




"""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      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__=0		):
				# Format the message.
				if name is None:
								_lowerCamelCase     :			Optional[Any]    =    None
				else:
								_lowerCamelCase     :			List[Any]    =    '.' * max(0							,    spaces - 2		) + '# {:' + str(50 - spaces		) + 's}'
								_lowerCamelCase     :			Optional[Any]    =    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      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__		):
				# 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     :			Optional[Any]    =    param.size()
				if checkpoint_version == 1.0:
								# version 1.0 stores [num_heads * hidden_size * num_splits, :]
								_lowerCamelCase     :			List[Any]    =    (num_heads, hidden_size, num_splits) + input_shape[1:]
								_lowerCamelCase     :			List[Any]    =    param.view(*lowercase__		)
								_lowerCamelCase     :			List[Any]    =    param.transpose(0							,    2		)
								_lowerCamelCase     :			int    =    param.transpose(1							,    2		).contiguous()
				elif checkpoint_version >= 2.0:
								# other versions store [num_heads * num_splits * hidden_size, :]
								_lowerCamelCase     :			List[Any]    =    (num_heads, num_splits, hidden_size) + input_shape[1:]
								_lowerCamelCase     :			Dict    =    param.view(*lowercase__		)
								_lowerCamelCase     :			Union[str, Any]    =    param.transpose(0							,    1		).contiguous()
				_lowerCamelCase     :			Any    =    param.view(*lowercase__		)
				return param







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__		):
				# The converted output model.
				_lowerCamelCase     :			List[Any]    =    {}

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

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

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

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

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

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

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

				# The simple map of names for "automated" rules.
				_lowerCamelCase     :			Any    =    {
				    '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     :			List[str]    =    layer_re.match(lowercase__		)

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

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

								# The name of the layer.
								_lowerCamelCase     :			Optional[Any]    =    f'''transformer.h.{layer_idx}'''

								# For layernorm(s), simply store the layer norm.
								if op_name.endswith('layernorm'		):
												_lowerCamelCase     :			Dict    =    'ln_1' if op_name.startswith('input'		) else 'ln_2'
												_lowerCamelCase     :			List[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":
												# Insert a tensor of 1x1xDxD bias.
												_lowerCamelCase     :			Optional[int]    =    torch.tril(torch.ones((n_positions, n_positions)							,    dtype=torch.floataa		)		).view(
												    1							,    1							,    lowercase__							,    lowercase__		)
												_lowerCamelCase     :			Optional[Any]    =    causal_mask

												# Insert a "dummy" tensor for masked_bias.
												_lowerCamelCase     :			Dict    =    torch.tensor(-1E4							,    dtype=torch.floataa		)
												_lowerCamelCase     :			Tuple    =    masked_bias

												_lowerCamelCase     :			Dict    =    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     :			Dict    =    out_val.transpose(0							,    1		).contiguous()
												# Store.
												_lowerCamelCase     :			int    =    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     :			Dict    =    fix_query_key_value_ordering(lowercase__							,    lowercase__							,    3							,    lowercase__							,    lowercase__		)
												# Store. No change of shape.
												_lowerCamelCase     :			List[str]    =    out_val

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

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

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

				# The final layernorm.
				_lowerCamelCase     :			Any    =    transformer['final_layernorm.weight']
				_lowerCamelCase     :			Union[str, Any]    =    transformer['final_layernorm.bias']

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

				# It should be done!
				return output_state_dict







def      _snake_case			(				):
				# 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=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     :			Dict    =    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     :			Any    =    torch.load(lowercase__							,    map_location='cpu'		)
				else:
								_lowerCamelCase     :			int    =    torch.load(args.path_to_checkpoint							,    map_location='cpu'		)

				_lowerCamelCase     :			List[Any]    =    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     :			Union[str, Any]    =    'gelu_fast'
												elif ds_args.openai_gelu:
																_lowerCamelCase     :			Tuple    =    'gelu_new'
												else:
																_lowerCamelCase     :			int    =    'gelu'
								else:
												# in the very early days this used to be "gelu_new"
												_lowerCamelCase     :			int    =    'gelu_new'

								# Spell out all parameters in case the defaults change.
								_lowerCamelCase     :			Tuple    =    GPTaConfig(
								    vocab_size=50257							,    n_positions=1024							,    n_embd=1024							,    n_layer=24							,    n_head=16							,    n_inner=4096							,    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=50256							,    eos_token_id=50256							,    )
				else:
								_lowerCamelCase     :			Union[str, Any]    =    GPTaConfig.from_json_file(args.config_file		)

