

import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowerCAmelCase_       	=	get_tests_dir('''fixtures/test_sentencepiece.model''')





@require_sentencepiece
@require_tokenizers
class 	snake_case_   (		__A	,       unittest.TestCase					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   Optional[int]									=				ReformerTokenizer
			SCREAMING_SNAKE_CASE     :   int									=				ReformerTokenizerFast
			SCREAMING_SNAKE_CASE     :   int									=				True
			SCREAMING_SNAKE_CASE     :   Optional[Any]									=				False
			SCREAMING_SNAKE_CASE     :   str									=				True







			def 				snake_case__(     self :				Dict							)      ->List[str]:
									super().setUp()

									snake_case_   =	ReformerTokenizer(_UpperCamelCase							,   keep_accents=_UpperCamelCase							)
									tokenizer.save_pretrained(self.tmpdirname							)







			def 				snake_case__(     self :				Optional[Any]							)      ->int:
									snake_case_   =	'''<s>'''
									snake_case_   =	1

									self.assertEqual(self.get_tokenizer()._convert_token_to_id(_UpperCamelCase							)							,   _UpperCamelCase							)
									self.assertEqual(self.get_tokenizer()._convert_id_to_token(_UpperCamelCase							)							,   _UpperCamelCase							)







			def 				snake_case__(     self :				Optional[Any]							)      ->Dict:
									snake_case_   =	list(self.get_tokenizer().get_vocab().keys()							)

									self.assertEqual(vocab_keys[0]							,   '''<unk>'''							)
									self.assertEqual(vocab_keys[1]							,   '''<s>'''							)
									self.assertEqual(vocab_keys[-1]							,   '''j'''							)
									self.assertEqual(len(_UpperCamelCase							)							,   1_0_0_0							)







			def 				snake_case__(     self :				Union[str, Any]							)      ->Dict:
									self.assertEqual(self.get_tokenizer().vocab_size							,   1_0_0_0							)







			def 				snake_case__(     self :				List[Any]							)      ->str:
									if not self.test_rust_tokenizer:
															return

									snake_case_   =	self.get_tokenizer()
									snake_case_   =	self.get_rust_tokenizer()

									snake_case_   =	'''I was born in 92000, and this is falsé.'''

									snake_case_   =	tokenizer.tokenize(_UpperCamelCase							)
									snake_case_   =	rust_tokenizer.tokenize(_UpperCamelCase							)
									self.assertListEqual(_UpperCamelCase							,   _UpperCamelCase							)

									snake_case_   =	tokenizer.encode(_UpperCamelCase							,   add_special_tokens=_UpperCamelCase							)
									snake_case_   =	rust_tokenizer.encode(_UpperCamelCase							,   add_special_tokens=_UpperCamelCase							)
									self.assertListEqual(_UpperCamelCase							,   _UpperCamelCase							)

									snake_case_   =	self.get_rust_tokenizer()
									snake_case_   =	tokenizer.encode(_UpperCamelCase							)
									snake_case_   =	rust_tokenizer.encode(_UpperCamelCase							)
									self.assertListEqual(_UpperCamelCase							,   _UpperCamelCase							)







			def 				snake_case__(     self :				Optional[int]							,   _UpperCamelCase :				Tuple=1_5							)      ->Tuple:
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
																					snake_case_   =	self.rust_tokenizer_class.from_pretrained(_UpperCamelCase							,   **_UpperCamelCase							)

																					# Simple input
																					snake_case_   =	'''This is a simple input'''
																					snake_case_   =	['''This is a simple input 1''', '''This is a simple input 2''']
																					snake_case_   =	('''This is a simple input''', '''This is a pair''')
																					snake_case_   =	[
																					    ('''This is a simple input 1''', '''This is a simple input 2'''),
																					    ('''This is a simple pair 1''', '''This is a simple pair 2'''),
																					]

																					# Simple input tests
																					self.assertRaises(_UpperCamelCase							,   tokenizer_r.encode							,   _UpperCamelCase							,   max_length=_UpperCamelCase							,   padding='''max_length'''							)

																					# Simple input
																					self.assertRaises(_UpperCamelCase							,   tokenizer_r.encode_plus							,   _UpperCamelCase							,   max_length=_UpperCamelCase							,   padding='''max_length'''							)

