"""simple docstring"""






import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
lowerCAmelCase__						:						Tuple		  =			'src/transformers'
lowerCAmelCase__						:						Union[str, Any]		  =			'docs/source/en'
lowerCAmelCase__						:						Tuple		  =			'.'



def 						a_		(      lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase ):
				with open(lowerCamelCase      ,    'r'      ,    encoding='utf-8'      ,    newline='\n' ) as f:
								UpperCAmelCase__    =							f.readlines()
				# Find the start prompt.
				UpperCAmelCase__    =							0
				while not lines[start_index].startswith(lowerCamelCase ):
								start_index += 1
				start_index += 1

				UpperCAmelCase__    =							start_index
				while not lines[end_index].startswith(lowerCamelCase ):
								end_index += 1
				end_index -= 1

				while len(lines[start_index] ) <= 1:
								start_index += 1
				while len(lines[end_index] ) <= 1:
								end_index -= 1
				end_index += 1
				return "".join(lines[start_index:end_index] ), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
lowerCAmelCase__						:						Union[str, Any]		  =			'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
lowerCAmelCase__						:						List[Any]		  =			re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
lowerCAmelCase__						:						Optional[int]		  =			re.compile(r'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
lowerCAmelCase__						:						Optional[int]		  =			re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# This is to make sure the transformers module imported is the one in the repo.
lowerCAmelCase__						:						Any		  =			direct_transformers_import(TRANSFORMERS_PATH)



def 						a_		(      lowerCamelCase ):
				UpperCAmelCase__    =							re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'      ,    lowerCamelCase )
				return [m.group(0 ) for m in matches]



def 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				UpperCAmelCase__    =							2 if text == '✅' or text == '❌' else len(lowerCamelCase )
				UpperCAmelCase__    =							(width - text_length) // 2
				UpperCAmelCase__    =							width - text_length - left_indent
				return " " * left_indent + text + " " * right_indent



def 						a_		(      ):
				UpperCAmelCase__    =							transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
				UpperCAmelCase__    =							{
				    name: config_maping_names[code]
				    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
				    if code in config_maping_names
				}
				UpperCAmelCase__    =							{name: config.replace('Config'      ,    '' ) for name, config in model_name_to_config.items()}

				# Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
				UpperCAmelCase__    =							collections.defaultdict(lowerCamelCase )
				UpperCAmelCase__    =							collections.defaultdict(lowerCamelCase )
				UpperCAmelCase__    =							collections.defaultdict(lowerCamelCase )
				UpperCAmelCase__    =							collections.defaultdict(lowerCamelCase )
				UpperCAmelCase__    =							collections.defaultdict(lowerCamelCase )

				# Let's lookup through all transformers object (once).
				for attr_name in dir(lowerCamelCase ):
								UpperCAmelCase__    =							None
								if attr_name.endswith('Tokenizer' ):
												UpperCAmelCase__    =							slow_tokenizers
												UpperCAmelCase__    =							attr_name[:-9]
								elif attr_name.endswith('TokenizerFast' ):
												UpperCAmelCase__    =							fast_tokenizers
												UpperCAmelCase__    =							attr_name[:-1_3]
								elif _re_tf_models.match(lowerCamelCase ) is not None:
												UpperCAmelCase__    =							tf_models
												UpperCAmelCase__    =							_re_tf_models.match(lowerCamelCase ).groups()[0]
								elif _re_flax_models.match(lowerCamelCase ) is not None:
												UpperCAmelCase__    =							flax_models
												UpperCAmelCase__    =							_re_flax_models.match(lowerCamelCase ).groups()[0]
								elif _re_pt_models.match(lowerCamelCase ) is not None:
												UpperCAmelCase__    =							pt_models
												UpperCAmelCase__    =							_re_pt_models.match(lowerCamelCase ).groups()[0]

								if lookup_dict is not None:
												while len(lowerCamelCase ) > 0:
																if attr_name in model_name_to_prefix.values():
																				UpperCAmelCase__    =							True
																				break
																# Try again after removing the last word in the name
																UpperCAmelCase__    =							''.join(camel_case_split(lowerCamelCase )[:-1] )

    # Let's build that table!
				UpperCAmelCase__    =							list(model_name_to_config.keys() )
				model_names.sort(key=str.lower )
				UpperCAmelCase__    =							['Model', 'Tokenizer slow', 'Tokenizer fast', 'PyTorch support', 'TensorFlow support', 'Flax Support']
				# We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
				UpperCAmelCase__    =							[len(lowerCamelCase ) + 2 for c in columns]
				UpperCAmelCase__    =							max([len(lowerCamelCase ) for name in model_names] ) + 2

