
import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
A		: Optional[int]								=     {
    '''/attention/''': '''/0/SelfAttention/''',
    '''/self_attention/''': '''/0/SelfAttention/''',
    '''/encoder_decoder_attention/''': '''/1/EncDecAttention/''',
    '''value''': '''v''',
    '''query''': '''q''',
    '''key''': '''k''',
    '''out''': '''o''',
    '''pre_self_attention_layer_norm''': '''0/layer_norm''',
    '''pre_cross_attention_layer_norm''': '''1/layer_norm''',
    '''pre_attention_layer_norm''': '''0/layer_norm''',  # previously 1, but seems wrong
    '''token_embedder''': '''shared''',
    '''encoder_norm''': '''final_layer_norm''',
    '''decoder_norm''': '''final_layer_norm''',
    '''relpos_bias/rel_embedding''': '''block/0/layer/0/SelfAttention/relative_attention_bias/weight''',
    '''router/router_weights/w/''': '''router/classifier/''',
    '''roer/roer_weights/w/''': '''router/classifier/''',
    '''logits_dense''': '''lm_head''',
}




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Any       )  ->     List[Any]:
						# 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
						# the original model
						_lowercase					       =					list(s_dict.keys()       )
						for key in keys:
												_lowercase					       =					r""".*/layers_(\d+)"""
												_lowercase					       =					key
												if re.match(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       ):
																		_lowercase					       =					re.sub(r"""layers_(\d+)"""		,   r"""block/\1/layer"""		,   SCREAMING_SNAKE_CASE_       )

												_lowercase					       =					r"""(encoder|decoder)\/"""

												if re.match(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       ):
																		_lowercase					       =					re.match(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       ).groups()
																		if groups[0] == "encoder":
																								_lowercase					       =					re.sub(r"""/mlp/"""		,   r"""/1/mlp/"""		,   SCREAMING_SNAKE_CASE_       )
																								_lowercase					       =					re.sub(r"""/pre_mlp_layer_norm/"""		,   r"""/1/layer_norm/"""		,   SCREAMING_SNAKE_CASE_       )

																		elif groups[0] == "decoder":
																								_lowercase					       =					re.sub(r"""/mlp/"""		,   r"""/2/mlp/"""		,   SCREAMING_SNAKE_CASE_       )
																								_lowercase					       =					re.sub(r"""/pre_mlp_layer_norm/"""		,   r"""/2/layer_norm/"""		,   SCREAMING_SNAKE_CASE_       )

        # 2. Convert other classic mappings
												for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
																		if old_key in new_key:
																								_lowercase					       =					new_key.replace(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )

												print(f"""{key} -> {new_key}"""       )
												_lowercase					       =					s_dict.pop(SCREAMING_SNAKE_CASE_       )

						if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
												_lowercase					       =					s_dict[
												    """encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"""
												].T
						if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
												_lowercase					       =					s_dict[
												    """decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"""
												].T

						# 3. Take extra care of the EXPERTS layer
						for key in list(s_dict.keys()       ):
												if "expert" in key:
																		_lowercase					       =					s_dict[key].shape[0]
																		_lowercase					       =					s_dict[key]
																		for idx in range(SCREAMING_SNAKE_CASE_       ):
																								_lowercase					       =					expert_weihts[idx]
																								print(f"""{key} -> {key.replace("expert/"		,   "nested fstring"       )}"""       )

																		s_dict.pop(SCREAMING_SNAKE_CASE_       )

						return s_dict


A		: Optional[Any]								=     {
    '''NUM_ENCODER_LAYERS''': '''num_layers''',
    '''NUM_DECODER_LAYERS''': '''num_decoder_layers''',
    '''NUM_HEADS''': '''num_heads''',
    '''HEAD_DIM''': '''d_kv''',
    '''EMBED_DIM''': '''d_model''',
    '''MLP_DIM''': '''d_ff''',
    '''NUM_SELECTED_EXPERTS''': '''num_selected_experts''',
    '''NUM_ENCODER_SPARSE_LAYERS''': '''num_sparse_encoder_layers''',
    '''NUM_DECODER_SPARSE_LAYERS''': '''num_sparse_decoder_layers''',
    '''dense.MlpBlock.activations''': '''feed_forward_proj''',
}




