import unittest

from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate




def     UpperCamelCase						(	__lowercase						:			Optional[int]     ):
		'''simple docstring'''




		return x + 2





class UpperCAmelCase						( unittest.TestCase  ):


				'''simple docstring'''






				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    str         =				'x = 3'
						A_		:    str         =				{}
						A_		:    Dict         =				evaluate(lowercase		,     {}		,     state=lowercase )
						assert result == 3
						self.assertDictEqual(lowercase		,     {'x': 3} )

						A_		:    Union[str, Any]         =				'x = y'
						A_		:    List[str]         =				{'y': 5}
						A_		:    Dict         =				evaluate(lowercase		,     {}		,     state=lowercase )
						# evaluate returns the value of the last assignment.
						assert result == 5
						self.assertDictEqual(lowercase		,     {'x': 5, 'y': 5} )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    List[str]         =				'y = add_two(x)'
						A_		:    int         =				{'x': 3}
						A_		:    Any         =				evaluate(lowercase		,     {'add_two': add_two}		,     state=lowercase )
						assert result == 5
						self.assertDictEqual(lowercase		,     {'x': 3, 'y': 5} )

						# Won't work without the tool
						with CaptureStdout() as out:
								A_		:    List[str]         =				evaluate(lowercase		,     {}		,     state=lowercase )
						assert result is None
						assert "tried to execute add_two" in out.out
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    List[Any]         =				'x = 3'
						A_		:    Optional[Any]         =				{}
						A_		:    List[Any]         =				evaluate(lowercase		,     {}		,     state=lowercase )
						assert result == 3
						self.assertDictEqual(lowercase		,     {'x': 3} )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Optional[int]         =				'test_dict = {\'x\': x, \'y\': add_two(x)}'
						A_		:    Optional[int]         =				{'x': 3}
						A_		:    Tuple         =				evaluate(lowercase		,     {'add_two': add_two}		,     state=lowercase )
						self.assertDictEqual(lowercase		,     {'x': 3, 'y': 5} )
						self.assertDictEqual(lowercase		,     {'x': 3, 'test_dict': {'x': 3, 'y': 5}} )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    List[Any]         =				'x = 3\ny = 5'
						A_		:    List[Any]         =				{}
						A_		:    Optional[int]         =				evaluate(lowercase		,     {}		,     state=lowercase )
						# evaluate returns the value of the last assignment.
						assert result == 5
						self.assertDictEqual(lowercase		,     {'x': 3, 'y': 5} )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Dict         =				'text = f\'This is x: {x}.\''
						A_		:    Optional[Any]         =				{'x': 3}
						A_		:    int         =				evaluate(lowercase		,     {}		,     state=lowercase )
						# evaluate returns the value of the last assignment.
						assert result == "This is x: 3."
						self.assertDictEqual(lowercase		,     {'x': 3, 'text': 'This is x: 3.'} )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    str         =				'if x <= 3:\n    y = 2\nelse:\n    y = 5'
						A_		:    Dict         =				{'x': 3}
						A_		:    Dict         =				evaluate(lowercase		,     {}		,     state=lowercase )
						# evaluate returns the value of the last assignment.
						assert result == 2
						self.assertDictEqual(lowercase		,     {'x': 3, 'y': 2} )

