




# 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 		SCREAMING_SNAKE_CASE__   (       lowercase		,lowercase		,lowercase=0	)    ->  Dict:







   if name is None:
      snake_case :							List[Any]           =			None
   else:
      snake_case :							int           =			""".""" * max(0		,spaces - 2	) + """# {:""" + str(50 - spaces	) + """s}"""
      snake_case :							str           =			fmt.format(_UpperCamelCase	)

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




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




def 		SCREAMING_SNAKE_CASE__   (       lowercase		,lowercase		,lowercase	)    ->  List[Any]:
   snake_case :							Any           =			{}

   # old versions did not store training args
   snake_case :							Tuple           =			input_state_dict.get("""args"""		,_UpperCamelCase	)
   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 :							Dict           =			ds_args.padded_vocab_size
      snake_case :							Union[str, Any]           =			ds_args.max_position_embeddings
      snake_case :							List[str]           =			ds_args.hidden_size
      snake_case :							Optional[Any]           =			ds_args.num_layers
      snake_case :							Tuple           =			ds_args.num_attention_heads
      snake_case :							str           =			ds_args.ffn_hidden_size
      # pprint(config)

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

   # The model.
   snake_case :							Dict           =			input_state_dict["""model"""]
   # The language model.
   snake_case :							int           =			model["""language_model"""]
   # The embeddings.
   snake_case :							Union[str, Any]           =			lm["""embedding"""]

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

   # The position embeddings.
   snake_case :							Optional[int]           =			embeddings["""position_embeddings"""]["""weight"""]
   # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
   snake_case :							List[Any]           =			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 :							List[str]           =			pos_embeddings

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

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

   # The simple map of names for "automated" rules.
   snake_case :							Dict           =			{
       """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 :							Optional[int]           =			layer_re.match(_UpperCamelCase	)

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

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

      # The name of the layer.
      snake_case :							Tuple           =			f"""transformer.h.{layer_idx}"""

      # For layernorm(s), simply store the layer norm.
      if op_name.endswith("""layernorm"""	):
         snake_case :							int           =			"""ln_1""" if op_name.startswith("""input"""	) else """ln_2"""
         snake_case :							Optional[int]           =			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 :							List[str]           =			torch.tril(torch.ones((n_positions, n_positions)		,dtype=torch.floataa	)	).view(
             1		,1		,_UpperCamelCase		,_UpperCamelCase	)
         snake_case :							Any           =			causal_mask

         # Insert a "dummy" tensor for masked_bias.
         snake_case :							Optional[int]           =			torch.tensor(-1E4		,dtype=torch.floataa	)
         snake_case :							Tuple           =			masked_bias

         snake_case :							str           =			fix_query_key_value_ordering(_UpperCamelCase		,_UpperCamelCase		,3		,_UpperCamelCase		,_UpperCamelCase	)
         # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
         snake_case :							Optional[int]           =			out_val.transpose(0		,1	).contiguous()
         # Store.
         snake_case :							Union[str, 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":
         snake_case :							List[Any]           =			fix_query_key_value_ordering(_UpperCamelCase		,_UpperCamelCase		,3		,_UpperCamelCase		,_UpperCamelCase	)
         # Store. No change of shape.
         snake_case :							str           =			out_val

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

      # Copy the bias.
      elif weight_or_bias == "bias":
         snake_case :							Tuple           =			megatron_to_transformers[op_name]
         snake_case :							Union[str, Any]           =			val

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

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

   # For LM head, transformers' wants the matrix to weight embeddings.
   snake_case :							Optional[Any]           =			word_embeddings

   # It should be done!
   return output_state_dict




def 		SCREAMING_SNAKE_CASE__   (       )    ->  List[Any]:
   snake_case :							int           =			argparse.ArgumentParser()
   parser.add_argument("""--print-checkpoint-structure"""		,action="""store_true"""	)
   parser.add_argument(
       """path_to_checkpoint"""		,type=_UpperCamelCase		,help="""Path to the checkpoint file (.zip archive or direct .pt file)"""		,)
   parser.add_argument(
       """--config_file"""		,default=""""""		,type=_UpperCamelCase		,help="""An optional config json file describing the pre-trained model."""		,)
   snake_case :							Union[str, Any]           =			parser.parse_args()

   # Extract the basename.
   snake_case :							List[Any]           =			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 :							Optional[Any]           =			torch.load(_UpperCamelCase		,map_location="""cpu"""	)
   else:
      snake_case :							Optional[int]           =			torch.load(args.path_to_checkpoint		,map_location="""cpu"""	)

   snake_case :							List[str]           =			input_state_dict.get("""args"""		,_UpperCamelCase	)

