



"""simple docstring"""





import argparse

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    CLIPTokenizer,
    CLIPTokenizerFast,
    VideoMAEImageProcessor,
    XCLIPConfig,
    XCLIPModel,
    XCLIPProcessor,
    XCLIPTextConfig,
    XCLIPVisionConfig,
)
def 	__lowerCAmelCase						(		__UpperCamelCase      :			str					, __UpperCamelCase      :			Tuple    ):


						'''simple docstring'''






						snake_case_   : Any						=			XCLIPTextConfig()

						# derive patch size from model name
						snake_case_   : List[str]						=			model_name.find("""patch"""    )
						snake_case_   : Optional[Any]						=			int(model_name[start_idx + len("""patch"""    ) : start_idx + len("""patch"""    ) + 2]    )
						snake_case_   : Tuple						=			XCLIPVisionConfig(patch_size=__UpperCamelCase					, num_frames=__UpperCamelCase    )

						if "large" in model_name:
												snake_case_   : int						=			7_6_8
												snake_case_   : Optional[int]						=			3_0_7_2
												snake_case_   : List[Any]						=			1_2

												snake_case_   : List[Any]						=			1_0_2_4
												snake_case_   : List[str]						=			4_0_9_6
												snake_case_   : Tuple						=			1_6
												snake_case_   : Optional[Any]						=			2_4
												snake_case_   : Any						=			7_6_8
												snake_case_   : Optional[Any]						=			3_0_7_2

						if model_name == "xclip-large-patch14-16-frames":
												snake_case_   : int						=			3_3_6

						snake_case_   : str						=			XCLIPConfig.from_text_vision_configs(__UpperCamelCase					, __UpperCamelCase    )

						if "large" in model_name:
												snake_case_   : Any						=			7_6_8

						return config
def 	__lowerCAmelCase						(		__UpperCamelCase      :			List[Any]    ):


						'''simple docstring'''






						if name == "token_embedding.weight":
												snake_case_   : List[Any]						=			name.replace("""token_embedding.weight"""					, """text_model.embeddings.token_embedding.weight"""    )
						if name == "positional_embedding":
												snake_case_   : Tuple						=			name.replace("""positional_embedding"""					, """text_model.embeddings.position_embedding.weight"""    )
						if "ln_1" in name:
												snake_case_   : str						=			name.replace("""ln_1"""					, """layer_norm1"""    )
						if "ln_2" in name:
												snake_case_   : Any						=			name.replace("""ln_2"""					, """layer_norm2"""    )
						if "c_fc" in name:
												snake_case_   : Dict						=			name.replace("""c_fc"""					, """fc1"""    )
						if "c_proj" in name:
												snake_case_   : Optional[Any]						=			name.replace("""c_proj"""					, """fc2"""    )
						if name.startswith("""transformer.resblocks"""    ):
												snake_case_   : Tuple						=			name.replace("""transformer.resblocks"""					, """text_model.encoder.layers"""    )
						if "attn.out_proj" in name and "message" not in name:
												snake_case_   : List[Any]						=			name.replace("""attn.out_proj"""					, """self_attn.out_proj"""    )
						if "ln_final" in name:
												snake_case_   : List[str]						=			name.replace("""ln_final"""					, """text_model.final_layer_norm"""    )
						# visual encoder
						if name == "visual.class_embedding":
												snake_case_   : Optional[Any]						=			name.replace("""visual.class_embedding"""					, """vision_model.embeddings.class_embedding"""    )
						if name == "visual.positional_embedding":
												snake_case_   : Optional[Any]						=			name.replace("""visual.positional_embedding"""					, """vision_model.embeddings.position_embedding.weight"""    )
						if name.startswith("""visual.transformer.resblocks"""    ):
												snake_case_   : List[Any]						=			name.replace("""visual.transformer.resblocks"""					, """vision_model.encoder.layers"""    )
						if "visual.conv1" in name:
												snake_case_   : List[Any]						=			name.replace("""visual.conv1"""					, """vision_model.embeddings.patch_embedding"""    )
						if "visual.ln_pre" in name:
												snake_case_   : str						=			name.replace("""visual.ln_pre"""					, """vision_model.pre_layernorm"""    )
						if "visual.ln_post" in name:
												snake_case_   : Optional[Any]						=			name.replace("""visual.ln_post"""					, """vision_model.post_layernorm"""    )
						if "visual.proj" in name:
												snake_case_   : Dict						=			name.replace("""visual.proj"""					, """visual_projection.weight"""    )
						if "text_projection" in name:
												snake_case_   : Optional[Any]						=			name.replace("""text_projection"""					, """text_projection.weight"""    )
						# things on top
						if "prompts_visual_proj" in name:
												snake_case_   : Dict						=			name.replace("""prompts_visual_proj"""					, """prompts_visual_projection"""    )
						if "prompts_visual_ln" in name:
												snake_case_   : Optional[Any]						=			name.replace("""prompts_visual_ln"""					, """prompts_visual_layernorm"""    )
						# mit
						if name == "mit.positional_embedding":
												snake_case_   : Dict						=			name.replace("""positional"""					, """position"""    )
						if name.startswith("""mit.resblocks"""    ):
												snake_case_   : Tuple						=			name.replace("""mit.resblocks"""					, """mit.encoder.layers"""    )
						# prompts generator
						if name.startswith("""prompts_generator.norm"""    ):
												snake_case_   : int						=			name.replace("""prompts_generator.norm"""					, """prompts_generator.layernorm"""    )

