


'''simple docstring'''






import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
__UpperCAmelCase       =							logging.get_logger(__name__)

__UpperCAmelCase       =							{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}
__UpperCAmelCase       =							[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]


def  _snake_case       (					A			,   A			,   A			,   A			,   A	)						->   Tuple:
							for attribute in key.split('''.'''	):
														lowerCAmelCase__	       =     getattr(A			,   A	)

							if weight_type is not None:
														lowerCAmelCase__	       =     getattr(A			,   A	).shape
							else:
														lowerCAmelCase__	       =     hf_pointer.shape

							assert hf_shape == value.shape, (
							    F"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
							    F""" {value.shape} for {full_name}"""
							)

							if weight_type == "weight":
														lowerCAmelCase__	       =     value
							elif weight_type == "weight_g":
														lowerCAmelCase__	       =     value
							elif weight_type == "weight_v":
														lowerCAmelCase__	       =     value
							elif weight_type == "bias":
														lowerCAmelCase__	       =     value
							else:
														lowerCAmelCase__	       =     value

							logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""	)


def  _snake_case       (					A			,   A	)						->   List[str]:
							lowerCAmelCase__	       =     []
							lowerCAmelCase__	       =     fairseq_model.state_dict()

							lowerCAmelCase__	       =     hf_model.feature_extractor
							lowerCAmelCase__	       =     hf_model.adapter

							for name, value in fairseq_dict.items():
														lowerCAmelCase__	       =     False
														if "conv_layers" in name:
																					load_conv_layer(
																					    A			,   A			,   A			,   A			,   hf_model.config.feat_extract_norm == '''group'''			,   )
																					lowerCAmelCase__	       =     True
														elif any(x in name for x in ['''adaptor''', '''w2v_encoder.proj.''', '''w2v_proj_ln.''']	):
																					load_adapter(A			,   A			,   A			,   A	)
																					lowerCAmelCase__	       =     True
														else:
																					for key, mapped_key in MAPPING.items():
																												if key in name or key.split('''w2v_model.'''	)[-1] == name.split('''.'''	)[0]:
																																			lowerCAmelCase__	       =     True
																																			if "*" in mapped_key:
																																										lowerCAmelCase__	       =     name.split(A	)[0].split('''.'''	)[-2]
																																										lowerCAmelCase__	       =     mapped_key.replace('''*'''			,   A	)
																																			if "weight_g" in name:
																																										lowerCAmelCase__	       =     '''weight_g'''
																																			elif "weight_v" in name:
																																										lowerCAmelCase__	       =     '''weight_v'''
																																			elif "bias" in name:
																																										lowerCAmelCase__	       =     '''bias'''
																																			elif "weight" in name:
																																										lowerCAmelCase__	       =     '''weight'''
																																			else:
																																										lowerCAmelCase__	       =     None
																																			set_recursively(A			,   A			,   A			,   A			,   A	)
																												continue
														if not is_used:
																					unused_weights.append(A	)

							logger.warning(F"""Unused weights: {unused_weights}"""	)


def  _snake_case       (					A			,   A			,   A			,   A			,   A	)						->   int:
							lowerCAmelCase__	       =     full_name.split('''conv_layers.'''	)[-1]
							lowerCAmelCase__	       =     name.split('''.'''	)
							lowerCAmelCase__	       =     int(items[0]	)
							lowerCAmelCase__	       =     int(items[1]	)

							if type_id == 0:
														if "bias" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
																					    F"""{full_name} has size {value.shape}, but"""
																					    F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
																					)
																					lowerCAmelCase__	       =     value
																					logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""	)
														elif "weight" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
																					    F"""{full_name} has size {value.shape}, but"""
																					    F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
																					)
																					lowerCAmelCase__	       =     value
																					logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""	)
							elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
														if "bias" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
																					    F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
																					    " found."
																					)
																					lowerCAmelCase__	       =     value
																					logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""	)
														elif "weight" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
																					    F"""{full_name} has size {value.shape}, but"""
																					    F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
																					)
																					lowerCAmelCase__	       =     value
																					logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""	)
							else:
														unused_weights.append(A	)