   # 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 :							Optional[int]           =			"""gelu_fast"""
         elif ds_args.openai_gelu:
            snake_case :							Optional[int]           =			"""gelu_new"""
         else:
            snake_case :							Dict           =			"""gelu"""
      else:
         # in the very early days this used to be "gelu_new"
         snake_case :							Any           =			"""gelu_new"""

      # Spell out all parameters in case the defaults change.
      snake_case :							Union[str, Any]           =			GPTaConfig(
          vocab_size=50257		,n_positions=1024		,n_embd=1024		,n_layer=24		,n_head=16		,n_inner=4096		,activation_function=_UpperCamelCase		,resid_pdrop=0.1		,embd_pdrop=0.1		,attn_pdrop=0.1		,layer_norm_epsilon=1E-5		,initializer_range=0.02		,summary_type="""cls_index"""		,summary_use_proj=_UpperCamelCase		,summary_activation=_UpperCamelCase		,summary_proj_to_labels=_UpperCamelCase		,summary_first_dropout=0.1		,scale_attn_weights=_UpperCamelCase		,use_cache=_UpperCamelCase		,bos_token_id=50256		,eos_token_id=50256		,)
   else:
      snake_case :							Union[str, Any]           =			GPTaConfig.from_json_file(args.config_file	)

   snake_case :							str           =			["""GPT2LMHeadModel"""]

   # Convert.
   print("""Converting"""	)
   snake_case :							Any           =			convert_megatron_checkpoint(_UpperCamelCase		,_UpperCamelCase		,_UpperCamelCase	)

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

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

   snake_case :							Tuple           =			AutoTokenizer.from_pretrained(_UpperCamelCase	)
   snake_case :							Tuple           =			type(_UpperCamelCase	).__name__
   snake_case :							Union[str, Any]           =			tokenizer_class

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

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

   # Store the state_dict to file.
   snake_case :							int           =			os.path.join(_UpperCamelCase		,"""pytorch_model.bin"""	)
   print(f"""Saving checkpoint to \"{output_checkpoint_file}\""""	)
   torch.save(_UpperCamelCase		,_UpperCamelCase	)


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

if __name__ == "__main__":
       main()

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









import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import DonutImageProcessor
class 						lowerCamelCase_							(					unittest.TestCase					):



					'''simple docstring'''







					def __init__(					self			,							snake_case_			,							snake_case_=7			,							snake_case_=3			,							snake_case_=1_8			,							snake_case_=3_0			,							snake_case_=4_0_0			,							snake_case_=True			,							snake_case_=None			,							snake_case_=True			,							snake_case_=False			,							snake_case_=True			,							snake_case_=True			,							snake_case_=[0.5, 0.5, 0.5]			,							snake_case_=[0.5, 0.5, 0.5]			,							) ->      Optional[Any]:







								'''simple docstring'''

								__lowercase			       = parent
								__lowercase			       = batch_size
								__lowercase			       = num_channels
								__lowercase			       = image_size
								__lowercase			       = min_resolution
								__lowercase			       = max_resolution
								__lowercase			       = do_resize
								__lowercase			       = size if size is not None else {'''height''': 1_8, '''width''': 2_0}
								__lowercase			       = do_thumbnail
								__lowercase			       = do_align_axis
								__lowercase			       = do_pad
								__lowercase			       = do_normalize
								__lowercase			       = image_mean
								__lowercase			       = image_std






					def A   (					self    ) ->      Dict:







								'''simple docstring'''

								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_thumbnail": self.do_thumbnail,
								    "do_align_long_axis": self.do_align_axis,
								    "do_pad": self.do_pad,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								}




@require_torch
@require_vision
class 						lowerCamelCase_							(					lowerCAmelCase__  ,  unittest.TestCase					):



					'''simple docstring'''







					__UpperCAmelCase							  =					DonutImageProcessor if is_vision_available() else None






					def A   (					self    ) ->      Optional[Any]:







								'''simple docstring'''

								__lowercase			       = DonutImageProcessingTester(self    )






					@property
					def A   (					self    ) ->      str:







								'''simple docstring'''

								return self.image_processor_tester.prepare_image_processor_dict()






					def A   (					self    ) ->      Optional[int]:







								'''simple docstring'''