						return name
def 	__lowerCAmelCase						(		__UpperCamelCase      :			str					, __UpperCamelCase      :			Tuple    ):


						'''simple docstring'''






						for key in orig_state_dict.copy().keys():
												snake_case_   : str						=			orig_state_dict.pop(__UpperCamelCase    )

												if "attn.in_proj" in key:
																		snake_case_   : List[Any]						=			key.split("""."""    )
																		if key.startswith("""visual"""    ):
																								snake_case_   : Union[str, Any]						=			key_split[3]
																								snake_case_   : Optional[Any]						=			config.vision_config.hidden_size
																								if "message_attn" in key:
																														if "weight" in key:
																																				snake_case_   : str						=			val[
																																				    :dim, :
																																				]
																																				snake_case_   : Dict						=			val[
																																				    dim : dim * 2, :
																																				]
																																				snake_case_   : Union[str, Any]						=			val[
																																				    -dim:, :
																																				]
																														else:
																																				snake_case_   : Tuple						=			val[
																																				    :dim
																																				]
																																				snake_case_   : int						=			val[
																																				    dim : dim * 2
																																				]
																																				snake_case_   : Any						=			val[
																																				    -dim:
																																				]
																								else:
																														if "weight" in key:
																																				snake_case_   : Optional[Any]						=			val[
																																				    :dim, :
																																				]
																																				snake_case_   : List[str]						=			val[
																																				    dim : dim * 2, :
																																				]
																																				snake_case_   : str						=			val[
																																				    -dim:, :
																																				]
																														else:
																																				snake_case_   : List[Any]						=			val[:dim]
																																				snake_case_   : List[Any]						=			val[
																																				    dim : dim * 2
																																				]
																																				snake_case_   : Any						=			val[-dim:]
																		elif key.startswith("""mit"""    ):
																								snake_case_   : Tuple						=			key_split[2]
																								snake_case_   : Any						=			config.vision_config.mit_hidden_size
																								if "weight" in key:
																														snake_case_   : Dict						=			val[:dim, :]
																														snake_case_   : str						=			val[dim : dim * 2, :]
																														snake_case_   : Optional[int]						=			val[-dim:, :]
																								else:
																														snake_case_   : int						=			val[:dim]
																														snake_case_   : Optional[Any]						=			val[dim : dim * 2]
																														snake_case_   : int						=			val[-dim:]
																		else:
																								snake_case_   : Any						=			key_split[2]
																								snake_case_   : int						=			config.text_config.hidden_size
																								if "weight" in key:
																														snake_case_   : List[str]						=			val[:dim, :]
																														snake_case_   : List[str]						=			val[
																														    dim : dim * 2, :
																														]
																														snake_case_   : Tuple						=			val[-dim:, :]
																								else:
																														snake_case_   : Tuple						=			val[:dim]
																														snake_case_   : int						=			val[
																														    dim : dim * 2
																														]
																														snake_case_   : Tuple						=			val[-dim:]
												else:
																		snake_case_   : str						=			rename_key(__UpperCamelCase    )
																		if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
																								snake_case_   : Tuple						=			val.T
																		snake_case_   : Any						=			val

						return orig_state_dict
def 	__lowerCAmelCase						(		__UpperCamelCase      :			List[str]    ):


