
"""simple docstring"""




import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel

def __snake_case (      UpperCamelCase						,					UpperCamelCase=1    )       ->    int:


		"""simple docstring"""





		if n_shave_prefix_segments >= 0:
				return ".".join(path.split('''.'''    )[n_shave_prefix_segments:]    )
		else:
				return ".".join(path.split('''.'''    )[:n_shave_prefix_segments]    )

def __snake_case (      UpperCamelCase						,					UpperCamelCase=0    )       ->    Tuple:


		"""simple docstring"""





		a__   		=     []
		for old_item in old_list:
				a__   		=     old_item
				a__   		=     new_item.replace('''block.'''						,					'''resnets.'''    )
				a__   		=     new_item.replace('''conv_shorcut'''						,					'''conv1'''    )
				a__   		=     new_item.replace('''in_shortcut'''						,					'''conv_shortcut'''    )
				a__   		=     new_item.replace('''temb_proj'''						,					'''time_emb_proj'''    )

				a__   		=     shave_segments(UpperCamelCase						,					n_shave_prefix_segments=UpperCamelCase    )

				mapping.append({'''old''': old_item, '''new''': new_item}    )

		return mapping

def __snake_case (      UpperCamelCase						,					UpperCamelCase=0						,					UpperCamelCase=False    )       ->    List[Any]:


		"""simple docstring"""





		a__   		=     []
		for old_item in old_list:
				a__   		=     old_item

				# In `model.mid`, the layer is called `attn`.
				if not in_mid:
						a__   		=     new_item.replace('''attn'''						,					'''attentions'''    )
				a__   		=     new_item.replace('''.k.'''						,					'''.key.'''    )
				a__   		=     new_item.replace('''.v.'''						,					'''.value.'''    )
				a__   		=     new_item.replace('''.q.'''						,					'''.query.'''    )

				a__   		=     new_item.replace('''proj_out'''						,					'''proj_attn'''    )
				a__   		=     new_item.replace('''norm'''						,					'''group_norm'''    )

				a__   		=     shave_segments(UpperCamelCase						,					n_shave_prefix_segments=UpperCamelCase    )
				mapping.append({'''old''': old_item, '''new''': new_item}    )

		return mapping

def __snake_case (      UpperCamelCase						,					UpperCamelCase						,					UpperCamelCase						,					UpperCamelCase=None						,					UpperCamelCase=None						,					UpperCamelCase=None    )       ->    List[Any]:


		"""simple docstring"""





		assert isinstance(UpperCamelCase						,					UpperCamelCase    ), "Paths should be a list of dicts containing 'old' and 'new' keys."

		if attention_paths_to_split is not None:
				if config is None:
						raise ValueError('''Please specify the config if setting \'attention_paths_to_split\' to \'True\'.'''    )

				for path, path_map in attention_paths_to_split.items():
						a__   		=     old_checkpoint[path]
						a__   		=     old_tensor.shape[0] // 3

						a__   		=     (-1, channels) if len(old_tensor.shape    ) == 3 else (-1)

						a__   		=     old_tensor.shape[0] // config.get('''num_head_channels'''						,					1    ) // 3

						a__   		=     old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]    )
						a__		,	a__		,	a__   		=     old_tensor.split(channels // num_heads						,					dim=1    )

						a__   		=     query.reshape(UpperCamelCase    ).squeeze()
						a__   		=     key.reshape(UpperCamelCase    ).squeeze()
						a__   		=     value.reshape(UpperCamelCase    ).squeeze()

		for path in paths:
				a__   		=     path['''new''']

				if attention_paths_to_split is not None and new_path in attention_paths_to_split:
						continue

				a__   		=     new_path.replace('''down.'''						,					'''down_blocks.'''    )
				a__   		=     new_path.replace('''up.'''						,					'''up_blocks.'''    )

				if additional_replacements is not None:
						for replacement in additional_replacements:
								a__   		=     new_path.replace(replacement['''old''']						,					replacement['''new''']    )

				if "attentions" in new_path:
						a__   		=     old_checkpoint[path['''old''']].squeeze()
				else:
						a__   		=     old_checkpoint[path['''old''']]

def __snake_case (      UpperCamelCase						,					UpperCamelCase    )       ->    List[str]:


		"""simple docstring"""





		a__   		=     {}

		a__   		=     checkpoint['''temb.dense.0.weight''']
		a__   		=     checkpoint['''temb.dense.0.bias''']
		a__   		=     checkpoint['''temb.dense.1.weight''']
		a__   		=     checkpoint['''temb.dense.1.bias''']

		a__   		=     checkpoint['''norm_out.weight''']
		a__   		=     checkpoint['''norm_out.bias''']

		a__   		=     checkpoint['''conv_in.weight''']
		a__   		=     checkpoint['''conv_in.bias''']
		a__   		=     checkpoint['''conv_out.weight''']
		a__   		=     checkpoint['''conv_out.bias''']

		a__   		=     len({'''.'''.join(layer.split('''.'''    )[:2]    ) for layer in checkpoint if '''down''' in layer}    )
		a__   		=     {
		    layer_id: [key for key in checkpoint if f"down.{layer_id}" in key] for layer_id in range(UpperCamelCase    )
		}

		a__   		=     len({'''.'''.join(layer.split('''.'''    )[:2]    ) for layer in checkpoint if '''up''' in layer}    )
		a__   		=     {layer_id: [key for key in checkpoint if f"up.{layer_id}" in key] for layer_id in range(UpperCamelCase    )}

		for i in range(UpperCamelCase    ):
				a__   		=     (i - 1) // (config['''layers_per_block'''] + 1)

				if any('''downsample''' in layer for layer in down_blocks[i]    ):
						a__   		=     checkpoint[
						    f"down.{i}.downsample.op.weight"
						]
						a__   		=     checkpoint[f"down.{i}.downsample.op.bias"]
				#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
				#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

				if any('''block''' in layer for layer in down_blocks[i]    ):
						a__   		=     len(
						    {'''.'''.join(shave_segments(UpperCamelCase						,					2    ).split('''.'''    )[:2]    ) for layer in down_blocks[i] if '''block''' in layer}    )
						a__   		=     {
						    layer_id: [key for key in down_blocks[i] if f"block.{layer_id}" in key]
						    for layer_id in range(UpperCamelCase    )
						}

						if num_blocks > 0:
								for j in range(config['''layers_per_block''']    ):
										a__   		=     renew_resnet_paths(blocks[j]    )
										assign_to_checkpoint(UpperCamelCase						,					UpperCamelCase						,					UpperCamelCase    )

				if any('''attn''' in layer for layer in down_blocks[i]    ):
						a__   		=     len(
						    {'''.'''.join(shave_segments(UpperCamelCase						,					2    ).split('''.'''    )[:2]    ) for layer in down_blocks[i] if '''attn''' in layer}    )
						a__   		=     {
						    layer_id: [key for key in down_blocks[i] if f"attn.{layer_id}" in key]
						    for layer_id in range(UpperCamelCase    )
						}

						if num_attn > 0:
								for j in range(config['''layers_per_block''']    ):
										a__   		=     renew_attention_paths(attns[j]    )
										assign_to_checkpoint(UpperCamelCase						,					UpperCamelCase						,					UpperCamelCase						,					config=UpperCamelCase    )

		a__   		=     [key for key in checkpoint if '''mid.block_1''' in key]
		a__   		=     [key for key in checkpoint if '''mid.block_2''' in key]
		a__   		=     [key for key in checkpoint if '''mid.attn_1''' in key]

		# Mid new 2
		a__   		=     renew_resnet_paths(UpperCamelCase    )
		assign_to_checkpoint(
		    UpperCamelCase						,					UpperCamelCase						,					UpperCamelCase						,					additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_1''', '''new''': '''resnets.0'''}]						,					)

		a__   		=     renew_resnet_paths(UpperCamelCase    )
		assign_to_checkpoint(
		    UpperCamelCase						,					UpperCamelCase						,					UpperCamelCase						,					additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_2''', '''new''': '''resnets.1'''}]						,					)

		a__   		=     renew_attention_paths(UpperCamelCase						,					in_mid=UpperCamelCase    )
		assign_to_checkpoint(
		    UpperCamelCase						,					UpperCamelCase						,					UpperCamelCase						,					additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''attn_1''', '''new''': '''attentions.0'''}]						,					)

