



'''simple docstring'''

import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						Tuple							,		_SCREAMING_SNAKE_CASE     :						Union[str, Any]							,		_SCREAMING_SNAKE_CASE     :						List[Any]=0							)			->						Tuple:







	"""simple docstring"""
	# Format the message.
	if name is None:
		lowerCAmelCase    =	None
	else:
		lowerCAmelCase    =	""".""" * max(0							,		spaces - 2							) + """# {:""" + str(50 - spaces							) + """s}"""
		lowerCAmelCase    =	fmt.format(_SCREAMING_SNAKE_CASE							)

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






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						str							,		_SCREAMING_SNAKE_CASE     :						Tuple							,		_SCREAMING_SNAKE_CASE     :						Tuple							,		_SCREAMING_SNAKE_CASE     :						Tuple							)			->						str:







	"""simple docstring"""
	# 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()
	# other versions store [num_heads * num_splits * hidden_size, :]
	lowerCAmelCase    =	(num_heads, num_splits, hidden_size) + input_shape[1:]
	lowerCAmelCase    =	param.view(*_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase    =	param.transpose(0							,		1							).contiguous()
	lowerCAmelCase    =	param.view(*_SCREAMING_SNAKE_CASE							)
	return param






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						int							,		_SCREAMING_SNAKE_CASE     :						List[str]							)			->						List[str]:







	"""simple docstring"""
	lowerCAmelCase    =	config.n_positions
	lowerCAmelCase    =	config.n_layer
	lowerCAmelCase    =	config.vocab_size
	lowerCAmelCase    =	config.n_head
	lowerCAmelCase    =	config.n_embd // config.n_head

	lowerCAmelCase    =	sd_megatron["""model.language_model.embedding.word_embeddings.weight"""][:vocab_size, :]
	lowerCAmelCase    =	{
	    """transformer.wte.weight""": word_embeddings,
	    """transformer.wpe.weight""": sd_megatron["""model.language_model.embedding.position_embeddings.weight"""],
	    """transformer.ln_f.weight""": sd_megatron["""model.language_model.encoder.final_layernorm.weight"""],
	    """transformer.ln_f.bias""": sd_megatron["""model.language_model.encoder.final_layernorm.bias"""],
	}

	lowerCAmelCase    =	"""model.language_model.encoder.layers."""
	for i in range(_SCREAMING_SNAKE_CASE							):
		lowerCAmelCase    =	torch.tril(torch.ones((n_positions, n_positions)							,		dtype=torch.bool							)							)
		lowerCAmelCase    =	causal_mask.view(1							,		1							,		_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)
		lowerCAmelCase    =	causal_mask
		lowerCAmelCase    =	torch.tensor(-1E4							,		dtype=torch.bfloataa							)

		lowerCAmelCase    =	sd_megatron[f'{pf}{i}.input_layernorm.weight']
		lowerCAmelCase    =	sd_megatron[f'{pf}{i}.input_layernorm.bias']

		lowerCAmelCase    =	sd_megatron[f'{pf}{i}.self_attention.query_key_value.weight']
		lowerCAmelCase    =	fix_query_key_value_ordering(_SCREAMING_SNAKE_CASE							,		3							,		_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)
		lowerCAmelCase    =	vala.transpose(0							,		1							).contiguous()

		lowerCAmelCase    =	sd_megatron[f'{pf}{i}.self_attention.query_key_value.bias']
		lowerCAmelCase    =	fix_query_key_value_ordering(_SCREAMING_SNAKE_CASE							,		3							,		_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)
		lowerCAmelCase    =	vala

		lowerCAmelCase    =	sd_megatron[f'{pf}{i}.self_attention.dense.weight'].transpose(
		    0							,		1							)
		lowerCAmelCase    =	sd_megatron[f'{pf}{i}.self_attention.dense.bias']
		lowerCAmelCase    =	sd_megatron[f'{pf}{i}.post_attention_layernorm.weight']
		lowerCAmelCase    =	sd_megatron[f'{pf}{i}.post_attention_layernorm.bias']
		lowerCAmelCase    =	sd_megatron[f'{pf}{i}.mlp.dense_h_to_4h.weight'].transpose(0							,		1							)
		lowerCAmelCase    =	sd_megatron[f'{pf}{i}.mlp.dense_h_to_4h.bias']
		lowerCAmelCase    =	sd_megatron[f'{pf}{i}.mlp.dense_4h_to_h.weight'].transpose(
		    0							,		1							)
		lowerCAmelCase    =	sd_megatron[f'{pf}{i}.mlp.dense_4h_to_h.bias']