						'''simple docstring'''






						if num_frames == 8:
												snake_case_   : Optional[int]						=			"""eating_spaghetti_8_frames.npy"""
						elif num_frames == 1_6:
												snake_case_   : Tuple						=			"""eating_spaghetti.npy"""
						elif num_frames == 3_2:
												snake_case_   : Union[str, Any]						=			"""eating_spaghetti_32_frames.npy"""
						snake_case_   : str						=			hf_hub_download(
						    repo_id="""hf-internal-testing/spaghetti-video"""					, filename=__UpperCamelCase					, repo_type="""dataset"""					, )
						snake_case_   : str						=			np.load(__UpperCamelCase    )
						return list(__UpperCamelCase    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			List[Any]					, __UpperCamelCase      :			List[str]=None					, __UpperCamelCase      :			Any=False    ):


						'''simple docstring'''






						snake_case_   : int						=			{
						    # fully supervised kinetics-400 checkpoints
						    """xclip-base-patch32""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth""",
						    """xclip-base-patch32-16-frames""": (
						        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth"""
						    ),
						    """xclip-base-patch16""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth""",
						    """xclip-base-patch16-16-frames""": (
						        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth"""
						    ),
						    """xclip-large-patch14""": """https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&amp;export=download&amp;confirm=t&amp;uuid=b26caedc-88e2-473e-830a-9d158b653cdb""",
						    """xclip-large-patch14-16-frames""": """https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&amp;export=download&amp;confirm=t&amp;uuid=538fa810-e671-4050-b385-9a623f89804f""",
						    # fully supervised kinetics-600 checkpoints
						    """xclip-base-patch16-kinetics-600""": (
						        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth"""
						    ),
						    """xclip-base-patch16-kinetics-600-16-frames""": (
						        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth"""
						    ),
						    """xclip-large-patch14-kinetics-600""": """https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&amp;export=download&amp;confirm=t&amp;uuid=141d4977-4a65-44ae-864f-4b0c19f838be""",
						    # few shot
						    """xclip-base-patch16-hmdb-2-shot""": (
						        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth"""
						    ),
						    """xclip-base-patch16-hmdb-4-shot""": (
						        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth"""
						    ),
						    """xclip-base-patch16-hmdb-8-shot""": (
						        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth"""
						    ),
						    """xclip-base-patch16-hmdb-16-shot""": (
						        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth"""
						    ),
						    """xclip-base-patch16-ucf-2-shot""": (
						        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth"""
						    ),
						    """xclip-base-patch16-ucf-4-shot""": (
						        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth"""
						    ),
						    """xclip-base-patch16-ucf-8-shot""": (
						        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth"""
						    ),
						    """xclip-base-patch16-ucf-16-shot""": (
						        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth"""
						    ),
						    # zero shot
						    """xclip-base-patch16-zero-shot""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth""",
						}

						snake_case_   : Optional[int]						=			model_to_url[model_name]
						snake_case_   : str						=			8
						if "16-frames" in model_name:
												snake_case_   : str						=			1_6
						elif "shot" in model_name:
												snake_case_   : Optional[int]						=			3_2

						snake_case_   : Optional[Any]						=			get_xclip_config(__UpperCamelCase					, __UpperCamelCase    )
						snake_case_   : List[str]						=			XCLIPModel(__UpperCamelCase    )
						model.eval()

						if "drive" in checkpoint_url:
												snake_case_   : Any						=			"""pytorch_model.bin"""
												gdown.cached_download(__UpperCamelCase					, __UpperCamelCase					, quiet=__UpperCamelCase    )
												snake_case_   : List[Any]						=			torch.load(__UpperCamelCase					, map_location="""cpu"""    )["""model"""]
						else:
												snake_case_   : List[Any]						=			torch.hub.load_state_dict_from_url(__UpperCamelCase    )["""model"""]

