
'''simple docstring'''
import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM







def 		A_(     A						:		str		,     A						:		List[Any]		,     A						:		Optional[Any]):
							UpperCamelCase   			=     AutoConfig.from_pretrained(A)
							UpperCamelCase   			=     FlaxAutoModelForSeqaSeqLM.from_config(config=A)
							UpperCamelCase   			=     checkpoints.load_tax_checkpoint(A)

							UpperCamelCase   			=     'wi_0' in tax_model['target']['encoder']['layers_0']['mlp']

							if config.model_type == "t5":
														UpperCamelCase   			=     'SelfAttention'
							if config.model_type == "longt5" and config.encoder_attention_type == "local":
														UpperCamelCase   			=     'LocalSelfAttention'
							elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
														UpperCamelCase   			=     'TransientGlobalSelfAttention'
							else:
														raise ValueError(
														    'Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'
														    ' attribute with a value from [\'local\', \'transient-global].')

							# Encoder
							for layer_index in range(config.num_layers):
														UpperCamelCase   			=     f'''layers_{str(A)}'''

														# Self-Attention
														UpperCamelCase   			=     tax_model['target']['encoder'][layer_name]['attention']['key']['kernel']
														UpperCamelCase   			=     tax_model['target']['encoder'][layer_name]['attention']['out']['kernel']
														UpperCamelCase   			=     tax_model['target']['encoder'][layer_name]['attention']['query']['kernel']
														UpperCamelCase   			=     tax_model['target']['encoder'][layer_name]['attention']['value']['kernel']

														# Global input layer norm
														if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
																					UpperCamelCase   			=     tax_model['target']['encoder'][layer_name]['attention']['T5LayerNorm_0']['scale']

														# Layer Normalization
														UpperCamelCase   			=     tax_model['target']['encoder'][layer_name]['pre_attention_layer_norm']['scale']

														if split_mlp_wi:
																					UpperCamelCase   			=     tax_model['target']['encoder'][layer_name]['mlp']['wi_0']['kernel']
																					UpperCamelCase   			=     tax_model['target']['encoder'][layer_name]['mlp']['wi_1']['kernel']
														else:
																					UpperCamelCase   			=     tax_model['target']['encoder'][layer_name]['mlp']['wi']['kernel']

														UpperCamelCase   			=     tax_model['target']['encoder'][layer_name]['mlp']['wo']['kernel']

														# Layer Normalization
														UpperCamelCase   			=     tax_model['target']['encoder'][layer_name]['pre_mlp_layer_norm']['scale']

														# Assigning
														UpperCamelCase   			=     flax_model.params['encoder']['block'][str(A)]['layer']
														UpperCamelCase   			=     tax_attention_key
														UpperCamelCase   			=     tax_attention_out
														UpperCamelCase   			=     tax_attention_query
														UpperCamelCase   			=     tax_attention_value

														UpperCamelCase   			=     tax_attention_layer_norm

														# Global input layer norm
														if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
																					UpperCamelCase   			=     tax_global_layer_norm

														if split_mlp_wi:
																					UpperCamelCase   			=     tax_mlp_wi_a
																					UpperCamelCase   			=     tax_mlp_wi_a
														else:
																					UpperCamelCase   			=     tax_mlp_wi

														UpperCamelCase   			=     tax_mlp_wo
														UpperCamelCase   			=     tax_mlp_layer_norm

														UpperCamelCase   			=     flax_model_encoder_layer_block

							# Only for layer 0:
							UpperCamelCase   			=     tax_model['target']['encoder']['relpos_bias']['rel_embedding'].T
							UpperCamelCase   			=     tax_encoder_rel_embedding

							# Side/global relative position_bias + layer norm
							if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
														UpperCamelCase   			=     tax_model['target']['encoder']['side_relpos_bias']['rel_embedding'].T
														UpperCamelCase   			=     tax_encoder_global_rel_embedding

							# Assigning
							UpperCamelCase   			=     tax_model['target']['encoder']['encoder_norm']['scale']
							UpperCamelCase   			=     tax_encoder_norm

							# Decoder
							for layer_index in range(config.num_layers):
														UpperCamelCase   			=     f'''layers_{str(A)}'''

														# Self-Attention
														UpperCamelCase   			=     tax_model['target']['decoder'][layer_name]['self_attention']['key']['kernel']
														UpperCamelCase   			=     tax_model['target']['decoder'][layer_name]['self_attention']['out']['kernel']
														UpperCamelCase   			=     tax_model['target']['decoder'][layer_name]['self_attention']['query']['kernel']
														UpperCamelCase   			=     tax_model['target']['decoder'][layer_name]['self_attention']['value']['kernel']

