

"""simple docstring"""






import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def    UpperCamelCase       (      _lowerCAmelCase  :    List[str],	_lowerCAmelCase  :    Tuple=1			)					->      Union[str, Any]:







	if n_shave_prefix_segments >= 0:
		return ".".join(path.split("""."""			)[n_shave_prefix_segments:]			)
	else:
		return ".".join(path.split("""."""			)[:n_shave_prefix_segments]			)
def    UpperCamelCase       (      _lowerCAmelCase  :    Optional[int],	_lowerCAmelCase  :    int=0			)					->      Union[str, Any]:
	_UpperCAmelCase     :				str          =       []
	for old_item in old_list:
		_UpperCAmelCase     :				Any          =       old_item.replace("""in_layers.0""",	"""norm1"""			)
		_UpperCAmelCase     :				Any          =       new_item.replace("""in_layers.2""",	"""conv1"""			)

		_UpperCAmelCase     :				Tuple          =       new_item.replace("""out_layers.0""",	"""norm2"""			)
		_UpperCAmelCase     :				List[Any]          =       new_item.replace("""out_layers.3""",	"""conv2"""			)

		_UpperCAmelCase     :				str          =       new_item.replace("""emb_layers.1""",	"""time_emb_proj"""			)
		_UpperCAmelCase     :				int          =       new_item.replace("""skip_connection""",	"""conv_shortcut"""			)

		_UpperCAmelCase     :				Optional[int]          =       shave_segments(__UpperCAmelCase,	n_shave_prefix_segments=__UpperCAmelCase			)

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

	return mapping
def    UpperCamelCase       (      _lowerCAmelCase  :    Union[str, Any],	_lowerCAmelCase  :    Dict=0			)					->      List[Any]:
	_UpperCAmelCase     :				Tuple          =       []
	for old_item in old_list:
		_UpperCAmelCase     :				Dict          =       old_item

		_UpperCAmelCase     :				Dict          =       new_item.replace("""norm.weight""",	"""group_norm.weight"""			)
		_UpperCAmelCase     :				Union[str, Any]          =       new_item.replace("""norm.bias""",	"""group_norm.bias"""			)

		_UpperCAmelCase     :				Any          =       new_item.replace("""proj_out.weight""",	"""proj_attn.weight"""			)
		_UpperCAmelCase     :				Optional[int]          =       new_item.replace("""proj_out.bias""",	"""proj_attn.bias"""			)

		_UpperCAmelCase     :				Dict          =       shave_segments(__UpperCAmelCase,	n_shave_prefix_segments=__UpperCAmelCase			)

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

	return mapping
def    UpperCamelCase       (      _lowerCAmelCase  :    Dict,	_lowerCAmelCase  :    Optional[int],	_lowerCAmelCase  :    int,	_lowerCAmelCase  :    Optional[Any]=None,	_lowerCAmelCase  :    Optional[Any]=None,	_lowerCAmelCase  :    Tuple=None			)					->      Any:
	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():
			_UpperCAmelCase     :				Any          =       old_checkpoint[path]
			_UpperCAmelCase     :				int          =       old_tensor.shape[0] // 3

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

			_UpperCAmelCase     :				Union[str, Any]          =       old_tensor.shape[0] // config['num_head_channels'] // 3

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

			_UpperCAmelCase     :				Tuple          =       query.reshape(__UpperCAmelCase			)
			_UpperCAmelCase     :				Optional[int]          =       key.reshape(__UpperCAmelCase			)
			_UpperCAmelCase     :				int          =       value.reshape(__UpperCAmelCase			)

	for path in paths:
		_UpperCAmelCase     :				List[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
		_UpperCAmelCase     :				Optional[int]          =       new_path.replace("""middle_block.0""",	"""mid_block.resnets.0"""			)
		_UpperCAmelCase     :				int          =       new_path.replace("""middle_block.1""",	"""mid_block.attentions.0"""			)
		_UpperCAmelCase     :				Dict          =       new_path.replace("""middle_block.2""",	"""mid_block.resnets.1"""			)

		if additional_replacements is not None:
			for replacement in additional_replacements:
				_UpperCAmelCase     :				Tuple          =       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:
			_UpperCAmelCase     :				Optional[Any]          =       old_checkpoint[path['old']][:, :, 0]
		else:
			_UpperCAmelCase     :				Dict          =       old_checkpoint[path['old']]
def    UpperCamelCase       (      _lowerCAmelCase  :    List[str],	_lowerCAmelCase  :    Optional[Any]			)					->      Dict:
	_UpperCAmelCase     :				Any          =       {}

	_UpperCAmelCase     :				Union[str, Any]          =       checkpoint['time_embed.0.weight']
	_UpperCAmelCase     :				Any          =       checkpoint['time_embed.0.bias']
	_UpperCAmelCase     :				Dict          =       checkpoint['time_embed.2.weight']
	_UpperCAmelCase     :				int          =       checkpoint['time_embed.2.bias']