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

	return sd_hf






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						List[Any]							,		_SCREAMING_SNAKE_CASE     :						Optional[int]							)			->						Dict:







	"""simple docstring"""
	lowerCAmelCase    =	64_000
	lowerCAmelCase    =	config_megatron["""encoder_seq_length"""]
	lowerCAmelCase    =	config_megatron["""hidden_size"""]
	lowerCAmelCase    =	config_megatron["""num_layers"""]
	lowerCAmelCase    =	config_megatron["""num_attention_heads"""]
	lowerCAmelCase    =	config_megatron["""ffn_hidden_size"""]
	lowerCAmelCase    =	"""gelu"""
	lowerCAmelCase    =	0.1
	lowerCAmelCase    =	0.1
	lowerCAmelCase    =	0.1
	lowerCAmelCase    =	config_megatron["""layernorm_epsilon"""]  # 1e-5
	lowerCAmelCase    =	config_megatron["""init_method_std"""]  # 0.02
	lowerCAmelCase    =	config_megatron["""apply_query_key_layer_scaling"""]  # True
	lowerCAmelCase    =	True
	lowerCAmelCase    =	True

	# This identifies the 6.7B (7B) model which uses a different tokenizer
	if config_megatron["hidden_size"] == 4_096:
		lowerCAmelCase    =	1  # <|endoftext|>
		lowerCAmelCase    =	1  # <|endoftext|>
		lowerCAmelCase    =	0  # <unk>
	else:
		lowerCAmelCase    =	2  # <s>
		lowerCAmelCase    =	3  # <|endoftext|>
		lowerCAmelCase    =	0  # <pad>

	return config_hf






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						int							)			->						List[str]:







	"""simple docstring"""
	print(_SCREAMING_SNAKE_CASE							)

	lowerCAmelCase    =	args.checkpoint_path
	lowerCAmelCase    =	args.save_path
	if isfile(_SCREAMING_SNAKE_CASE							):
		raise FileNotFoundError(f'ERROR! could not find file {checkpoint_path}'							)

	# Load the model.
	lowerCAmelCase    =	torch.load(_SCREAMING_SNAKE_CASE							,		map_location="""cpu"""							)

	# Load the config.
	lowerCAmelCase    =	checkpoint["""hyper_parameters"""]["""cfg"""]
	lowerCAmelCase    =	GPTaConfig()
	lowerCAmelCase    =	copy_config(config_hf=_SCREAMING_SNAKE_CASE							,		config_megatron=_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase    =	["""GPT2LMHeadModel"""]

	lowerCAmelCase    =	checkpoint["""state_dict"""]

	# Convert.
	print("""Converting"""							)
	lowerCAmelCase    =	convert_megatron_checkpoint(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)

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

	lowerCAmelCase    =	"""GPTSw3Tokenizer"""

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

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


if __name__ == "__main__":
				UpperCAmelCase			=    argparse.ArgumentParser()
				parser.add_argument(
				    '--checkpoint_path',
				    type=str,
				    required=True,
				    help='e.g. megatron_gpt--val_loss=2.42-step=38000-consumed_samples=54720000',
				)
				parser.add_argument('--save_path', type=str, required=True, help='e.g. /home/user/gpt-sw3/hf')
				parser.add_argument('--print-checkpoint-structure', action='store_true')
				UpperCAmelCase			=    parser.parse_args()
				main(_args)




'''simple docstring'''

import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

@skip_mps
class __snake_case(      _lowerCAmelCase		,							_lowerCAmelCase		,							_lowerCAmelCase		,							unittest.TestCase					):


						'''simple docstring'''



