



"""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_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_(   lowercase_     :							Optional[Any]						,   lowercase_     :							Optional[Any]						,   lowercase_     :							Optional[Any]=0							)	->       Optional[int]:
				# Format the message.
				if name is None:
								_lowerCamelCase        =				None
				else:
								_lowerCamelCase        =				'''.''' * max(0						,   spaces - 2							) + '''# {:''' + str(50 - spaces							) + '''s}'''
								_lowerCamelCase        =				fmt.format(lowercase_							)

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



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



def        lowerCAmelCase_(   lowercase_     :							List[str]						,   lowercase_     :							List[Any]						,   lowercase_     :							str							)	->       Tuple:
				# The converted output model.
				_lowerCamelCase        =				{}

				# old versions did not store training args
				_lowerCamelCase        =				input_state_dict.get('''args'''						,   lowercase_							)
				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        =				ds_args.padded_vocab_size
								_lowerCamelCase        =				ds_args.max_position_embeddings
								_lowerCamelCase        =				ds_args.hidden_size
								_lowerCamelCase        =				ds_args.num_layers
								_lowerCamelCase        =				ds_args.num_attention_heads
								_lowerCamelCase        =				ds_args.ffn_hidden_size
								# pprint(config)

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

				# The model.
				_lowerCamelCase        =				input_state_dict['''model''']
				# The language model.
				_lowerCamelCase        =				model['''language_model''']
				# The embeddings.
				_lowerCamelCase        =				lm['''embedding''']

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

				# The position embeddings.
				_lowerCamelCase        =				embeddings['''position_embeddings''']['''weight''']
				# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
				_lowerCamelCase        =				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.
				_lowerCamelCase        =				pos_embeddings

				# The transformer.
				_lowerCamelCase        =				lm['''transformer'''] if '''transformer''' in lm.keys() else lm['''encoder''']

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

				# The simple map of names for "automated" rules.
				_lowerCamelCase        =				{
				    '''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.
								_lowerCamelCase        =				layer_re.match(lowercase_							)

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

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

								# The name of the layer.
								_lowerCamelCase        =				F"""transformer.h.{layer_idx}"""

								# For layernorm(s), simply store the layer norm.
								if op_name.endswith('''layernorm'''							):
												_lowerCamelCase        =				'''ln_1''' if op_name.startswith('''input'''							) else '''ln_2'''
												_lowerCamelCase        =				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.
												_lowerCamelCase        =				torch.tril(torch.ones((n_positions, n_positions)						,   dtype=torch.floataa							)							).view(
												    1						,   1						,   lowercase_						,   lowercase_							)
												_lowerCamelCase        =				causal_mask

												# Insert a "dummy" tensor for masked_bias.
												_lowerCamelCase        =				torch.tensor(-1e4						,   dtype=torch.floataa							)
												_lowerCamelCase        =				masked_bias

												_lowerCamelCase        =				fix_query_key_value_ordering(lowercase_						,   lowercase_						,   3						,   lowercase_						,   lowercase_							)
												# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
												_lowerCamelCase        =				out_val.transpose(0						,   1							).contiguous()
												# Store.
												_lowerCamelCase        =				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":
												_lowerCamelCase        =				fix_query_key_value_ordering(lowercase_						,   lowercase_						,   3						,   lowercase_						,   lowercase_							)
												# Store. No change of shape.
												_lowerCamelCase        =				out_val

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

								# Copy the bias.
								elif weight_or_bias == "bias":
												_lowerCamelCase        =				megatron_to_transformers[op_name]
												_lowerCamelCase        =				val

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

				# The final layernorm.
				_lowerCamelCase        =				transformer['''final_layernorm.weight''']
				_lowerCamelCase        =				transformer['''final_layernorm.bias''']

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

				# It should be done!
				return output_state_dict



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

				# Extract the basename.
				_lowerCamelCase        =				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        =				torch.load(lowercase_						,   map_location='''cpu'''							)
				else:
								_lowerCamelCase        =				torch.load(args.path_to_checkpoint						,   map_location='''cpu'''							)

