


# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

####################################################################################################

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPTaConfig







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase=0			):
							# Format the message.
							if name is None:
														__snake_case	:    Optional[Any]											=     None
							else:
														__snake_case	:    Dict											=     "." * max(0							,			spaces - 2			) + "# {:" + str(5_0 - spaces			) + "s}"
														__snake_case	:    Dict											=     fmt.format(__lowerCamelCase			)

							# Print and recurse (if needed).
							if isinstance(__lowerCamelCase							,			__lowerCamelCase			):
														if msg is not None:
																					print(__lowerCamelCase			)
														for k in val.keys():
																					recursive_print(__lowerCamelCase							,			val[k]							,			spaces + 2			)
							elif isinstance(__lowerCamelCase							,			torch.Tensor			):
														print(__lowerCamelCase							,			":"							,			val.size()			)
							else:
														print(__lowerCamelCase							,			":"							,			__lowerCamelCase			)







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase			):
							# Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
							# for compatibility with later versions of NVIDIA Megatron-LM.
							# The inverse operation is performed inside Megatron-LM to read checkpoints:
							# https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
							# If param is the weight tensor of the self-attention block, the returned tensor
							# will have to be transposed one more time to be read by HuggingFace GPT2.
							__snake_case	:    Union[str, Any]											=     param.size()
							if checkpoint_version == 1.0:
														# version 1.0 stores [num_heads * hidden_size * num_splits, :]
														__snake_case	:    List[str]											=     (num_heads, hidden_size, num_splits) + input_shape[1:]
														__snake_case	:    int											=     param.view(*__lowerCamelCase			)
														__snake_case	:    List[Any]											=     param.transpose(0							,			2			)
														__snake_case	:    Optional[int]											=     param.transpose(1							,			2			).contiguous()
							elif checkpoint_version >= 2.0:
														# other versions store [num_heads * num_splits * hidden_size, :]
														__snake_case	:    Optional[Any]											=     (num_heads, num_splits, hidden_size) + input_shape[1:]
														__snake_case	:    List[str]											=     param.view(*__lowerCamelCase			)
														__snake_case	:    str											=     param.transpose(0							,			1			).contiguous()
							__snake_case	:    Any											=     param.view(*__lowerCamelCase			)
							return param







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase			):
							# The converted output model.
							__snake_case	:    List[Any]											=     {}

							# old versions did not store training args
							__snake_case	:    List[str]											=     input_state_dict.get("args"							,			__lowerCamelCase			)
							if ds_args is not None:
														# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
														# from pprint import pprint
														# pprint(vars(ds_args))

														__snake_case	:    Union[str, Any]											=     ds_args.padded_vocab_size
														__snake_case	:    int											=     ds_args.max_position_embeddings
														__snake_case	:    List[Any]											=     ds_args.hidden_size
														__snake_case	:    List[Any]											=     ds_args.num_layers
														__snake_case	:    Optional[Any]											=     ds_args.num_attention_heads
														__snake_case	:    Dict											=     ds_args.ffn_hidden_size
														# pprint(config)

							# The number of heads.
							__snake_case	:    str											=     config.n_head
							# The hidden_size per head.
							__snake_case	:    int											=     config.n_embd // config.n_head
							# Megatron-LM checkpoint version
							if "checkpoint_version" in input_state_dict.keys():
														__snake_case	:    Tuple											=     input_state_dict["checkpoint_version"]
							else:
														__snake_case	:    Any											=     0.0

							# The model.
							__snake_case	:    Dict											=     input_state_dict["model"]
							# The language model.
							__snake_case	:    str											=     model["language_model"]
							# The embeddings.
							__snake_case	:    List[str]											=     lm["embedding"]

							# The word embeddings.
							__snake_case	:    Any											=     embeddings["word_embeddings"]["weight"]
							# Truncate the embedding table to vocab_size rows.
							__snake_case	:    Tuple											=     word_embeddings[: config.vocab_size, :]
							__snake_case	:    Tuple											=     word_embeddings

							# The position embeddings.
							__snake_case	:    Tuple											=     embeddings["position_embeddings"]["weight"]
							# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
							__snake_case	:    Dict											=     pos_embeddings.size(0			)
							if n_positions != config.n_positions:
														raise ValueError(
														    F'pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don\'t match'			)
							# Store the position embeddings.
							__snake_case	:    Union[str, Any]											=     pos_embeddings