														# Layer Normalization
														UpperCamelCase   			=     tax_model['target']['decoder'][layer_name]['pre_self_attention_layer_norm'][
														    'scale'
														]

														# Encoder-Decoder-Attention
														UpperCamelCase   			=     tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']
														UpperCamelCase   			=     tax_enc_dec_attention_module['key']['kernel']
														UpperCamelCase   			=     tax_enc_dec_attention_module['out']['kernel']
														UpperCamelCase   			=     tax_enc_dec_attention_module['query']['kernel']
														UpperCamelCase   			=     tax_enc_dec_attention_module['value']['kernel']

														# Layer Normalization
														UpperCamelCase   			=     tax_model['target']['decoder'][layer_name]['pre_cross_attention_layer_norm']['scale']

														# MLP
														if split_mlp_wi:
																					UpperCamelCase   			=     tax_model['target']['decoder'][layer_name]['mlp']['wi_0']['kernel']
																					UpperCamelCase   			=     tax_model['target']['decoder'][layer_name]['mlp']['wi_1']['kernel']
														else:
																					UpperCamelCase   			=     tax_model['target']['decoder'][layer_name]['mlp']['wi']['kernel']

														UpperCamelCase   			=     tax_model['target']['decoder'][layer_name]['mlp']['wo']['kernel']

														# Layer Normalization
														UpperCamelCase   			=     tax_model['target']['decoder'][layer_name]['pre_mlp_layer_norm']['scale']

														# Assigning
														UpperCamelCase   			=     flax_model.params['decoder']['block'][str(A)]['layer']
														UpperCamelCase   			=     tax_attention_key
														UpperCamelCase   			=     tax_attention_out
														UpperCamelCase   			=     tax_attention_query
														UpperCamelCase   			=     tax_attention_value

														UpperCamelCase   			=     tax_pre_attention_layer_norm

														UpperCamelCase   			=     tax_enc_dec_attention_key
														UpperCamelCase   			=     tax_enc_dec_attention_out
														UpperCamelCase   			=     tax_enc_dec_attention_query
														UpperCamelCase   			=     tax_enc_dec_attention_value

														UpperCamelCase   			=     tax_cross_layer_norm

														if split_mlp_wi:
																					UpperCamelCase   			=     tax_mlp_wi_a
																					UpperCamelCase   			=     tax_mlp_wi_a
														else:
																					UpperCamelCase   			=     tax_mlp_wi

														UpperCamelCase   			=     tax_mlp_wo

														UpperCamelCase   			=     txa_mlp_layer_norm

														UpperCamelCase   			=     flax_model_decoder_layer_block

							# Decoder Normalization
							UpperCamelCase   			=     tax_model['target']['decoder']['decoder_norm']['scale']
							UpperCamelCase   			=     txa_decoder_norm

							# Only for layer 0:
							UpperCamelCase   			=     tax_model['target']['decoder']['relpos_bias']['rel_embedding'].T
							UpperCamelCase   			=     tax_decoder_rel_embedding

							# Token Embeddings
							UpperCamelCase   			=     tax_model['target']['token_embedder']['embedding']
							UpperCamelCase   			=     txa_token_embeddings

							# LM Head (only in v1.1 and LongT5 checkpoints)
							if "logits_dense" in tax_model["target"]["decoder"]:
														UpperCamelCase   			=     tax_model['target']['decoder']['logits_dense']['kernel']

							flax_model.save_pretrained(A)
							print('T5X Model was sucessfully converted!')


if __name__ == "__main__":
						lowerCAmelCase					:    Tuple           = argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the T5X checkpoint.'
						)
						parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of LongT5/T5 model.')
						parser.add_argument(
						    '--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.'
						)
						lowerCAmelCase					:    int           = parser.parse_args()
						convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)






'''simple docstring'''
import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase					:    int           = logging.get_logger(__name__)

lowerCAmelCase					:    Tuple           = {
    'microsoft/unispeech-sat-base-100h-libri-ft': (
        'https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json'
    ),
    # See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							"""unispeech-sat"""