				_lowerCamelCase     :			Any    =    ['GPT2LMHeadModel']

				# Convert.
				print('Converting'		)
				_lowerCamelCase     :			int    =    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     :			Optional[int]    =    ds_args.tokenizer_type
								if tokenizer_type == "GPT2BPETokenizer":
												_lowerCamelCase     :			Optional[int]    =    'gpt2'
								elif tokenizer_type == "PretrainedFromHF":
												_lowerCamelCase     :			Any    =    ds_args.tokenizer_name_or_path
								else:
												raise ValueError(f'''Unrecognized tokenizer_type {tokenizer_type}'''		)
				else:
								_lowerCamelCase     :			Any    =    'gpt2'

				_lowerCamelCase     :			Optional[Any]    =    AutoTokenizer.from_pretrained(lowercase__		)
				_lowerCamelCase     :			List[Any]    =    type(lowercase__		).__name__
				_lowerCamelCase     :			Tuple    =    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     :			Dict    =    os.path.join(lowercase__							,    'pytorch_model.bin'		)
				print(f'''Saving checkpoint to "{output_checkpoint_file}"'''		)
				torch.save(lowercase__							,    lowercase__		)


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

if __name__ == "__main__":
			main()

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

import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
		import jax

		from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel




class  lowerCamelCase__							(				unittest.TestCase):




	'''simple docstring'''




	def __init__(					self		:List[str]     ,   a		:Any     ,   a		:List[Any]=1_3     ,   a		:Any=3_0     ,   a		:Dict=2     ,   a		:List[str]=3     ,   a		:Union[str, Any]=True     ,   a		:Optional[int]=True     ,   a		:Tuple=3_2     ,   a		:Any=5     ,   a		:List[str]=4     ,   a		:Optional[Any]=3_7     ,   a		:List[Any]="gelu"     ,   a		:Any=0.1     ,   a		:Any=0.1     ,   a		:List[str]=1_0     ,   a		:str=0.02     ,   )		-> Union[str, Any]:
								__UpperCamelCase	:   int			   =					parent
								__UpperCamelCase	:   List[Any]			   =					batch_size
								__UpperCamelCase	:   Optional[Any]			   =					image_size
								__UpperCamelCase	:   int			   =					patch_size
								__UpperCamelCase	:   Optional[Any]			   =					num_channels
								__UpperCamelCase	:   Optional[int]			   =					is_training
								__UpperCamelCase	:   Union[str, Any]			   =					use_labels
								__UpperCamelCase	:   Tuple			   =					hidden_size
								__UpperCamelCase	:   List[Any]			   =					num_hidden_layers
								__UpperCamelCase	:   List[Any]			   =					num_attention_heads
								__UpperCamelCase	:   Dict			   =					intermediate_size
								__UpperCamelCase	:   Optional[int]			   =					hidden_act
								__UpperCamelCase	:   List[Any]			   =					hidden_dropout_prob
								__UpperCamelCase	:   str			   =					attention_probs_dropout_prob
								__UpperCamelCase	:   List[str]			   =					type_sequence_label_size
								__UpperCamelCase	:   List[str]			   =					initializer_range

								# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
								__UpperCamelCase	:   Union[str, Any]			   =					(image_size // patch_size) ** 2
								__UpperCamelCase	:   Optional[int]			   =					num_patches + 1







	def    _lowerCamelCase				(					self		:Optional[int]						)		-> Union[str, Any]:
								__UpperCamelCase	:   Optional[int]			   =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

								__UpperCamelCase	:   Tuple			   =					ViTConfig(
								    image_size=self.image_size     ,   patch_size=self.patch_size     ,   num_channels=self.num_channels     ,   hidden_size=self.hidden_size     ,   num_hidden_layers=self.num_hidden_layers     ,   num_attention_heads=self.num_attention_heads     ,   intermediate_size=self.intermediate_size     ,   hidden_act=self.hidden_act     ,   hidden_dropout_prob=self.hidden_dropout_prob     ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,   is_decoder=a     ,   initializer_range=self.initializer_range     ,   )

								return config, pixel_values







	def    _lowerCamelCase				(					self		:Tuple     ,   a		:Optional[int]     ,   a		:Optional[int]						)		-> Optional[int]:
								__UpperCamelCase	:   Optional[Any]			   =					FlaxViTModel(config=a						)
								__UpperCamelCase	:   int			   =					model(a						)
								# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
								__UpperCamelCase	:   Any			   =					(self.image_size, self.image_size)
								__UpperCamelCase	:   Optional[int]			   =					(self.patch_size, self.patch_size)
								__UpperCamelCase	:   Optional[int]			   =					(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
								self.parent.assertEqual(result.last_hidden_state.shape     ,   (self.batch_size, num_patches + 1, self.hidden_size)						)







