
# 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    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__=0	):







				'''simple docstring'''


				if name is None:
								lowercase						    =			None
				else:
								lowercase						    =			"." * max(0		,						spaces - 2	) + "# {:" + str(50 - spaces	) + "s}"
								lowercase						    =			fmt.format(__magic_name__	)

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

def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__		,						__magic_name__		,						__magic_name__	):







				'''simple docstring'''


				lowercase						    =			param.size()
				if checkpoint_version == 1.0:
								# version 1.0 stores [num_heads * hidden_size * num_splits, :]
								lowercase						    =			(num_heads, hidden_size, num_splits) + input_shape[1:]
								lowercase						    =			param.view(*__magic_name__	)
								lowercase						    =			param.transpose(0		,						2	)
								lowercase						    =			param.transpose(1		,						2	).contiguous()
				elif checkpoint_version >= 2.0:
								# other versions store [num_heads * num_splits * hidden_size, :]
								lowercase						    =			(num_heads, num_splits, hidden_size) + input_shape[1:]
								lowercase						    =			param.view(*__magic_name__	)
								lowercase						    =			param.transpose(0		,						1	).contiguous()
				lowercase						    =			param.view(*__magic_name__	)
				return param

def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__	):







				'''simple docstring'''


				lowercase						    =			{}

				# old versions did not store training args
				lowercase						    =			input_state_dict.get("args"		,						__magic_name__	)
				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))

								lowercase						    =			ds_args.tokenizer_type
								lowercase						    =			ds_args.padded_vocab_size
								lowercase						    =			ds_args.max_position_embeddings
								lowercase						    =			ds_args.hidden_size
								lowercase						    =			ds_args.num_layers
								lowercase						    =			ds_args.num_attention_heads
								lowercase						    =			ds_args.ffn_hidden_size if "ffn_hidden_size" in ds_args else 4 * ds_args.hidden_size
								# pprint(config)

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

				# The model.
				lowercase						    =			input_state_dict["model"]
				# The language model.
				lowercase						    =			model["language_model"]
				# The embeddings.
				lowercase						    =			lm["embedding"]

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

				# The position embeddings.
				lowercase						    =			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.
				lowercase						    =			pos_embeddings

				# The token-type embeddings.
				lowercase						    =			embeddings["tokentype_embeddings"]["weight"]
				# Store the position embeddings.
				lowercase						    =			tokentype_embeddings

				# The transformer.
				lowercase						    =			lm["transformer"] if "transformer" in lm.keys() else lm["encoder"]

				# The regex to extract layer names.
				lowercase						    =			re.compile(r"layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)"	)

				# The simple map of names for "automated" rules.
				lowercase						    =			{
				    "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.
				lowercase						    =			None

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

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

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

								# The name of the layer.
								lowercase						    =			F'''bert.encoder.layer.{layer_idx}'''

								# For layernorm(s), simply store the layer norm.
								if op_name.endswith("layernorm"	):
												lowercase						    =			"attention.ln" if op_name.startswith("input"	) else "ln"
												lowercase						    =			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, ""

												lowercase						    =			fix_query_key_value_ordering(__magic_name__		,						__magic_name__		,						3		,						__magic_name__		,						__magic_name__	)
												# Store the tensor as we need the bias as well to interleave QKV and biases.
												lowercase						    =			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.
												lowercase						    =			attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
												lowercase						    =			attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
												lowercase						    =			attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

												lowercase						    =			fix_query_key_value_ordering(__magic_name__		,						__magic_name__		,						3		,						__magic_name__		,						__magic_name__	)
												# Split the bias.
												lowercase						    =			out_val[0 * config.hidden_size : 1 * config.hidden_size]
												lowercase						    =			out_val[1 * config.hidden_size : 2 * config.hidden_size]
												lowercase						    =			out_val[2 * config.hidden_size : 3 * config.hidden_size]

												# Store.
												lowercase						    =			q
												lowercase						    =			q_bias
												lowercase						    =			k
												lowercase						    =			k_bias
												lowercase						    =			v
												lowercase						    =			v_bias

												# Clear the stored tensor.
												lowercase						    =			None

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

    # The final layernorm.
				lowercase						    =			transformer["final_layernorm.weight"]
				lowercase						    =			transformer["final_layernorm.bias"]

				# The pooler.
				lowercase						    =			lm["pooler"]

				# Store the matrix and the bias.
				lowercase						    =			pooler["dense.weight"]
				lowercase						    =			pooler["dense.bias"]

				# The LM head from Megatron (for RACE).
				lowercase						    =			model["lm_head"]

				# The transform matrix.
				lowercase						    =			lm_head["dense.weight"]
				lowercase						    =			lm_head["dense.bias"]

