import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE="attention"     ):
				'''simple docstring'''







				__UpperCamelCase					:Union[str, Any]        =		params[f"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
				__UpperCamelCase					:List[Any]        =		params[f"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
				__UpperCamelCase					:Optional[Any]        =		params[f"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
				__UpperCamelCase					:str        =		params[f"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
				return k, o, q, v



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE=False     ):
				'''simple docstring'''







				if split_mlp_wi:
								__UpperCamelCase					:Dict        =		params[f"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
								__UpperCamelCase					:Optional[Any]        =		params[f"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
								__UpperCamelCase					:Optional[Any]        =		(wi_a, wi_a)
				else:
								__UpperCamelCase					:List[str]        =		params[f"""{prefix}/layers_{i}/mlp/wi/kernel"""]

				__UpperCamelCase					:str        =		params[f"""{prefix}/layers_{i}/mlp/wo/kernel"""]
				return wi, wo



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
				'''simple docstring'''







				return params[f"""{prefix}/layers_{i}/{layer_name}/scale"""]



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      *, SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
				'''simple docstring'''







				__UpperCamelCase					:Optional[Any]        =		traverse_util.flatten_dict(variables['''target''']     )
				__UpperCamelCase					:Tuple        =		{'''/'''.join(lowerCamelCase_     ): v for k, v in old.items()}

				# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
				__UpperCamelCase					:int        =		'''encoder/layers_0/mlp/wi_0/kernel''' in old
				print('''Split MLP:'''  ,      lowerCamelCase_     )

				__UpperCamelCase					:int        =		collections.OrderedDict()

				# Shared embeddings.
				__UpperCamelCase					:Union[str, Any]        =		old['''token_embedder/embedding''']

				# Encoder.
				for i in range(lowerCamelCase_     ):
								# Block i, layer 0 (Self Attention).
								__UpperCamelCase					:Optional[int]        =		tax_layer_norm_lookup(lowerCamelCase_  ,      lowerCamelCase_  ,      '''encoder'''  ,      '''pre_attention_layer_norm'''     )
								__UpperCamelCase					:Optional[int]        =		tax_attention_lookup(lowerCamelCase_  ,      lowerCamelCase_  ,      '''encoder'''  ,      '''attention'''     )
								__UpperCamelCase					:str        =		layer_norm
								__UpperCamelCase					:Any        =		k.T
								__UpperCamelCase					:List[str]        =		o.T
								__UpperCamelCase					:Tuple        =		q.T
								__UpperCamelCase					:List[str]        =		v.T

								# Block i, layer 1 (MLP).
								__UpperCamelCase					:Tuple        =		tax_layer_norm_lookup(lowerCamelCase_  ,      lowerCamelCase_  ,      '''encoder'''  ,      '''pre_mlp_layer_norm'''     )
								__UpperCamelCase					:List[Any]        =		tax_mlp_lookup(lowerCamelCase_  ,      lowerCamelCase_  ,      '''encoder'''  ,      lowerCamelCase_     )
								__UpperCamelCase					:Optional[int]        =		layer_norm
								if split_mlp_wi:
												__UpperCamelCase					:Any        =		wi[0].T
												__UpperCamelCase					:str        =		wi[1].T
								else:
												__UpperCamelCase					:List[str]        =		wi.T
								__UpperCamelCase					:str        =		wo.T

				__UpperCamelCase					:Dict        =		old[
				    '''encoder/relpos_bias/rel_embedding'''
				].T
				__UpperCamelCase					:Any        =		old['''encoder/encoder_norm/scale''']

				if not is_encoder_only:
								# Decoder.
								for i in range(lowerCamelCase_     ):
												# Block i, layer 0 (Self Attention).
												__UpperCamelCase					:Dict        =		tax_layer_norm_lookup(lowerCamelCase_  ,      lowerCamelCase_  ,      '''decoder'''  ,      '''pre_self_attention_layer_norm'''     )
												__UpperCamelCase					:Tuple        =		tax_attention_lookup(lowerCamelCase_  ,      lowerCamelCase_  ,      '''decoder'''  ,      '''self_attention'''     )
												__UpperCamelCase					:Optional[Any]        =		layer_norm
												__UpperCamelCase					:Dict        =		k.T
												__UpperCamelCase					:str        =		o.T
												__UpperCamelCase					:int        =		q.T
												__UpperCamelCase					:Dict        =		v.T

												# Block i, layer 1 (Cross Attention).
												__UpperCamelCase					:List[str]        =		tax_layer_norm_lookup(lowerCamelCase_  ,      lowerCamelCase_  ,      '''decoder'''  ,      '''pre_cross_attention_layer_norm'''     )
												__UpperCamelCase					:int        =		tax_attention_lookup(lowerCamelCase_  ,      lowerCamelCase_  ,      '''decoder'''  ,      '''encoder_decoder_attention'''     )
												__UpperCamelCase					:str        =		layer_norm
												__UpperCamelCase					:Tuple        =		k.T
												__UpperCamelCase					:Union[str, Any]        =		o.T
												__UpperCamelCase					:Tuple        =		q.T
												__UpperCamelCase					:Dict        =		v.T