						A_		:    Dict         =				{'x': 8}
						A_		:    Dict         =				evaluate(lowercase		,     {}		,     state=lowercase )
						# evaluate returns the value of the last assignment.
						assert result == 5
						self.assertDictEqual(lowercase		,     {'x': 8, 'y': 5} )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Union[str, Any]         =				'test_list = [x, add_two(x)]'
						A_		:    Any         =				{'x': 3}
						A_		:    str         =				evaluate(lowercase		,     {'add_two': add_two}		,     state=lowercase )
						self.assertListEqual(lowercase		,     [3, 5] )
						self.assertDictEqual(lowercase		,     {'x': 3, 'test_list': [3, 5]} )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Any         =				'y = x'
						A_		:    Dict         =				{'x': 3}
						A_		:    Any         =				evaluate(lowercase		,     {}		,     state=lowercase )
						assert result == 3
						self.assertDictEqual(lowercase		,     {'x': 3, 'y': 3} )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Any         =				'test_list = [x, add_two(x)]\ntest_list[1]'
						A_		:    int         =				{'x': 3}
						A_		:    Union[str, Any]         =				evaluate(lowercase		,     {'add_two': add_two}		,     state=lowercase )
						assert result == 5
						self.assertDictEqual(lowercase		,     {'x': 3, 'test_list': [3, 5]} )

						A_		:    Union[str, Any]         =				'test_dict = {\'x\': x, \'y\': add_two(x)}\ntest_dict[\'y\']'
						A_		:    Optional[Any]         =				{'x': 3}
						A_		:    Union[str, Any]         =				evaluate(lowercase		,     {'add_two': add_two}		,     state=lowercase )
						assert result == 5
						self.assertDictEqual(lowercase		,     {'x': 3, 'test_dict': {'x': 3, 'y': 5}} )







				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Any         =				'x = 0\nfor i in range(3):\n    x = i'
						A_		:    List[str]         =				{}
						A_		:    str         =				evaluate(lowercase		,     {'range': range}		,     state=lowercase )
						assert result == 2
						self.assertDictEqual(lowercase		,     {'x': 2, 'i': 2} )







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     UpperCamelCase						(	__lowercase						:			Optional[Any]						,__lowercase						:			Tuple						,__lowercase						:			str						,__lowercase						:			List[Any]						,__lowercase						:			Optional[int]     ):
		'''simple docstring'''




		for attribute in key.split('.'     ):
				A_		:    List[Any]         =				getattr(__lowercase						,__lowercase     )

		if weight_type is not None:
				A_		:    Dict         =				getattr(__lowercase						,__lowercase     ).shape
		else:
				A_		:    Union[str, Any]         =				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":
				A_		:    Dict         =				value
		elif weight_type == "weight_g":
				A_		:    str         =				value
		elif weight_type == "weight_v":
				A_		:    int         =				value
		elif weight_type == "bias":
				A_		:    int         =				value
		else:
				A_		:    List[Any]         =				value

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




def     UpperCamelCase						(	__lowercase						:			Optional[Any]						,__lowercase						:			Union[str, Any]     ):
		'''simple docstring'''




		A_		:    List[Any]         =				[]
		A_		:    int         =				fairseq_model.state_dict()

		A_		:    Optional[Any]         =				hf_model.feature_extractor
		A_		:    List[str]         =				hf_model.adapter

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

		logger.warning(f'''Unused weights: {unused_weights}'''     )




def     UpperCamelCase						(	__lowercase						:			Dict						,__lowercase						:			List[Any]						,__lowercase						:			Tuple						,__lowercase						:			Dict						,__lowercase						:			Any     ):
		'''simple docstring'''




		A_		:    List[Any]         =				full_name.split('conv_layers.'     )[-1]
		A_		:    Optional[int]         =				name.split('.'     )
		A_		:    Tuple         =				int(items[0]     )
		A_		:    int         =				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.'''
						)
						A_		:    Union[str, Any]         =				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.'''
						)
						A_		:    str         =				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."
						)
						A_		:    List[str]         =				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.'''
						)
						A_		:    List[Any]         =				value
						logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''     )
		else:
				unused_weights.append(__lowercase     )




def     UpperCamelCase						(	__lowercase						:			Any						,__lowercase						:			Tuple						,__lowercase						:			Optional[Any]						,__lowercase						:			Union[str, Any]     ):
		'''simple docstring'''




