
import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


UpperCamelCase_ 						=						[
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ['memory_attention', 'encoder_attn'],
    ['attention', 'attn'],
    ['/', '.'],
    ['.LayerNorm.gamma', '_layer_norm.weight'],
    ['.LayerNorm.beta', '_layer_norm.bias'],
    ['r.layer_', 'r.layers.'],
    ['output_proj', 'out_proj'],
    ['ffn.dense_1.', 'fc2.'],
    ['ffn.dense.', 'fc1.'],
    ['ffn_layer_norm', 'final_layer_norm'],
    ['kernel', 'weight'],
    ['encoder_layer_norm.', 'encoder.layer_norm.'],
    ['decoder_layer_norm.', 'decoder.layer_norm.'],
    ['embeddings.weights', 'shared.weight'],
]






def 		_UpperCAmelCase (						UpperCamelCase:  Dict	):






	"""simple docstring"""


	for pegasus_name, hf_name in PATTERNS:
		__lowerCAmelCase    =				k.replace(UpperCamelCase      ,				UpperCamelCase	)
	return k






def 		_UpperCAmelCase (						UpperCamelCase:  dict      ,				UpperCamelCase:  dict	):






	"""simple docstring"""


	__lowerCAmelCase    =				DEFAULTS.copy()
	cfg_kwargs.update(UpperCamelCase	)
	__lowerCAmelCase    =				PegasusConfig(**UpperCamelCase	)
	__lowerCAmelCase    =				PegasusForConditionalGeneration(UpperCamelCase	)
	__lowerCAmelCase    =				torch_model.model.state_dict()
	__lowerCAmelCase    =				{}
	for k, v in tf_weights.items():
		__lowerCAmelCase    =				rename_state_dict_key(UpperCamelCase	)
		if new_k not in sd:
			raise ValueError(F"could not find new key {new_k} in state dict. (converted from {k})"	)

		if "dense" in k or "proj" in new_k:
			__lowerCAmelCase    =				v.T
		__lowerCAmelCase    =				torch.tensor(UpperCamelCase      ,				dtype=sd[new_k].dtype	)
		assert v.shape == sd[new_k].shape, F"{new_k}, {k}, {v.shape}, {sd[new_k].shape}"
	# make sure embedding.padding_idx is respected
	__lowerCAmelCase    =				torch.zeros_like(mapping["shared.weight"][cfg.pad_token_id + 1]	)
	__lowerCAmelCase    =				mapping["shared.weight"]
	__lowerCAmelCase    =				mapping["shared.weight"]
	__lowerCAmelCase    =				{k: torch.zeros_like(UpperCamelCase	) for k, v in sd.items() if k.endswith("bias"	) and k not in mapping}
	mapping.update(**UpperCamelCase	)
	__lowerCAmelCase ,    __lowerCAmelCase    =				torch_model.model.load_state_dict(UpperCamelCase      ,				strict=UpperCamelCase	)
	__lowerCAmelCase    =				[
	    k for k in missing if k not in ["encoder.embed_positions.weight", "decoder.embed_positions.weight"]
	]
	assert unexpected_missing == [], F"no matches found for the following torch keys {unexpected_missing}"
	assert extra == [], F"no matches found for the following tf keys {extra}"
	return torch_model






def 		_UpperCAmelCase (						UpperCamelCase:  Tuple="./ckpt/aeslc/model.ckpt-32000"	):






	"""simple docstring"""


	__lowerCAmelCase    =				tf.train.list_variables(UpperCamelCase	)
	__lowerCAmelCase    =				{}
	__lowerCAmelCase    =				["Adafactor", "global_step"]
	for name, shape in tqdm(UpperCamelCase      ,				desc="converting tf checkpoint to dict"	):
		__lowerCAmelCase    =				any(pat in name for pat in ignore_name	)
		if skip_key:
			continue
		__lowerCAmelCase    =				tf.train.load_variable(UpperCamelCase      ,				UpperCamelCase	)
		__lowerCAmelCase    =				array
	return tf_weights






def 		_UpperCAmelCase (						UpperCamelCase:  str      ,				UpperCamelCase:  str	):






	"""simple docstring"""