def  _snake_case       (					A			,   A			,   A			,   A	)						->   Tuple:
							lowerCAmelCase__	       =     full_name.split('''adaptor.'''	)[-1]
							lowerCAmelCase__	       =     name.split('''.'''	)

							if items[1].isdigit():
														lowerCAmelCase__	       =     int(items[1]	)
							else:
														lowerCAmelCase__	       =     None

							if "adaptor" not in full_name:
														if "proj_ln" in full_name:
																					# has to be layer norm
																					if "bias" in name:
																												assert (
																												    value.shape == adapter.proj_layer_norm.bias.data.shape
																												), F"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found."""
																												lowerCAmelCase__	       =     value
																												logger.info(F"""Adapter proj layer norm bias was initialized from {full_name}."""	)
																					if "weight" in name:
																												assert (
																												    value.shape == adapter.proj_layer_norm.weight.data.shape
																												), F"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found."""
																												lowerCAmelCase__	       =     value
														else:
																					# has to be projection layer
																					if "bias" in name:
																												assert (
																												    value.shape == adapter.proj.bias.data.shape
																												), F"""{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found."""
																												lowerCAmelCase__	       =     value
																												logger.info(F"""Adapter proj layer bias was initialized from {full_name}."""	)
																					if "weight" in name:
																												assert (
																												    value.shape == adapter.proj.weight.data.shape
																												), F"""{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found."""
																												lowerCAmelCase__	       =     value
																												logger.info(F"""Adapter proj layer weight was initialized from {full_name}."""	)
							elif isinstance(A			,   A	):
														if "bias" in name:
																					assert (
																					    value.shape == adapter.layers[layer_id].conv.bias.data.shape
																					), F"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found."""
																					lowerCAmelCase__	       =     value
																					logger.info(F"""Adapter layer {layer_id} bias was initialized from {full_name}."""	)
														elif "weight" in name:
																					assert (
																					    value.shape == adapter.layers[layer_id].conv.weight.data.shape
																					), F"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found."""
																					lowerCAmelCase__	       =     value
																					logger.info(F"""Adapter layer {layer_id} bias was initialized from {full_name}."""	)
							else:
														unused_weights.append(A	)


def  _snake_case       (					A	)						->   Any:
							lowerCAmelCase__       , lowerCAmelCase__	       =     emb.weight.shape
							lowerCAmelCase__	       =     nn.Linear(A			,   A			,   bias=A	)
							lowerCAmelCase__	       =     emb.weight.data
							return lin_layer


@torch.no_grad()
def  _snake_case       (					A			,   A			,   A			,   A			,   A			,   A			,   A			,   A			,   A			,   A			,   A			,   )						->   str:
							lowerCAmelCase__	       =     WavaVecaConfig.from_pretrained(
							    A			,   add_adapter=A			,   adapter_stride=A			,   adapter_kernel_size=A			,   use_auth_token=A			,   output_hidden_size=A			,   )
							lowerCAmelCase__	       =     MBartConfig.from_pretrained(A	)

							# load model
							lowerCAmelCase__       , lowerCAmelCase__       , lowerCAmelCase__	       =     fairseq.checkpoint_utils.load_model_ensemble_and_task(
							    [checkpoint_path]			,   arg_overrides={
							        '''config_yaml''': config_yaml_path,
							        '''data''': '''/'''.join(dict_path.split('''/'''	)[:-1]	),
							        '''w2v_path''': checkpoint_path,
							        '''load_pretrained_decoder_from''': None,
							    }			,   )
							lowerCAmelCase__	       =     model[0].eval()

							# load feature extractor
							lowerCAmelCase__	       =     WavaVecaFeatureExtractor.from_pretrained(A			,   use_auth_token=A	)