				_lowerCamelCase        =				input_state_dict.get('''args'''						,   lowercase_							)

				# 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:
																_lowerCamelCase        =				'''gelu_fast'''
												elif ds_args.openai_gelu:
																_lowerCamelCase        =				'''gelu_new'''
												else:
																_lowerCamelCase        =				'''gelu'''
								else:
												# in the very early days this used to be "gelu_new"
												_lowerCamelCase        =				'''gelu_new'''

								# Spell out all parameters in case the defaults change.
								_lowerCamelCase        =				GPTaConfig(
								    vocab_size=5_02_57						,   n_positions=10_24						,   n_embd=10_24						,   n_layer=24						,   n_head=16						,   n_inner=40_96						,   activation_function=lowercase_						,   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=lowercase_						,   summary_activation=lowercase_						,   summary_proj_to_labels=lowercase_						,   summary_first_dropout=0.1						,   scale_attn_weights=lowercase_						,   use_cache=lowercase_						,   bos_token_id=5_02_56						,   eos_token_id=5_02_56						,   )
				else:
								_lowerCamelCase        =				GPTaConfig.from_json_file(args.config_file							)

				_lowerCamelCase        =				['''GPT2LMHeadModel''']

				# Convert.
				print('''Converting'''							)
				_lowerCamelCase        =				convert_megatron_checkpoint(lowercase_						,   lowercase_						,   lowercase_							)

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

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

				_lowerCamelCase        =				AutoTokenizer.from_pretrained(lowercase_							)
				_lowerCamelCase        =				type(lowercase_							).__name__
				_lowerCamelCase        =				tokenizer_class

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

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

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


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

if __name__ == "__main__":
			main()

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





"""simple docstring"""
import unittest

from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM


@require_torch
class 							lowerCamelCase_:





					'''simple docstring'''



					def __init__(    self				,       lowerCamelCase__				,       lowerCamelCase__=9_9				,       lowerCamelCase__=1_3				,       lowerCamelCase__=1_6				,       lowerCamelCase__=7				,       lowerCamelCase__=True				,       lowerCamelCase__=True				,       lowerCamelCase__=True				,       lowerCamelCase__=False				,       lowerCamelCase__=True				,       lowerCamelCase__=2				,       lowerCamelCase__=3_2				,       lowerCamelCase__=4				,       lowerCamelCase__=4				,       lowerCamelCase__=3_0				,       lowerCamelCase__=0				,       lowerCamelCase__=1				,       lowerCamelCase__=2				,       lowerCamelCase__=None				,       ):
									_lowerCamelCase        =				parent
									_lowerCamelCase        =				batch_size
									_lowerCamelCase        =				decoder_seq_length
									# For common tests
									_lowerCamelCase        =				self.decoder_seq_length
									_lowerCamelCase        =				is_training
									_lowerCamelCase        =				use_attention_mask
									_lowerCamelCase        =				use_labels

									_lowerCamelCase        =				vocab_size
									_lowerCamelCase        =				d_model
									_lowerCamelCase        =				d_model
									_lowerCamelCase        =				decoder_layers
									_lowerCamelCase        =				decoder_layers
									_lowerCamelCase        =				decoder_ffn_dim
									_lowerCamelCase        =				decoder_attention_heads
									_lowerCamelCase        =				decoder_attention_heads
									_lowerCamelCase        =				eos_token_id
									_lowerCamelCase        =				bos_token_id
									_lowerCamelCase        =				pad_token_id
									_lowerCamelCase        =				decoder_start_token_id
									_lowerCamelCase        =				use_cache
									_lowerCamelCase        =				max_position_embeddings

									_lowerCamelCase        =				None
									_lowerCamelCase        =				decoder_seq_length
									_lowerCamelCase        =				2
									_lowerCamelCase        =				1

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				ids_tensor([self.batch_size, self.decoder_seq_length]				,       self.vocab_size   )

									_lowerCamelCase        =				None
									if self.use_attention_mask:
													_lowerCamelCase        =				ids_tensor([self.batch_size, self.decoder_seq_length]				,       vocab_size=2   )