						UpperCAmelCase							:    Optional[Any]           =		StableDiffusionModelEditingPipeline
						UpperCAmelCase							:    List[str]           =		TEXT_TO_IMAGE_PARAMS
						UpperCAmelCase							:    Optional[Any]           =		TEXT_TO_IMAGE_BATCH_PARAMS
						UpperCAmelCase							:    Optional[int]           =		TEXT_TO_IMAGE_IMAGE_PARAMS
						UpperCAmelCase							:    Optional[int]           =		TEXT_TO_IMAGE_IMAGE_PARAMS


						def 				__snake_case    (					self      )	->	Optional[Any]:
							torch.manual_seed(0      )
							lowerCAmelCase    =	UNetaDConditionModel(
							    block_out_channels=(32, 64)	,       layers_per_block=2	,       sample_size=32	,       in_channels=4	,       out_channels=4	,       down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")	,       up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")	,       cross_attention_dim=32	,       )
							lowerCAmelCase    =	DDIMScheduler()
							torch.manual_seed(0      )
							lowerCAmelCase    =	AutoencoderKL(
							    block_out_channels=[32, 64]	,       in_channels=3	,       out_channels=3	,       down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]	,       up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]	,       latent_channels=4	,       )
							torch.manual_seed(0      )
							lowerCAmelCase    =	CLIPTextConfig(
							    bos_token_id=0	,       eos_token_id=2	,       hidden_size=32	,       intermediate_size=37	,       layer_norm_eps=1e-05	,       num_attention_heads=4	,       num_hidden_layers=5	,       pad_token_id=1	,       vocab_size=1000	,       )
							lowerCAmelCase    =	CLIPTextModel(A_      )
							lowerCAmelCase    =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""      )

							lowerCAmelCase    =	{
							    """unet""": unet,
							    """scheduler""": scheduler,
							    """vae""": vae,
							    """text_encoder""": text_encoder,
							    """tokenizer""": tokenizer,
							    """safety_checker""": None,
							    """feature_extractor""": None,
							}
							return components


						def 				__snake_case    (					self	,       A_	,       A_=0      )	->	Optional[int]:
							lowerCAmelCase    =	torch.manual_seed(A_      )
							lowerCAmelCase    =	{
							    """prompt""": """A field of roses""",
							    """generator""": generator,
							    # Setting height and width to None to prevent OOMs on CPU.
							    """height""": None,
							    """width""": None,
							    """num_inference_steps""": 2,
							    """guidance_scale""": 6.0,
							    """output_type""": """numpy""",
							}
							return inputs


						def 				__snake_case    (					self      )	->	List[str]:
							lowerCAmelCase    =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowerCAmelCase    =	self.get_dummy_components()
							lowerCAmelCase    =	StableDiffusionModelEditingPipeline(**A_      )
							lowerCAmelCase    =	sd_pipe.to(A_      )
							sd_pipe.set_progress_bar_config(disable=A_      )

							lowerCAmelCase    =	self.get_dummy_inputs(A_      )
							lowerCAmelCase    =	sd_pipe(**A_      ).images
							lowerCAmelCase    =	image[0, -3:, -3:, -1]
							assert image.shape == (1, 64, 64, 3)

							lowerCAmelCase    =	np.array([0.4_7_5_5, 0.5_1_3_2, 0.4_9_7_6, 0.3_9_0_4, 0.3_5_5_4, 0.4_7_6_5, 0.5_1_3_9, 0.5_1_5_8, 0.4_8_8_9]      )

							assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2


						def 				__snake_case    (					self      )	->	str:
							lowerCAmelCase    =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowerCAmelCase    =	self.get_dummy_components()
							lowerCAmelCase    =	StableDiffusionModelEditingPipeline(**A_      )
							lowerCAmelCase    =	sd_pipe.to(A_      )
							sd_pipe.set_progress_bar_config(disable=A_      )

							lowerCAmelCase    =	self.get_dummy_inputs(A_      )
							lowerCAmelCase    =	"""french fries"""
							lowerCAmelCase    =	sd_pipe(**A_	,       negative_prompt=A_      )
							lowerCAmelCase    =	output.images
							lowerCAmelCase    =	image[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)

							lowerCAmelCase    =	np.array([0.4_9_9_2, 0.5_1_0_1, 0.5_0_0_4, 0.3_9_4_9, 0.3_6_0_4, 0.4_7_3_5, 0.5_2_1_6, 0.5_2_0_4, 0.4_9_1_3]      )

							assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2


						def 				__snake_case    (					self      )	->	Optional[int]:
							lowerCAmelCase    =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowerCAmelCase    =	self.get_dummy_components()
							lowerCAmelCase    =	EulerAncestralDiscreteScheduler(
							    beta_start=0.0_0_0_8_5	,       beta_end=0.0_1_2	,       beta_schedule="""scaled_linear"""      )
							lowerCAmelCase    =	StableDiffusionModelEditingPipeline(**A_      )
							lowerCAmelCase    =	sd_pipe.to(A_      )
							sd_pipe.set_progress_bar_config(disable=A_      )

