





import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel






def 	lowerCamelCase_		(							_UpperCamelCase					,							_UpperCamelCase=1					)						->		List[Any]:






				"""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 	lowerCamelCase_		(							_UpperCamelCase					,							_UpperCamelCase=0					)						->		Tuple:






				"""simple docstring"""


				snake_case_		:       Tuple           =    []
				for old_item in old_list:
								snake_case_		:       str           =    old_item.replace('''in_layers.0'''					,							'''norm1'''					)
								snake_case_		:       Dict           =    new_item.replace('''in_layers.2'''					,							'''conv1'''					)

								snake_case_		:       Union[str, Any]           =    new_item.replace('''out_layers.0'''					,							'''norm2'''					)
								snake_case_		:       str           =    new_item.replace('''out_layers.3'''					,							'''conv2'''					)

								snake_case_		:       List[str]           =    new_item.replace('''emb_layers.1'''					,							'''time_emb_proj'''					)
								snake_case_		:       List[Any]           =    new_item.replace('''skip_connection'''					,							'''conv_shortcut'''					)

								snake_case_		:       Optional[Any]           =    shave_segments(_UpperCamelCase					,							n_shave_prefix_segments=_UpperCamelCase					)

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

				return mapping






def 	lowerCamelCase_		(							_UpperCamelCase					,							_UpperCamelCase=0					)						->		Dict:






				"""simple docstring"""


				snake_case_		:       List[Any]           =    []
				for old_item in old_list:
								snake_case_		:       Tuple           =    old_item

								snake_case_		:       List[Any]           =    new_item.replace('''norm.weight'''					,							'''group_norm.weight'''					)
								snake_case_		:       List[Any]           =    new_item.replace('''norm.bias'''					,							'''group_norm.bias'''					)

								snake_case_		:       Dict           =    new_item.replace('''proj_out.weight'''					,							'''proj_attn.weight'''					)
								snake_case_		:       Union[str, Any]           =    new_item.replace('''proj_out.bias'''					,							'''proj_attn.bias'''					)

								snake_case_		:       List[Any]           =    shave_segments(_UpperCamelCase					,							n_shave_prefix_segments=_UpperCamelCase					)

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

				return mapping






def 	lowerCamelCase_		(							_UpperCamelCase					,							_UpperCamelCase					,							_UpperCamelCase					,							_UpperCamelCase=None					,							_UpperCamelCase=None					,							_UpperCamelCase=None					)						->		Any:






				"""simple docstring"""


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

				# Splits the attention layers into three variables.
				if attention_paths_to_split is not None:
								for path, path_map in attention_paths_to_split.items():
												snake_case_		:       List[Any]           =    old_checkpoint[path]
												snake_case_		:       Tuple           =    old_tensor.shape[0] // 3

												snake_case_		:       int           =    (-1, channels) if len(old_tensor.shape					) == 3 else (-1)

												snake_case_		:       Dict           =    old_tensor.shape[0] // config['''num_head_channels'''] // 3

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

												snake_case_		:       List[str]           =    query.reshape(_UpperCamelCase					)
												snake_case_		:       Dict           =    key.reshape(_UpperCamelCase					)
												snake_case_		:       List[str]           =    value.reshape(_UpperCamelCase					)

				for path in paths:
								snake_case_		:       Any           =    path['''new''']

								# These have already been assigned
								if attention_paths_to_split is not None and new_path in attention_paths_to_split:
												continue

								# Global renaming happens here
								snake_case_		:       List[Any]           =    new_path.replace('''middle_block.0'''					,							'''mid_block.resnets.0'''					)
								snake_case_		:       Dict           =    new_path.replace('''middle_block.1'''					,							'''mid_block.attentions.0'''					)
								snake_case_		:       str           =    new_path.replace('''middle_block.2'''					,							'''mid_block.resnets.1'''					)

