





import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
UpperCAmelCase__						:			List[str]									=logging.get_logger('''transformers.models.speecht5''')

UpperCAmelCase__						:			str									={
    '''speech_encoder_prenet.layer_norm''': '''speecht5.encoder.prenet.feature_projection.layer_norm''',
    '''speech_encoder_prenet.post_extract_proj''': '''speecht5.encoder.prenet.feature_projection.projection''',
    '''speech_encoder_prenet.pos_conv.0''': '''speecht5.encoder.prenet.pos_conv_embed.conv''',
    '''speech_encoder_prenet.mask_emb''': '''speecht5.encoder.prenet.masked_spec_embed''',
}
UpperCAmelCase__						:			Any									={
    '''text_encoder_prenet.encoder_prenet.0''': '''speecht5.encoder.prenet.embed_tokens''',
    '''text_encoder_prenet.encoder_prenet.1.alpha''': '''speecht5.encoder.prenet.encode_positions.alpha''',
}
UpperCAmelCase__						:			Union[str, Any]									={
    '''speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0''': '''speecht5.decoder.prenet.layers.0''',
    '''speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0''': '''speecht5.decoder.prenet.layers.1''',
    '''speech_decoder_prenet.decoder_prenet.0.1''': '''speecht5.decoder.prenet.final_layer''',
    '''speech_decoder_prenet.decoder_prenet.1.alpha''': '''speecht5.decoder.prenet.encode_positions.alpha''',
    '''speech_decoder_prenet.spkembs_layer.0''': '''speecht5.decoder.prenet.speaker_embeds_layer''',
}
UpperCAmelCase__						:			Any									={
    '''speech_decoder_postnet.feat_out''': '''speech_decoder_postnet.feat_out''',
    '''speech_decoder_postnet.prob_out''': '''speech_decoder_postnet.prob_out''',
    '''speech_decoder_postnet.postnet.postnet.0.0''': '''speech_decoder_postnet.layers.0.conv''',
    '''speech_decoder_postnet.postnet.postnet.0.1''': '''speech_decoder_postnet.layers.0.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.1.0''': '''speech_decoder_postnet.layers.1.conv''',
    '''speech_decoder_postnet.postnet.postnet.1.1''': '''speech_decoder_postnet.layers.1.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.2.0''': '''speech_decoder_postnet.layers.2.conv''',
    '''speech_decoder_postnet.postnet.postnet.2.1''': '''speech_decoder_postnet.layers.2.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.3.0''': '''speech_decoder_postnet.layers.3.conv''',
    '''speech_decoder_postnet.postnet.postnet.3.1''': '''speech_decoder_postnet.layers.3.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.4.0''': '''speech_decoder_postnet.layers.4.conv''',
    '''speech_decoder_postnet.postnet.postnet.4.1''': '''speech_decoder_postnet.layers.4.batch_norm''',
}
UpperCAmelCase__						:			int									={
    '''text_decoder_prenet.embed_tokens''': '''speecht5.decoder.prenet.embed_tokens''',
}
UpperCAmelCase__						:			Tuple									={
    '''text_decoder_postnet.output_projection''': '''text_decoder_postnet.lm_head''',
}
UpperCAmelCase__						:			Tuple									={
    '''encoder.layers.*.self_attn.k_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj''',
    '''encoder.layers.*.self_attn.v_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj''',
    '''encoder.layers.*.self_attn.q_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj''',
    '''encoder.layers.*.self_attn.out_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj''',
    '''encoder.layers.*.self_attn_layer_norm''': '''speecht5.encoder.wrapped_encoder.layers.*.layer_norm''',
    '''encoder.layers.*.fc1''': '''speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense''',
    '''encoder.layers.*.fc2''': '''speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense''',
    '''encoder.layers.*.final_layer_norm''': '''speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''speecht5.encoder.wrapped_encoder.layer_norm''',
    '''encoder.pos_emb.pe_k''': '''speecht5.encoder.wrapped_encoder.embed_positions.pe_k''',
}
UpperCAmelCase__						:			List[Any]									={
    '''decoder.layers.*.self_attn.k_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj''',
    '''decoder.layers.*.self_attn.v_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj''',
    '''decoder.layers.*.self_attn.q_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj''',
    '''decoder.layers.*.self_attn.out_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj''',
    '''decoder.layers.*.self_attn_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm''',
    '''decoder.layers.*.encoder_attn.k_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj''',
    '''decoder.layers.*.encoder_attn.v_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj''',
    '''decoder.layers.*.encoder_attn.q_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj''',
    '''decoder.layers.*.encoder_attn.out_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj''',
    '''decoder.layers.*.encoder_attn_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm''',
    '''decoder.layers.*.fc1''': '''speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense''',
    '''decoder.layers.*.fc2''': '''speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense''',
    '''decoder.layers.*.final_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm''',
}
UpperCAmelCase__						:			Dict									={
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
UpperCAmelCase__						:			Union[str, Any]									={
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
UpperCAmelCase__						:			str									={
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
UpperCAmelCase__						:			int									=[]
UpperCAmelCase__						:			Any									=[
    '''encoder.version''',
    '''encoder.layers.*.norm_k.weight''',
    '''encoder.layers.*.norm_k.bias''',
    '''decoder.version''',
    '''decoder.layers.*.norm_k.weight''',
    '''decoder.layers.*.norm_k.bias''',
    '''decoder.pos_emb.pe_k''',
    '''speech_encoder_prenet.embed_positions._float_tensor''',
    '''text_decoder_prenet.embed_positions._float_tensor''',
]
UpperCAmelCase__						:			Dict									=IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''speech_decoder_prenet.*''',
    '''speech_decoder_postnet.*''',
]
UpperCAmelCase__						:			Optional[Any]									=IGNORE_KEYS + [
    '''encoder.proj''',
    '''speech_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]
UpperCAmelCase__						:			Any									=IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  int:
				for attribute in key.split("""."""				):
								lowerCamelCase	 =getattr(_UpperCAmelCase			,      _UpperCAmelCase				)