		for i in range(UpperCamelCase    ):
				a__   		=     num_up_blocks - 1 - i

				if any('''upsample''' in layer for layer in up_blocks[i]    ):
						a__   		=     checkpoint[
						    f"up.{i}.upsample.conv.weight"
						]
						a__   		=     checkpoint[f"up.{i}.upsample.conv.bias"]

				if any('''block''' in layer for layer in up_blocks[i]    ):
						a__   		=     len(
						    {'''.'''.join(shave_segments(UpperCamelCase						,					2    ).split('''.'''    )[:2]    ) for layer in up_blocks[i] if '''block''' in layer}    )
						a__   		=     {
						    layer_id: [key for key in up_blocks[i] if f"block.{layer_id}" in key] for layer_id in range(UpperCamelCase    )
						}

						if num_blocks > 0:
								for j in range(config['''layers_per_block'''] + 1    ):
										a__   		=     {'''old''': f"up_blocks.{i}", '''new''': f"up_blocks.{block_id}"}
										a__   		=     renew_resnet_paths(blocks[j]    )
										assign_to_checkpoint(UpperCamelCase						,					UpperCamelCase						,					UpperCamelCase						,					additional_replacements=[replace_indices]    )

				if any('''attn''' in layer for layer in up_blocks[i]    ):
						a__   		=     len(
						    {'''.'''.join(shave_segments(UpperCamelCase						,					2    ).split('''.'''    )[:2]    ) for layer in up_blocks[i] if '''attn''' in layer}    )
						a__   		=     {
						    layer_id: [key for key in up_blocks[i] if f"attn.{layer_id}" in key] for layer_id in range(UpperCamelCase    )
						}

						if num_attn > 0:
								for j in range(config['''layers_per_block'''] + 1    ):
										a__   		=     {'''old''': f"up_blocks.{i}", '''new''': f"up_blocks.{block_id}"}
										a__   		=     renew_attention_paths(attns[j]    )
										assign_to_checkpoint(UpperCamelCase						,					UpperCamelCase						,					UpperCamelCase						,					additional_replacements=[replace_indices]    )

		a__   		=     {k.replace('''mid_new_2'''						,					'''mid_block'''    ): v for k, v in new_checkpoint.items()}
		return new_checkpoint

def __snake_case (      UpperCamelCase						,					UpperCamelCase    )       ->    List[str]:


		"""simple docstring"""





		a__   		=     {}

		a__   		=     checkpoint['''encoder.norm_out.weight''']
		a__   		=     checkpoint['''encoder.norm_out.bias''']

		a__   		=     checkpoint['''encoder.conv_in.weight''']
		a__   		=     checkpoint['''encoder.conv_in.bias''']
		a__   		=     checkpoint['''encoder.conv_out.weight''']
		a__   		=     checkpoint['''encoder.conv_out.bias''']

		a__   		=     checkpoint['''decoder.norm_out.weight''']
		a__   		=     checkpoint['''decoder.norm_out.bias''']

		a__   		=     checkpoint['''decoder.conv_in.weight''']
		a__   		=     checkpoint['''decoder.conv_in.bias''']
		a__   		=     checkpoint['''decoder.conv_out.weight''']
		a__   		=     checkpoint['''decoder.conv_out.bias''']

		a__   		=     len({'''.'''.join(layer.split('''.'''    )[:3]    ) for layer in checkpoint if '''down''' in layer}    )
		a__   		=     {
		    layer_id: [key for key in checkpoint if f"down.{layer_id}" in key] for layer_id in range(UpperCamelCase    )
		}

		a__   		=     len({'''.'''.join(layer.split('''.'''    )[:3]    ) for layer in checkpoint if '''up''' in layer}    )
		a__   		=     {layer_id: [key for key in checkpoint if f"up.{layer_id}" in key] for layer_id in range(UpperCamelCase    )}

		for i in range(UpperCamelCase    ):
				a__   		=     (i - 1) // (config['''layers_per_block'''] + 1)

				if any('''downsample''' in layer for layer in down_blocks[i]    ):
						a__   		=     checkpoint[
						    f"encoder.down.{i}.downsample.conv.weight"
						]
						a__   		=     checkpoint[
						    f"encoder.down.{i}.downsample.conv.bias"
						]