				# The transform LN.
				lowercase						    =			lm_head["layernorm.weight"]
				lowercase						    =			lm_head["layernorm.bias"]

				# For the decoder, we replicate the weights.
				lowercase						    =			word_embeddings
				lowercase						    =			lm_head["bias"]

				# The classifier from Megatron (for MLNI).
				lowercase						    =			model["binary_head"]

				# Store the classifier.
				lowercase						    =			binary_head["weight"]
				lowercase						    =			binary_head["bias"]

				# It should be done!
				return output_state_dict

def    __snake_case       (  ):







				'''simple docstring'''


				lowercase						    =			argparse.ArgumentParser()
				parser.add_argument("--print-checkpoint-structure"		,						action="store_true"	)
				parser.add_argument("path_to_checkpoint"		,						type=__magic_name__		,						help="Path to the ZIP file containing the checkpoint"	)
				parser.add_argument(
				    "--config_file"		,						default=""		,						type=__magic_name__		,						help="An optional config json file describing the pre-trained model."		,						)
				lowercase						    =			parser.parse_args()

				# Extract the basename.
				lowercase						    =			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:
																lowercase						    =			torch.load(__magic_name__		,						map_location="cpu"	)
				else:
								lowercase						    =			torch.load(args.path_to_checkpoint		,						map_location="cpu"	)

				if args.config_file == "":
								# Default config of megatron-bert 345m
								lowercase						    =			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
								lowercase						    =			input_state_dict["model"]["lm_head"]["bias"].numel()
				else:
								lowercase						    =			MegatronBertConfig.from_json_file(args.config_file	)

				# Convert.
				print("Converting"	)
				lowercase						    =			convert_megatron_checkpoint(__magic_name__		,						__magic_name__		,						__magic_name__	)

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

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

				# Store the state_dict to file.
				lowercase						    =			os.path.join(__magic_name__		,						"pytorch_model.bin"	)
				print(F'''Saving checkpoint to "{output_checkpoint_file}"'''	)
				torch.save(__magic_name__		,						__magic_name__	)


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

if __name__ == "__main__":
						main()

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


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


_snake_case			:       Tuple      =						logging.get_logger(__name__)


if is_vision_available():
						import PIL





class      UpperCamelCase_			(			__a		):







						'''simple docstring'''



						UpperCamelCase			:     Optional[int]							      =				['''pixel_values''']





						def __init__( self       :List[str]		,						lowerCAmelCase__       :bool = True		,						lowerCAmelCase__       :Dict[str, int] = None		,						lowerCAmelCase__       :PILImageResampling = PILImageResampling.BILINEAR		,						lowerCAmelCase__       :bool = True		,						lowerCAmelCase__       :bool = False		,						lowerCAmelCase__       :bool = True		,						lowerCAmelCase__       :bool = True		,						lowerCAmelCase__       :Union[int, float] = 1 / 255		,						lowerCAmelCase__       :bool = True		,						lowerCAmelCase__       :Optional[Union[float, List[float]]] = None		,						lowerCAmelCase__       :Optional[Union[float, List[float]]] = None		,						**lowerCAmelCase__       :int		,						)     ->None:
										super().__init__(**lowerCAmelCase__    )

										lowercase						    =			size if size is not None else {"height": 2560, "width": 1920}
										if isinstance(lowerCAmelCase__		,						(tuple, list)    ):
														# The previous feature extractor size parameter was in (width, height) format
														lowercase						    =			size[::-1]
										lowercase						    =			get_size_dict(lowerCAmelCase__    )

										lowercase						    =			do_resize
										lowercase						    =			size
										lowercase						    =			resample
										lowercase						    =			do_thumbnail
										lowercase						    =			do_align_long_axis
										lowercase						    =			do_pad
										lowercase						    =			do_rescale
										lowercase						    =			rescale_factor
										lowercase						    =			do_normalize
										lowercase						    =			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										lowercase						    =			image_std if image_std is not None else IMAGENET_STANDARD_STD





						def 					SCREAMING_SNAKE_CASE( self       :Optional[Any]		,						lowerCAmelCase__       :np.ndarray		,						lowerCAmelCase__       :Dict[str, int]		,						lowerCAmelCase__       :Optional[Union[str, ChannelDimension]] = None    )     ->np.ndarray:
										lowercase    ,  lowercase						    =			get_image_size(lowerCAmelCase__    )
										lowercase    ,  lowercase						    =			size["height"], size["width"]

										if (output_width < output_height and input_width > input_height) or (
										    output_width > output_height and input_width < input_height
										):
														lowercase						    =			np.rotaa(lowerCAmelCase__		,						3    )