				if weight_type is not None:
								lowerCamelCase	 =getattr(_UpperCAmelCase			,      _UpperCAmelCase				).shape
				else:
								lowerCamelCase	 =hf_pointer.shape

				if hf_shape != value.shape:
								raise ValueError(
								    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
				elif weight_type == "running_mean":
								lowerCamelCase	 =value
				elif weight_type == "running_var":
								lowerCamelCase	 =value
				elif weight_type == "num_batches_tracked":
								lowerCamelCase	 =value
				else:
								lowerCamelCase	 =value

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





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase				)						->  List[str]:
				for key in ignore_keys:
								if key.endswith(""".*"""				):
												if name.startswith(key[:-1]				):
																return True
								elif ".*." in key:
												lowerCamelCase ,  lowerCamelCase	 =key.split(""".*."""				)
												if prefix in name and suffix in name:
																return True
								elif key in name:
												return True
				return False





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  Any:
				lowerCamelCase	 =[]

				if task == "s2t":
								lowerCamelCase	 =hf_model.speechta.encoder.prenet.feature_encoder
								lowerCamelCase	 =MAPPING_S2T
								lowerCamelCase	 =IGNORE_KEYS_S2T
				elif task == "t2s":
								lowerCamelCase	 =None
								lowerCamelCase	 =MAPPING_T2S
								lowerCamelCase	 =IGNORE_KEYS_T2S
				elif task == "s2s":
								lowerCamelCase	 =hf_model.speechta.encoder.prenet.feature_encoder
								lowerCamelCase	 =MAPPING_S2S
								lowerCamelCase	 =IGNORE_KEYS_S2S
				else:
								raise ValueError(F"""Unsupported task: {task}"""				)

				for name, value in fairseq_dict.items():
								if should_ignore(_UpperCAmelCase			,      _UpperCAmelCase				):
												logger.info(F"""{name} was ignored"""				)
												continue

								lowerCamelCase	 =False
								if "conv_layers" in name:
												load_conv_layer(
												    _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      hf_model.config.feat_extract_norm == """group"""			,      )
												lowerCamelCase	 =True
								else:
												for key, mapped_key in MAPPING.items():
																# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

																if "*" in key:
																				lowerCamelCase ,  lowerCamelCase	 =key.split(""".*."""				)
																				if prefix in name and suffix in name:
																								lowerCamelCase	 =suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
																if key in name:
																				lowerCamelCase	 =True
																				if "*" in mapped_key:
																								lowerCamelCase	 =name.split(_UpperCAmelCase				)[0].split("""."""				)[-2]
																								lowerCamelCase	 =mapped_key.replace("""*"""			,      _UpperCAmelCase				)
																				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"""
																				elif "running_mean" in name:
																								lowerCamelCase	 ="""running_mean"""
																				elif "running_var" in name:
																								lowerCamelCase	 ="""running_var"""
																				elif "num_batches_tracked" in name:
																								lowerCamelCase	 ="""num_batches_tracked"""
																				else:
																								lowerCamelCase	 =None
																				set_recursively(_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)
																continue
								if not is_used:
												unused_weights.append(_UpperCAmelCase				)

