





from ...processing_utils import ProcessorMixin

class 	A_       (a_			):
	UpperCAmelCase__					=			'''SpeechT5FeatureExtractor'''
	UpperCAmelCase__					=			'''SpeechT5Tokenizer'''
	def __init__(    self							,					_A							,					_A		):






				'''simple docstring'''







				super().__init__(_A							,					_A		)
	def __call__(    self							,					*_A							,					**_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				kwargs.pop('''audio'''							,					_A		)
				UpperCAmelCase   		=				kwargs.pop('''text'''							,					_A		)
				UpperCAmelCase   		=				kwargs.pop('''text_target'''							,					_A		)
				UpperCAmelCase   		=				kwargs.pop('''audio_target'''							,					_A		)
				UpperCAmelCase   		=				kwargs.pop('''sampling_rate'''							,					_A		)

				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:
							UpperCAmelCase   		=				self.feature_extractor(_A							,					*_A							,					sampling_rate=_A							,					**_A		)
				elif text is not None:
							UpperCAmelCase   		=				self.tokenizer(_A							,					**_A		)
				else:
							UpperCAmelCase   		=				None

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

				if inputs is None:
							return targets

				if targets is not None:
							UpperCAmelCase   		=				labels

							UpperCAmelCase   		=				targets.get('''attention_mask'''		)
							if decoder_attention_mask is not None:
										UpperCAmelCase   		=				decoder_attention_mask

				return inputs
	def      _lowercase				(    self							,					*_A							,					**_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				kwargs.pop('''input_values'''							,					_A		)
				UpperCAmelCase   		=				kwargs.pop('''input_ids'''							,					_A		)
				UpperCAmelCase   		=				kwargs.pop('''labels'''							,					_A		)

				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:
							UpperCAmelCase   		=				self.feature_extractor.pad(_A							,					*_A							,					**_A		)
				elif input_ids is not None:
							UpperCAmelCase   		=				self.tokenizer.pad(_A							,					**_A		)
				else:
							UpperCAmelCase   		=				None

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

				if inputs is None:
							return targets

				if targets is not None:
							UpperCAmelCase   		=				labels

							UpperCAmelCase   		=				targets.get('''attention_mask'''		)
							if decoder_attention_mask is not None:
										UpperCAmelCase   		=				decoder_attention_mask

				return inputs
	def      _lowercase				(    self							,					*_A							,					**_A		):






				'''simple docstring'''







				return self.tokenizer.batch_decode(*_A							,					**_A		)







	def      _lowercase				(    self							,					*_A							,					**_A		):






				'''simple docstring'''







				return self.tokenizer.decode(*_A							,					**_A		)










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				:    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 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				List[str]:







			'''simple docstring'''

			UpperCAmelCase   		=				list(s_dict.keys() )
			for key in keys:
						UpperCAmelCase   		=				R'''.*/layers_(\d+)'''
						UpperCAmelCase   		=				key
						if re.match(UpperCamelCase__			,			UpperCamelCase__ ):
									UpperCAmelCase   		=				re.sub(R'''layers_(\d+)'''			,			R'''block/\1/layer'''			,			UpperCamelCase__ )

						UpperCAmelCase   		=				R'''(encoder|decoder)\/'''

						if re.match(UpperCamelCase__			,			UpperCamelCase__ ):
									UpperCAmelCase   		=				re.match(UpperCamelCase__			,			UpperCamelCase__ ).groups()
									if groups[0] == "encoder":
												UpperCAmelCase   		=				re.sub(R'''/mlp/'''			,			R'''/1/mlp/'''			,			UpperCamelCase__ )
												UpperCAmelCase   		=				re.sub(R'''/pre_mlp_layer_norm/'''			,			R'''/1/layer_norm/'''			,			UpperCamelCase__ )

									elif groups[0] == "decoder":
												UpperCAmelCase   		=				re.sub(R'''/mlp/'''			,			R'''/2/mlp/'''			,			UpperCamelCase__ )
												UpperCAmelCase   		=				re.sub(R'''/pre_mlp_layer_norm/'''			,			R'''/2/layer_norm/'''			,			UpperCamelCase__ )

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

						print(F"""{key} -> {new_key}""" )
						UpperCAmelCase   		=				s_dict.pop(UpperCamelCase__ )

			if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
						UpperCAmelCase   		=				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:
						UpperCAmelCase   		=				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:
									UpperCAmelCase   		=				s_dict[key].shape[0]
									UpperCAmelCase   		=				s_dict[key]
									for idx in range(UpperCamelCase__ ):
												UpperCAmelCase   		=				expert_weihts[idx]
												print(F"""{key} -> {key.replace("expert/"			,			"nested fstring" )}""" )

									s_dict.pop(UpperCamelCase__ )

			return s_dict


__A				:    Optional[int]       =   {
    "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 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__ )  ->				List[str]:







			'''simple docstring'''

			import regex as re

			with open(UpperCamelCase__			,			'''r''' ) as f:
						UpperCAmelCase   		=				f.read()

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

			UpperCAmelCase   		=				re.findall(R'''(.*activations) = \(\'(.*)\',\)'''			,			UpperCamelCase__ )[0]
			UpperCAmelCase   		=				str(activation[1] )

			UpperCAmelCase   		=				num_experts
			UpperCAmelCase   		=				SwitchTransformersConfig(**UpperCamelCase__ )
			return config





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__=None			,			UpperCamelCase__="./"			,			UpperCamelCase__=8 )  ->				List[Any]:







			'''simple docstring'''

			print(F"""Loading flax weights from : {flax_checkpoint_path}""" )
			UpperCAmelCase   		=				checkpoints.load_tax_checkpoint(UpperCamelCase__ )

			if gin_file is not None:
						UpperCAmelCase   		=				convert_gin_to_config(UpperCamelCase__			,			UpperCamelCase__ )
			else:
						UpperCAmelCase   		=				SwitchTransformersConfig.from_pretrained(UpperCamelCase__ )

			UpperCAmelCase   		=				SwitchTransformersForConditionalGeneration(UpperCamelCase__ )

			UpperCAmelCase   		=				flax_params['''target''']
			UpperCAmelCase   		=				flatten_dict(UpperCamelCase__			,			sep='''/''' )
			UpperCAmelCase   		=				rename_keys(UpperCamelCase__ )
			UpperCAmelCase   		=				unflatten_dict(UpperCamelCase__			,			sep='''/''' )

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

			print(F"""Save PyTorch model to {pytorch_dump_path}""" )
			pt_model.save_pretrained(UpperCamelCase__ )


if __name__ == "__main__":
				__A				:    Tuple       =   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				:    Tuple       =   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,
				)



