
"""simple docstring"""


# 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_bert/convert_megatron_bert_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 MegatronBertConfig






def        lowerCAmelCase_       (		snake_case_		:      int							,   snake_case_		:      Dict							,   snake_case_		:      Union[str, Any]=0      )       ->List[Any]:
			# Format the message.
			if name is None:
						lowerCamelCase__     :					str		=None
			else:
						lowerCamelCase__     :					List[str]		="." * max(0							,   spaces - 2      ) + "# {:" + str(5_0 - spaces      ) + "s}"
						lowerCamelCase__     :					List[str]		=fmt.format(_a      )

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






def        lowerCAmelCase_       (		snake_case_		:      Any							,   snake_case_		:      str							,   snake_case_		:      List[Any]							,   snake_case_		:      Any							,   snake_case_		:      Optional[Any]      )       ->Optional[int]:
			# 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 BERT.
			lowerCamelCase__     :					List[Any]		=param.size()
			if checkpoint_version == 1.0:
						# version 1.0 stores [num_heads * hidden_size * num_splits, :]
						lowerCamelCase__     :					Optional[int]		=(num_heads, hidden_size, num_splits) + input_shape[1:]
						lowerCamelCase__     :					Optional[int]		=param.view(*_a      )
						lowerCamelCase__     :					int		=param.transpose(0							,   2      )
						lowerCamelCase__     :					List[str]		=param.transpose(1							,   2      ).contiguous()
			elif checkpoint_version >= 2.0:
						# other versions store [num_heads * num_splits * hidden_size, :]
						lowerCamelCase__     :					str		=(num_heads, num_splits, hidden_size) + input_shape[1:]
						lowerCamelCase__     :					str		=param.view(*_a      )
						lowerCamelCase__     :					List[str]		=param.transpose(0							,   1      ).contiguous()
			lowerCamelCase__     :					Union[str, Any]		=param.view(*_a      )
			return param






def        lowerCAmelCase_       (		snake_case_		:      Tuple							,   snake_case_		:      str							,   snake_case_		:      Any      )       ->str:
			# The converted output model.
			lowerCamelCase__     :					Tuple		={}

			# old versions did not store training args
			lowerCamelCase__     :					Dict		=input_state_dict.get('args'							,   _a      )
			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))

						lowerCamelCase__     :					Union[str, Any]		=ds_args.tokenizer_type
						lowerCamelCase__     :					Optional[Any]		=ds_args.padded_vocab_size
						lowerCamelCase__     :					List[Any]		=ds_args.max_position_embeddings
						lowerCamelCase__     :					int		=ds_args.hidden_size
						lowerCamelCase__     :					List[Any]		=ds_args.num_layers
						lowerCamelCase__     :					Tuple		=ds_args.num_attention_heads
						lowerCamelCase__     :					str		=ds_args.ffn_hidden_size if "ffn_hidden_size" in ds_args else 4 * ds_args.hidden_size
						# pprint(config)

			# The number of heads.
			lowerCamelCase__     :					Dict		=config.num_attention_heads
			# The hidden_size per head.
			lowerCamelCase__     :					Dict		=config.hidden_size // heads
			# Megatron-LM checkpoint version
			if "checkpoint_version" in input_state_dict.keys():
						lowerCamelCase__     :					Optional[Any]		=input_state_dict["checkpoint_version"]
			else:
						lowerCamelCase__     :					str		=0.0

			# The model.
			lowerCamelCase__     :					Optional[Any]		=input_state_dict["model"]
			# The language model.
			lowerCamelCase__     :					Optional[int]		=model["language_model"]
			# The embeddings.
			lowerCamelCase__     :					Dict		=lm["embedding"]

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

			# The position embeddings.
			lowerCamelCase__     :					int		=embeddings["position_embeddings"]["weight"]
			assert pos_embeddings.size(0      ) == config.max_position_embeddings and pos_embeddings.size(1      ) == config.hidden_size
			# Store the position embeddings.
			lowerCamelCase__     :					Optional[int]		=pos_embeddings