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





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  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:
												if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																raise ValueError(
																    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:
												if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																raise ValueError(
																    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:
												if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																raise ValueError(
																    F"""{full_name} has size {value.shape}, but"""
																    F""" {feature_extractor.conv_layers[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:
												if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																raise ValueError(
																    F"""{full_name} has size {value.shape}, but"""
																    F""" {feature_extractor.conv_layers[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(_UpperCAmelCase				)





@torch.no_grad()
def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase=None			,      _UpperCAmelCase=None			,      _UpperCAmelCase=None			,      )						->  List[str]:
				if config_path is not None:
								lowerCamelCase	 =SpeechTaConfig.from_pretrained(_UpperCAmelCase				)
				else:
								lowerCamelCase	 =SpeechTaConfig()

				if task == "s2t":
								lowerCamelCase	 =config.max_text_positions
								lowerCamelCase	 =SpeechTaForSpeechToText(_UpperCAmelCase				)
				elif task == "t2s":
								lowerCamelCase	 =18_76
								lowerCamelCase	 =6_00
								lowerCamelCase	 =config.max_speech_positions
								lowerCamelCase	 =SpeechTaForTextToSpeech(_UpperCAmelCase				)
				elif task == "s2s":
								lowerCamelCase	 =18_76
								lowerCamelCase	 =config.max_speech_positions
								lowerCamelCase	 =SpeechTaForSpeechToSpeech(_UpperCAmelCase				)
				else:
								raise ValueError(F"""Unknown task name: {task}"""				)

				if vocab_path:
								lowerCamelCase	 =SpeechTaTokenizer(_UpperCAmelCase			,      model_max_length=config.max_text_positions				)

								# Mask token behaves like a normal word, i.e. include the space before it
								lowerCamelCase	 =AddedToken("""<mask>"""			,      lstrip=_UpperCAmelCase			,      rstrip=_UpperCAmelCase				)
								lowerCamelCase	 =mask_token
								tokenizer.add_special_tokens({"""mask_token""": mask_token}				)
								tokenizer.add_tokens(["""<ctc_blank>"""]				)

				lowerCamelCase	 =SpeechTaFeatureExtractor()
				lowerCamelCase	 =SpeechTaProcessor(tokenizer=_UpperCAmelCase			,      feature_extractor=_UpperCAmelCase				)
				processor.save_pretrained(_UpperCAmelCase				)

				lowerCamelCase	 =torch.load(_UpperCAmelCase				)
				recursively_load_weights(fairseq_checkpoint["""model"""]			,      _UpperCAmelCase			,      _UpperCAmelCase				)

				model.save_pretrained(_UpperCAmelCase				)

				if repo_id:
								print("""Pushing to the hub..."""				)
								processor.push_to_hub(_UpperCAmelCase				)
								model.push_to_hub(_UpperCAmelCase				)


if __name__ == "__main__":
							UpperCAmelCase__						:			str									=argparse.ArgumentParser()
							parser.add_argument(
							    '''--task''',
							    default='''s2t''',
							    type=str,
							    help='''Type of the SpeechT5 model you\'d like to convert. Should be one of \'s2t\', \'t2s\', \'s2s\'.''',
							)
							parser.add_argument('''--checkpoint_path''', required=True, default=None, type=str, help='''Path to fairseq checkpoint''')
							parser.add_argument('''--vocab_path''', default=None, type=str, help='''Path to SentencePiece model''')
							parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
							parser.add_argument(
							    '''--pytorch_dump_folder_path''', required=True, default=None, type=str, help='''Path to the output PyTorch model.'''
							)
							parser.add_argument(
							    '''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
							)

