






"""simple docstring"""



import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

__A		:       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'''),
]

__A		:       List[str]  				=		[
    # (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'''),
]

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

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

for j in range(2):
						__A		:       List[Any]  				=		F"""mid_block.resnets.{j}."""
						__A		:       List[Any]  				=		F"""middle_block.{2*j}."""
						unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))



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


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

__A		:       Union[str, Any]  				=		[
    # (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):
												__A		:       int  				=		F"""encoder.down_blocks.{i}.resnets.{j}."""
												__A		:       Optional[int]  				=		F"""encoder.down.{i}.block.{j}."""
												vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

						if i < 3:
												__A		:       List[str]  				=		F"""down_blocks.{i}.downsamplers.0."""
												__A		:       str  				=		F"""down.{i}.downsample."""
												vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

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


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



def 						lowercase    (				__snake_case				: Optional[Any]	):
		return w.reshape(*w.shape		,					1		,					1	)



def 						lowercase    (				__snake_case				: Any	):
		lowercase_ : int							       =   {k: k for k in vae_state_dict.keys()}
		for k, v in mapping.items():
				for sd_part, hf_part in vae_conversion_map:
						lowercase_ : str							       =   v.replace(lowerCAmelCase_		,					lowerCAmelCase_	)
				lowercase_ : Optional[Any]							       =   v
		for k, v in mapping.items():
				if "attentions" in k:
						for sd_part, hf_part in vae_conversion_map_attn:
								lowercase_ : Optional[int]							       =   v.replace(lowerCAmelCase_		,					lowerCAmelCase_	)
						lowercase_ : Optional[int]							       =   v
		lowercase_ : List[Any]							       =   {v: vae_state_dict[k] for k, v in mapping.items()}
		lowercase_ : Optional[Any]							       =   ['''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'''	)
								lowercase_ : Dict							       =   reshape_weight_for_sd(lowerCAmelCase_	)
		return new_state_dict


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


__A		:       str  				=		[
    # (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'''),
]
__A		:       Tuple  				=		{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
__A		:       str  				=		re.compile('''|'''.join(protected.keys()))

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



def 						lowercase    (				__snake_case				: Dict	):
		lowercase_ : Optional[Any]							       =   {}
		lowercase_ : Optional[Any]							       =   {}
		lowercase_ : List[str]							       =   {}
		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'''	)
				):
						lowercase_ : Union[str, Any]							       =   k[: -len('''.q_proj.weight'''	)]
						lowercase_ : Optional[int]							       =   k[-len('''q_proj.weight'''	)]
						if k_pre not in capture_qkv_weight:
								lowercase_ : str							       =   [None, None, None]
						lowercase_ : Tuple							       =   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'''	)
				):
						lowercase_ : Tuple							       =   k[: -len('''.q_proj.bias'''	)]
						lowercase_ : int							       =   k[-len('''q_proj.bias'''	)]
						if k_pre not in capture_qkv_bias:
								lowercase_ : int							       =   [None, None, None]
						lowercase_ : Union[str, Any]							       =   v
						continue

				lowercase_ : Any							       =   textenc_pattern.sub(lambda __snake_case	: protected[re.escape(m.group(0	)	)]		,					lowerCAmelCase_	)
				lowercase_ : Dict							       =   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'''	)
				lowercase_ : List[Any]							       =   textenc_pattern.sub(lambda __snake_case	: protected[re.escape(m.group(0	)	)]		,					lowerCAmelCase_	)
				lowercase_ : Union[str, Any]							       =   torch.cat(lowerCAmelCase_	)

		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'''	)
				lowercase_ : Tuple							       =   textenc_pattern.sub(lambda __snake_case	: protected[re.escape(m.group(0	)	)]		,					lowerCAmelCase_	)
				lowercase_ : Optional[int]							       =   torch.cat(lowerCAmelCase_	)

		return new_state_dict



def 						lowercase    (				__snake_case				: Union[str, Any]	):
		return text_enc_dict


if __name__ == "__main__":
						__A		:       Optional[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.'''
						)

						__A		:       Any  				=		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
						__A		:       Any  				=		osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.safetensors''')
						__A		:       Any  				=		osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.safetensors''')
						__A		:       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):
												__A		:       Union[str, Any]  				=		load_file(unet_path, device='''cpu''')
						else:
												__A		:       str  				=		osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.bin''')
												__A		:       Union[str, Any]  				=		torch.load(unet_path, map_location='''cpu''')

						if osp.exists(vae_path):
												__A		:       str  				=		load_file(vae_path, device='''cpu''')
						else:
												__A		:       List[str]  				=		osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.bin''')
												__A		:       Any  				=		torch.load(vae_path, map_location='''cpu''')

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

						# Convert the UNet model
						__A		:       Tuple  				=		convert_unet_state_dict(unet_state_dict)
						__A		:       List[str]  				=		{'''model.diffusion_model.''' + k: v for k, v in unet_state_dict.items()}