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Union[str, Any]		,   SCREAMING_SNAKE_CASE_	:	List[Any]       )  ->     Optional[Any]:
						# Convert a google style config to the hugging face fromat
						import regex as re

						with open(SCREAMING_SNAKE_CASE_		,   """r"""       ) as f:
												_lowercase					       =					f.read()

						_lowercase					       =					re.findall(r"""(.*) = ([0-9.]*)"""		,   SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					{}
						for param, value in regex_match:
												if param in GIN_TO_CONFIG_MAPPING and value != "":
																		_lowercase					       =					float(SCREAMING_SNAKE_CASE_       ) if """.""" in value else int(SCREAMING_SNAKE_CASE_       )

						_lowercase					       =					re.findall(r"""(.*activations) = \(\'(.*)\',\)"""		,   SCREAMING_SNAKE_CASE_       )[0]
						_lowercase					       =					str(activation[1]       )

						_lowercase					       =					num_experts
						_lowercase					       =					SwitchTransformersConfig(**SCREAMING_SNAKE_CASE_       )
						return config




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	List[str]		,   SCREAMING_SNAKE_CASE_	:	Dict		,   SCREAMING_SNAKE_CASE_	:	Any=None		,   SCREAMING_SNAKE_CASE_	:	Union[str, Any]="./"		,   SCREAMING_SNAKE_CASE_	:	str=8       )  ->     Tuple:
						# Initialise PyTorch model

						print(f"""Loading flax weights from : {flax_checkpoint_path}"""       )
						_lowercase					       =					checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE_       )

						if gin_file is not None:
												_lowercase					       =					convert_gin_to_config(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )
						else:
												_lowercase					       =					SwitchTransformersConfig.from_pretrained(SCREAMING_SNAKE_CASE_       )

						_lowercase					       =					SwitchTransformersForConditionalGeneration(SCREAMING_SNAKE_CASE_       )

						_lowercase					       =					flax_params["""target"""]
						_lowercase					       =					flatten_dict(SCREAMING_SNAKE_CASE_		,   sep="""/"""       )
						_lowercase					       =					rename_keys(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					unflatten_dict(SCREAMING_SNAKE_CASE_		,   sep="""/"""       )

						# Load the flax params in the PT model
						load_flax_weights_in_pytorch_model(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )

						print(f"""Save PyTorch model to {pytorch_dump_path}"""       )
						pt_model.save_pretrained(SCREAMING_SNAKE_CASE_       )


if __name__ == "__main__":
		A		: int								=     argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--switch_t5x_checkpoint_path''',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        '''The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the'''
		        ''' model architecture. If not provided, a `gin_file` has to be provided.'''
		    ),
		)
		parser.add_argument(
		    '''--gin_file''',
		    default=None,
		    type=str,
		    required=False,
		    help='''Path to the gin config file. If not provided, a `config_file` has to be passed   ''',
		)
		parser.add_argument(
		    '''--config_name''', default=None, type=str, required=False, help='''Config name of SwitchTransformers model.'''
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output pytorch model.'''
		)
		parser.add_argument('''--num_experts''', default=8, type=int, required=False, help='''Number of experts''')
		A		: Any								=     parser.parse_args()
		convert_flax_checkpoint_to_pytorch(
		    args.switch_tax_checkpoint_path,
		    args.config_name,
		    args.gin_file,
		    args.pytorch_dump_folder_path,
		    args.num_experts,
		)

from ...processing_utils import ProcessorMixin



class    a_ (			_a    ):
			a	:			Optional[int]					   =							'''SpeechT5FeatureExtractor'''
			a	:			List[Any]					   =							'''SpeechT5Tokenizer'''






			def __init__(      self				,			__UpperCamelCase				,			__UpperCamelCase  ):
									super().__init__(__UpperCamelCase				,			__UpperCamelCase  )