							# The transformer.
							__snake_case	:    List[str]											=     lm["transformer"] if "transformer" in lm.keys() else lm["encoder"]

							# The regex to extract layer names.
							__snake_case	:    Optional[int]											=     re.compile(R"layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)"			)

							# The simple map of names for "automated" rules.
							__snake_case	:    Optional[Any]											=     {
							    "attention.dense": ".attn.c_proj.",
							    "self_attention.dense": ".attn.c_proj.",
							    "mlp.dense_h_to_4h": ".mlp.c_fc.",
							    "mlp.dense_4h_to_h": ".mlp.c_proj.",
							}

							# Extract the layers.
							for key, val in transformer.items():
														# Match the name.
														__snake_case	:    List[str]											=     layer_re.match(__lowerCamelCase			)

														# Stop if that's not a layer
														if m is None:
																					break

														# The index of the layer.
														__snake_case	:    int											=     int(m.group(1			)			)
														# The name of the operation.
														__snake_case	:    Dict											=     m.group(2			)
														# Is it a weight or a bias?
														__snake_case	:    Any											=     m.group(3			)

														# The name of the layer.
														__snake_case	:    List[Any]											=     F'transformer.h.{layer_idx}'

														# For layernorm(s), simply store the layer norm.
														if op_name.endswith("layernorm"			):
																					__snake_case	:    Optional[int]											=     "ln_1" if op_name.startswith("input"			) else "ln_2"
																					__snake_case	:    Optional[Any]											=     val

														# Transpose the QKV matrix.
														elif (
														    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
														) and weight_or_bias == "weight":
																					# Insert a tensor of 1x1xDxD bias.
																					__snake_case	:    Dict											=     torch.tril(torch.ones((n_positions, n_positions)							,			dtype=torch.floataa			)			).view(
																					    1							,			1							,			__lowerCamelCase							,			__lowerCamelCase			)
																					__snake_case	:    List[str]											=     causal_mask

																					# Insert a "dummy" tensor for masked_bias.
																					__snake_case	:    Union[str, Any]											=     torch.tensor(-1e4							,			dtype=torch.floataa			)
																					__snake_case	:    Dict											=     masked_bias

																					__snake_case	:    Tuple											=     fix_query_key_value_ordering(__lowerCamelCase							,			__lowerCamelCase							,			3							,			__lowerCamelCase							,			__lowerCamelCase			)
																					# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
																					__snake_case	:    List[Any]											=     out_val.transpose(0							,			1			).contiguous()
																					# Store.
																					__snake_case	:    Optional[int]											=     out_val

														# Transpose the bias.
														elif (
														    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
														) and weight_or_bias == "bias":
																					__snake_case	:    Any											=     fix_query_key_value_ordering(__lowerCamelCase							,			__lowerCamelCase							,			3							,			__lowerCamelCase							,			__lowerCamelCase			)
																					# Store. No change of shape.
																					__snake_case	:    Any											=     out_val

														# Transpose the weights.
														elif weight_or_bias == "weight":
																					__snake_case	:    str											=     megatron_to_transformers[op_name]
																					__snake_case	:    Union[str, Any]											=     val.transpose(0							,			1			)

														# Copy the bias.
														elif weight_or_bias == "bias":
																					__snake_case	:    Any											=     megatron_to_transformers[op_name]
																					__snake_case	:    List[Any]											=     val

    # DEBUG.
							assert config.n_layer == layer_idx + 1

							# The final layernorm.
							__snake_case	:    List[Any]											=     transformer["final_layernorm.weight"]
							__snake_case	:    List[str]											=     transformer["final_layernorm.bias"]

							# For LM head, transformers' wants the matrix to weight embeddings.
							__snake_case	:    Tuple											=     word_embeddings

							# It should be done!
							return output_state_dict







def        lowerCAmelCase_  (      ):
							# Create the argument parser.
							__snake_case	:    int											=     argparse.ArgumentParser()
							parser.add_argument("--print-checkpoint-structure"							,			action="store_true"			)
							parser.add_argument(
							    "path_to_checkpoint"							,			type=__lowerCamelCase							,			help="Path to the checkpoint file (.zip archive or direct .pt file)"							,			)
							parser.add_argument(
							    "--config_file"							,			default=""							,			type=__lowerCamelCase							,			help="An optional config json file describing the pre-trained model."							,			)
							__snake_case	:    Optional[Any]											=     parser.parse_args()