								__lowercase			       = self.image_processing_class(**self.image_processor_dict    )
								self.assertTrue(hasattr(snake_case_			,							'''do_resize'''    )    )
								self.assertTrue(hasattr(snake_case_			,							'''size'''    )    )
								self.assertTrue(hasattr(snake_case_			,							'''do_thumbnail'''    )    )
								self.assertTrue(hasattr(snake_case_			,							'''do_align_long_axis'''    )    )
								self.assertTrue(hasattr(snake_case_			,							'''do_pad'''    )    )
								self.assertTrue(hasattr(snake_case_			,							'''do_normalize'''    )    )
								self.assertTrue(hasattr(snake_case_			,							'''image_mean'''    )    )
								self.assertTrue(hasattr(snake_case_			,							'''image_std'''    )    )






					def A   (					self    ) ->      List[str]:







								'''simple docstring'''

								__lowercase			       = self.image_processing_class.from_dict(self.image_processor_dict    )
								self.assertEqual(image_processor.size			,							{'''height''': 1_8, '''width''': 2_0}    )

								__lowercase			       = self.image_processing_class.from_dict(self.image_processor_dict			,							size=4_2    )
								self.assertEqual(image_processor.size			,							{'''height''': 4_2, '''width''': 4_2}    )

								# Previous config had dimensions in (width, height) order
								__lowercase			       = self.image_processing_class.from_dict(self.image_processor_dict			,							size=(4_2, 8_4)    )
								self.assertEqual(image_processor.size			,							{'''height''': 8_4, '''width''': 4_2}    )






					def A   (					self    ) ->      Optional[Any]:







								'''simple docstring'''

								pass






					@is_flaky()
					def A   (					self    ) ->      Optional[Any]:







								'''simple docstring'''

								__lowercase			       = self.image_processing_class(**self.image_processor_dict    )
								# create random PIL images
								__lowercase			       = prepare_image_inputs(self.image_processor_tester			,							equal_resolution=snake_case_    )
								for image in image_inputs:
											self.assertIsInstance(snake_case_			,							Image.Image    )

								# Test not batched input
								__lowercase			       = image_processing(image_inputs[0]			,							return_tensors='''pt'''    ).pixel_values
								self.assertEqual(
								    encoded_images.shape			,							(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size['''height'''],
								        self.image_processor_tester.size['''width'''],
								    )			,							)

								# Test batched
								__lowercase			       = image_processing(snake_case_			,							return_tensors='''pt'''    ).pixel_values
								self.assertEqual(
								    encoded_images.shape			,							(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size['''height'''],
								        self.image_processor_tester.size['''width'''],
								    )			,							)






					@is_flaky()
					def A   (					self    ) ->      Dict:







								'''simple docstring'''

								__lowercase			       = self.image_processing_class(**self.image_processor_dict    )
								# create random numpy tensors
								__lowercase			       = prepare_image_inputs(self.image_processor_tester			,							equal_resolution=snake_case_			,							numpify=snake_case_    )
								for image in image_inputs:
											self.assertIsInstance(snake_case_			,							np.ndarray    )

								# Test not batched input
								__lowercase			       = image_processing(image_inputs[0]			,							return_tensors='''pt'''    ).pixel_values
								self.assertEqual(
								    encoded_images.shape			,							(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size['''height'''],
								        self.image_processor_tester.size['''width'''],
								    )			,							)

								# Test batched
								__lowercase			       = image_processing(snake_case_			,							return_tensors='''pt'''    ).pixel_values
								self.assertEqual(
								    encoded_images.shape			,							(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size['''height'''],
								        self.image_processor_tester.size['''width'''],
								    )			,							)






					@is_flaky()
					def A   (					self    ) ->      Dict:







								'''simple docstring'''

								__lowercase			       = self.image_processing_class(**self.image_processor_dict    )
								# create random PyTorch tensors
								__lowercase			       = prepare_image_inputs(self.image_processor_tester			,							equal_resolution=snake_case_			,							torchify=snake_case_    )
								for image in image_inputs:
											self.assertIsInstance(snake_case_			,							torch.Tensor    )

								# Test not batched input
								__lowercase			       = image_processing(image_inputs[0]			,							return_tensors='''pt'''    ).pixel_values
								self.assertEqual(
								    encoded_images.shape			,							(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size['''height'''],
								        self.image_processor_tester.size['''width'''],
								    )			,							)

								# Test batched
								__lowercase			       = image_processing(snake_case_			,							return_tensors='''pt'''    ).pixel_values
								self.assertEqual(
								    encoded_images.shape			,							(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size['''height'''],
								        self.image_processor_tester.size['''width'''],
								    )			,							)