		A_		:    Union[str, Any]         =				full_name.split('adaptor.'     )[-1]
		A_		:    List[Any]         =				name.split('.'     )

		if items[1].isdigit():
				A_		:    Union[str, Any]         =				int(items[1]     )
		else:
				A_		:    Tuple         =				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.'''
								A_		:    Dict         =				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.'''
								A_		:    int         =				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.'''
								A_		:    Dict         =				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.'''
								A_		:    Tuple         =				value
								logger.info(f'''Adapter proj layer weight was initialized from {full_name}.'''     )
		elif isinstance(__lowercase						,__lowercase     ):
				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.'''
						A_		:    Tuple         =				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.'''
						A_		:    str         =				value
						logger.info(f'''Adapter layer {layer_id} bias was initialized from {full_name}.'''     )
		else:
				unused_weights.append(__lowercase     )




def     UpperCamelCase						(	__lowercase						:			List[Any]     ):
		'''simple docstring'''




		A_       ,      A_		:    Any         =				emb.weight.shape
		A_		:    Tuple         =				nn.Linear(__lowercase						,__lowercase						,bias=__lowercase     )
		A_		:    Optional[Any]         =				emb.weight.data
		return lin_layer




@torch.no_grad()
def     UpperCamelCase						(	__lowercase						:			Any						,__lowercase						:			Optional[int]						,__lowercase						:			Any						,__lowercase						:			str						,__lowercase						:			Dict						,__lowercase						:			Dict						,__lowercase						:			Tuple						,__lowercase						:			Optional[int]						,__lowercase						:			List[str]						,__lowercase						:			List[Any]						,__lowercase						:			str						,):
		'''simple docstring'''




		A_		:    Optional[int]         =				WavaVecaConfig.from_pretrained(
		    __lowercase						,add_adapter=__lowercase						,adapter_stride=__lowercase						,adapter_kernel_size=__lowercase						,use_auth_token=__lowercase						,output_hidden_size=__lowercase						,)
		A_		:    Any         =				MBartConfig.from_pretrained(__lowercase     )

		# load model
		A_       ,      A_       ,      A_		:    Union[str, Any]         =				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,
		    }						,)
		A_		:    Union[str, Any]         =				model[0].eval()

		# load feature extractor
		A_		:    Any         =				WavaVecaFeatureExtractor.from_pretrained(__lowercase						,use_auth_token=__lowercase     )

		# set weights for wav2vec2 encoder
		A_		:    Optional[Any]         =				WavaVecaModel(__lowercase     )

		recursively_load_weights_wavaveca(model.encoder						,__lowercase     )

		# load decoder weights
		A_		:    Dict         =				MBartForCausalLM(__lowercase     )
		A_       ,      A_		:    Union[str, Any]         =				hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()						,strict=__lowercase     )
		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}'''     )

		A_		:    Optional[int]         =				SpeechEncoderDecoderModel(encoder=__lowercase						,decoder=__lowercase     )
		A_		:    Any         =				False

		A_		:    List[Any]         =				MBartaaTokenizer(__lowercase     )
		tokenizer.save_pretrained(__lowercase     )

		A_		:    Dict         =				hf_wavavec.config.to_dict()
		A_		:    Any         =				tokenizer.pad_token_id
		A_		:    Optional[Any]         =				tokenizer.bos_token_id
		A_		:    Union[str, Any]         =				tokenizer.eos_token_id
		A_		:    Dict         =				'mbart50'
		A_		:    str         =				'wav2vec2'

		A_		:    int         =				tokenizer.eos_token_id
		A_		:    List[str]         =				25_00_04
		A_		:    int         =				tokenizer.eos_token_id

		A_		:    Optional[Any]         =				SpeechEncoderDecoderConfig.from_dict(__lowercase     )

		hf_wavavec.save_pretrained(__lowercase     )
		feature_extractor.save_pretrained(__lowercase     )


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=1024, type=int, help="""encoder output dim""")
	parser.add_argument("""--start_token_id""", default=250004, 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,
	)