																					# Simple input
																					self.assertRaises(
																					    _UpperCamelCase							,   tokenizer_r.batch_encode_plus							,   _UpperCamelCase							,   max_length=_UpperCamelCase							,   padding='''max_length'''							,   )

																					# Pair input
																					self.assertRaises(_UpperCamelCase							,   tokenizer_r.encode							,   _UpperCamelCase							,   max_length=_UpperCamelCase							,   padding='''max_length'''							)

																					# Pair input
																					self.assertRaises(_UpperCamelCase							,   tokenizer_r.encode_plus							,   _UpperCamelCase							,   max_length=_UpperCamelCase							,   padding='''max_length'''							)

																					# Pair input
																					self.assertRaises(
																					    _UpperCamelCase							,   tokenizer_r.batch_encode_plus							,   _UpperCamelCase							,   max_length=_UpperCamelCase							,   padding='''max_length'''							,   )







			def 				snake_case__(     self :				Dict							)      ->int:
									pass







			def 				snake_case__(     self :				Dict							)      ->str:
									snake_case_   =	ReformerTokenizer(_UpperCamelCase							,   keep_accents=_UpperCamelCase							)

									snake_case_   =	tokenizer.tokenize('''This is a test'''							)
									self.assertListEqual(_UpperCamelCase							,   ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']							)

									self.assertListEqual(
									    tokenizer.convert_tokens_to_ids(_UpperCamelCase							)							,   [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]							,   )

									snake_case_   =	tokenizer.tokenize('''I was born in 92000, and this is falsé.'''							)
									self.assertListEqual(
									    _UpperCamelCase							,   [
									        SPIECE_UNDERLINE + '''I''',
									        SPIECE_UNDERLINE + '''was''',
									        SPIECE_UNDERLINE + '''b''',
									        '''or''',
									        '''n''',
									        SPIECE_UNDERLINE + '''in''',
									        SPIECE_UNDERLINE + '''''',
									        '''9''',
									        '''2''',
									        '''0''',
									        '''0''',
									        '''0''',
									        ''',''',
									        SPIECE_UNDERLINE + '''and''',
									        SPIECE_UNDERLINE + '''this''',
									        SPIECE_UNDERLINE + '''is''',
									        SPIECE_UNDERLINE + '''f''',
									        '''al''',
									        '''s''',
									        '''é''',
									        '''.''',
									    ]							,   )
									snake_case_   =	tokenizer.convert_tokens_to_ids(_UpperCamelCase							)
									self.assertListEqual(
									    _UpperCamelCase							,   [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4]							,   )

									snake_case_   =	tokenizer.convert_ids_to_tokens(_UpperCamelCase							)
									self.assertListEqual(
									    _UpperCamelCase							,   [
									        SPIECE_UNDERLINE + '''I''',
									        SPIECE_UNDERLINE + '''was''',
									        SPIECE_UNDERLINE + '''b''',
									        '''or''',
									        '''n''',
									        SPIECE_UNDERLINE + '''in''',
									        SPIECE_UNDERLINE + '''''',
									        '''<unk>''',
									        '''2''',
									        '''0''',
									        '''0''',
									        '''0''',
									        ''',''',
									        SPIECE_UNDERLINE + '''and''',
									        SPIECE_UNDERLINE + '''this''',
									        SPIECE_UNDERLINE + '''is''',
									        SPIECE_UNDERLINE + '''f''',
									        '''al''',
									        '''s''',
									        '''<unk>''',
									        '''.''',
									    ]							,   )







			@cached_property
			def 				snake_case__(     self :				Optional[int]							)      ->Tuple:
									return ReformerTokenizer.from_pretrained('''google/reformer-crime-and-punishment'''							)







			@slow
			def 				snake_case__(     self :				Optional[Any]							)      ->int:
									snake_case_   =	'''Hello World!'''
									snake_case_   =	[1_2_6, 3_2, 2_6_2, 1_5_2, 3_8, 7_2, 2_8_7]

									self.assertListEqual(_UpperCamelCase							,   self.big_tokenizer.encode(_UpperCamelCase							)							)