										if data_format is not None:
														lowercase						    =			to_channel_dimension_format(lowerCAmelCase__		,						lowerCAmelCase__    )

										return image





						def 					SCREAMING_SNAKE_CASE( self       :Tuple		,						lowerCAmelCase__       :np.ndarray		,						lowerCAmelCase__       :Dict[str, int]		,						lowerCAmelCase__       :bool = False		,						lowerCAmelCase__       :Optional[Union[str, ChannelDimension]] = None		,						)     ->np.ndarray:
										lowercase    ,  lowercase						    =			size["height"], size["width"]
										lowercase    ,  lowercase						    =			get_image_size(lowerCAmelCase__    )

										lowercase						    =			output_width - input_width
										lowercase						    =			output_height - input_height

										if random_padding:
														lowercase						    =			np.random.randint(low=0		,						high=delta_height + 1    )
														lowercase						    =			np.random.randint(low=0		,						high=delta_width + 1    )
										else:
														lowercase						    =			delta_height // 2
														lowercase						    =			delta_width // 2

										lowercase						    =			delta_height - pad_top
										lowercase						    =			delta_width - pad_left

										lowercase						    =			((pad_top, pad_bottom), (pad_left, pad_right))
										return pad(lowerCAmelCase__		,						lowerCAmelCase__		,						data_format=lowerCAmelCase__    )





						def 					SCREAMING_SNAKE_CASE( self       :Dict		,						*lowerCAmelCase__       :Union[str, Any]		,						**lowerCAmelCase__       :str    )     ->List[str]:
										logger.info("pad is deprecated and will be removed in version 4.27. Please use pad_image instead."    )
										return self.pad_image(*lowerCAmelCase__		,						**lowerCAmelCase__    )





						def 					SCREAMING_SNAKE_CASE( self       :Tuple		,						lowerCAmelCase__       :np.ndarray		,						lowerCAmelCase__       :Dict[str, int]		,						lowerCAmelCase__       :PILImageResampling = PILImageResampling.BICUBIC		,						lowerCAmelCase__       :Optional[Union[str, ChannelDimension]] = None		,						**lowerCAmelCase__       :Dict		,						)     ->np.ndarray:
										lowercase    ,  lowercase						    =			get_image_size(lowerCAmelCase__    )
										lowercase    ,  lowercase						    =			size["height"], size["width"]

										# We always resize to the smallest of either the input or output size.
										lowercase						    =			min(lowerCAmelCase__		,						lowerCAmelCase__    )
										lowercase						    =			min(lowerCAmelCase__		,						lowerCAmelCase__    )

										if height == input_height and width == input_width:
														return image

										if input_height > input_width:
														lowercase						    =			int(input_width * height / input_height    )
										elif input_width > input_height:
														lowercase						    =			int(input_height * width / input_width    )

										return resize(
										    lowerCAmelCase__		,						size=(height, width)		,						resample=lowerCAmelCase__		,						reducing_gap=2.0		,						data_format=lowerCAmelCase__		,						**lowerCAmelCase__    )





						def 					SCREAMING_SNAKE_CASE( self       :List[str]		,						lowerCAmelCase__       :np.ndarray		,						lowerCAmelCase__       :Dict[str, int]		,						lowerCAmelCase__       :PILImageResampling = PILImageResampling.BICUBIC		,						lowerCAmelCase__       :Optional[Union[str, ChannelDimension]] = None		,						**lowerCAmelCase__       :List[str]		,						)     ->np.ndarray:
										lowercase						    =			get_size_dict(lowerCAmelCase__    )
										lowercase						    =			min(size["height"]		,						size["width"]    )
										lowercase						    =			get_resize_output_image_size(lowerCAmelCase__		,						size=lowerCAmelCase__		,						default_to_square=lowerCAmelCase__    )
										lowercase						    =			resize(lowerCAmelCase__		,						size=lowerCAmelCase__		,						resample=lowerCAmelCase__		,						data_format=lowerCAmelCase__		,						**lowerCAmelCase__    )
										return resized_image





						def 					SCREAMING_SNAKE_CASE( self       :Any		,						lowerCAmelCase__       :np.ndarray		,						lowerCAmelCase__       :Union[int, float]		,						lowerCAmelCase__       :Optional[Union[str, ChannelDimension]] = None		,						**lowerCAmelCase__       :List[Any]		,						)     ->List[Any]:
										return rescale(lowerCAmelCase__		,						scale=lowerCAmelCase__		,						data_format=lowerCAmelCase__		,						**lowerCAmelCase__    )