						snake_case_   : int						=			convert_state_dict(__UpperCamelCase					, __UpperCamelCase    )

						snake_case_   : int						=			XCLIPModel(__UpperCamelCase    )
						snake_case_			,		snake_case_   : int						=			model.load_state_dict(__UpperCamelCase					, strict=__UpperCamelCase    )
						assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
						model.eval()

						snake_case_   : Union[str, Any]						=			3_3_6 if model_name == """xclip-large-patch14-16-frames""" else 2_2_4
						snake_case_   : List[Any]						=			VideoMAEImageProcessor(size=__UpperCamelCase    )
						snake_case_   : List[str]						=			CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""    )
						snake_case_   : Tuple						=			CLIPTokenizerFast.from_pretrained("""openai/clip-vit-base-patch32"""    )
						snake_case_   : List[str]						=			XCLIPProcessor(image_processor=__UpperCamelCase					, tokenizer=__UpperCamelCase    )

						snake_case_   : List[str]						=			prepare_video(__UpperCamelCase    )
						snake_case_   : List[str]						=			processor(
						    text=["""playing sports""", """eating spaghetti""", """go shopping"""]					, videos=__UpperCamelCase					, return_tensors="""pt"""					, padding=__UpperCamelCase    )

						print("""Shape of pixel values:"""					, inputs.pixel_values.shape    )

						with torch.no_grad():
												snake_case_   : str						=			model(**__UpperCamelCase    )

						# Verify outputs
						snake_case_   : Optional[Any]						=			outputs.logits_per_video
						snake_case_   : List[Any]						=			logits_per_video.softmax(dim=1    )
						print("""Probs:"""					, __UpperCamelCase    )
						# kinetics-400
						if model_name == "xclip-base-patch32":
												snake_case_   : Optional[Any]						=			torch.tensor([[0.0_019, 0.9_951, 0.0_030]]    )
						elif model_name == "xclip-base-patch32-16-frames":
												snake_case_   : List[str]						=			torch.tensor([[7.0_9_9_9E-0_4, 9.9_8_8_3E-0_1, 4.5_5_8_0E-0_4]]    )
						elif model_name == "xclip-base-patch16":
												snake_case_   : int						=			torch.tensor([[0.0_083, 0.9_681, 0.0_236]]    )
						elif model_name == "xclip-base-patch16-16-frames":
												snake_case_   : Tuple						=			torch.tensor([[7.6_9_3_7E-0_4, 9.9_7_2_8E-0_1, 1.9_4_7_3E-0_3]]    )
						elif model_name == "xclip-large-patch14":
												snake_case_   : int						=			torch.tensor([[0.0_062, 0.9_864, 0.0_075]]    )
						elif model_name == "xclip-large-patch14-16-frames":
												snake_case_   : str						=			torch.tensor([[3.3_8_7_7E-0_4, 9.9_9_3_7E-0_1, 2.8_8_8_8E-0_4]]    )
						# kinetics-600
						elif model_name == "xclip-base-patch16-kinetics-600":
												snake_case_   : Optional[Any]						=			torch.tensor([[0.0_555, 0.8_914, 0.0_531]]    )
						elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
												snake_case_   : Optional[Any]						=			torch.tensor([[3.8_5_5_4E-0_4, 9.9_9_2_9E-0_1, 3.2_7_5_4E-0_4]]    )
						elif model_name == "xclip-large-patch14-kinetics-600":
												snake_case_   : Dict						=			torch.tensor([[0.0_036, 0.9_920, 0.0_045]]    )
						# few shot
						elif model_name == "xclip-base-patch16-hmdb-2-shot":
												snake_case_   : List[str]						=			torch.tensor([[7.1_8_9_0E-0_6, 9.9_9_9_4E-0_1, 5.6_5_5_9E-0_5]]    )
						elif model_name == "xclip-base-patch16-hmdb-4-shot":
												snake_case_   : Optional[int]						=			torch.tensor([[1.0_3_2_0E-0_5, 9.9_9_9_3E-0_1, 6.2_4_3_5E-0_5]]    )
						elif model_name == "xclip-base-patch16-hmdb-8-shot":
												snake_case_   : str						=			torch.tensor([[4.1_3_7_7E-0_6, 9.9_9_9_0E-0_1, 9.8_3_8_6E-0_5]]    )
						elif model_name == "xclip-base-patch16-hmdb-16-shot":
												snake_case_   : Optional[Any]						=			torch.tensor([[4.1_3_4_7E-0_5, 9.9_9_6_2E-0_1, 3.3_4_1_1E-0_4]]    )
						elif model_name == "xclip-base-patch16-ucf-2-shot":
												snake_case_   : Optional[int]						=			torch.tensor([[8.5_8_5_7E-0_5, 9.9_9_2_8E-0_1, 6.3_2_9_1E-0_4]]    )
						elif model_name == "xclip-base-patch16-ucf-4-shot":
												snake_case_   : List[str]						=			torch.tensor([[8.5_8_5_7E-0_5, 9.9_9_2_8E-0_1, 6.3_2_9_1E-0_4]]    )
						elif model_name == "xclip-base-patch16-ucf-8-shot":
												snake_case_   : Tuple						=			torch.tensor([[0.0_027, 0.9_904, 0.0_070]]    )
						elif model_name == "xclip-base-patch16-ucf-16-shot":
												snake_case_   : List[Any]						=			torch.tensor([[9.8_2_1_9E-0_4, 9.9_5_9_3E-0_1, 3.0_8_6_3E-0_3]]    )
						# zero shot
						elif model_name == "xclip-base-patch16-zero-shot":
												snake_case_   : Union[str, Any]						=			torch.tensor([[3.5_0_8_2E-0_4, 9.9_7_8_5E-0_1, 1.7_9_6_6E-0_3]]    )
						else:
												raise ValueError(F'Model name {model_name} not supported'    )
						assert torch.allclose(__UpperCamelCase					, __UpperCamelCase					, atol=1E-3    )
						print("""Looks ok!"""    )