							# Extract the basename.
							__snake_case	:    int											=     os.path.dirname(args.path_to_checkpoint			)

							# Load the model.
							# the .zip is very optional, let's keep it for backward compatibility
							print(F'Extracting PyTorch state dictionary from {args.path_to_checkpoint}'			)
							if args.path_to_checkpoint.endswith(".zip"			):
														with zipfile.ZipFile(args.path_to_checkpoint							,			"r"			) as checkpoint:
																					with checkpoint.open("release/mp_rank_00/model_optim_rng.pt"			) as pytorch_dict:
																												__snake_case	:    List[Any]											=     torch.load(__lowerCamelCase							,			map_location="cpu"			)
							else:
														__snake_case	:    Any											=     torch.load(args.path_to_checkpoint							,			map_location="cpu"			)

							__snake_case	:    List[str]											=     input_state_dict.get("args"							,			__lowerCamelCase			)

							# Read the config, or default to the model released by NVIDIA.
							if args.config_file == "":
														if ds_args is not None:
																					if ds_args.bias_gelu_fusion:
																												__snake_case	:    List[str]											=     "gelu_fast"
																					elif ds_args.openai_gelu:
																												__snake_case	:    Optional[int]											=     "gelu_new"
																					else:
																												__snake_case	:    int											=     "gelu"
														else:
																					# in the very early days this used to be "gelu_new"
																					__snake_case	:    Tuple											=     "gelu_new"

														# Spell out all parameters in case the defaults change.
														__snake_case	:    Dict											=     GPTaConfig(
														    vocab_size=5_0_2_5_7							,			n_positions=1_0_2_4							,			n_embd=1_0_2_4							,			n_layer=2_4							,			n_head=1_6							,			n_inner=4_0_9_6							,			activation_function=__lowerCamelCase							,			resid_pdrop=0.1							,			embd_pdrop=0.1							,			attn_pdrop=0.1							,			layer_norm_epsilon=1e-5							,			initializer_range=0.0_2							,			summary_type="cls_index"							,			summary_use_proj=__lowerCamelCase							,			summary_activation=__lowerCamelCase							,			summary_proj_to_labels=__lowerCamelCase							,			summary_first_dropout=0.1							,			scale_attn_weights=__lowerCamelCase							,			use_cache=__lowerCamelCase							,			bos_token_id=5_0_2_5_6							,			eos_token_id=5_0_2_5_6							,			)
							else:
														__snake_case	:    List[str]											=     GPTaConfig.from_json_file(args.config_file			)

							__snake_case	:    Tuple											=     ["GPT2LMHeadModel"]

							# Convert.
							print("Converting"			)
							__snake_case	:    Union[str, Any]											=     convert_megatron_checkpoint(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase			)

							# Print the structure of converted state dict.
							if args.print_checkpoint_structure:
														recursive_print(__lowerCamelCase							,			__lowerCamelCase			)

							# Add tokenizer class info to config
							# see https://github.com/huggingface/transformers/issues/13906)
							if ds_args is not None:
														__snake_case	:    Union[str, Any]											=     ds_args.tokenizer_type
														if tokenizer_type == "GPT2BPETokenizer":
																					__snake_case	:    Optional[int]											=     "gpt2"
														elif tokenizer_type == "PretrainedFromHF":
																					__snake_case	:    Any											=     ds_args.tokenizer_name_or_path
														else:
																					raise ValueError(F'Unrecognized tokenizer_type {tokenizer_type}'			)
							else:
														__snake_case	:    Optional[int]											=     "gpt2"

							__snake_case	:    Optional[Any]											=     AutoTokenizer.from_pretrained(__lowerCamelCase			)
							__snake_case	:    List[str]											=     type(__lowerCamelCase			).__name__
							__snake_case	:    Optional[int]											=     tokenizer_class

							# Store the config to file.
							print("Saving config"			)
							config.save_pretrained(__lowerCamelCase			)

							# Save tokenizer based on args
							print(F'Adding {tokenizer_class} tokenizer files'			)
							tokenizer.save_pretrained(__lowerCamelCase			)

							# Store the state_dict to file.
							__snake_case	:    Optional[int]											=     os.path.join(__lowerCamelCase							,			"pytorch_model.bin"			)
							print(F'Saving checkpoint to "{output_checkpoint_file}"'			)
							torch.save(__lowerCamelCase							,			__lowerCamelCase			)