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

        # proj_attn.weight has to be converted from conv 1D to linear
								if "proj_attn.weight" in new_path:
												snake_case_		:       Dict           =    old_checkpoint[path['''old''']][:, :, 0]
								else:
												snake_case_		:       int           =    old_checkpoint[path['''old''']]






def 	lowerCamelCase_		(							_UpperCamelCase					,							_UpperCamelCase					)						->		List[Any]:






				"""simple docstring"""


				snake_case_		:       Optional[int]           =    {}

				snake_case_		:       Tuple           =    checkpoint['''time_embed.0.weight''']
				snake_case_		:       Any           =    checkpoint['''time_embed.0.bias''']
				snake_case_		:       Optional[Any]           =    checkpoint['''time_embed.2.weight''']
				snake_case_		:       List[Any]           =    checkpoint['''time_embed.2.bias''']

				snake_case_		:       List[str]           =    checkpoint['''input_blocks.0.0.weight''']
				snake_case_		:       Optional[int]           =    checkpoint['''input_blocks.0.0.bias''']

				snake_case_		:       Any           =    checkpoint['''out.0.weight''']
				snake_case_		:       Union[str, Any]           =    checkpoint['''out.0.bias''']
				snake_case_		:       Optional[Any]           =    checkpoint['''out.2.weight''']
				snake_case_		:       Union[str, Any]           =    checkpoint['''out.2.bias''']

				# Retrieves the keys for the input blocks only
				snake_case_		:       Union[str, Any]           =    len({'''.'''.join(layer.split('''.'''					)[:2]					) for layer in checkpoint if '''input_blocks''' in layer}					)
				snake_case_		:       Optional[Any]           =    {
				    layer_id: [key for key in checkpoint if f'''input_blocks.{layer_id}''' in key]
				    for layer_id in range(_UpperCamelCase					)
				}

				# Retrieves the keys for the middle blocks only
				snake_case_		:       List[Any]           =    len({'''.'''.join(layer.split('''.'''					)[:2]					) for layer in checkpoint if '''middle_block''' in layer}					)
				snake_case_		:       List[Any]           =    {
				    layer_id: [key for key in checkpoint if f'''middle_block.{layer_id}''' in key]
				    for layer_id in range(_UpperCamelCase					)
				}

				# Retrieves the keys for the output blocks only
				snake_case_		:       Any           =    len({'''.'''.join(layer.split('''.'''					)[:2]					) for layer in checkpoint if '''output_blocks''' in layer}					)
				snake_case_		:       int           =    {
				    layer_id: [key for key in checkpoint if f'''output_blocks.{layer_id}''' in key]
				    for layer_id in range(_UpperCamelCase					)
				}

				for i in range(1					,							_UpperCamelCase					):
								snake_case_		:       Optional[Any]           =    (i - 1) // (config['''num_res_blocks'''] + 1)
								snake_case_		:       List[Any]           =    (i - 1) % (config['''num_res_blocks'''] + 1)

								snake_case_		:       Dict           =    [key for key in input_blocks[i] if f'''input_blocks.{i}.0''' in key]
								snake_case_		:       int           =    [key for key in input_blocks[i] if f'''input_blocks.{i}.1''' in key]

								if f'''input_blocks.{i}.0.op.weight''' in checkpoint:
												snake_case_		:       Optional[int]           =    checkpoint[
												    f'''input_blocks.{i}.0.op.weight'''
												]
												snake_case_		:       Union[str, Any]           =    checkpoint[
												    f'''input_blocks.{i}.0.op.bias'''
												]
												continue

								snake_case_		:       Optional[Any]           =    renew_resnet_paths(_UpperCamelCase					)
								snake_case_		:       Union[str, Any]           =    {'''old''': f'''input_blocks.{i}.0''', '''new''': f'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
								snake_case_		:       Dict           =    {'''old''': '''resnets.2.op''', '''new''': '''downsamplers.0.op'''}
								assign_to_checkpoint(
								    _UpperCamelCase					,							_UpperCamelCase					,							_UpperCamelCase					,							additional_replacements=[meta_path, resnet_op]					,							config=_UpperCamelCase					)