				if any('''block''' in layer for layer in down_blocks[i]    ):
						a__   		=     len(
						    {'''.'''.join(shave_segments(UpperCamelCase						,					3    ).split('''.'''    )[:3]    ) for layer in down_blocks[i] if '''block''' in layer}    )
						a__   		=     {
						    layer_id: [key for key in down_blocks[i] if f"block.{layer_id}" in key]
						    for layer_id in range(UpperCamelCase    )
						}

						if num_blocks > 0:
								for j in range(config['''layers_per_block''']    ):
										a__   		=     renew_resnet_paths(blocks[j]    )
										assign_to_checkpoint(UpperCamelCase						,					UpperCamelCase						,					UpperCamelCase    )

				if any('''attn''' in layer for layer in down_blocks[i]    ):
						a__   		=     len(
						    {'''.'''.join(shave_segments(UpperCamelCase						,					3    ).split('''.'''    )[:3]    ) for layer in down_blocks[i] if '''attn''' in layer}    )
						a__   		=     {
						    layer_id: [key for key in down_blocks[i] if f"attn.{layer_id}" in key]
						    for layer_id in range(UpperCamelCase    )
						}

						if num_attn > 0:
								for j in range(config['''layers_per_block''']    ):
										a__   		=     renew_attention_paths(attns[j]    )
										assign_to_checkpoint(UpperCamelCase						,					UpperCamelCase						,					UpperCamelCase						,					config=UpperCamelCase    )

		a__   		=     [key for key in checkpoint if '''mid.block_1''' in key]
		a__   		=     [key for key in checkpoint if '''mid.block_2''' in key]
		a__   		=     [key for key in checkpoint if '''mid.attn_1''' in key]

		# Mid new 2
		a__   		=     renew_resnet_paths(UpperCamelCase    )
		assign_to_checkpoint(
		    UpperCamelCase						,					UpperCamelCase						,					UpperCamelCase						,					additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_1''', '''new''': '''resnets.0'''}]						,					)

		a__   		=     renew_resnet_paths(UpperCamelCase    )
		assign_to_checkpoint(
		    UpperCamelCase						,					UpperCamelCase						,					UpperCamelCase						,					additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_2''', '''new''': '''resnets.1'''}]						,					)

		a__   		=     renew_attention_paths(UpperCamelCase						,					in_mid=UpperCamelCase    )
		assign_to_checkpoint(
		    UpperCamelCase						,					UpperCamelCase						,					UpperCamelCase						,					additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''attn_1''', '''new''': '''attentions.0'''}]						,					)

		for i in range(UpperCamelCase    ):
				a__   		=     num_up_blocks - 1 - i

				if any('''upsample''' in layer for layer in up_blocks[i]    ):
						a__   		=     checkpoint[
						    f"decoder.up.{i}.upsample.conv.weight"
						]
						a__   		=     checkpoint[
						    f"decoder.up.{i}.upsample.conv.bias"
						]

				if any('''block''' in layer for layer in up_blocks[i]    ):
						a__   		=     len(
						    {'''.'''.join(shave_segments(UpperCamelCase						,					3    ).split('''.'''    )[:3]    ) for layer in up_blocks[i] if '''block''' in layer}    )
						a__   		=     {
						    layer_id: [key for key in up_blocks[i] if f"block.{layer_id}" in key] for layer_id in range(UpperCamelCase    )
						}

						if num_blocks > 0:
								for j in range(config['''layers_per_block'''] + 1    ):
										a__   		=     {'''old''': f"up_blocks.{i}", '''new''': f"up_blocks.{block_id}"}
										a__   		=     renew_resnet_paths(blocks[j]    )
										assign_to_checkpoint(UpperCamelCase						,					UpperCamelCase						,					UpperCamelCase						,					additional_replacements=[replace_indices]    )

				if any('''attn''' in layer for layer in up_blocks[i]    ):
						a__   		=     len(
						    {'''.'''.join(shave_segments(UpperCamelCase						,					3    ).split('''.'''    )[:3]    ) for layer in up_blocks[i] if '''attn''' in layer}    )
						a__   		=     {
						    layer_id: [key for key in up_blocks[i] if f"attn.{layer_id}" in key] for layer_id in range(UpperCamelCase    )
						}