							UpperCAmelCase__						:			Optional[Any]									=parser.parse_args()
							convert_speechta_checkpoint(
							    args.task,
							    args.checkpoint_path,
							    args.pytorch_dump_folder_path,
							    args.config_path,
							    args.vocab_path,
							    args.push_to_hub,
							)












from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch

@require_torch
@require_tf
class       __A     (						a    ):


				def _snake_case       (		self							):
								lowerCamelCase	 =SMALL_MODEL_IDENTIFIER
								lowerCamelCase	 ="""pt"""
								lowerCamelCase	 ="""tf"""


				def _snake_case       (		self   , UpperCAmelCase_							):
								lowerCamelCase	 =AutoModel.from_pretrained(self.test_model							)
								model_pt.save_pretrained(UpperCAmelCase_							)


				def _snake_case       (		self   , UpperCAmelCase_							):
								lowerCamelCase	 =TFAutoModel.from_pretrained(self.test_model   , from_pt=UpperCAmelCase_							)
								model_tf.save_pretrained(UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase	 ="""mock_framework"""

								# Framework provided - return whatever the user provides
								lowerCamelCase	 =FeaturesManager.determine_framework(self.test_model   , UpperCAmelCase_							)
								self.assertEqual(UpperCAmelCase_   , UpperCAmelCase_							)

								# Local checkpoint and framework provided - return provided framework
								# PyTorch checkpoint
								with TemporaryDirectory() as local_pt_ckpt:
												self._setup_pt_ckpt(UpperCAmelCase_							)
												lowerCamelCase	 =FeaturesManager.determine_framework(UpperCAmelCase_   , UpperCAmelCase_							)
												self.assertEqual(UpperCAmelCase_   , UpperCAmelCase_							)

								# TensorFlow checkpoint
								with TemporaryDirectory() as local_tf_ckpt:
												self._setup_tf_ckpt(UpperCAmelCase_							)
												lowerCamelCase	 =FeaturesManager.determine_framework(UpperCAmelCase_   , UpperCAmelCase_							)
												self.assertEqual(UpperCAmelCase_   , UpperCAmelCase_							)


				def _snake_case       (		self							):
								# PyTorch checkpoint
								with TemporaryDirectory() as local_pt_ckpt:
												self._setup_pt_ckpt(UpperCAmelCase_							)
												lowerCamelCase	 =FeaturesManager.determine_framework(UpperCAmelCase_							)
												self.assertEqual(UpperCAmelCase_   , self.framework_pt							)

								# TensorFlow checkpoint
								with TemporaryDirectory() as local_tf_ckpt:
												self._setup_tf_ckpt(UpperCAmelCase_							)
												lowerCamelCase	 =FeaturesManager.determine_framework(UpperCAmelCase_							)
												self.assertEqual(UpperCAmelCase_   , self.framework_tf							)

								# Invalid local checkpoint
								with TemporaryDirectory() as local_invalid_ckpt:
												with self.assertRaises(UpperCAmelCase_							):
																lowerCamelCase	 =FeaturesManager.determine_framework(UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase	 =MagicMock(return_value=UpperCAmelCase_							)
								with patch("""transformers.onnx.features.is_tf_available"""   , UpperCAmelCase_							):
												lowerCamelCase	 =FeaturesManager.determine_framework(self.test_model							)
												self.assertEqual(UpperCAmelCase_   , self.framework_pt							)

								# PyTorch not in environment -> use TensorFlow
								lowerCamelCase	 =MagicMock(return_value=UpperCAmelCase_							)
								with patch("""transformers.onnx.features.is_torch_available"""   , UpperCAmelCase_							):
												lowerCamelCase	 =FeaturesManager.determine_framework(self.test_model							)
												self.assertEqual(UpperCAmelCase_   , self.framework_tf							)

								# Both in environment -> use PyTorch
								lowerCamelCase	 =MagicMock(return_value=UpperCAmelCase_							)
								lowerCamelCase	 =MagicMock(return_value=UpperCAmelCase_							)
								with patch("""transformers.onnx.features.is_tf_available"""   , UpperCAmelCase_							), patch(
								    """transformers.onnx.features.is_torch_available"""   , UpperCAmelCase_							):
												lowerCamelCase	 =FeaturesManager.determine_framework(self.test_model							)
												self.assertEqual(UpperCAmelCase_   , self.framework_pt							)

								# Both not in environment -> raise error
								lowerCamelCase	 =MagicMock(return_value=UpperCAmelCase_							)
								lowerCamelCase	 =MagicMock(return_value=UpperCAmelCase_							)
								with patch("""transformers.onnx.features.is_tf_available"""   , UpperCAmelCase_							), patch(
								    """transformers.onnx.features.is_torch_available"""   , UpperCAmelCase_							):
												with self.assertRaises(UpperCAmelCase_							):
																lowerCamelCase	 =FeaturesManager.determine_framework(self.test_model							)