####################################################################################################

if __name__ == "__main__":
			main()

####################################################################################################





import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


_snake_case  :	int			      =       {
    "text_branch": "text_model",
    "audio_branch": "audio_model.audio_encoder",
    "attn": "attention.self",
    "self.proj": "output.dense",
    "attention.self_mask": "attn_mask",
    "mlp.fc1": "intermediate.dense",
    "mlp.fc2": "output.dense",
    "norm1": "layernorm_before",
    "norm2": "layernorm_after",
    "bn0": "batch_norm",
}

_snake_case  :	Dict			      =       AutoFeatureExtractor.from_pretrained("laion/clap-htsat-unfused", truncation="rand_trunc")







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase=False			):
							__snake_case				,    __snake_case	:    Any											=     create_model(
							    "HTSAT-tiny"							,			"roberta"							,			__lowerCamelCase							,			precision="fp32"							,			device="cuda:0" if torch.cuda.is_available() else "cpu"							,			enable_fusion=__lowerCamelCase							,			fusion_type="aff_2d" if enable_fusion else None							,			)
							return model, model_cfg







def        lowerCAmelCase_  (      __lowerCamelCase			):
							__snake_case	:    int											=     {}

							__snake_case	:    List[Any]											=     R".*sequential.(\d+).*"
							__snake_case	:    Any											=     R".*_projection.(\d+).*"

							for key, value in state_dict.items():
														# check if any key needs to be modified
														for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
																					if key_to_modify in key:
																												__snake_case	:    Optional[int]											=     key.replace(__lowerCamelCase							,			__lowerCamelCase			)

														if re.match(__lowerCamelCase							,			__lowerCamelCase			):
																					# replace sequential layers with list
																					__snake_case	:    List[Any]											=     re.match(__lowerCamelCase							,			__lowerCamelCase			).group(1			)

																					__snake_case	:    str											=     key.replace(F'sequential.{sequential_layer}.'							,			F'layers.{int(__lowerCamelCase			)//3}.linear.'			)
														elif re.match(__lowerCamelCase							,			__lowerCamelCase			):
																					__snake_case	:    Any											=     int(re.match(__lowerCamelCase							,			__lowerCamelCase			).group(1			)			)

																					# Because in CLAP they use `nn.Sequential`...
																					__snake_case	:    List[str]											=     1 if projecton_layer == 0 else 2

																					__snake_case	:    Tuple											=     key.replace(F'_projection.{projecton_layer}.'							,			F'_projection.linear{transformers_projection_layer}.'			)

														if "audio" and "qkv" in key:
																					# split qkv into query key and value
																					__snake_case	:    List[str]											=     value
																					__snake_case	:    Optional[int]											=     mixed_qkv.size(0			) // 3

																					__snake_case	:    List[str]											=     mixed_qkv[:qkv_dim]
																					__snake_case	:    int											=     mixed_qkv[qkv_dim : qkv_dim * 2]
																					__snake_case	:    str											=     mixed_qkv[qkv_dim * 2 :]

																					__snake_case	:    int											=     query_layer
																					__snake_case	:    Tuple											=     key_layer
																					__snake_case	:    List[str]											=     value_layer
														else:
																					__snake_case	:    Tuple											=     value

							return model_state_dict







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase=False			):
							__snake_case				,    __snake_case	:    List[str]											=     init_clap(__lowerCamelCase							,			enable_fusion=__lowerCamelCase			)

							clap_model.eval()
							__snake_case	:    Union[str, Any]											=     clap_model.state_dict()
							__snake_case	:    Any											=     rename_state_dict(__lowerCamelCase			)

							__snake_case	:    Dict											=     ClapConfig()
							__snake_case	:    Dict											=     enable_fusion
							__snake_case	:    Optional[Any]											=     ClapModel(__lowerCamelCase			)

							# ignore the spectrogram embedding layer
							model.load_state_dict(__lowerCamelCase							,			strict=__lowerCamelCase			)

							model.save_pretrained(__lowerCamelCase			)
							transformers_config.save_pretrained(__lowerCamelCase			)


if __name__ == "__main__":
			_snake_case  :	Union[str, Any]			      =       argparse.ArgumentParser()
			parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
			parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
			parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
			parser.add_argument("--enable_fusion", action="store_true", help="Whether to enable fusion or not")
			_snake_case  :	Tuple			      =       parser.parse_args()

			convert_clap_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.enable_fusion)

