocks.{i}.upsamplers.0."""
  _lowerCAmelCase      						=	F"""up.{3-i}.upsample."""
  vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

 # up_blocks have three resnets
 # also, up blocks in hf are numbered in reverse from sd
 for j in range(3):
  _lowerCAmelCase      						=	F"""decoder.up_blocks.{i}.resnets.{j}."""
  _lowerCAmelCase      						=	F"""decoder.up.{3-i}.block.{j}."""
  vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
 _lowerCAmelCase      						=	F"""mid_block.resnets.{i}."""
 _lowerCAmelCase      						=	F"""mid.block_{i+1}."""
 vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


_lowerCAmelCase      						=	[
    # (stable-diffusion, HF Diffusers)
    ('''norm.''', '''group_norm.'''),
    ('''q.''', '''query.'''),
    ('''k.''', '''key.'''),
    ('''v.''', '''value.'''),
    ('''proj_out.''', '''proj_attn.'''),
]
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    return w.reshape(*w.shape							,    1							,    1			)
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  Optional[int]							      =					{k: k for k in vae_state_dict.keys()}
    for k, v in mapping.items():
        for sd_part, hf_part in vae_conversion_map:
            lowerCAmelCase__ :  str							      =					v.replace(UpperCamelCase							,    UpperCamelCase			)
        lowerCAmelCase__ :  Optional[Any]							      =					v
    for k, v in mapping.items():
        if "attentions" in k:
            for sd_part, hf_part in vae_conversion_map_attn:
                lowerCAmelCase__ :  Dict							      =					v.replace(UpperCamelCase							,    UpperCamelCase			)
            lowerCAmelCase__ :  List[Any]							      =					v
    lowerCAmelCase__ :  Union[str, Any]							      =					{v: vae_state_dict[k] for k, v in mapping.items()}
    lowerCAmelCase__ :  Tuple							      =					["""q""", """k""", """v""", """proj_out"""]
    for k, v in new_state_dict.items():
        for weight_name in weights_to_convert:
            if f"""mid.attn_1.{weight_name}.weight""" in k:
                print(f"""Reshaping {k} for SD format"""			)
                lowerCAmelCase__ :  Optional[int]							      =					reshape_weight_for_sd(UpperCamelCase			)
    return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


_lowerCAmelCase      						=	[
    # (stable-diffusion, HF Diffusers)
    ('''resblocks.''', '''text_model.encoder.layers.'''),
    ('''ln_1''', '''layer_norm1'''),
    ('''ln_2''', '''layer_norm2'''),
    ('''.c_fc.''', '''.fc1.'''),
    ('''.c_proj.''', '''.fc2.'''),
    ('''.attn''', '''.self_attn'''),
    ('''ln_final.''', '''transformer.text_model.final_layer_norm.'''),
    ('''token_embedding.weight''', '''transformer.text_model.embeddings.token_embedding.weight'''),
    ('''positional_embedding''', '''transformer.text_model.embeddings.position_embedding.weight'''),
]
_lowerCAmelCase      						=	{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
_lowerCAmelCase      						=	re.compile('''|'''.join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
_lowerCAmelCase      						=	{'''q''': 0, '''k''': 1, '''v''': 2}
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  Optional[Any]							      =					{}
    lowerCAmelCase__ :  int							      =					{}
    lowerCAmelCase__ :  List[Any]							      =					{}
    for k, v in text_enc_dict.items():
        if (
            k.endswith(""".self_attn.q_proj.weight"""			)
            or k.endswith(""".self_attn.k_proj.weight"""			)
            or k.endswith(""".self_attn.v_proj.weight"""			)
        ):
            lowerCAmelCase__ :  Optional[int]							      =					k[: -len(""".q_proj.weight"""			)]
            lowerCAmelCase__ :  Tuple							      =					k[-len("""q_proj.weight"""			)]
            if k_pre not in capture_qkv_weight:
                lowerCAmelCase__ :  List[Any]							      =					[None, None, None]
            lowerCAmelCase__ :  Dict							      =					v
            continue

        if (
            k.endswith(""".self_attn.q_proj.bias"""			)
            or k.endswith(""".self_attn.k_proj.bias"""			)
            or k.endswith(""".self_attn.v_proj.bias"""			)
        ):
            lowerCAmelCase__ :  str							      =					k[: -len(""".q_proj.bias"""			)]
            lowerCAmelCase__ :  List[str]							      =					k[-len("""q_proj.bias"""			)]
            if k_pre not in capture_qkv_bias:
                lowerCAmelCase__ :  Union[str, Any]							      =					[None, None, None]
            lowerCAmelCase__ :  Any							      =					v
            continue

