





'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE					=    {
    '''configuration_whisper''': ['''WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WhisperConfig''', '''WhisperOnnxConfig'''],
    '''feature_extraction_whisper''': ['''WhisperFeatureExtractor'''],
    '''processing_whisper''': ['''WhisperProcessor'''],
    '''tokenization_whisper''': ['''WhisperTokenizer'''],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE					=    ['''WhisperTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE					=    [
	    '''WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''WhisperForConditionalGeneration''',
	    '''WhisperModel''',
	    '''WhisperPreTrainedModel''',
	    '''WhisperForAudioClassification''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE					=    [
	    '''TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFWhisperForConditionalGeneration''',
	    '''TFWhisperModel''',
	    '''TFWhisperPreTrainedModel''',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE					=    [
	    '''FlaxWhisperForConditionalGeneration''',
	    '''FlaxWhisperModel''',
	    '''FlaxWhisperPreTrainedModel''',
	    '''FlaxWhisperForAudioClassification''',
	]


if TYPE_CHECKING:
	from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
	from .feature_extraction_whisper import WhisperFeatureExtractor
	from .processing_whisper import WhisperProcessor
	from .tokenization_whisper import WhisperTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_whisper_fast import WhisperTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_whisper import (
		    WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    WhisperForAudioClassification,
		    WhisperForConditionalGeneration,
		    WhisperModel,
		    WhisperPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_whisper import (
		    TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFWhisperForConditionalGeneration,
		    TFWhisperModel,
		    TFWhisperPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_whisper import (
		    FlaxWhisperForAudioClassification,
		    FlaxWhisperForConditionalGeneration,
		    FlaxWhisperModel,
		    FlaxWhisperPreTrainedModel,
		)

else:
	import sys

	_SCREAMING_SNAKE_CASE					=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)












'''simple docstring'''



import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


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




def    _lowerCAmelCase						(				lowerCamelCase_			:  int				):
			__lowercase										=							torch.load(lowerCamelCase_		,       map_location='''cpu'''				)
			if "model" in sd.keys():
						__lowercase										=							torch.load(lowerCamelCase_		,       map_location='''cpu'''				)['''model''']

			# pop unnecessary weights
			__lowercase										=							[
			    '''decoder.version''',
			    '''decoder.output_projection.weight''',
			]
			for key in keys_to_delete:
						if key in sd:
									sd.pop(lowerCamelCase_				)

			__lowercase										=							{
			    '''decoder.project_in_dim.weight''': '''decoder.project_in.weight''',
			    '''decoder.project_out_dim.weight''': '''decoder.project_out.weight''',
			    '''decoder.layer_norm.weight''': '''decoder.final_layer_norm.weight''',
			    '''decoder.layer_norm.bias''': '''decoder.final_layer_norm.bias''',
			}
			for old_key, new_key in keys_to_rename.items():
						if old_key in sd:
									__lowercase										=							sd.pop(lowerCamelCase_				)

			__lowercase										=							list(sd.keys()				)
			for key in keys:
						if ".qkv_proj." in key:
									__lowercase										=							sd[key]
									# We split QKV in separate Q,K,V

									__lowercase										=							key.replace('''.qkv_proj.'''		,       '''.q_proj.'''				)
									__lowercase										=							key.replace('''.qkv_proj.'''		,       '''.k_proj.'''				)
									__lowercase										=							key.replace('''.qkv_proj.'''		,       '''.v_proj.'''				)

									__lowercase										=							value.shape[0]
									assert depth % 3 == 0
									# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
									# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
									__lowercase						,		__lowercase						,		__lowercase										=							torch.split(lowerCamelCase_		,       depth // 3		,       dim=0				)

									__lowercase										=							q
									__lowercase										=							k
									__lowercase										=							v
									del sd[key]

			return sd




@torch.no_grad()
def    _lowerCAmelCase						(				lowerCamelCase_			:  Union[str, Any]		,       lowerCamelCase_			:  Tuple		,       lowerCamelCase_			:  Union[str, Any]=None				):
			__lowercase										=							load_checkpoint(lowerCamelCase_				)

			if config is not None:
						__lowercase										=							OPTConfig.from_pretrained(lowerCamelCase_				)
			else:
						__lowercase										=							OPTConfig()

			__lowercase										=							OPTModel(lowerCamelCase_				).half().eval()
			model.load_state_dict(lowerCamelCase_				)

			# Check results
			Path(lowerCamelCase_				).mkdir(exist_ok=lowerCamelCase_				)
			model.save_pretrained(lowerCamelCase_				)


if __name__ == "__main__":
	_SCREAMING_SNAKE_CASE					=    argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--fairseq_path''',
	    type=str,
	    help=(
	        '''path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'''
	        ''' https://huggingface.co/models?other=opt_metasq'''
	    ),
	)
	parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
	parser.add_argument('''--hf_config''', default=None, type=str, help='''Define HF config.''')
	_SCREAMING_SNAKE_CASE					=    parser.parse_args()
	convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)