						def 					SCREAMING_SNAKE_CASE( self       :List[str]		,						lowerCAmelCase__       :np.ndarray		,						lowerCAmelCase__       :Union[float, List[float]]		,						lowerCAmelCase__       :Union[float, List[float]]		,						lowerCAmelCase__       :Optional[Union[str, ChannelDimension]] = None		,						**lowerCAmelCase__       :str		,						)     ->np.ndarray:
										return normalize(lowerCAmelCase__		,						mean=lowerCAmelCase__		,						std=lowerCAmelCase__		,						data_format=lowerCAmelCase__		,						**lowerCAmelCase__    )





						def 					SCREAMING_SNAKE_CASE( self       :Optional[Any]		,						lowerCAmelCase__       :ImageInput		,						lowerCAmelCase__       :bool = None		,						lowerCAmelCase__       :Dict[str, int] = None		,						lowerCAmelCase__       :PILImageResampling = None		,						lowerCAmelCase__       :bool = None		,						lowerCAmelCase__       :bool = None		,						lowerCAmelCase__       :bool = None		,						lowerCAmelCase__       :bool = False		,						lowerCAmelCase__       :bool = None		,						lowerCAmelCase__       :float = None		,						lowerCAmelCase__       :bool = None		,						lowerCAmelCase__       :Optional[Union[float, List[float]]] = None		,						lowerCAmelCase__       :Optional[Union[float, List[float]]] = None		,						lowerCAmelCase__       :Optional[Union[str, TensorType]] = None		,						lowerCAmelCase__       :Optional[ChannelDimension] = ChannelDimension.FIRST		,						**lowerCAmelCase__       :Tuple		,						)     ->PIL.Image.Image:
										lowercase						    =			do_resize if do_resize is not None else self.do_resize
										lowercase						    =			size if size is not None else self.size
										if isinstance(lowerCAmelCase__		,						(tuple, list)    ):
														# Previous feature extractor had size in (width, height) format
														lowercase						    =			size[::-1]
										lowercase						    =			get_size_dict(lowerCAmelCase__    )
										lowercase						    =			resample if resample is not None else self.resample
										lowercase						    =			do_thumbnail if do_thumbnail is not None else self.do_thumbnail
										lowercase						    =			do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
										lowercase						    =			do_pad if do_pad is not None else self.do_pad
										lowercase						    =			do_rescale if do_rescale is not None else self.do_rescale
										lowercase						    =			rescale_factor if rescale_factor is not None else self.rescale_factor
										lowercase						    =			do_normalize if do_normalize is not None else self.do_normalize
										lowercase						    =			image_mean if image_mean is not None else self.image_mean
										lowercase						    =			image_std if image_std is not None else self.image_std

										lowercase						    =			make_list_of_images(lowerCAmelCase__    )

										if not valid_images(lowerCAmelCase__    ):
														raise ValueError(
														    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
														    "torch.Tensor, tf.Tensor or jax.ndarray."    )

										if do_resize and size is None:
														raise ValueError("Size must be specified if do_resize is True."    )

										if do_rescale and rescale_factor is None:
														raise ValueError("Rescale factor must be specified if do_rescale is True."    )

										if do_pad and size is None:
														raise ValueError("Size must be specified if do_pad is True."    )

										if do_normalize and (image_mean is None or image_std is None):
														raise ValueError("Image mean and std must be specified if do_normalize is True."    )

										# All transformations expect numpy arrays.
										lowercase						    =			[to_numpy_array(lowerCAmelCase__    ) for image in images]

										if do_align_long_axis:
														lowercase						    =			[self.align_long_axis(lowerCAmelCase__		,						size=lowerCAmelCase__    ) for image in images]

										if do_resize:
														lowercase						    =			[self.resize(image=lowerCAmelCase__		,						size=lowerCAmelCase__		,						resample=lowerCAmelCase__    ) for image in images]

										if do_thumbnail:
														lowercase						    =			[self.thumbnail(image=lowerCAmelCase__		,						size=lowerCAmelCase__    ) for image in images]

										if do_pad:
														lowercase						    =			[self.pad_image(image=lowerCAmelCase__		,						size=lowerCAmelCase__		,						random_padding=lowerCAmelCase__    ) for image in images]

										if do_rescale:
														lowercase						    =			[self.rescale(image=lowerCAmelCase__		,						scale=lowerCAmelCase__    ) for image in images]

										if do_normalize:
														lowercase						    =			[self.normalize(image=lowerCAmelCase__		,						mean=lowerCAmelCase__		,						std=lowerCAmelCase__    ) for image in images]

										lowercase						    =			[to_channel_dimension_format(lowerCAmelCase__		,						lowerCAmelCase__    ) for image in images]

										lowercase						    =			{"pixel_values": images}
										return BatchFeature(data=lowerCAmelCase__		,						tensor_type=lowerCAmelCase__    )