			@slow
			def 				snake_case__(     self :				str							)      ->str:
									snake_case_   =	(
									    '''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
									    ''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
									)
									snake_case_   =	[
									    1_0_8,
									    2_6_5,
									    2_4,
									    1_1_1,
									    4,
									    2_5_8,
									    1_5_6,
									    3_5,
									    2_8,
									    2_7_5,
									    3,
									    2_5_9,
									    2_9_7,
									    2_6_0,
									    8_4,
									    4,
									    3_5,
									    1_1_0,
									    4_4,
									    8,
									    2_5_9,
									    9_1,
									    2_6_8,
									    2_1,
									    1_1,
									    2_0_9,
									    2_7_4,
									    1_0_9,
									    2_6_6,
									    2_7_7,
									    1_1_7,
									    8_6,
									    9_3,
									    3_1_5,
									    2_5_8,
									    2_7_8,
									    2_5_8,
									    2_7_7,
									    2_5_8,
									    0,
									    2_5_8,
									    2_8_8,
									    2_5_8,
									    3_1_9,
									    2_5_8,
									    0,
									    2_5_8,
									    0,
									    2_5_8,
									    0,
									    2_5_8,
									    0,
									    2_5_8,
									    2_8_7,
									    2_5_8,
									    3_1_5,
									    2_5_8,
									    2_8_9,
									    2_5_8,
									    2_7_8,
									    9_9,
									    2_6_9,
									    2_6_6,
									    2_6_2,
									    8,
									    2_5_9,
									    2_4_1,
									    4,
									    2_1_7,
									    2_3_0,
									    2_6_8,
									    2_6_6,
									    5_5,
									    1_6_8,
									    1_0_6,
									    7_5,
									    1_9_3,
									    2_6_6,
									    2_2_3,
									    2_7,
									    4_9,
									    2_6,
									    2_8_2,
									    2_5,
									    2_6_4,
									    2_9_9,
									    1_9,
									    2_6,
									    0,
									    2_5_8,
									    2_7_7,
									    1_1_7,
									    8_6,
									    9_3,
									    1_7_6,
									    1_8_3,
									    2_7_0,
									    1_1,
									    2_6_2,
									    4_2,
									    6_1,
									    2_6_5,
									]

									self.assertListEqual(_UpperCamelCase							,   self.big_tokenizer.encode(_UpperCamelCase							)							)







			@require_torch
			@slow
			def 				snake_case__(     self :				List[str]							)      ->List[str]:
									import torch

									from transformers import ReformerConfig, ReformerModel

									# Build sequence
									snake_case_   =	list(self.big_tokenizer.get_vocab().keys()							)[:1_0]
									snake_case_   =	''' '''.join(_UpperCamelCase							)
									snake_case_   =	self.big_tokenizer.encode_plus(_UpperCamelCase							,   return_tensors='''pt'''							)
									snake_case_   =	self.big_tokenizer.batch_encode_plus([sequence, sequence]							,   return_tensors='''pt'''							)

									snake_case_   =	ReformerConfig()
									# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
									snake_case_   =	encoded_sequence['''input_ids'''].shape
									snake_case_   =	ReformerModel(_UpperCamelCase							)

									# Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
									assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

									with torch.no_grad():
															model(**_UpperCamelCase							)
															model(**_UpperCamelCase							)







			@slow
			def 				snake_case__(     self :				List[Any]							)      ->Dict:
									# fmt: off
									snake_case_   =	{'''input_ids''': [[1_0_8, 2_6_5, 2_4, 1_1_1, 4, 2_5_8, 1_5_6, 7, 5_1, 2_7_9, 5_8, 7, 7_6, 2_5, 6_9, 2_7_8], [1_4_0, 2_4_3, 2_6_4, 1_3_4, 1_7, 2_6_7, 7_7, 2_6_3, 2_2, 2_6_2, 2_9_7, 2_5_8, 3_0_4, 1_7_7, 2_7_9, 2_6_6, 1_4, 8_9, 1_3, 3_5, 2_6_1, 2_9_9, 2_7_2, 1_3_7, 2_7_5, 2_7_8]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
									# fmt: on