						if pytorch_dump_folder_path is not None:
												print(F'Saving model {model_name} to {pytorch_dump_folder_path}'    )
												model.save_pretrained(__UpperCamelCase    )

						if push_to_hub:
												print("""Pushing model, processor and slow tokenizer files to the hub..."""    )
												model.push_to_hub(__UpperCamelCase					, organization="""nielsr"""    )
												processor.push_to_hub(__UpperCamelCase					, organization="""nielsr"""    )
												slow_tokenizer.push_to_hub(__UpperCamelCase					, organization="""nielsr"""    )


if __name__ == "__main__":
					__lowerCAmelCase       :					List[str]      					=					argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '''--model_name''',
					    default='''xclip-base-patch32''',
					    type=str,
					    help='''Name of the model.''',
					)
					parser.add_argument(
					    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
					)
					parser.add_argument(
					    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
					)

					__lowerCAmelCase       :					List[Any]      					=					parser.parse_args()
					convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






"""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						(		__UpperCamelCase      :			Tuple					, __UpperCamelCase      :			Tuple					, __UpperCamelCase      :			Any=0    ):


						'''simple docstring'''






						if name is None:
												snake_case_   : Dict						=			None
						else:
												snake_case_   : Dict						=			""".""" * max(0					, spaces - 2    ) + """# {:""" + str(5_0 - spaces    ) + """s}"""
												snake_case_   : Any						=			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 	__lowerCAmelCase						(		__UpperCamelCase      :			str					, __UpperCamelCase      :			List[Any]					, __UpperCamelCase      :			Dict					, __UpperCamelCase      :			str					, __UpperCamelCase      :			Any    ):


						'''simple docstring'''






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


						'''simple docstring'''






						snake_case_   : Dict						=			{}

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

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

						# The model.
						snake_case_   : List[str]						=			input_state_dict["""model"""]
						# The language model.
						snake_case_   : str						=			model["""language_model"""]
						# The embeddings.
						snake_case_   : Tuple						=			lm["""embedding"""]

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

						# The position embeddings.
						snake_case_   : List[Any]						=			embeddings["""position_embeddings"""]["""weight"""]
						# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
						snake_case_   : Tuple						=			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_   : Union[str, Any]						=			pos_embeddings

