# 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    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_=0  ):



			"""simple docstring"""





			if name is None:
						_snake_case              =      None
			else:
						_snake_case              =      "." * max(0							,	spaces - 2  ) + "# {:" + str(50 - spaces  ) + "s}"
						_snake_case              =      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    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_  ):



			"""simple docstring"""





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






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_  ):



			"""simple docstring"""





			_snake_case              =      {}

			# old versions did not store training args
			_snake_case              =      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              =      ds_args.tokenizer_type
						_snake_case              =      ds_args.padded_vocab_size
						_snake_case              =      ds_args.max_position_embeddings
						_snake_case              =      ds_args.hidden_size
						_snake_case              =      ds_args.num_layers
						_snake_case              =      ds_args.num_attention_heads
						_snake_case              =      ds_args.ffn_hidden_size if "ffn_hidden_size" in ds_args else 4 * ds_args.hidden_size
						# pprint(config)

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

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

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

			# The position embeddings.
			_snake_case              =      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.
			_snake_case              =      pos_embeddings

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

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

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

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

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

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

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

						# The name of the layer.
						_snake_case              =      f'bert.encoder.layer.{layer_idx}'

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

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

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

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

									# Clear the stored tensor.
									_snake_case              =      None

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

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

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

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

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

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

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

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

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

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

			# It should be done!
			return output_state_dict






def    UpperCAmelCase__(						):



			"""simple docstring"""





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

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

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

			# Convert.
			print("Converting"  )
			_snake_case              =      convert_megatron_checkpoint(lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_  )

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

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

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


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

if __name__ == "__main__":
			main()

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


import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey

class 	_SCREAMING_SNAKE_CASE							:






		"""simple docstring"""







		def __init__(			self    :				int  ,					__SCREAMING_SNAKE_CASE    :				List[Any]  ,					__SCREAMING_SNAKE_CASE    :				int   ):






					"""simple docstring"""
					_snake_case              =      0
					# max greyscale value for #FFFFFF
					_snake_case              =      int(self.get_greyscale(255  ,					255  ,					255   )   )

					if not self.min_threshold < threshold < self.max_threshold:
								_snake_case              =      F'Factor value should be from 0 to {self.max_threshold}'
								raise ValueError(__SCREAMING_SNAKE_CASE   )

					_snake_case              =      input_img
					_snake_case              =      threshold
					_snake_case       , _snake_case              =      self.input_img.shape[1], self.input_img.shape[0]

					# error table size (+4 columns and +1 row) greater than input image because of
					# lack of if statements
					_snake_case              =      [
					    [0 for _ in range(self.height + 4   )] for __ in range(self.width + 1   )
					]
					_snake_case              =      np.ones((self.width, self.height, 3)  ,					np.uinta   ) * 255



		@classmethod
		def 				_a				(			cls    :				Optional[int]  ,					__SCREAMING_SNAKE_CASE    :				int  ,					__SCREAMING_SNAKE_CASE    :				int  ,					__SCREAMING_SNAKE_CASE    :				int   ):






					"""simple docstring"""
					return 0.1_1_4 * blue + 0.5_8_7 * green + 0.2_1_2_6 * red







		def 				_a				(			self    :				Union[str, Any]   ):






					"""simple docstring"""
					for y in range(self.height   ):
								for x in range(self.width   ):
											_snake_case              =      int(self.get_greyscale(*self.input_img[y][x]   )   )
											if self.threshold > greyscale + self.error_table[y][x]:
														_snake_case              =      (0, 0, 0)
														_snake_case              =      greyscale + self.error_table[x][y]
											else:
														_snake_case              =      (255, 255, 255)
														_snake_case              =      greyscale + self.error_table[x][y] - 255
											self.error_table[y][x + 1] += int(8 / 32 * current_error   )
											self.error_table[y][x + 2] += int(4 / 32 * current_error   )
											self.error_table[y + 1][x] += int(8 / 32 * current_error   )
											self.error_table[y + 1][x + 1] += int(4 / 32 * current_error   )
											self.error_table[y + 1][x + 2] += int(2 / 32 * current_error   )
											self.error_table[y + 1][x - 1] += int(4 / 32 * current_error   )
											self.error_table[y + 1][x - 2] += int(2 / 32 * current_error   )


if __name__ == "__main__":
			# create Burke's instances with original images in greyscale
			_SCREAMING_SNAKE_CASE:						Tuple    	=       [
			    Burkes(imread('''image_data/lena.jpg''', 1), threshold)
			    for threshold in (1, 126, 130, 140)
			]

			for burkes in burkes_instances:
						burkes.process()

			for burkes in burkes_instances:
						imshow(
						    F"Original image with dithering threshold: {burkes.threshold}",
						    burkes.output_img,
						)

			waitKey(0)
			destroyAllWindows()