							lowerCAmelCase    =	self.get_dummy_inputs(A_      )
							lowerCAmelCase    =	sd_pipe(**A_      ).images
							lowerCAmelCase    =	image[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)

							lowerCAmelCase    =	np.array([0.4_7_4_7, 0.5_3_7_2, 0.4_7_7_9, 0.4_9_8_2, 0.5_5_4_3, 0.4_8_1_6, 0.5_2_3_8, 0.4_9_0_4, 0.5_0_2_7]      )

							assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2


						def 				__snake_case    (					self      )	->	Optional[Any]:
							lowerCAmelCase    =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowerCAmelCase    =	self.get_dummy_components()
							lowerCAmelCase    =	PNDMScheduler()
							lowerCAmelCase    =	StableDiffusionModelEditingPipeline(**A_      )
							lowerCAmelCase    =	sd_pipe.to(A_      )
							sd_pipe.set_progress_bar_config(disable=A_      )

							lowerCAmelCase    =	self.get_dummy_inputs(A_      )
							# the pipeline does not expect pndm so test if it raises error.
							with self.assertRaises(A_      ):
								lowerCAmelCase    =	sd_pipe(**A_      ).images


						def 				__snake_case    (					self      )	->	str:
							super().test_inference_batch_single_identical(expected_max_diff=5e-3      )




						def 				__snake_case    (					self      )	->	Any:
							super().test_attention_slicing_forward_pass(expected_max_diff=5e-3      )







@slow
@require_torch_gpu
class __snake_case(      unittest.TestCase					):


						'''simple docstring'''



						def 				__snake_case    (					self      )	->	int:
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()


						def 				__snake_case    (					self	,       A_=0      )	->	Dict:
							lowerCAmelCase    =	torch.manual_seed(A_      )
							lowerCAmelCase    =	{
							    """prompt""": """A field of roses""",
							    """generator""": generator,
							    """num_inference_steps""": 3,
							    """guidance_scale""": 7.5,
							    """output_type""": """numpy""",
							}
							return inputs


						def 				__snake_case    (					self      )	->	int:
							lowerCAmelCase    =	"""CompVis/stable-diffusion-v1-4"""
							lowerCAmelCase    =	StableDiffusionModelEditingPipeline.from_pretrained(A_	,       safety_checker=A_      )
							pipe.to(A_      )
							pipe.set_progress_bar_config(disable=A_      )
							pipe.enable_attention_slicing()

							lowerCAmelCase    =	self.get_inputs()
							lowerCAmelCase    =	pipe(**A_      ).images
							lowerCAmelCase    =	image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 512, 512, 3)

							lowerCAmelCase    =	np.array(
							    [0.6_7_4_9_4_9_6, 0.6_3_8_6_4_5_3, 0.5_1_4_4_3_2_6_7, 0.6_6_0_9_4_9_0_5, 0.6_1_9_2_1_2_1_5, 0.5_4_9_1_3_3_2, 0.5_7_4_4_4_1_7, 0.5_8_0_7_5_1_0_6, 0.5_1_7_4_6_5_8]      )

							assert np.abs(expected_slice - image_slice      ).max() < 1e-2

							# make sure image changes after editing
							pipe.edit_model("""A pack of roses"""	,       """A pack of blue roses"""      )

							lowerCAmelCase    =	pipe(**A_      ).images
							lowerCAmelCase    =	image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 512, 512, 3)

							assert np.abs(expected_slice - image_slice      ).max() > 1e-1




						def 				__snake_case    (					self      )	->	str:
							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()

							lowerCAmelCase    =	"""CompVis/stable-diffusion-v1-4"""
							lowerCAmelCase    =	DDIMScheduler.from_pretrained(A_	,       subfolder="""scheduler"""      )
							lowerCAmelCase    =	StableDiffusionModelEditingPipeline.from_pretrained(
							    A_	,       scheduler=A_	,       safety_checker=A_      )
							lowerCAmelCase    =	pipe.to(A_      )
							pipe.set_progress_bar_config(disable=A_      )
							pipe.enable_attention_slicing(1      )
							pipe.enable_sequential_cpu_offload()

							lowerCAmelCase    =	self.get_inputs()
							lowerCAmelCase    =	pipe(**A_      )

							lowerCAmelCase    =	torch.cuda.max_memory_allocated()
							# make sure that less than 4.4 GB is allocated
							assert mem_bytes < 4.4 * 10**9