
import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPTaConfig






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE=0				):
							# Format the message.
							if name is None:
														_snake_case       =  None
							else:
														_snake_case       =  "." * max(0							, spaces - 2				) + "# {:" + str(50 - spaces				) + "s}"
														_snake_case       =  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  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							# 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.
							_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(*__SCREAMING_SNAKE_CASE				)
														_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(*__SCREAMING_SNAKE_CASE				)
														_snake_case       =  param.transpose(0							, 1				).contiguous()
							_snake_case       =  param.view(*__SCREAMING_SNAKE_CASE				)
							return param






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							# The converted output model.
							_snake_case       =  {}

							# old versions did not store training args
							_snake_case       =  input_state_dict.get("""args"""							, __SCREAMING_SNAKE_CASE				)
							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.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
														# pprint(config)

							# The number of heads.
							_snake_case       =  config.n_head
							# The hidden_size per head.
							_snake_case       =  config.n_embd // config.n_head
							# 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, :]
							_snake_case       =  word_embeddings

							# The position embeddings.
							_snake_case       =  embeddings["position_embeddings"]["weight"]
							# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
							_snake_case       =  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       =  pos_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": ".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       =  layer_re.match(__SCREAMING_SNAKE_CASE				)

														# 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"""transformer.h.{layer_idx}"""

														# For layernorm(s), simply store the layer norm.
														if op_name.endswith("""layernorm"""				):
																					_snake_case       =  "ln_1" if op_name.startswith("""input"""				) else "ln_2"
																					_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":
																					# Insert a tensor of 1x1xDxD bias.
																					_snake_case       =  torch.tril(torch.ones((n_positions, n_positions)							, dtype=torch.floataa				)				).view(
																					    1							, 1							, __SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE				)
																					_snake_case       =  causal_mask

																					# Insert a "dummy" tensor for masked_bias.
																					_snake_case       =  torch.tensor(-1E4							, dtype=torch.floataa				)
																					_snake_case       =  masked_bias

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

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

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

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

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

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

							# It should be done!
							return output_state_dict






def  __SCREAMING_SNAKE_CASE				(  ):
							# Create the argument parser.
							_snake_case       =  argparse.ArgumentParser()
							parser.add_argument("""--print-checkpoint-structure"""							, action="""store_true"""				)
							parser.add_argument(
							    """path_to_checkpoint"""							, type=__SCREAMING_SNAKE_CASE							, help="""Path to the checkpoint file (.zip archive or direct .pt file)"""							, )
							parser.add_argument(
							    """--config_file"""							, default=""""""							, type=__SCREAMING_SNAKE_CASE							, 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(__SCREAMING_SNAKE_CASE							, map_location="""cpu"""				)
							else:
														_snake_case       =  torch.load(args.path_to_checkpoint							, map_location="""cpu"""				)

							_snake_case       =  input_state_dict.get("""args"""							, __SCREAMING_SNAKE_CASE				)

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

														# Spell out all parameters in case the defaults change.
														_snake_case       =  GPTaConfig(
														    vocab_size=5_0257							, n_positions=1024							, n_embd=1024							, n_layer=24							, n_head=16							, n_inner=4096							, activation_function=__SCREAMING_SNAKE_CASE							, 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=__SCREAMING_SNAKE_CASE							, summary_activation=__SCREAMING_SNAKE_CASE							, summary_proj_to_labels=__SCREAMING_SNAKE_CASE							, summary_first_dropout=0.1							, scale_attn_weights=__SCREAMING_SNAKE_CASE							, use_cache=__SCREAMING_SNAKE_CASE							, bos_token_id=5_0256							, eos_token_id=5_0256							, )
							else:
														_snake_case       =  GPTaConfig.from_json_file(args.config_file				)

							_snake_case       =  ["GPT2LMHeadModel"]

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

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

							_snake_case       =  AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE				)
							_snake_case       =  type(__SCREAMING_SNAKE_CASE				).__name__
							_snake_case       =  tokenizer_class

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

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

							# Store the state_dict to file.
							_snake_case       =  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__":
				main()