			# The token-type embeddings.
			lowerCamelCase__     :					Any		=embeddings["tokentype_embeddings"]["weight"]
			# Store the position embeddings.
			lowerCamelCase__     :					List[str]		=tokentype_embeddings

			# The transformer.
			lowerCamelCase__     :					Optional[Any]		=lm["transformer"] if "transformer" in lm.keys() else lm["encoder"]

			# The regex to extract layer names.
			lowerCamelCase__     :					List[str]		=re.compile(R'layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)'      )

			# The simple map of names for "automated" rules.
			lowerCamelCase__     :					Union[str, Any]		={
			    "attention.dense": ".attention.output.dense.",
			    "self_attention.dense": ".attention.output.dense.",
			    "mlp.dense_h_to_4h": ".intermediate.dense.",
			    "mlp.dense_4h_to_h": ".output.dense.",
			}

			# Keep track of the attention/query/value tensor.
			lowerCamelCase__     :					List[str]		=None

			# Extract the layers.
			for key, val in transformer.items():
						# Match the name.
						lowerCamelCase__     :					Any		=layer_re.match(_a      )

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

						# The index of the layer.
						lowerCamelCase__     :					int		=int(m.group(1      )      )
						# The name of the operation.
						lowerCamelCase__     :					Optional[Any]		=m.group(2      )
						# Is it a weight or a bias?
						lowerCamelCase__     :					Union[str, Any]		=m.group(3      )

						# The name of the layer.
						lowerCamelCase__     :					str		=f"""bert.encoder.layer.{layer_idx}"""

						# For layernorm(s), simply store the layer norm.
						if op_name.endswith('layernorm'      ):
									lowerCamelCase__     :					str		="attention.ln" if op_name.startswith('input'      ) else "ln"
									lowerCamelCase__     :					Union[str, 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":
									# Make sure the QKV pointer is nil.
									assert attention_qkv_weight is None, ""

									lowerCamelCase__     :					Optional[Any]		=fix_query_key_value_ordering(_a							,   _a							,   3							,   _a							,   _a      )
									# Store the tensor as we need the bias as well to interleave QKV and biases.
									lowerCamelCase__     :					Any		=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":
									# Make sure we read the weight tensor.
									assert attention_qkv_weight is not None, ""

									# Split the QKV matrix into Q, K and V. Megatron stores Q,K,V interleaved.
									lowerCamelCase__     :					Dict		=attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
									lowerCamelCase__     :					Optional[int]		=attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
									lowerCamelCase__     :					Union[str, Any]		=attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

									lowerCamelCase__     :					List[Any]		=fix_query_key_value_ordering(_a							,   _a							,   3							,   _a							,   _a      )
									# Split the bias.
									lowerCamelCase__     :					Optional[int]		=out_val[0 * config.hidden_size : 1 * config.hidden_size]
									lowerCamelCase__     :					List[str]		=out_val[1 * config.hidden_size : 2 * config.hidden_size]
									lowerCamelCase__     :					Dict		=out_val[2 * config.hidden_size : 3 * config.hidden_size]

									# Store.
									lowerCamelCase__     :					List[Any]		=q
									lowerCamelCase__     :					str		=q_bias
									lowerCamelCase__     :					Dict		=k
									lowerCamelCase__     :					Optional[int]		=k_bias
									lowerCamelCase__     :					str		=v
									lowerCamelCase__     :					List[Any]		=v_bias

									# Clear the stored tensor.
									lowerCamelCase__     :					str		=None

						# Copy weights and biases as is.
						elif weight_or_bias in ["weight", "bias"]:
									lowerCamelCase__     :					Any		=megatron_to_transformers[op_name]
									lowerCamelCase__     :					int		=val

    # The final layernorm.
			lowerCamelCase__     :					Any		=transformer["final_layernorm.weight"]
			lowerCamelCase__     :					Tuple		=transformer["final_layernorm.bias"]