						if num_attn > 0:
								for j in range(config['''layers_per_block'''] + 1    ):
										a__   		=     {'''old''': f"up_blocks.{i}", '''new''': f"up_blocks.{block_id}"}
										a__   		=     renew_attention_paths(attns[j]    )
										assign_to_checkpoint(UpperCamelCase						,					UpperCamelCase						,					UpperCamelCase						,					additional_replacements=[replace_indices]    )

		a__   		=     {k.replace('''mid_new_2'''						,					'''mid_block'''    ): v for k, v in new_checkpoint.items()}
		a__   		=     checkpoint['''quant_conv.weight''']
		a__   		=     checkpoint['''quant_conv.bias''']
		if "quantize.embedding.weight" in checkpoint:
				a__   		=     checkpoint['''quantize.embedding.weight''']
		a__   		=     checkpoint['''post_quant_conv.weight''']
		a__   		=     checkpoint['''post_quant_conv.bias''']

		return new_checkpoint


if __name__ == "__main__":
					__lowerCAmelCase							:  Optional[Any] 				=		argparse.ArgumentParser()

					parser.add_argument(
					    '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
					)

					parser.add_argument(
					    '''--config_file''',
					    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.''')

					__lowerCAmelCase							:  List[str] 				=		parser.parse_args()
					__lowerCAmelCase							:  Optional[int] 				=		torch.load(args.checkpoint_path)

					with open(args.config_file) as f:
										__lowerCAmelCase							:  Dict 				=		json.loads(f.read())

					# unet case
					__lowerCAmelCase							:  Any 				=		{key.split('''.''')[0] for key in checkpoint.keys()}
					if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
										__lowerCAmelCase							:  Tuple 				=		convert_vq_autoenc_checkpoint(checkpoint, config)
					else:
										__lowerCAmelCase							:  Dict 				=		convert_ddpm_checkpoint(checkpoint, config)

					if "ddpm" in config:
										del config["ddpm"]

					if config["_class_name"] == "VQModel":
										__lowerCAmelCase							:  Any 				=		VQModel(**config)
										model.load_state_dict(converted_checkpoint)
										model.save_pretrained(args.dump_path)
					elif config["_class_name"] == "AutoencoderKL":
										__lowerCAmelCase							:  str 				=		AutoencoderKL(**config)
										model.load_state_dict(converted_checkpoint)
										model.save_pretrained(args.dump_path)
					else:
										__lowerCAmelCase							:  str 				=		UNetaDModel(**config)
										model.load_state_dict(converted_checkpoint)

										__lowerCAmelCase							:  Union[str, Any] 				=		DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

										__lowerCAmelCase							:  Any 				=		DDPMPipeline(unet=model, scheduler=scheduler)
										pipe.save_pretrained(args.dump_path)






"""simple docstring"""




import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_with_pabee

from transformers.testing_utils import TestCasePlus


logging.basicConfig(level=logging.DEBUG)

__lowerCAmelCase							:  Dict 				=		logging.getLogger()

def __snake_case (      )       ->    Optional[int]:


		"""simple docstring"""





		a__   		=     argparse.ArgumentParser()
		parser.add_argument('''-f'''    )
		a__   		=     parser.parse_args()
		return args.f




class SCREAMING_SNAKE_CASE       (      lowerCAmelCase					):






	'''simple docstring'''







	def    _UpperCamelCase							(  self :Tuple			)			->   Union[str, Any]:

			'''simple docstring'''






			a__   		=     logging.StreamHandler(sys.stdout			)
			logger.addHandler(__magic_name__			)

			a__   		=     self.get_auto_remove_tmp_dir()
			a__   		=     F"\n            run_glue_with_pabee.py\n            --model_type albert\n            --model_name_or_path albert-base-v2\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --task_name mrpc\n            --do_train\n            --do_eval\n            --per_gpu_train_batch_size=2\n            --per_gpu_eval_batch_size=1\n            --learning_rate=2e-5\n            --max_steps=50\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            ".split()

			with patch.object(__magic_name__       ,    '''argv'''       ,    __magic_name__			):
					a__   		=     run_glue_with_pabee.main()
					for value in result.values():
							self.assertGreaterEqual(__magic_name__       ,    0.75			)