        lowerCAmelCase__ :  Dict							      =					textenc_pattern.sub(lambda UpperCamelCase			: protected[re.escape(m.group(0			)			)]							,    UpperCamelCase			)
        lowerCAmelCase__ :  Union[str, Any]							      =					v

    for k_pre, tensors in capture_qkv_weight.items():
        if None in tensors:
            raise Exception("""CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"""			)
        lowerCAmelCase__ :  Any							      =					textenc_pattern.sub(lambda UpperCamelCase			: protected[re.escape(m.group(0			)			)]							,    UpperCamelCase			)
        lowerCAmelCase__ :  Tuple							      =					torch.cat(UpperCamelCase			)

    for k_pre, tensors in capture_qkv_bias.items():
        if None in tensors:
            raise Exception("""CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"""			)
        lowerCAmelCase__ :  str							      =					textenc_pattern.sub(lambda UpperCamelCase			: protected[re.escape(m.group(0			)			)]							,    UpperCamelCase			)
        lowerCAmelCase__ :  List[Any]							      =					torch.cat(UpperCamelCase			)

    return new_state_dict
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    return text_enc_dict


if __name__ == "__main__":
 _lowerCAmelCase      						=	argparse.ArgumentParser()

 parser.add_argument('''--model_path''', default=None, type=str, required=True, help='''Path to the model to convert.''')
 parser.add_argument('''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the output model.''')
 parser.add_argument('''--half''', action='''store_true''', help='''Save weights in half precision.''')
 parser.add_argument(
     '''--use_safetensors''', action='''store_true''', help='''Save weights use safetensors, default is ckpt.'''
 )

 _lowerCAmelCase      						=	parser.parse_args()

 assert args.model_path is not None, "Must provide a model path!"

 assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

 # Path for safetensors
 _lowerCAmelCase      						=	osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.safetensors''')
 _lowerCAmelCase      						=	osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.safetensors''')
 _lowerCAmelCase      						=	osp.join(args.model_path, '''text_encoder''', '''model.safetensors''')

 # Load models from safetensors if it exists, if it doesn't pytorch
 if osp.exists(unet_path):
  _lowerCAmelCase      						=	load_file(unet_path, device='''cpu''')
 else:
  _lowerCAmelCase      						=	osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.bin''')
  _lowerCAmelCase      						=	torch.load(unet_path, map_location='''cpu''')

 if osp.exists(vae_path):
  _lowerCAmelCase      						=	load_file(vae_path, device='''cpu''')
 else:
  _lowerCAmelCase      						=	osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.bin''')
  _lowerCAmelCase      						=	torch.load(vae_path, map_location='''cpu''')

 if osp.exists(text_enc_path):
  _lowerCAmelCase      						=	load_file(text_enc_path, device='''cpu''')
 else:
  _lowerCAmelCase      						=	osp.join(args.model_path, '''text_encoder''', '''pytorch_model.bin''')
  _lowerCAmelCase      						=	torch.load(text_enc_path, map_location='''cpu''')

 # Convert the UNet model
 _lowerCAmelCase      						=	convert_unet_state_dict(unet_state_dict)
 _lowerCAmelCase      						=	{'''model.diffusion_model.''' + k: v for k, v in unet_state_dict.items()}

 # Convert the VAE model
 _lowerCAmelCase      						=	convert_vae_state_dict(vae_state_dict)
 _lowerCAmelCase      						=	{'''first_stage_model.''' + k: v for k, v in vae_state_dict.items()}

 # Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
 _lowerCAmelCase      						=	'''text_model.encoder.layers.22.layer_norm2.bias''' in text_enc_dict

 if is_vaa_model:
  # Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
  _lowerCAmelCase      						=	{'''transformer.''' + k: v for k, v in text_enc_dict.items()}
  _lowerCAmelCase      						=	convert_text_enc_state_dict_vaa(text_enc_dict)
  _lowerCAmelCase      						=	{'''cond_stage_model.model.''' + k: v for k, v in text_enc_dict.items()}
 else:
  _lowerCAmelCase      						=	convert_text_enc_state_dict(text_enc_dict)
  _lowerCAmelCase      						=	{'''cond_stage_model.transformer.''' + k: v for k, v in text_enc_dict.items()}

 # Put together new checkpoint
 _lowerCAmelCase      						=	{**unet_state_dict, **vae_state_dict, **text_enc_dict}
 if args.half:
  _lowerCAmelCase      						=	{k: v.half() for k, v in state_dict.items()}

 if args.use_safetensors:
  save_file(state_dict, args.checkpoint_path)
 else:
  _lowerCAmelCase      						=	{'''state_dict''': state_dict}
  torch.save(state_dict, args.checkpoint_path)