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

'''simple docstring'''
import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class       _lowerCAmelCase   (   __snake_case					,							unittest.TestCase				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						DiTPipeline
							lowerCAmelCase_ 						=						CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
							lowerCAmelCase_ 						=						PipelineTesterMixin.required_optional_params - {
							    "latents",
							    "num_images_per_prompt",
							    "callback",
							    "callback_steps",
							}
							lowerCAmelCase_ 						=						CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
							lowerCAmelCase_ 						=						False

							def 	lowercase							(self  )					->       Union[str, Any]:
														torch.manual_seed(0  )
														_snake_case       =  TransformeraDModel(
														    sample_size=16  ,  num_layers=2  ,  patch_size=4  ,  attention_head_dim=8  ,  num_attention_heads=2  ,  in_channels=4  ,  out_channels=8  ,  attention_bias=UpperCAmelCase  ,  activation_fn="""gelu-approximate"""  ,  num_embeds_ada_norm=1000  ,  norm_type="""ada_norm_zero"""  ,  norm_elementwise_affine=UpperCAmelCase  ,  )
														_snake_case       =  AutoencoderKL()
														_snake_case       =  DDIMScheduler()
														_snake_case       =  {"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
														return components

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase=0  )					->       List[str]:
														if str(UpperCAmelCase  ).startswith("""mps"""  ):
																					_snake_case       =  torch.manual_seed(UpperCAmelCase  )
														else:
																					_snake_case       =  torch.Generator(device=UpperCAmelCase  ).manual_seed(UpperCAmelCase  )
														_snake_case       =  {
														    """class_labels""": [1],
														    """generator""": generator,
														    """num_inference_steps""": 2,
														    """output_type""": """numpy""",
														}
														return inputs

							def 	lowercase							(self  )					->       Union[str, Any]:
														_snake_case       =  """cpu"""

														_snake_case       =  self.get_dummy_components()
														_snake_case       =  self.pipeline_class(**UpperCAmelCase  )
														pipe.to(UpperCAmelCase  )
														pipe.set_progress_bar_config(disable=UpperCAmelCase  )

														_snake_case       =  self.get_dummy_inputs(UpperCAmelCase  )
														_snake_case       =  pipe(**UpperCAmelCase  ).images
														_snake_case       =  image[0, -3:, -3:, -1]

														self.assertEqual(image.shape  ,  (1, 16, 16, 3)  )
														_snake_case       =  np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457]  )
														_snake_case       =  np.abs(image_slice.flatten() - expected_slice  ).max()
														self.assertLessEqual(UpperCAmelCase  ,  1e-3  )

							def 	lowercase							(self  )					->       List[str]:
														self._test_inference_batch_single_identical(relax_max_difference=UpperCAmelCase  ,  expected_max_diff=1e-3  )





							@unittest.skipIf(
							    torch_device != """cuda""" or not is_xformers_available()  ,  reason="""XFormers attention is only available with CUDA and `xformers` installed"""  ,  )
							def 	lowercase							(self  )					->       Union[str, Any]:
														self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3  )


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







							'''simple docstring'''

							def 	lowercase							(self  )					->       Tuple:
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()

							def 	lowercase							(self  )					->       Any:
														_snake_case       =  torch.manual_seed(0  )

														_snake_case       =  DiTPipeline.from_pretrained("""facebook/DiT-XL-2-256"""  )
														pipe.to("""cuda"""  )

														_snake_case       =  ["""vase""", """umbrella""", """white shark""", """white wolf"""]
														_snake_case       =  pipe.get_label_ids(UpperCAmelCase  )

														_snake_case       =  pipe(UpperCAmelCase  ,  generator=UpperCAmelCase  ,  num_inference_steps=40  ,  output_type="""np"""  ).images

														for word, image in zip(UpperCAmelCase  ,  UpperCAmelCase  ):
																					_snake_case       =  load_numpy(
																					    f"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy"""  )
																					assert np.abs((expected_image - image).max()  ) < 1e-2





							def 	lowercase							(self  )					->       Union[str, Any]:
														_snake_case       =  DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512"""  )
														_snake_case       =  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config  )
														pipe.to("""cuda"""  )

														_snake_case       =  ["""vase""", """umbrella"""]
														_snake_case       =  pipe.get_label_ids(UpperCAmelCase  )

														_snake_case       =  torch.manual_seed(0  )
														_snake_case       =  pipe(UpperCAmelCase  ,  generator=UpperCAmelCase  ,  num_inference_steps=25  ,  output_type="""np"""  ).images

														for word, image in zip(UpperCAmelCase  ,  UpperCAmelCase  ):
																					_snake_case       =  load_numpy(
																					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
																					    f"""/dit/{word}_512.npy"""  )

																					assert np.abs((expected_image - image).max()  ) < 1e-1