			# The pooler.
			lowerCamelCase__     :					Union[str, Any]		=lm["pooler"]

			# Store the matrix and the bias.
			lowerCamelCase__     :					Optional[int]		=pooler["dense.weight"]
			lowerCamelCase__     :					Optional[Any]		=pooler["dense.bias"]

			# The LM head from Megatron (for RACE).
			lowerCamelCase__     :					List[str]		=model["lm_head"]

			# The transform matrix.
			lowerCamelCase__     :					Dict		=lm_head["dense.weight"]
			lowerCamelCase__     :					Union[str, Any]		=lm_head["dense.bias"]

			# The transform LN.
			lowerCamelCase__     :					int		=lm_head["layernorm.weight"]
			lowerCamelCase__     :					Optional[Any]		=lm_head["layernorm.bias"]

			# For the decoder, we replicate the weights.
			lowerCamelCase__     :					List[Any]		=word_embeddings
			lowerCamelCase__     :					Dict		=lm_head["bias"]

			# The classifier from Megatron (for MLNI).
			lowerCamelCase__     :					Optional[int]		=model["binary_head"]

			# Store the classifier.
			lowerCamelCase__     :					Dict		=binary_head["weight"]
			lowerCamelCase__     :					List[Any]		=binary_head["bias"]

			# It should be done!
			return output_state_dict






def        lowerCAmelCase_       (		)       ->Optional[Any]:
			# Create the argument parser.
			lowerCamelCase__     :					Union[str, Any]		=argparse.ArgumentParser()
			parser.add_argument('--print-checkpoint-structure'							,   action='store_true'      )
			parser.add_argument('path_to_checkpoint'							,   type=_a							,   help='Path to the ZIP file containing the checkpoint'      )
			parser.add_argument(
			    '--config_file'							,   default=''							,   type=_a							,   help='An optional config json file describing the pre-trained model.'							,   )
			lowerCamelCase__     :					Tuple		=parser.parse_args()

			# Extract the basename.
			lowerCamelCase__     :					Optional[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:
												lowerCamelCase__     :					Optional[Any]		=torch.load(_a							,   map_location='cpu'      )
			else:
						lowerCamelCase__     :					Union[str, Any]		=torch.load(args.path_to_checkpoint							,   map_location='cpu'      )

			if args.config_file == "":
						# Default config of megatron-bert 345m
						lowerCamelCase__     :					Any		=MegatronBertConfig()

						# different megatron-bert-*-345m models have different vocab sizes, so override the default
						# config (which is for megatron-bert-cased-345m) with the actual vocab dimension
						lowerCamelCase__     :					Optional[int]		=input_state_dict["model"]["lm_head"]["bias"].numel()
			else:
						lowerCamelCase__     :					int		=MegatronBertConfig.from_json_file(args.config_file      )

			# Convert.
			print('Converting'      )
			lowerCamelCase__     :					Tuple		=convert_megatron_checkpoint(_a							,   _a							,   _a      )

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

			# Store the config to file.
			print('Saving config'      )
			config.save_pretrained(_a      )

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


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

if __name__ == "__main__":
							main()

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

"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCAmelCase								=						{
    """configuration_mobilenet_v1""": [
        """MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileNetV1Config""",
        """MobileNetV1OnnxConfig""",
    ],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase								=						["""MobileNetV1FeatureExtractor"""]
							lowerCAmelCase								=						["""MobileNetV1ImageProcessor"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase								=						[
							    """MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """MobileNetV1ForImageClassification""",
							    """MobileNetV1Model""",
							    """MobileNetV1PreTrainedModel""",
							    """load_tf_weights_in_mobilenet_v1""",
							]


if TYPE_CHECKING:
							from .configuration_mobilenet_va import (
							    MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    MobileNetVaConfig,
							    MobileNetVaOnnxConfig,
							)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
														from .image_processing_mobilenet_va import MobileNetVaImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mobilenet_va import (
														    MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MobileNetVaForImageClassification,
														    MobileNetVaModel,
														    MobileNetVaPreTrainedModel,
														    load_tf_weights_in_mobilenet_va,
														)


else:
							import sys

							lowerCAmelCase								=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)