				# Build the table per se
				UpperCAmelCase__    =							'|' + '|'.join([_center_text(lowerCamelCase      ,    lowerCamelCase ) for c, w in zip(lowerCamelCase      ,    lowerCamelCase )] ) + '|\n'
				# Use ":-----:" format to center-aligned table cell texts
				table += "|" + "|".join([':' + '-' * (w - 2) + ':' for w in widths] ) + "|\n"

				UpperCAmelCase__    =							{True: '✅', False: '❌'}
				for name in model_names:
								UpperCAmelCase__    =							model_name_to_prefix[name]
								UpperCAmelCase__    =							[
								    name,
								    check[slow_tokenizers[prefix]],
								    check[fast_tokenizers[prefix]],
								    check[pt_models[prefix]],
								    check[tf_models[prefix]],
								    check[flax_models[prefix]],
								]
								table += "|" + "|".join([_center_text(lowerCamelCase      ,    lowerCamelCase ) for l, w in zip(lowerCamelCase      ,    lowerCamelCase )] ) + "|\n"
				return table



def 						a_		(      lowerCamelCase=False ):
				UpperCAmelCase__   ,			UpperCAmelCase__   ,			UpperCAmelCase__   ,			UpperCAmelCase__    =							_find_text_in_file(
				    filename=os.path.join(lowerCamelCase      ,    'index.md' )      ,    start_prompt='<!--This table is updated automatically from the auto modules'      ,    end_prompt='<!-- End table-->'      ,    )
				UpperCAmelCase__    =							get_model_table_from_auto_modules()

				if current_table != new_table:
								if overwrite:
												with open(os.path.join(lowerCamelCase      ,    'index.md' )      ,    'w'      ,    encoding='utf-8'      ,    newline='\n' ) as f:
																f.writelines(lines[:start_index] + [new_table] + lines[end_index:] )
								else:
												raise ValueError(
												    'The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this.' )


if __name__ == "__main__":
						lowerCAmelCase__						:						Tuple		  =			argparse.ArgumentParser()
						parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
						lowerCAmelCase__						:						Dict		  =			parser.parse_args()

						check_model_table(args.fix_and_overwrite)




"""simple docstring"""






import argparse
import os

import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints

from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder


lowerCAmelCase__						:						str		  =			'base_with_context'



def 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(weights['token_embedder']['embedding'] ) )
				UpperCAmelCase__    =							nn.Parameter(
				    torch.FloatTensor(weights['Embed_0']['embedding'] )      ,    requires_grad=lowerCamelCase )
				for lyr_num, lyr in enumerate(model.encoders ):
								UpperCAmelCase__    =							weights[f'''layers_{lyr_num}''']
								UpperCAmelCase__    =							nn.Parameter(
								    torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale'] ) )

								UpperCAmelCase__    =							ly_weight['attention']
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T ) )

								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale'] ) )

								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T ) )

				UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(weights['encoder_norm']['scale'] ) )
				return model



def 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(weights['input_proj']['kernel'].T ) )

				UpperCAmelCase__    =							nn.Parameter(
				    torch.FloatTensor(weights['Embed_0']['embedding'] )      ,    requires_grad=lowerCamelCase )

				for lyr_num, lyr in enumerate(model.encoders ):
								UpperCAmelCase__    =							weights[f'''layers_{lyr_num}''']
								UpperCAmelCase__    =							ly_weight['attention']

								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(
								    torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale'] ) )

								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale'] ) )

				UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(weights['encoder_norm']['scale'] ) )

				return model



def 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(weights['time_emb_dense0']['kernel'].T ) )
				UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(weights['time_emb_dense1']['kernel'].T ) )

				UpperCAmelCase__    =							nn.Parameter(
				    torch.FloatTensor(weights['Embed_0']['embedding'] )      ,    requires_grad=lowerCamelCase )

				UpperCAmelCase__    =							nn.Parameter(
				    torch.FloatTensor(weights['continuous_inputs_projection']['kernel'].T ) )

				for lyr_num, lyr in enumerate(model.decoders ):
								UpperCAmelCase__    =							weights[f'''layers_{lyr_num}''']
								UpperCAmelCase__    =							nn.Parameter(
								    torch.FloatTensor(ly_weight['pre_self_attention_layer_norm']['scale'] ) )

								UpperCAmelCase__    =							nn.Parameter(
								    torch.FloatTensor(ly_weight['FiLMLayer_0']['DenseGeneral_0']['kernel'].T ) )

								UpperCAmelCase__    =							ly_weight['self_attention']
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T ) )

								UpperCAmelCase__    =							ly_weight['MultiHeadDotProductAttention_0']
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(
								    torch.FloatTensor(ly_weight['pre_cross_attention_layer_norm']['scale'] ) )

								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale'] ) )
								UpperCAmelCase__    =							nn.Parameter(
								    torch.FloatTensor(ly_weight['FiLMLayer_1']['DenseGeneral_0']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T ) )

				UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(weights['decoder_norm']['scale'] ) )

				UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(weights['spec_out_dense']['kernel'].T ) )

				return model



def 						a_		(      lowerCamelCase ):
				UpperCAmelCase__    =							checkpoints.load_tax_checkpoint(args.checkpoint_path )
				UpperCAmelCase__    =							jnp.tree_util.tree_map(onp.array      ,    lowerCamelCase )

				UpperCAmelCase__    =							[
				    'from __gin__ import dynamic_registration',
				    'from music_spectrogram_diffusion.models.diffusion import diffusion_utils',
				    'diffusion_utils.ClassifierFreeGuidanceConfig.eval_condition_weight = 2.0',
				    'diffusion_utils.DiffusionConfig.classifier_free_guidance = @diffusion_utils.ClassifierFreeGuidanceConfig()',
				]

				UpperCAmelCase__    =							os.path.join(args.checkpoint_path      ,    '..'      ,    'config.gin' )
				UpperCAmelCase__    =							inference.parse_training_gin_file(lowerCamelCase      ,    lowerCamelCase )
				UpperCAmelCase__    =							inference.InferenceModel(args.checkpoint_path      ,    lowerCamelCase )

				UpperCAmelCase__    =							DDPMScheduler(beta_schedule='squaredcos_cap_v2'      ,    variance_type='fixed_large' )

				UpperCAmelCase__    =							SpectrogramNotesEncoder(
				    max_length=synth_model.sequence_length['inputs']      ,    vocab_size=synth_model.model.module.config.vocab_size      ,    d_model=synth_model.model.module.config.emb_dim      ,    dropout_rate=synth_model.model.module.config.dropout_rate      ,    num_layers=synth_model.model.module.config.num_encoder_layers      ,    num_heads=synth_model.model.module.config.num_heads      ,    d_kv=synth_model.model.module.config.head_dim      ,    d_ff=synth_model.model.module.config.mlp_dim      ,    feed_forward_proj='gated-gelu'      ,    )

				UpperCAmelCase__    =							SpectrogramContEncoder(
				    input_dims=synth_model.audio_codec.n_dims      ,    targets_context_length=synth_model.sequence_length['targets_context']      ,    d_model=synth_model.model.module.config.emb_dim      ,    dropout_rate=synth_model.model.module.config.dropout_rate      ,    num_layers=synth_model.model.module.config.num_encoder_layers      ,    num_heads=synth_model.model.module.config.num_heads      ,    d_kv=synth_model.model.module.config.head_dim      ,    d_ff=synth_model.model.module.config.mlp_dim      ,    feed_forward_proj='gated-gelu'      ,    )

				UpperCAmelCase__    =							TaFilmDecoder(
				    input_dims=synth_model.audio_codec.n_dims      ,    targets_length=synth_model.sequence_length['targets_context']      ,    max_decoder_noise_time=synth_model.model.module.config.max_decoder_noise_time      ,    d_model=synth_model.model.module.config.emb_dim      ,    num_layers=synth_model.model.module.config.num_decoder_layers      ,    num_heads=synth_model.model.module.config.num_heads      ,    d_kv=synth_model.model.module.config.head_dim      ,    d_ff=synth_model.model.module.config.mlp_dim      ,    dropout_rate=synth_model.model.module.config.dropout_rate      ,    )

				UpperCAmelCase__    =							load_notes_encoder(ta_checkpoint['target']['token_encoder']      ,    lowerCamelCase )
				UpperCAmelCase__    =							load_continuous_encoder(ta_checkpoint['target']['continuous_encoder']      ,    lowerCamelCase )
				UpperCAmelCase__    =							load_decoder(ta_checkpoint['target']['decoder']      ,    lowerCamelCase )

				UpperCAmelCase__    =							OnnxRuntimeModel.from_pretrained('kashif/soundstream_mel_decoder' )

				UpperCAmelCase__    =							SpectrogramDiffusionPipeline(
				    notes_encoder=lowerCamelCase      ,    continuous_encoder=lowerCamelCase      ,    decoder=lowerCamelCase      ,    scheduler=lowerCamelCase      ,    melgan=lowerCamelCase      ,    )
				if args.save:
								pipe.save_pretrained(args.output_path )


if __name__ == "__main__":
						lowerCAmelCase__						:						Tuple		  =			argparse.ArgumentParser()

						parser.add_argument('--output_path', default=None, type=str, required=True, help='Path to the converted model.')
						parser.add_argument(
						    '--save', default=True, type=bool, required=False, help='Whether to save the converted model or not.'
						)
						parser.add_argument(
						    '--checkpoint_path',
						    default=F"""{MODEL}/checkpoint_500000""",
						    type=str,
						    required=False,
						    help='Path to the original jax model checkpoint.',
						)
						lowerCAmelCase__						:						List[str]		  =			parser.parse_args()

						main(args)