								if len(_UpperCamelCase					):
												snake_case_		:       Union[str, Any]           =    renew_attention_paths(_UpperCamelCase					)
												snake_case_		:       int           =    {
												    '''old''': f'''input_blocks.{i}.1''',
												    '''new''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
												}
												snake_case_		:       Optional[Any]           =    {
												    f'''input_blocks.{i}.1.qkv.bias''': {
												        '''key''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
												        '''query''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
												        '''value''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
												    },
												    f'''input_blocks.{i}.1.qkv.weight''': {
												        '''key''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
												        '''query''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
												        '''value''': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
												    },
												}
												assign_to_checkpoint(
												    _UpperCamelCase					,							_UpperCamelCase					,							_UpperCamelCase					,							additional_replacements=[meta_path]					,							attention_paths_to_split=_UpperCamelCase					,							config=_UpperCamelCase					,							)

				snake_case_		:       int           =    middle_blocks[0]
				snake_case_		:       List[str]           =    middle_blocks[1]
				snake_case_		:       Optional[int]           =    middle_blocks[2]

				snake_case_		:       List[str]           =    renew_resnet_paths(_UpperCamelCase					)
				assign_to_checkpoint(_UpperCamelCase					,							_UpperCamelCase					,							_UpperCamelCase					,							config=_UpperCamelCase					)

				snake_case_		:       Tuple           =    renew_resnet_paths(_UpperCamelCase					)
				assign_to_checkpoint(_UpperCamelCase					,							_UpperCamelCase					,							_UpperCamelCase					,							config=_UpperCamelCase					)

				snake_case_		:       Optional[Any]           =    renew_attention_paths(_UpperCamelCase					)
				snake_case_		:       Optional[Any]           =    {
				    '''middle_block.1.qkv.bias''': {
				        '''key''': '''mid_block.attentions.0.key.bias''',
				        '''query''': '''mid_block.attentions.0.query.bias''',
				        '''value''': '''mid_block.attentions.0.value.bias''',
				    },
				    '''middle_block.1.qkv.weight''': {
				        '''key''': '''mid_block.attentions.0.key.weight''',
				        '''query''': '''mid_block.attentions.0.query.weight''',
				        '''value''': '''mid_block.attentions.0.value.weight''',
				    },
				}
				assign_to_checkpoint(
				    _UpperCamelCase					,							_UpperCamelCase					,							_UpperCamelCase					,							attention_paths_to_split=_UpperCamelCase					,							config=_UpperCamelCase					)

				for i in range(_UpperCamelCase					):
								snake_case_		:       Tuple           =    i // (config['''num_res_blocks'''] + 1)
								snake_case_		:       List[str]           =    i % (config['''num_res_blocks'''] + 1)
								snake_case_		:       str           =    [shave_segments(_UpperCamelCase					,							2					) for name in output_blocks[i]]
								snake_case_		:       Optional[Any]           =    {}

								for layer in output_block_layers:
												snake_case_     ,				snake_case_		:       Any           =    layer.split('''.'''					)[0], shave_segments(_UpperCamelCase					,							1					)
												if layer_id in output_block_list:
																output_block_list[layer_id].append(_UpperCamelCase					)
												else:
																snake_case_		:       int           =    [layer_name]

								if len(_UpperCamelCase					) > 1:
												snake_case_		:       Tuple           =    [key for key in output_blocks[i] if f'''output_blocks.{i}.0''' in key]
												snake_case_		:       Any           =    [key for key in output_blocks[i] if f'''output_blocks.{i}.1''' in key]

												snake_case_		:       List[Any]           =    renew_resnet_paths(_UpperCamelCase					)
												snake_case_		:       Dict           =    renew_resnet_paths(_UpperCamelCase					)

												snake_case_		:       Tuple           =    {'''old''': f'''output_blocks.{i}.0''', '''new''': f'''up_blocks.{block_id}.resnets.{layer_in_block_id}'''}
												assign_to_checkpoint(_UpperCamelCase					,							_UpperCamelCase					,							_UpperCamelCase					,							additional_replacements=[meta_path]					,							config=_UpperCamelCase					)

												if ["conv.weight", "conv.bias"] in output_block_list.values():
																snake_case_		:       Optional[Any]           =    list(output_block_list.values()					).index(['''conv.weight''', '''conv.bias''']					)
																snake_case_		:       Optional[Any]           =    checkpoint[
																    f'''output_blocks.{i}.{index}.conv.weight'''
																]
																snake_case_		:       Any           =    checkpoint[
																    f'''output_blocks.{i}.{index}.conv.bias'''
																]