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

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

						# The simple map of names for "automated" rules.
						snake_case_   : List[str]						=			{
						    """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_   : int						=			layer_re.match(__UpperCamelCase    )

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

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

												# The name of the layer.
												snake_case_   : str						=			F'transformer.h.{layer_idx}'

												# For layernorm(s), simply store the layer norm.
												if op_name.endswith("""layernorm"""    ):
																		snake_case_   : Dict						=			"""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_   : Optional[Any]						=			torch.tril(torch.ones((n_positions, n_positions)					, dtype=torch.floataa    )    ).view(
																		    1					, 1					, __UpperCamelCase					, __UpperCamelCase    )
																		snake_case_   : List[Any]						=			causal_mask

																		# Insert a "dummy" tensor for masked_bias.
																		snake_case_   : str						=			torch.tensor(-1E4					, dtype=torch.floataa    )
																		snake_case_   : List[Any]						=			masked_bias

																		snake_case_   : Optional[int]						=			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_   : str						=			out_val.transpose(0					, 1    ).contiguous()
																		# Store.
																		snake_case_   : Tuple						=			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_   : Optional[Any]						=			fix_query_key_value_ordering(__UpperCamelCase					, __UpperCamelCase					, 3					, __UpperCamelCase					, __UpperCamelCase    )
																		# Store. No change of shape.
																		snake_case_   : List[Any]						=			out_val

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

												# Copy the bias.
												elif weight_or_bias == "bias":
																		snake_case_   : List[str]						=			megatron_to_transformers[op_name]
																		snake_case_   : Tuple						=			val

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

						# The final layernorm.
						snake_case_   : Dict						=			transformer["""final_layernorm.weight"""]
						snake_case_   : Dict						=			transformer["""final_layernorm.bias"""]

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

						# It should be done!
						return output_state_dict
def 	__lowerCAmelCase						(		):


						'''simple docstring'''






						snake_case_   : List[str]						=			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_   : str						=			parser.parse_args()

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

						snake_case_   : Any						=			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_   : Any						=			"""gelu_fast"""
																		elif ds_args.openai_gelu:
																								snake_case_   : Tuple						=			"""gelu_new"""
																		else:
																								snake_case_   : List[str]						=			"""gelu"""
												else:
																		# in the very early days this used to be "gelu_new"
																		snake_case_   : Dict						=			"""gelu_new"""

												# Spell out all parameters in case the defaults change.
												snake_case_   : List[str]						=			GPTaConfig(
												    vocab_size=5_0_2_5_7					, n_positions=1_0_2_4					, n_embd=1_0_2_4					, n_layer=2_4					, n_head=1_6					, n_inner=4_0_9_6					, 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=5_0_2_5_6					, eos_token_id=5_0_2_5_6					, )
						else:
												snake_case_   : List[Any]						=			GPTaConfig.from_json_file(args.config_file    )

						snake_case_   : int						=			["""GPT2LMHeadModel"""]

						# Convert.
						print("""Converting"""    )
						snake_case_   : Tuple						=			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_   : str						=			ds_args.tokenizer_type
												if tokenizer_type == "GPT2BPETokenizer":
																		snake_case_   : Optional[Any]						=			"""gpt2"""
												elif tokenizer_type == "PretrainedFromHF":
																		snake_case_   : str						=			ds_args.tokenizer_name_or_path
												else:
																		raise ValueError(F'Unrecognized tokenizer_type {tokenizer_type}'    )
						else:
												snake_case_   : List[str]						=			"""gpt2"""

						snake_case_   : List[Any]						=			AutoTokenizer.from_pretrained(__UpperCamelCase    )
						snake_case_   : List[str]						=			type(__UpperCamelCase    ).__name__
						snake_case_   : Optional[int]						=			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_   : List[Any]						=			os.path.join(__UpperCamelCase					, """pytorch_model.bin"""    )
						print(F'Saving checkpoint to "{output_checkpoint_file}"'    )
						torch.save(__UpperCamelCase					, __UpperCamelCase    )


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

if __name__ == "__main__":
					main()

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