	_UpperCAmelCase     :				List[Any]          =       checkpoint['input_blocks.0.0.weight']
	_UpperCAmelCase     :				str          =       checkpoint['input_blocks.0.0.bias']

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

	# Retrieves the keys for the input blocks only
	_UpperCAmelCase     :				str          =       len({""".""".join(layer.split("""."""			)[:2]			) for layer in checkpoint if """input_blocks""" in layer}			)
	_UpperCAmelCase     :				Union[str, 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
	_UpperCAmelCase     :				int          =       len({""".""".join(layer.split("""."""			)[:2]			) for layer in checkpoint if """middle_block""" in layer}			)
	_UpperCAmelCase     :				Optional[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
	_UpperCAmelCase     :				Dict          =       len({""".""".join(layer.split("""."""			)[:2]			) for layer in checkpoint if """output_blocks""" in layer}			)
	_UpperCAmelCase     :				List[str]          =       {
	    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			):
		_UpperCAmelCase     :				Optional[int]          =       (i - 1) // (config['num_res_blocks'] + 1)
		_UpperCAmelCase     :				str          =       (i - 1) % (config['num_res_blocks'] + 1)

		_UpperCAmelCase     :				Union[str, Any]          =       [key for key in input_blocks[i] if f'''input_blocks.{i}.0''' in key]
		_UpperCAmelCase     :				str          =       [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:
			_UpperCAmelCase     :				Tuple          =       checkpoint[
			    f'''input_blocks.{i}.0.op.weight'''
			]
			_UpperCAmelCase     :				Any          =       checkpoint[
			    f'''input_blocks.{i}.0.op.bias'''
			]
			continue

		_UpperCAmelCase     :				Optional[int]          =       renew_resnet_paths(__UpperCAmelCase			)
		_UpperCAmelCase     :				Any          =       {'old': f'''input_blocks.{i}.0''', 'new': f'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
		_UpperCAmelCase     :				List[Any]          =       {'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			):
			_UpperCAmelCase     :				List[Any]          =       renew_attention_paths(__UpperCAmelCase			)
			_UpperCAmelCase     :				Any          =       {
			    'old': f'''input_blocks.{i}.1''',
			    'new': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
			}
			_UpperCAmelCase     :				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,	)

	_UpperCAmelCase     :				int          =       middle_blocks[0]
	_UpperCAmelCase     :				Tuple          =       middle_blocks[1]
	_UpperCAmelCase     :				Optional[Any]          =       middle_blocks[2]

	_UpperCAmelCase     :				Optional[Any]          =       renew_resnet_paths(__UpperCAmelCase			)
	assign_to_checkpoint(__UpperCAmelCase,	__UpperCAmelCase,	__UpperCAmelCase,	config=__UpperCAmelCase			)

	_UpperCAmelCase     :				Any          =       renew_resnet_paths(__UpperCAmelCase			)
	assign_to_checkpoint(__UpperCAmelCase,	__UpperCAmelCase,	__UpperCAmelCase,	config=__UpperCAmelCase			)

	_UpperCAmelCase     :				Optional[int]          =       renew_attention_paths(__UpperCAmelCase			)
	_UpperCAmelCase     :				Union[str, 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			):
		_UpperCAmelCase     :				List[Any]          =       i // (config['num_res_blocks'] + 1)
		_UpperCAmelCase     :				Any          =       i % (config['num_res_blocks'] + 1)
		_UpperCAmelCase     :				List[str]          =       [shave_segments(__UpperCAmelCase,	2			) for name in output_blocks[i]]
		_UpperCAmelCase     :				List[str]          =       {}

		for layer in output_block_layers:
			_UpperCAmelCase     :				List[Any]          =       layer.split("""."""			)[0], shave_segments(__UpperCAmelCase,	1			)
			if layer_id in output_block_list:
				output_block_list[layer_id].append(__UpperCAmelCase			)
			else:
				_UpperCAmelCase     :				int          =       [layer_name]

		if len(__UpperCAmelCase			) > 1:
			_UpperCAmelCase     :				Dict          =       [key for key in output_blocks[i] if f'''output_blocks.{i}.0''' in key]
			_UpperCAmelCase     :				str          =       [key for key in output_blocks[i] if f'''output_blocks.{i}.1''' in key]

			_UpperCAmelCase     :				int          =       renew_resnet_paths(__UpperCAmelCase			)
			_UpperCAmelCase     :				Optional[int]          =       renew_resnet_paths(__UpperCAmelCase			)

			_UpperCAmelCase     :				List[str]          =       {'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():
				_UpperCAmelCase     :				Optional[Any]          =       list(output_block_list.values()			).index(["""conv.weight""", """conv.bias"""]			)
				_UpperCAmelCase     :				int          =       checkpoint[
				    f'''output_blocks.{i}.{index}.conv.weight'''
				]
				_UpperCAmelCase     :				List[str]          =       checkpoint[
				    f'''output_blocks.{i}.{index}.conv.bias'''
				]