				def __init__(       self						,				A_=32						,				A_=768						,				A_=12						,				A_=12						,				A_=3072						,				A_="gelu"						,				A_=0.1						,				A_=0.1						,				A_=0.1						,				A_=0.0						,				A_=0.0						,				A_=0.1						,				A_=0.1						,				A_=0.02						,				A_=1e-5						,				A_="group"						,				A_="gelu"						,				A_=(512, 512, 512, 512, 512, 512, 512)						,				A_=(5, 2, 2, 2, 2, 2, 2)						,				A_=(10, 3, 3, 3, 3, 2, 2)						,				A_=False						,				A_=128						,				A_=16						,				A_=False						,				A_=True						,				A_=0.05						,				A_=10						,				A_=2						,				A_=0.0						,				A_=10						,				A_=0						,				A_=320						,				A_=2						,				A_=0.1						,				A_=100						,				A_=256						,				A_=256						,				A_=0.1						,				A_="mean"						,				A_=False						,				A_=False						,				A_=256						,				A_=(512, 512, 512, 512, 1500)						,				A_=(5, 3, 3, 1, 1)						,				A_=(1, 2, 3, 1, 1)						,				A_=512						,				A_=0						,				A_=1						,				A_=2						,				A_=504						,				**A_						,				)->		Tuple:







											'''simple docstring'''






											super().__init__(**A_						,				pad_token_id=A_						,				bos_token_id=A_						,				eos_token_id=A_     )
											UpperCamelCase   			=     hidden_size
											UpperCamelCase   			=     feat_extract_norm
											UpperCamelCase   			=     feat_extract_activation
											UpperCamelCase   			=     list(A_     )
											UpperCamelCase   			=     list(A_     )
											UpperCamelCase   			=     list(A_     )
											UpperCamelCase   			=     conv_bias
											UpperCamelCase   			=     num_conv_pos_embeddings
											UpperCamelCase   			=     num_conv_pos_embedding_groups
											UpperCamelCase   			=     len(self.conv_dim     )
											UpperCamelCase   			=     num_hidden_layers
											UpperCamelCase   			=     intermediate_size
											UpperCamelCase   			=     hidden_act
											UpperCamelCase   			=     num_attention_heads
											UpperCamelCase   			=     hidden_dropout
											UpperCamelCase   			=     attention_dropout
											UpperCamelCase   			=     activation_dropout
											UpperCamelCase   			=     feat_proj_dropout
											UpperCamelCase   			=     final_dropout
											UpperCamelCase   			=     layerdrop
											UpperCamelCase   			=     layer_norm_eps
											UpperCamelCase   			=     initializer_range
											UpperCamelCase   			=     vocab_size
											UpperCamelCase   			=     num_clusters
											UpperCamelCase   			=     do_stable_layer_norm
											UpperCamelCase   			=     use_weighted_layer_sum

											if (
											    (len(self.conv_stride     ) != self.num_feat_extract_layers)
											    or (len(self.conv_kernel     ) != self.num_feat_extract_layers)
											    or (len(self.conv_dim     ) != self.num_feat_extract_layers)
											):
																		raise ValueError(
																		    'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
																		    ' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
																		    F''' {len(self.conv_dim     )}`, `len(config.conv_stride) = {len(self.conv_stride     )}`,'''
																		    F''' `len(config.conv_kernel) = {len(self.conv_kernel     )}`.'''     )

											# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
											UpperCamelCase   			=     apply_spec_augment
											UpperCamelCase   			=     mask_time_prob
											UpperCamelCase   			=     mask_time_length
											UpperCamelCase   			=     mask_time_min_masks
											UpperCamelCase   			=     mask_feature_prob
											UpperCamelCase   			=     mask_feature_length
											UpperCamelCase   			=     mask_feature_min_masks

											# parameters for pretraining with codevector quantized representations
											UpperCamelCase   			=     num_codevectors_per_group
											UpperCamelCase   			=     num_codevector_groups
											UpperCamelCase   			=     contrastive_logits_temperature
											UpperCamelCase   			=     feat_quantizer_dropout
											UpperCamelCase   			=     num_negatives
											UpperCamelCase   			=     codevector_dim
											UpperCamelCase   			=     proj_codevector_dim
											UpperCamelCase   			=     diversity_loss_weight

											# ctc loss
											UpperCamelCase   			=     ctc_loss_reduction
											UpperCamelCase   			=     ctc_zero_infinity

											# SequenceClassification-specific parameter. Feel free to ignore for other classes.
											UpperCamelCase   			=     classifier_proj_size

											# XVector-specific parameters. Feel free to ignore for other classes.
											UpperCamelCase   			=     list(A_     )
											UpperCamelCase   			=     list(A_     )
											UpperCamelCase   			=     list(A_     )
											UpperCamelCase   			=     xvector_output_dim







				@property
				def 						UpperCAmelCase_    (       self     )->		Optional[Any]:







											'''simple docstring'''






											return functools.reduce(operator.mul						,				self.conv_stride						,				1     )