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

						# Put together new checkpoint
						__A		:       List[str]  				=		{**unet_state_dict, **vae_state_dict, **text_enc_dict}
						if args.half:
												__A		:       Optional[int]  				=		{k: v.half() for k, v in state_dict.items()}

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







"""simple docstring"""







import json
import os
import unittest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class  UpperCamelCase_	( UpperCamelCase			,  unittest.TestCase):

	"""simple docstring"""





	snake_case__				:  Any    					=			CLIPTokenizer
	snake_case__				:  Dict    					=			CLIPTokenizerFast
	snake_case__				:  List[Any]    					=			True
	snake_case__				:  Optional[Any]    					=			{}
	snake_case__				:  Dict    					=			False




	def   UpperCAmelCase_      ( self			:				Any   )       ->	Any:
						super().setUp()

						# fmt: off
						__SCREAMING_SNAKE_CASE						   =				["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
						# fmt: on
						__SCREAMING_SNAKE_CASE						   =				dict(zip(UpperCAmelCase__			,			range(len(UpperCAmelCase__   )   )   )   )
						__SCREAMING_SNAKE_CASE						   =				["#version: 0.2", "l o", "lo w</w>", "e r</w>"]
						__SCREAMING_SNAKE_CASE						   =				{"unk_token": "<unk>"}

						__SCREAMING_SNAKE_CASE						   =				os.path.join(self.tmpdirname			,			VOCAB_FILES_NAMES["vocab_file"]   )
						__SCREAMING_SNAKE_CASE						   =				os.path.join(self.tmpdirname			,			VOCAB_FILES_NAMES["merges_file"]   )
						with open(self.vocab_file			,			"w"			,			encoding="utf-8"   ) as fp:
											fp.write(json.dumps(UpperCAmelCase__   ) + "\n"   )
						with open(self.merges_file			,			"w"			,			encoding="utf-8"   ) as fp:
											fp.write("\n".join(UpperCAmelCase__   )   )




	def   UpperCAmelCase_      ( self			:				List[Any]			,			**UpperCAmelCase__			:				Tuple   )       ->	List[Any]:
						kwargs.update(self.special_tokens_map   )
						return CLIPTokenizer.from_pretrained(self.tmpdirname			,			**UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				Any			,			**UpperCAmelCase__			:				Optional[Any]   )       ->	List[str]:
						kwargs.update(self.special_tokens_map   )
						return CLIPTokenizerFast.from_pretrained(self.tmpdirname			,			**UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				List[Any]			,			UpperCAmelCase__			:				int   )       ->	Union[str, Any]:
						__SCREAMING_SNAKE_CASE						   =				"lower newer"
						__SCREAMING_SNAKE_CASE						   =				"lower newer"
						return input_text, output_text




	def   UpperCAmelCase_      ( self			:				int   )       ->	List[str]:
						__SCREAMING_SNAKE_CASE						   =				CLIPTokenizer(self.vocab_file			,			self.merges_file			,			**self.special_tokens_map   )
						__SCREAMING_SNAKE_CASE						   =				"lower newer"
						__SCREAMING_SNAKE_CASE						   =				["lo", "w", "er</w>", "n", "e", "w", "er</w>"]
						__SCREAMING_SNAKE_CASE						   =				tokenizer.tokenize(UpperCAmelCase__   )
						self.assertListEqual(UpperCAmelCase__			,			UpperCAmelCase__   )

						__SCREAMING_SNAKE_CASE						   =				tokens + [tokenizer.unk_token]
						__SCREAMING_SNAKE_CASE						   =				[1_0, 2, 1_6, 9, 3, 2, 1_6, 2_0]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase__   )			,			UpperCAmelCase__   )




	@require_ftfy
	def   UpperCAmelCase_      ( self			:				Optional[Any]   )       ->	int:
						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""   ):
																__SCREAMING_SNAKE_CASE						   =				self.tokenizer_class.from_pretrained(UpperCAmelCase__			,			**UpperCAmelCase__   )
																__SCREAMING_SNAKE_CASE						   =				self.rust_tokenizer_class.from_pretrained(UpperCAmelCase__			,			**UpperCAmelCase__   )

																__SCREAMING_SNAKE_CASE						   =				"A\n'll 11p223RF☆ho!!to?'d'd''d of a cat to-$''d."
																__SCREAMING_SNAKE_CASE						   =				tokenizer_s.tokenize(UpperCAmelCase__   )
																__SCREAMING_SNAKE_CASE						   =				tokenizer_r.tokenize(UpperCAmelCase__   )

																self.assertListEqual(UpperCAmelCase__			,			UpperCAmelCase__   )