									# This tokenizer does not know some characters like ")".
									# That is the reason why we use very simple texts here.
									# Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
									snake_case_   =	[
									    '''This is a very simple sentence.''',
									    '''The quick brown fox jumps over the lazy dog.''',
									]

									self.tokenizer_integration_test_util(
									    expected_encoding=_UpperCamelCase							,   model_name='''google/reformer-crime-and-punishment'''							,   revision='''0e6c3decb8211d49bf881013425dc8b0448b3f5a'''							,   padding=_UpperCamelCase							,   sequences=_UpperCamelCase							,   )





import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

lowercase__				:     Optional[int] 		=						[
    # (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"),
]

lowercase__				:     List[Any] 		=						[
    # (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"),
]

lowercase__				:     Optional[Any] 		=						[]
# 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
				lowercase__				:     str 		=						f'''down_blocks.{i}.resnets.{j}.'''
				lowercase__				:     Union[str, Any] 		=						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
						lowercase__				:     Tuple 		=						f'''down_blocks.{i}.attentions.{j}.'''
						lowercase__				:     Dict 		=						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
				lowercase__				:     List[Any] 		=						f'''up_blocks.{i}.resnets.{j}.'''
				lowercase__				:     int 		=						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
						lowercase__				:     List[str] 		=						f'''up_blocks.{i}.attentions.{j}.'''
						lowercase__				:     Tuple 		=						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
				lowercase__				:     List[str] 		=						f'''down_blocks.{i}.downsamplers.0.conv.'''
				lowercase__				:     Any 		=						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
				lowercase__				:     Optional[int] 		=						f'''up_blocks.{i}.upsamplers.0.'''
				lowercase__				:     int 		=						f'''output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'''
				unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

lowercase__				:     Union[str, Any] 		=						"mid_block.attentions.0."
lowercase__				:     List[Any] 		=						"middle_block.1."
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
		lowercase__				:     Tuple 		=						f'''mid_block.resnets.{j}.'''
		lowercase__				:     List[str] 		=						f'''middle_block.{2*j}.'''
		unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	snake_case_		    =     {k: k for k in unet_state_dict.keys()}
	for sd_name, hf_name in unet_conversion_map:
		snake_case_		    =     sd_name
	for k, v in mapping.items():
		if "resnets" in k:
			for sd_part, hf_part in unet_conversion_map_resnet:
				snake_case_		    =     v.replace(_A     ,	_A							)
			snake_case_		    =     v
	for k, v in mapping.items():
		for sd_part, hf_part in unet_conversion_map_layer:
			snake_case_		    =     v.replace(_A     ,	_A							)
		snake_case_		    =     v
	snake_case_		    =     {v: unet_state_dict[k] for k, v in mapping.items()}
	return new_state_dict


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

lowercase__				:     Dict 		=						[
    # (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):
				lowercase__				:     Any 		=						f'''encoder.down_blocks.{i}.resnets.{j}.'''
				lowercase__				:     List[Any] 		=						f'''encoder.down.{i}.block.{j}.'''
				vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

		if i < 3:
				lowercase__				:     Optional[int] 		=						f'''down_blocks.{i}.downsamplers.0.'''
				lowercase__				:     Tuple 		=						f'''down.{i}.downsample.'''
				vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

				lowercase__				:     List[str] 		=						f'''up_blocks.{i}.upsamplers.0.'''
				lowercase__				:     Optional[int] 		=						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):
				lowercase__				:     int 		=						f'''decoder.up_blocks.{i}.resnets.{j}.'''
				lowercase__				:     Union[str, Any] 		=						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):
		lowercase__				:     Dict 		=						f'''mid_block.resnets.{i}.'''
		lowercase__				:     int 		=						f'''mid.block_{i+1}.'''
		vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


lowercase__				:     Optional[Any] 		=						[
    # (stable-diffusion, HF Diffusers)
    ("norm.", "group_norm."),
    ("q.", "query."),
    ("k.", "key."),
    ("v.", "value."),
    ("proj_out.", "proj_attn."),
]


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	return w.reshape(*w.shape     ,	1     ,	1							)


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	snake_case_		    =     {k: k for k in vae_state_dict.keys()}
	for k, v in mapping.items():
		for sd_part, hf_part in vae_conversion_map:
			snake_case_		    =     v.replace(_A     ,	_A							)
		snake_case_		    =     v
	for k, v in mapping.items():
		if "attentions" in k:
			for sd_part, hf_part in vae_conversion_map_attn:
				snake_case_		    =     v.replace(_A     ,	_A							)
			snake_case_		    =     v
	snake_case_		    =     {v: vae_state_dict[k] for k, v in mapping.items()}
	snake_case_		    =     ["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"							)
				snake_case_		    =     reshape_weight_for_sd(_A							)
	return new_state_dict