																# Clear attentions as they have been attributed above.
																if len(_UpperCamelCase					) == 2:
																				snake_case_		:       str           =    []

												if len(_UpperCamelCase					):
																snake_case_		:       Tuple           =    renew_attention_paths(_UpperCamelCase					)
																snake_case_		:       str           =    {
																    '''old''': f'''output_blocks.{i}.1''',
																    '''new''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
																}
																snake_case_		:       List[str]           =    {
																    f'''output_blocks.{i}.1.qkv.bias''': {
																        '''key''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
																        '''query''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
																        '''value''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
																    },
																    f'''output_blocks.{i}.1.qkv.weight''': {
																        '''key''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
																        '''query''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
																        '''value''': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
																    },
																}
																assign_to_checkpoint(
																    _UpperCamelCase					,							_UpperCamelCase					,							_UpperCamelCase					,							additional_replacements=[meta_path]					,							attention_paths_to_split=to_split if any('''qkv''' in key for key in attentions					) else None					,							config=_UpperCamelCase					,							)
								else:
												snake_case_		:       Union[str, Any]           =    renew_resnet_paths(_UpperCamelCase					,							n_shave_prefix_segments=1					)
												for path in resnet_0_paths:
																snake_case_		:       Dict           =    '''.'''.join(['''output_blocks''', str(_UpperCamelCase					), path['''old''']]					)
																snake_case_		:       Optional[Any]           =    '''.'''.join(['''up_blocks''', str(_UpperCamelCase					), '''resnets''', str(_UpperCamelCase					), path['''new''']]					)

																snake_case_		:       List[Any]           =    checkpoint[old_path]

				return new_checkpoint


if __name__ == "__main__":
	lowerCAmelCase_        =							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_        =							parser.parse_args()

	lowerCAmelCase_        =							torch.load(args.checkpoint_path)

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

	lowerCAmelCase_        =							convert_ldm_checkpoint(checkpoint, config)

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

	lowerCAmelCase_        =							UNetaDModel(**config)
	model.load_state_dict(converted_checkpoint)

	try:
		lowerCAmelCase_        =							DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
		lowerCAmelCase_        =							VQModel.from_pretrained('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

		lowerCAmelCase_        =							LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
		pipe.save_pretrained(args.dump_path)
	except:  # noqa: E722
		model.save_pretrained(args.dump_path)







'''simple docstring'''


import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
       import torch

       from transformers import AutoModelForCausalLM

@require_torch
class  lowerCAmelCase_		(		unittest.TestCase		):




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
     _lowerCAmelCase						      =   AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   -1

     _lowerCAmelCase						      =   ids_tensor((1, 5)      ,  vocab_size=model.config.vocab_size		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   model.generate(_lowerCAmelCase      ,  max_new_tokens=10      ,  do_sample=_lowerCAmelCase		)
     _lowerCAmelCase						      =   tokenizer.decode(greedy_ids[0]		)

     with CaptureStdout() as cs:
         _lowerCAmelCase						      =   TextStreamer(_lowerCAmelCase		)
         model.generate(_lowerCAmelCase      ,  max_new_tokens=10      ,  do_sample=_lowerCAmelCase      ,  streamer=_lowerCAmelCase		)
     # The greedy text should be printed to stdout, except for the final "\n" in the streamer
     _lowerCAmelCase						      =   cs.out[:-1]

     self.assertEqual(_lowerCAmelCase      ,  _lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
     _lowerCAmelCase						      =   AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   -1