				# Clear attentions as they have been attributed above.
				if len(__UpperCAmelCase			) == 2:
					_UpperCAmelCase     :				Union[str, Any]          =       []

			if len(__UpperCAmelCase			):
				_UpperCAmelCase     :				Optional[int]          =       renew_attention_paths(__UpperCAmelCase			)
				_UpperCAmelCase     :				str          =       {
				    'old': f'''output_blocks.{i}.1''',
				    'new': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
				}
				_UpperCAmelCase     :				List[Any]          =       {
				    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:
			_UpperCAmelCase     :				Union[str, Any]          =       renew_resnet_paths(__UpperCAmelCase,	n_shave_prefix_segments=1			)
			for path in resnet_0_paths:
				_UpperCAmelCase     :				int          =       '.'.join(["""output_blocks""", str(__UpperCAmelCase			), path["""old"""]]			)
				_UpperCAmelCase     :				Tuple          =       '.'.join(["""up_blocks""", str(__UpperCAmelCase			), """resnets""", str(__UpperCAmelCase			), path["""new"""]]			)

				_UpperCAmelCase     :				List[Any]          =       checkpoint[old_path]

	return new_checkpoint


if __name__ == "__main__":
						lowerCamelCase__					:	Union[str, 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__					:	str				    =	parser.parse_args()

						lowerCamelCase__					:	int				    =	torch.load(args.checkpoint_path)

						with open(args.config_file) as f:
												lowerCamelCase__					:	List[Any]				    =	json.loads(f.read())

						lowerCamelCase__					:	Union[str, Any]				    =	convert_ldm_checkpoint(checkpoint, config)

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

						lowerCamelCase__					:	Union[str, Any]				    =	UNetaDModel(**config)
						model.load_state_dict(converted_checkpoint)

						try:
												lowerCamelCase__					:	Optional[int]				    =	DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
												lowerCamelCase__					:	List[Any]				    =	VQModel.from_pretrained('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

												lowerCamelCase__					:	Tuple				    =	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 os
import unittest

from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 						a_		(     snake_case    ,   unittest.TestCase    ):
							UpperCAmelCase			:		int        =				FunnelTokenizer
							UpperCAmelCase			:		Tuple        =				FunnelTokenizerFast
							UpperCAmelCase			:		List[str]        =				True
							UpperCAmelCase			:		Tuple        =				True


							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					Optional[Any]:
										super().setUp()

										snake_case:    Optional[Any]      							=[
										    '<unk>',
										    '<cls>',
										    '<sep>',
										    'want',
										    '##want',
										    '##ed',
										    'wa',
										    'un',
										    'runn',
										    '##ing',
										    ',',
										    'low',
										    'lowest',
										]
										snake_case:    Any      							=os.path.join(self.tmpdirname      ,						VOCAB_FILES_NAMES['vocab_file']			)
										with open(self.vocab_file      ,						'w'      ,						encoding='utf-8'			) as vocab_writer:
													vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]			)			)


							def    UpperCamelCase     (				self   :						Tuple      ,						**a_   :						str			)			->					Any:
										return FunnelTokenizer.from_pretrained(self.tmpdirname      ,						**a_			)


							def    UpperCamelCase     (				self   :						List[Any]      ,						**a_   :						int			)			->					str:
										return FunnelTokenizerFast.from_pretrained(self.tmpdirname      ,						**a_			)


							def    UpperCamelCase     (				self   :						Any      ,						a_   :						Union[str, Any]			)			->					Union[str, Any]:
										snake_case:    List[str]      							='UNwant\u00E9d,running'
										snake_case:    Dict      							='unwanted, running'
										return input_text, output_text


							def    UpperCamelCase     (				self   :						Optional[int]			)			->					List[Any]:
										snake_case:    Union[str, Any]      							=self.tokenizer_class(self.vocab_file			)

										snake_case:    Dict      							=tokenizer.tokenize('UNwant\u00E9d,running'			)
										self.assertListEqual(a_      ,						['un', '##want', '##ed', ',', 'runn', '##ing']			)
										self.assertListEqual(tokenizer.convert_tokens_to_ids(a_			)      ,						[7, 4, 5, 1_0, 8, 9]			)




							def    UpperCamelCase     (				self   :						List[str]			)			->					Dict:
										snake_case:    Optional[Any]      							=self.get_tokenizers(do_lower_case=a_			)
										for tokenizer in tokenizers:
													snake_case:    str      							=tokenizer('UNwant\u00E9d,running'			)
													snake_case:    Union[str, Any]      							=len(inputs['input_ids']			) - 1
													self.assertListEqual(inputs['token_type_ids']      ,						[2] + [0] * sentence_len			)

													snake_case:    List[str]      							=tokenizer('UNwant\u00E9d,running'      ,						'UNwant\u00E9d,running'			)
													self.assertListEqual(inputs['token_type_ids']      ,						[2] + [0] * sentence_len + [1] * sentence_len			)