							# set weights for wav2vec2 encoder
							lowerCAmelCase__	       =     WavaVecaModel(A	)

							recursively_load_weights_wavaveca(model.encoder			,   A	)

							# load decoder weights
							lowerCAmelCase__	       =     MBartForCausalLM(A	)
							lowerCAmelCase__       , lowerCAmelCase__	       =     hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()			,   strict=A	)
							logger.warning(F"""The following keys are missing when loading the decoder weights: {missing_keys}"""	)
							logger.warning(F"""The following keys are unexpected when loading the decoder weights: {unexpected_keys}"""	)

							lowerCAmelCase__	       =     SpeechEncoderDecoderModel(encoder=A			,   decoder=A	)
							lowerCAmelCase__	       =     False

							lowerCAmelCase__	       =     MBartaaTokenizer(A	)
							tokenizer.save_pretrained(A	)

							lowerCAmelCase__	       =     hf_wavavec.config.to_dict()
							lowerCAmelCase__	       =     tokenizer.pad_token_id
							lowerCAmelCase__	       =     tokenizer.bos_token_id
							lowerCAmelCase__	       =     tokenizer.eos_token_id
							lowerCAmelCase__	       =     '''mbart50'''
							lowerCAmelCase__	       =     '''wav2vec2'''

							lowerCAmelCase__	       =     tokenizer.eos_token_id
							lowerCAmelCase__	       =     250004
							lowerCAmelCase__	       =     tokenizer.eos_token_id

							lowerCAmelCase__	       =     SpeechEncoderDecoderConfig.from_dict(A	)

							hf_wavavec.save_pretrained(A	)
							feature_extractor.save_pretrained(A	)


if __name__ == "__main__":
		__UpperCAmelCase       =							argparse.ArgumentParser()
		parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
		parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
		parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
		parser.add_argument('''--config_yaml_path''', default=None, type=str, help='''Path to yaml file of fine-tuned model''')
		parser.add_argument(
		    '''--encoder_config_path''',
		    default='''facebook/wav2vec2-xls-r-1b''',
		    type=str,
		    help='''Path to hf encoder wav2vec2 checkpoint config''',
		)
		parser.add_argument(
		    '''--decoder_config_path''',
		    default='''facebook/mbart-large-50-one-to-many-mmt''',
		    type=str,
		    help='''Path to hf decoder checkpoint config''',
		)
		parser.add_argument('''--add_adapter''', default=True, type=bool, help='''whethere to add model adapter layers''')
		parser.add_argument('''--adapter_stride''', default=2, type=int, help='''stride of adapter layers''')
		parser.add_argument('''--adapter_kernel_size''', default=3, type=int, help='''kernel size of adapter layers''')
		parser.add_argument('''--encoder_output_dim''', default=1_024, type=int, help='''encoder output dim''')
		parser.add_argument('''--start_token_id''', default=250_004, type=int, help='''`decoder_start_token_id` of model config''')

		__UpperCAmelCase       =							parser.parse_args()
		convert_wavaveca_checkpoint(
		    args.checkpoint_path,
		    args.pytorch_dump_folder_path,
		    args.dict_path,
		    args.config_yaml_path,
		    encoder_config_path=args.encoder_config_path,
		    decoder_config_path=args.decoder_config_path,
		    add_adapter=args.add_adapter,
		    adapter_kernel_size=args.adapter_kernel_size,
		    adapter_stride=args.adapter_stride,
		    decoder_start_token_id=args.start_token_id,
		    encoder_output_dim=args.encoder_output_dim,
		)





'''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 		a__    (							a__    ,    unittest.TestCase  ):





							'''simple docstring'''






							lowercase__      :       List[Any]     						=				CLIPTokenizer
							lowercase__      :       List[str]     						=				CLIPTokenizerFast
							lowercase__      :       Dict     						=				True
							lowercase__      :       Any     						=				{}
							lowercase__      :       Optional[int]     						=				False