	__lowerCAmelCase    =				Path(UpperCamelCase	).parent.name
	__lowerCAmelCase    =				task_specific_params[F"summarization_{dataset}"]["max_position_embeddings"]
	__lowerCAmelCase    =				PegasusTokenizer.from_pretrained("sshleifer/pegasus"      ,				model_max_length=UpperCamelCase	)
	assert tok.model_max_length == desired_max_model_length
	tok.save_pretrained(UpperCamelCase	)

	# convert model
	__lowerCAmelCase    =				get_tf_weights_as_numpy(UpperCamelCase	)
	__lowerCAmelCase    =				task_specific_params[F"summarization_{dataset}"]
	if dataset == "large":
		__lowerCAmelCase    =				task_specific_params
	__lowerCAmelCase    =				convert_pegasus(UpperCamelCase      ,				UpperCamelCase	)
	torch_model.save_pretrained(UpperCamelCase	)
	__lowerCAmelCase    =				torch_model.state_dict()
	sd.pop("model.decoder.embed_positions.weight"	)
	sd.pop("model.encoder.embed_positions.weight"	)
	torch.save(UpperCamelCase      ,				Path(UpperCamelCase	) / "pytorch_model.bin"	)


if __name__ == "__main__":
					UpperCamelCase_ 						=						argparse.ArgumentParser()
					# Required parameters
					parser.add_argument("tf_ckpt_path", type=str, help="passed to tf.train.list_variables")
					parser.add_argument("save_dir", default=None, type=str, help="Path to the output PyTorch model.")
					UpperCamelCase_ 						=						parser.parse_args()
					if args.save_dir is None:
										UpperCamelCase_ 						=						Path(args.tf_ckpt_path).parent.name
										UpperCamelCase_ 						=						os.path.join("pegasus", dataset)
					convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)








'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging


lowerCAmelCase					:    Optional[Any]           = logging.get_logger(__name__)

lowerCAmelCase					:    Dict           = {
    'speechbrain/m-ctc-t-large': 'https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json',
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							"""mctct"""







				def __init__(       self						,				A_=8065						,				A_=1536						,				A_=36						,				A_=6144						,				A_=4						,				A_=384						,				A_=920						,				A_=1e-5						,				A_=0.3						,				A_="relu"						,				A_=0.02						,				A_=0.3						,				A_=0.3						,				A_=1						,				A_=0						,				A_=2						,				A_=1						,				A_=0.3						,				A_=1						,				A_=(7,)						,				A_=(3,)						,				A_=80						,				A_=1						,				A_=None						,				A_="sum"						,				A_=False						,				**A_						,				)->		str:







											'''simple docstring'''






											super().__init__(**A_						,				pad_token_id=A_						,				bos_token_id=A_						,				eos_token_id=A_     )
											UpperCamelCase   			=     vocab_size
											UpperCamelCase   			=     hidden_size
											UpperCamelCase   			=     num_hidden_layers
											UpperCamelCase   			=     intermediate_size
											UpperCamelCase   			=     num_attention_heads
											UpperCamelCase   			=     attention_head_dim
											UpperCamelCase   			=     max_position_embeddings
											UpperCamelCase   			=     layer_norm_eps
											UpperCamelCase   			=     layerdrop
											UpperCamelCase   			=     hidden_act
											UpperCamelCase   			=     initializer_range
											UpperCamelCase   			=     hidden_dropout_prob
											UpperCamelCase   			=     attention_probs_dropout_prob
											UpperCamelCase   			=     pad_token_id
											UpperCamelCase   			=     bos_token_id
											UpperCamelCase   			=     eos_token_id
											UpperCamelCase   			=     conv_glu_dim
											UpperCamelCase   			=     conv_dropout
											UpperCamelCase   			=     num_conv_layers
											UpperCamelCase   			=     input_feat_per_channel
											UpperCamelCase   			=     input_channels
											UpperCamelCase   			=     conv_channels
											UpperCamelCase   			=     ctc_loss_reduction
											UpperCamelCase   			=     ctc_zero_infinity

											# prevents config testing fail with exporting to json
											UpperCamelCase   			=     list(A_     )
											UpperCamelCase   			=     list(A_     )

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