     _lowerCAmelCase						      =   ids_tensor((1, 5)      ,  vocab_size=model.config.vocab_size		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   model.generate(_lowerCAmelCase      ,  max_new_tokens=10      ,  do_sample=_lowerCAmelCase		)
     _lowerCAmelCase						      =   tokenizer.decode(greedy_ids[0]		)

     _lowerCAmelCase						      =   TextIteratorStreamer(_lowerCAmelCase		)
     _lowerCAmelCase						      =   {"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
     _lowerCAmelCase						      =   Thread(target=model.generate      ,  kwargs=_lowerCAmelCase		)
     thread.start()
     _lowerCAmelCase						      =   ""
     for new_text in streamer:
         streamer_text += new_text

     self.assertEqual(_lowerCAmelCase      ,  _lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				List[str]:
     _lowerCAmelCase						      =   AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
     _lowerCAmelCase						      =   AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   -1

     _lowerCAmelCase						      =   ids_tensor((1, 5)      ,  vocab_size=model.config.vocab_size		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   model.generate(_lowerCAmelCase      ,  max_new_tokens=10      ,  do_sample=_lowerCAmelCase		)
     _lowerCAmelCase						      =   greedy_ids[:, input_ids.shape[1] :]
     _lowerCAmelCase						      =   tokenizer.decode(new_greedy_ids[0]		)

     with CaptureStdout() as cs:
         _lowerCAmelCase						      =   TextStreamer(_lowerCAmelCase      ,  skip_prompt=_lowerCAmelCase		)
         model.generate(_lowerCAmelCase      ,  max_new_tokens=10      ,  do_sample=_lowerCAmelCase      ,  streamer=_lowerCAmelCase		)
     # The greedy text should be printed to stdout, except for the final "\n" in the streamer
     _lowerCAmelCase						      =   cs.out[:-1]

     self.assertEqual(_lowerCAmelCase      ,  _lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Dict:
     # Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
     # with actual models -- the dummy models' tokenizers are not aligned with their models, and
     # `skip_special_tokens=True` has no effect on them
     _lowerCAmelCase						      =   AutoTokenizer.from_pretrained("distilgpt2"		)
     _lowerCAmelCase						      =   AutoModelForCausalLM.from_pretrained("distilgpt2"		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   -1

     _lowerCAmelCase						      =   torch.ones((1, 5)      ,  device=_lowerCAmelCase		).long() * model.config.bos_token_id
     with CaptureStdout() as cs:
         _lowerCAmelCase						      =   TextStreamer(_lowerCAmelCase      ,  skip_special_tokens=_lowerCAmelCase		)
         model.generate(_lowerCAmelCase      ,  max_new_tokens=1      ,  do_sample=_lowerCAmelCase      ,  streamer=_lowerCAmelCase		)

     # The prompt contains a special token, so the streamer should not print it. As such, the output text, when
     # re-tokenized, must only contain one token
     _lowerCAmelCase						      =   cs.out[:-1]  # Remove the final "\n"
     _lowerCAmelCase						      =   tokenizer(_lowerCAmelCase      ,  return_tensors="pt"		)
     self.assertEqual(streamer_text_tokenized.input_ids.shape      ,  (1, 1)		)





 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
     _lowerCAmelCase						      =   AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   -1

     _lowerCAmelCase						      =   ids_tensor((1, 5)      ,  vocab_size=model.config.vocab_size		).to(_lowerCAmelCase		)
     _lowerCAmelCase						      =   TextIteratorStreamer(_lowerCAmelCase      ,  timeout=0.001		)
     _lowerCAmelCase						      =   {"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
     _lowerCAmelCase						      =   Thread(target=model.generate      ,  kwargs=_lowerCAmelCase		)
     thread.start()

     # The streamer will timeout after 0.001 seconds, so an exception will be raised
     with self.assertRaises(_lowerCAmelCase		):
         _lowerCAmelCase						      =   ""
         for new_text in streamer:
             streamer_text += new_text