												# Block i, layer 2 (MLP).
												__UpperCamelCase					:int        =		tax_layer_norm_lookup(lowerCamelCase_  ,      lowerCamelCase_  ,      '''decoder'''  ,      '''pre_mlp_layer_norm'''     )
												__UpperCamelCase					:Tuple        =		tax_mlp_lookup(lowerCamelCase_  ,      lowerCamelCase_  ,      '''decoder'''  ,      lowerCamelCase_     )
												__UpperCamelCase					:List[Any]        =		layer_norm
												if split_mlp_wi:
																__UpperCamelCase					:List[Any]        =		wi[0].T
																__UpperCamelCase					:Tuple        =		wi[1].T
												else:
																__UpperCamelCase					:int        =		wi.T
												__UpperCamelCase					:Union[str, Any]        =		wo.T

								__UpperCamelCase					:str        =		old['''decoder/decoder_norm/scale''']
								__UpperCamelCase					:Any        =		old[
								    '''decoder/relpos_bias/rel_embedding'''
								].T

								# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
								if "decoder/logits_dense/kernel" in old:
												__UpperCamelCase					:Dict        =		old['''decoder/logits_dense/kernel'''].T

				return new



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
				'''simple docstring'''







				__UpperCamelCase					:List[str]        =		collections.OrderedDict([(k, torch.from_numpy(v.copy()     )) for (k, v) in converted_params.items()]     )

				# Add what is missing.
				if "encoder.embed_tokens.weight" not in state_dict:
								__UpperCamelCase					:Tuple        =		state_dict['''shared.weight''']

				if not is_encoder_only:
								if "decoder.embed_tokens.weight" not in state_dict:
												__UpperCamelCase					:Optional[Any]        =		state_dict['''shared.weight''']

								if "lm_head.weight" not in state_dict:  # For old 1.0 models.
												print('''Using shared word embeddings as lm_head.'''     )
												__UpperCamelCase					:Optional[int]        =		state_dict['''shared.weight''']

				return state_dict



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
				'''simple docstring'''







				__UpperCamelCase					:str        =		checkpoints.load_tax_checkpoint(lowerCamelCase_     )
				__UpperCamelCase					:Tuple        =		convert_tax_to_pytorch(lowerCamelCase_  ,      num_layers=config.num_layers  ,      is_encoder_only=lowerCamelCase_     )
				__UpperCamelCase					:Optional[int]        =		make_state_dict(lowerCamelCase_  ,      lowerCamelCase_     )
				model.load_state_dict(lowerCamelCase_  ,      strict=lowerCamelCase_     )



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE = False     ):
				'''simple docstring'''







				__UpperCamelCase					:Optional[Any]        =		TaConfig.from_json_file(lowerCamelCase_     )
				print(f"""Building PyTorch model from configuration: {config}"""     )
				# Non-v1.1 checkpoints could also use T5Model, but this works for all.
				# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
				if is_encoder_only:
								__UpperCamelCase					:Any        =		TaEncoderModel(lowerCamelCase_     )
				else:
								__UpperCamelCase					:List[str]        =		TaForConditionalGeneration(lowerCamelCase_     )

				# Load weights from tf checkpoint
				load_tax_weights_in_ta(lowerCamelCase_  ,      lowerCamelCase_  ,      lowerCamelCase_  ,      lowerCamelCase_     )

				# Save pytorch-model
				print(f"""Save PyTorch model to {pytorch_dump_path}"""     )
				model.save_pretrained(lowerCamelCase_     )

				# Verify that we can load the checkpoint.
				model.from_pretrained(lowerCamelCase_     )
				print('''Done'''     )


if __name__ == "__main__":
				__lowercase										= argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
				# Required parameters
				parser.add_argument(
				    '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
				)
				parser.add_argument(
				    '''--config_file''',
				    default=None,
				    type=str,
				    required=True,
				    help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
				)
				parser.add_argument(
				    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
				)
				parser.add_argument(
				    '''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
				)
				__lowercase										= parser.parse_args()
				convert_tax_checkpoint_to_pytorch(
				    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
				)






from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


try:
    if not (is_transformers_available() and is_torch_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
    from .notes_encoder import SpectrogramNotesEncoder
    from .continous_encoder import SpectrogramContEncoder
    from .pipeline_spectrogram_diffusion import (
        SpectrogramContEncoder,
        SpectrogramDiffusionPipeline,
        TaFilmDecoder,
    )

try:
    if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ...utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
    from .midi_utils import MidiProcessor