									_lowerCamelCase        =				None
									if self.use_labels:
													_lowerCamelCase        =				ids_tensor([self.batch_size, self.decoder_seq_length]				,       self.vocab_size   )

									_lowerCamelCase        =				TrOCRConfig(
									    vocab_size=self.vocab_size				,       d_model=self.d_model				,       decoder_layers=self.decoder_layers				,       decoder_ffn_dim=self.decoder_ffn_dim				,       decoder_attention_heads=self.decoder_attention_heads				,       eos_token_id=self.eos_token_id				,       bos_token_id=self.bos_token_id				,       use_cache=self.use_cache				,       pad_token_id=self.pad_token_id				,       decoder_start_token_id=self.decoder_start_token_id				,       max_position_embeddings=self.max_position_embeddings				,       )

									return (config, input_ids, attention_mask, lm_labels)

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       ):
									_lowerCamelCase        =				True
									_lowerCamelCase        =				TrOCRDecoder(config=lowerCamelCase__   ).to(lowerCamelCase__   ).eval()
									_lowerCamelCase        =				input_ids[:2]

									input_ids[input_ids == 0] += 1
									# first forward pass
									_lowerCamelCase        =				model(lowerCamelCase__				,       use_cache=lowerCamelCase__   )
									_lowerCamelCase        =				model(lowerCamelCase__   )
									_lowerCamelCase        =				model(lowerCamelCase__				,       use_cache=lowerCamelCase__   )

									self.parent.assertTrue(len(lowerCamelCase__   ) == len(lowerCamelCase__   )   )
									self.parent.assertTrue(len(lowerCamelCase__   ) == len(lowerCamelCase__   ) + 1   )

									_lowerCamelCase        =				outputs['''past_key_values''']

									# create hypothetical next token and extent to next_input_ids
									_lowerCamelCase        =				ids_tensor((2, 1)				,       config.vocab_size - 1   ) + 1

									# append to next input_ids and
									_lowerCamelCase        =				torch.cat([input_ids, next_tokens]				,       dim=-1   )

									_lowerCamelCase        =				model(lowerCamelCase__   )['''last_hidden_state''']
									_lowerCamelCase        =				model(lowerCamelCase__				,       past_key_values=lowerCamelCase__   )['''last_hidden_state''']

									# select random slice
									_lowerCamelCase        =				ids_tensor((1,)				,       output_from_past.shape[-1]   ).item()
									_lowerCamelCase        =				output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
									_lowerCamelCase        =				output_from_past[:, 0, random_slice_idx].detach()

									# test that outputs are equal for slice
									assert torch.allclose(lowerCamelCase__				,       lowerCamelCase__				,       atol=1e-3   )





					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.prepare_config_and_inputs()
									_lowerCamelCase  ,     _lowerCamelCase  ,     _lowerCamelCase  ,     _lowerCamelCase        =				config_and_inputs

									_lowerCamelCase        =				{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
									return config, inputs_dict


@require_torch
class 							lowerCamelCase_(   A__,				A__,				A__,				unittest.TestCase    ):





					'''simple docstring'''



					lowercase__   :       int										= (TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
					lowercase__   :       List[str]										= (TrOCRForCausalLM,) if is_torch_available() else ()
					lowercase__   :       Tuple										= {'text-generation': TrOCRForCausalLM} if is_torch_available() else {}
					lowercase__   :       Dict										= True
					lowercase__   :       Optional[Any]										= False

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				TrOCRStandaloneDecoderModelTester(self				,       is_training=lowerCamelCase__   )
									_lowerCamelCase        =				ConfigTester(self				,       config_class=lowerCamelCase__   )

					def 			snake_case__      (    self   ):
									pass

					def 			snake_case__      (    self   ):
									pass

					def 			snake_case__      (    self   ):
									pass

					def 			snake_case__      (    self   ):
									self.config_tester.run_common_tests()

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_decoder_model_past(*lowerCamelCase__   )

					def 			snake_case__      (    self   ):
									return





					@unittest.skip('''The model doesn\'t support left padding'''   )  # and it's not used enough to be worth fixing :)
					def 			snake_case__      (    self   ):
									pass