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

														# fmt: off
														lowerCAmelCase__	       =     ['''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
														lowerCAmelCase__	       =     dict(zip(lowerCamelCase_ ,   range(len(lowerCamelCase_       )       )       )       )
														lowerCAmelCase__	       =     ['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''']
														lowerCAmelCase__	       =     {'''unk_token''': '''<unk>'''}

														lowerCAmelCase__	       =     os.path.join(self.tmpdirname ,   VOCAB_FILES_NAMES['''vocab_file''']       )
														lowerCAmelCase__	       =     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(lowerCamelCase_       ) + '''\n'''       )
														with open(self.merges_file ,   '''w''' ,   encoding='''utf-8'''       ) as fp:
																					fp.write('''\n'''.join(lowerCamelCase_       )       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   **lowerCamelCase_       )	->							Any:
														kwargs.update(self.special_tokens_map       )
														return CLIPTokenizer.from_pretrained(self.tmpdirname ,   **lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   **lowerCamelCase_       )	->							int:
														kwargs.update(self.special_tokens_map       )
														return CLIPTokenizerFast.from_pretrained(self.tmpdirname ,   **lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							Any:
														lowerCAmelCase__	       =     '''lower newer'''
														lowerCAmelCase__	       =     '''lower newer'''
														return input_text, output_text







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Union[str, Any]:
														lowerCAmelCase__	       =     CLIPTokenizer(self.vocab_file ,   self.merges_file ,   **self.special_tokens_map       )
														lowerCAmelCase__	       =     '''lower newer'''
														lowerCAmelCase__	       =     ['''lo''', '''w''', '''er</w>''', '''n''', '''e''', '''w''', '''er</w>''']
														lowerCAmelCase__	       =     tokenizer.tokenize(lowerCamelCase_       )
														self.assertListEqual(lowerCamelCase_ ,   lowerCamelCase_       )

														lowerCAmelCase__	       =     tokens + [tokenizer.unk_token]
														lowerCAmelCase__	       =     [10, 2, 16, 9, 3, 2, 16, 20]
														self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase_       ) ,   lowerCamelCase_       )







							@require_ftfy
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Tuple:
														for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																					with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""       ):
																												lowerCAmelCase__	       =     self.tokenizer_class.from_pretrained(lowerCamelCase_ ,   **lowerCamelCase_       )
																												lowerCAmelCase__	       =     self.rust_tokenizer_class.from_pretrained(lowerCamelCase_ ,   **lowerCamelCase_       )

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

																												self.assertListEqual(lowerCamelCase_ ,   lowerCamelCase_       )

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

																												self.assertListEqual(lowerCamelCase_ ,   lowerCamelCase_       )

																												# Test that the tokenization is identical on unicode of space type
																												lowerCAmelCase__	       =     [
																												    '''\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:
																																			lowerCAmelCase__	       =     tokenizer_s.tokenize(lowerCamelCase_       )
																																			lowerCAmelCase__	       =     tokenizer_r.tokenize(lowerCamelCase_       )

																																			self.assertListEqual(lowerCamelCase_ ,   lowerCamelCase_       )

																												# Test that the tokenization is identical on unicode of line break type
																												lowerCAmelCase__	       =     [
																												    '''\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:
																																			lowerCAmelCase__	       =     tokenizer_s.tokenize(lowerCamelCase_       )
																																			lowerCAmelCase__	       =     tokenizer_r.tokenize(lowerCamelCase_       )

																																			self.assertListEqual(lowerCamelCase_ ,   lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							str:
														# 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})"""       ):
																												lowerCAmelCase__	       =     '''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
																												lowerCAmelCase__	       =     F"""{text_of_1_token} {text_of_1_token}"""

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

																												lowerCAmelCase__	       =     F""" {text}"""

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







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Union[str, Any]:
														# 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(lowerCamelCase_       ) 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   __SCREAMING_SNAKE_CASE							(		self       )	->							List[Any]:
														super().test_tokenization_python_rust_equals()







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Any:
														# CLIP always lower cases letters
														pass