	def    _lowerCamelCase				(					self		:Dict     ,   a		:List[str]     ,   a		:Any						)		-> str:
								__UpperCamelCase	:   Dict			   =					self.type_sequence_label_size
								__UpperCamelCase	:   Dict			   =					FlaxViTForImageClassification(config=a						)
								__UpperCamelCase	:   Tuple			   =					model(a						)
								self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.type_sequence_label_size)						)

								# test greyscale images
								__UpperCamelCase	:   Dict			   =					1
								__UpperCamelCase	:   Optional[Any]			   =					FlaxViTForImageClassification(a						)

								__UpperCamelCase	:   List[str]			   =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
								__UpperCamelCase	:   str			   =					model(a						)







	def    _lowerCamelCase				(					self		:str						)		-> Optional[int]:
								__UpperCamelCase	:   str			   =					self.prepare_config_and_inputs()
								(
								    (
								    __UpperCamelCase
								)   ,						(
								    __UpperCamelCase
								)   ,						
								)	:   List[Any]			   =					config_and_inputs
								__UpperCamelCase	:   str			   =					{"pixel_values": pixel_values}
								return config, inputs_dict




@require_flax
class  lowerCamelCase__							(				__lowercase     ,						unittest.TestCase):




	'''simple docstring'''




	_A								=     (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()







	def    _lowerCamelCase				(					self		:Optional[int]						)		-> None:
								__UpperCamelCase	:   int			   =					FlaxViTModelTester(self						)
								__UpperCamelCase	:   Any			   =					ConfigTester(self     ,   config_class=a     ,   has_text_modality=a     ,   hidden_size=3_7						)







	def    _lowerCamelCase				(					self		:List[Any]						)		-> str:
								self.config_tester.run_common_tests()







	def    _lowerCamelCase				(					self		:List[Any]						)		-> List[str]:
								__UpperCamelCase	:   int			   =					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*a						)







	def    _lowerCamelCase				(					self		:List[str]						)		-> Union[str, Any]:
								__UpperCamelCase	:   Any			   =					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*a						)







	def    _lowerCamelCase				(					self		:Tuple						)		-> Any:
								__UpperCamelCase   ,						__UpperCamelCase	:   Optional[Any]			   =					self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
															__UpperCamelCase	:   List[Any]			   =					model_class(a						)
															__UpperCamelCase	:   Optional[int]			   =					inspect.signature(model.__call__						)
															# signature.parameters is an OrderedDict => so arg_names order is deterministic
															__UpperCamelCase	:   Dict			   =					[*signature.parameters.keys()]

															__UpperCamelCase	:   Any			   =					["pixel_values"]
															self.assertListEqual(arg_names[:1]     ,   a						)







	def    _lowerCamelCase				(					self		:str						)		-> Any:
								__UpperCamelCase   ,						__UpperCamelCase	:   List[str]			   =					self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__						):
																						__UpperCamelCase	:   List[Any]			   =					self._prepare_for_class(a     ,   a						)
																						__UpperCamelCase	:   List[Any]			   =					model_class(a						)

																						@jax.jit
																						def model_jitted(a		:List[str]     ,   **a		:List[Any]						):
																													return model(pixel_values=a     ,   **a						)

																						with self.subTest("JIT Enabled"						):
																													__UpperCamelCase	:   int			   =					model_jitted(**a						).to_tuple()

																						with self.subTest("JIT Disabled"						):
																													with jax.disable_jit():
																																				__UpperCamelCase	:   Optional[Any]			   =					model_jitted(**a						).to_tuple()

																						self.assertEqual(len(a						)     ,   len(a						)						)
																						for jitted_output, output in zip(a     ,   a						):
																													self.assertEqual(jitted_output.shape     ,   output.shape						)







	@slow
	def    _lowerCamelCase				(					self		:Dict						)		-> str:
								for model_class_name in self.all_model_classes:
															__UpperCamelCase	:   List[Any]			   =					model_class_name.from_pretrained("google/vit-base-patch16-224"						)
															__UpperCamelCase	:   Optional[int]			   =					model(np.ones((1, 3, 2_2_4, 2_2_4)						)						)
															self.assertIsNotNone(a						)