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


lowercase__				:     int 		=						[
    # (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"),
]
lowercase__				:     Dict 		=						{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
lowercase__				:     Tuple 		=						re.compile("|".join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
lowercase__				:     Dict 		=						{"q": 0, "k": 1, "v": 2}


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	snake_case_		    =     {}
	snake_case_		    =     {}
	snake_case_		    =     {}
	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"							)
		):
			snake_case_		    =     k[: -len(".q_proj.weight"							)]
			snake_case_		    =     k[-len("q_proj.weight"							)]
			if k_pre not in capture_qkv_weight:
				snake_case_		    =     [None, None, None]
			snake_case_		    =     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"							)
		):
			snake_case_		    =     k[: -len(".q_proj.bias"							)]
			snake_case_		    =     k[-len("q_proj.bias"							)]
			if k_pre not in capture_qkv_bias:
				snake_case_		    =     [None, None, None]
			snake_case_		    =     v
			continue

		snake_case_		    =     textenc_pattern.sub(lambda _A							: protected[re.escape(m.group(0							)							)]     ,	_A							)
		snake_case_		    =     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"							)
		snake_case_		    =     textenc_pattern.sub(lambda _A							: protected[re.escape(m.group(0							)							)]     ,	_A							)
		snake_case_		    =     torch.cat(_A							)

	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"							)
		snake_case_		    =     textenc_pattern.sub(lambda _A							: protected[re.escape(m.group(0							)							)]     ,	_A							)
		snake_case_		    =     torch.cat(_A							)

	return new_state_dict


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	return text_enc_dict


if __name__ == "__main__":
		lowercase__				:     int 		=						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."
		)

		lowercase__				:     Dict 		=						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
		lowercase__				:     Tuple 		=						osp.join(args.model_path, "unet", "diffusion_pytorch_model.safetensors")
		lowercase__				:     int 		=						osp.join(args.model_path, "vae", "diffusion_pytorch_model.safetensors")
		lowercase__				:     Any 		=						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):
				lowercase__				:     str 		=						load_file(unet_path, device="cpu")
		else:
				lowercase__				:     Optional[Any] 		=						osp.join(args.model_path, "unet", "diffusion_pytorch_model.bin")
				lowercase__				:     Any 		=						torch.load(unet_path, map_location="cpu")

		if osp.exists(vae_path):
				lowercase__				:     Tuple 		=						load_file(vae_path, device="cpu")
		else:
				lowercase__				:     Any 		=						osp.join(args.model_path, "vae", "diffusion_pytorch_model.bin")
				lowercase__				:     Dict 		=						torch.load(vae_path, map_location="cpu")

		if osp.exists(text_enc_path):
				lowercase__				:     Union[str, Any] 		=						load_file(text_enc_path, device="cpu")
		else:
				lowercase__				:     Union[str, Any] 		=						osp.join(args.model_path, "text_encoder", "pytorch_model.bin")
				lowercase__				:     Optional[int] 		=						torch.load(text_enc_path, map_location="cpu")

		# Convert the UNet model
		lowercase__				:     Dict 		=						convert_unet_state_dict(unet_state_dict)
		lowercase__				:     Any 		=						{"model.diffusion_model." + k: v for k, v in unet_state_dict.items()}

		# Convert the VAE model
		lowercase__				:     Dict 		=						convert_vae_state_dict(vae_state_dict)
		lowercase__				:     Union[str, Any] 		=						{"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
		lowercase__				:     Optional[Any] 		=						"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
				lowercase__				:     Any 		=						{"transformer." + k: v for k, v in text_enc_dict.items()}
				lowercase__				:     List[Any] 		=						convert_text_enc_state_dict_vaa(text_enc_dict)
				lowercase__				:     List[str] 		=						{"cond_stage_model.model." + k: v for k, v in text_enc_dict.items()}
		else:
				lowercase__				:     Tuple 		=						convert_text_enc_state_dict(text_enc_dict)
				lowercase__				:     Any 		=						{"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()}

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

		if args.use_safetensors:
				save_file(state_dict, args.checkpoint_path)
		else:
				lowercase__				:     Union[str, Any] 		=						{"state_dict": state_dict}
				torch.save(state_dict, args.checkpoint_path)