																# Test that the tokenization is identical on an example containing a character (Latin Small Letter A
																# with Tilde) encoded in 2 different ways
																__SCREAMING_SNAKE_CASE						   =				"xa\u0303y" + " " + "x\xe3y"
																__SCREAMING_SNAKE_CASE						   =				tokenizer_s.tokenize(UpperCAmelCase__   )
																__SCREAMING_SNAKE_CASE						   =				tokenizer_r.tokenize(UpperCAmelCase__   )

																self.assertListEqual(UpperCAmelCase__			,			UpperCAmelCase__   )

																# Test that the tokenization is identical on unicode of space type
																__SCREAMING_SNAKE_CASE						   =				[
																    "\u0009",  # (horizontal tab, '\t')
																    "\u000B",  # (vertical tab)
																    "\u000C",  # (form feed)
																    "\u0020",  # (space, ' ')
																    "\u200E",  # (left-to-right mark):w
																    "\u200F",  # (right-to-left mark)
																]
																for unicode_seq in spaces_unicodes:
																					__SCREAMING_SNAKE_CASE						   =				tokenizer_s.tokenize(UpperCAmelCase__   )
																					__SCREAMING_SNAKE_CASE						   =				tokenizer_r.tokenize(UpperCAmelCase__   )

																					self.assertListEqual(UpperCAmelCase__			,			UpperCAmelCase__   )

																# Test that the tokenization is identical on unicode of line break type
																__SCREAMING_SNAKE_CASE						   =				[
																    "\u000A",  # (line feed, '\n')
																    "\r\n",  # (carriage return and line feed, '\r\n')
																    "\u000D",  # (carriage return, '\r')
																    "\r",  # (carriage return, '\r')
																    "\u000D",  # (carriage return, '\r')
																    "\u2028",  # (line separator)
																    "\u2029",  # (paragraph separator)
																    # "\u0085", # (next line)
																]

																# The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
																# it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
																# space (and thus into an empty list).

																for unicode_seq in line_break_unicodes:
																					__SCREAMING_SNAKE_CASE						   =				tokenizer_s.tokenize(UpperCAmelCase__   )
																					__SCREAMING_SNAKE_CASE						   =				tokenizer_r.tokenize(UpperCAmelCase__   )

																					self.assertListEqual(UpperCAmelCase__			,			UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				Tuple   )       ->	Optional[Any]:
						# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space`
						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""   ):
																__SCREAMING_SNAKE_CASE						   =				"hello"  # `hello` is a token in the vocabulary of `pretrained_name`
																__SCREAMING_SNAKE_CASE						   =				F"""{text_of_1_token} {text_of_1_token}"""

																__SCREAMING_SNAKE_CASE						   =				self.rust_tokenizer_class.from_pretrained(
																    UpperCAmelCase__			,			use_fast=UpperCAmelCase__			,			)
																__SCREAMING_SNAKE_CASE						   =				tokenizer_r(UpperCAmelCase__			,			return_offsets_mapping=UpperCAmelCase__			,			add_special_tokens=UpperCAmelCase__   )
																self.assertEqual(encoding.offset_mapping[0]			,			(0, len(UpperCAmelCase__   ))   )
																self.assertEqual(
																    encoding.offset_mapping[1]			,			(len(UpperCAmelCase__   ) + 1, len(UpperCAmelCase__   ) + 1 + len(UpperCAmelCase__   ))			,			)

																__SCREAMING_SNAKE_CASE						   =				F""" {text}"""

																__SCREAMING_SNAKE_CASE						   =				self.rust_tokenizer_class.from_pretrained(
																    UpperCAmelCase__			,			use_fast=UpperCAmelCase__			,			)
																__SCREAMING_SNAKE_CASE						   =				tokenizer_r(UpperCAmelCase__			,			return_offsets_mapping=UpperCAmelCase__			,			add_special_tokens=UpperCAmelCase__   )
																self.assertEqual(encoding.offset_mapping[0]			,			(1, 1 + len(UpperCAmelCase__   ))   )
																self.assertEqual(
																    encoding.offset_mapping[1]			,			(1 + len(UpperCAmelCase__   ) + 1, 1 + len(UpperCAmelCase__   ) + 1 + len(UpperCAmelCase__   ))			,			)




	def   UpperCAmelCase_      ( self			:				str   )       ->	Optional[int]:
						# Test related to the breaking change introduced in transformers v4.17.0
						# We need to check that an error in raised when the user try to load a previous version of the tokenizer.
						with self.assertRaises(UpperCAmelCase__   ) as context:
											self.rust_tokenizer_class.from_pretrained("robot-test/old-clip-tokenizer"   )

						self.assertTrue(
						    context.exception.args[0].startswith(
						        "The `backend_tokenizer` provided does not match the expected format."   )   )




	@require_ftfy
	def   UpperCAmelCase_      ( self			:				Optional[int]   )       ->	int:
						super().test_tokenization_python_rust_equals()




	def   UpperCAmelCase_      ( self			:				Optional[int]   )       ->	Optional[Any]:
						# CLIP always lower cases letters
						pass