			def __call__(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									_lowercase					       =					kwargs.pop("""audio"""				,			__UpperCamelCase  )
									_lowercase					       =					kwargs.pop("""text"""				,			__UpperCamelCase  )
									_lowercase					       =					kwargs.pop("""text_target"""				,			__UpperCamelCase  )
									_lowercase					       =					kwargs.pop("""audio_target"""				,			__UpperCamelCase  )
									_lowercase					       =					kwargs.pop("""sampling_rate"""				,			__UpperCamelCase  )

									if audio is not None and text is not None:
															raise ValueError(
															    """Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?"""  )
									if audio_target is not None and text_target is not None:
															raise ValueError(
															    """Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?"""  )
									if audio is None and audio_target is None and text is None and text_target is None:
															raise ValueError(
															    """You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process."""  )

									if audio is not None:
															_lowercase					       =					self.feature_extractor(__UpperCamelCase				,			*__UpperCamelCase				,			sampling_rate=__UpperCamelCase				,			**__UpperCamelCase  )
									elif text is not None:
															_lowercase					       =					self.tokenizer(__UpperCamelCase				,			**__UpperCamelCase  )
									else:
															_lowercase					       =					None

									if audio_target is not None:
															_lowercase					       =					self.feature_extractor(audio_target=__UpperCamelCase				,			*__UpperCamelCase				,			sampling_rate=__UpperCamelCase				,			**__UpperCamelCase  )
															_lowercase					       =					targets["""input_values"""]
									elif text_target is not None:
															_lowercase					       =					self.tokenizer(__UpperCamelCase				,			**__UpperCamelCase  )
															_lowercase					       =					targets["""input_ids"""]
									else:
															_lowercase					       =					None

									if inputs is None:
															return targets

									if targets is not None:
															_lowercase					       =					labels

															_lowercase					       =					targets.get("""attention_mask"""  )
															if decoder_attention_mask is not None:
																					_lowercase					       =					decoder_attention_mask

									return inputs






			def    UpperCamelCase_				(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									_lowercase					       =					kwargs.pop("""input_values"""				,			__UpperCamelCase  )
									_lowercase					       =					kwargs.pop("""input_ids"""				,			__UpperCamelCase  )
									_lowercase					       =					kwargs.pop("""labels"""				,			__UpperCamelCase  )

									if input_values is not None and input_ids is not None:
															raise ValueError("""Cannot process both `input_values` and `input_ids` inputs."""  )
									if input_values is None and input_ids is None and labels is None:
															raise ValueError(
															    """You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded."""  )

									if input_values is not None:
															_lowercase					       =					self.feature_extractor.pad(__UpperCamelCase				,			*__UpperCamelCase				,			**__UpperCamelCase  )
									elif input_ids is not None:
															_lowercase					       =					self.tokenizer.pad(__UpperCamelCase				,			**__UpperCamelCase  )
									else:
															_lowercase					       =					None

									if labels is not None:
															if "input_ids" in labels or (isinstance(__UpperCamelCase				,			__UpperCamelCase  ) and "input_ids" in labels[0]):
																					_lowercase					       =					self.tokenizer.pad(__UpperCamelCase				,			**__UpperCamelCase  )
																					_lowercase					       =					targets["""input_ids"""]
															else:
																					_lowercase					       =					self.feature_extractor.feature_size
																					_lowercase					       =					self.feature_extractor.num_mel_bins
																					_lowercase					       =					self.feature_extractor.pad(__UpperCamelCase				,			*__UpperCamelCase				,			**__UpperCamelCase  )
																					_lowercase					       =					feature_size_hack
																					_lowercase					       =					targets["""input_values"""]
									else:
															_lowercase					       =					None

									if inputs is None:
															return targets

									if targets is not None:
															_lowercase					       =					labels

															_lowercase					       =					targets.get("""attention_mask"""  )
															if decoder_attention_mask is not None:
																					_lowercase					       =					decoder_attention_mask

									return inputs






			def    UpperCamelCase_				(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									return self.tokenizer.batch_decode(*__UpperCamelCase				,			**__UpperCamelCase  )






			def    UpperCamelCase_				(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									return self.tokenizer.decode(*__UpperCamelCase				,			**__UpperCamelCase  )