


'''simple docstring'''






import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling


def  _snake_case       (					A			,   A	)						->   str:
							lowerCAmelCase__	       =     old_name

							if "patch_embed" in old_name:
														lowerCAmelCase__       , lowerCAmelCase__       , lowerCAmelCase__	       =     old_name.split('''.'''	)

														if layer == "0":
																					lowerCAmelCase__	       =     old_name.replace('''0'''			,   '''convolution1'''	)
														elif layer == "1":
																					lowerCAmelCase__	       =     old_name.replace('''1'''			,   '''batchnorm_before'''	)
														elif layer == "3":
																					lowerCAmelCase__	       =     old_name.replace('''3'''			,   '''convolution2'''	)
														else:
																					lowerCAmelCase__	       =     old_name.replace('''4'''			,   '''batchnorm_after'''	)

							if "network" in old_name and re.search(R'''\d\.\d'''			,   A	):
														lowerCAmelCase__	       =     R'''\b\d{2}\b'''
														if bool(re.search(A			,   A	)	):
																					lowerCAmelCase__	       =     re.search(R'''\d\.\d\d.'''			,   A	).group()
														else:
																					lowerCAmelCase__	       =     re.search(R'''\d\.\d.'''			,   A	).group()
														if int(match[0]	) < 6:
																					lowerCAmelCase__	       =     old_name.replace(A			,   ''''''	)
																					lowerCAmelCase__	       =     trimmed_name.replace('''network'''			,   match[0] + '''.meta4D_layers.blocks.''' + match[2:-1]	)
																					lowerCAmelCase__	       =     '''intermediate_stages.''' + trimmed_name
														else:
																					lowerCAmelCase__	       =     old_name.replace(A			,   ''''''	)
																					if int(match[2]	) < num_meta4D_last_stage:
																												lowerCAmelCase__	       =     trimmed_name.replace('''network'''			,   '''meta4D_layers.blocks.''' + match[2]	)
																					else:
																												lowerCAmelCase__	       =     str(int(match[2]	) - num_meta4D_last_stage	)
																												lowerCAmelCase__	       =     trimmed_name.replace('''network'''			,   '''meta3D_layers.blocks.''' + layer_index	)
																												if "norm1" in old_name:
																																			lowerCAmelCase__	       =     trimmed_name.replace('''norm1'''			,   '''layernorm1'''	)
																												elif "norm2" in old_name:
																																			lowerCAmelCase__	       =     trimmed_name.replace('''norm2'''			,   '''layernorm2'''	)
																												elif "fc1" in old_name:
																																			lowerCAmelCase__	       =     trimmed_name.replace('''fc1'''			,   '''linear_in'''	)
																												elif "fc2" in old_name:
																																			lowerCAmelCase__	       =     trimmed_name.replace('''fc2'''			,   '''linear_out'''	)

																					lowerCAmelCase__	       =     '''last_stage.''' + trimmed_name

							elif "network" in old_name and re.search(R'''.\d.'''			,   A	):
														lowerCAmelCase__	       =     old_name.replace('''network'''			,   '''intermediate_stages'''	)

							if "fc" in new_name:
														lowerCAmelCase__	       =     new_name.replace('''fc'''			,   '''convolution'''	)
							elif ("norm1" in new_name) and ("layernorm1" not in new_name):
														lowerCAmelCase__	       =     new_name.replace('''norm1'''			,   '''batchnorm_before'''	)
							elif ("norm2" in new_name) and ("layernorm2" not in new_name):
														lowerCAmelCase__	       =     new_name.replace('''norm2'''			,   '''batchnorm_after'''	)
							if "proj" in new_name:
														lowerCAmelCase__	       =     new_name.replace('''proj'''			,   '''projection'''	)
							if "dist_head" in new_name:
														lowerCAmelCase__	       =     new_name.replace('''dist_head'''			,   '''distillation_classifier'''	)
							elif "head" in new_name:
														lowerCAmelCase__	       =     new_name.replace('''head'''			,   '''classifier'''	)
							elif "patch_embed" in new_name:
														lowerCAmelCase__	       =     '''efficientformer.''' + new_name
							elif new_name == "norm.weight" or new_name == "norm.bias":
														lowerCAmelCase__	       =     new_name.replace('''norm'''			,   '''layernorm'''	)
														lowerCAmelCase__	       =     '''efficientformer.''' + new_name
							else:
														lowerCAmelCase__	       =     '''efficientformer.encoder.''' + new_name

							return new_name


def  _snake_case       (					A			,   A	)						->   Optional[int]:
							for key in checkpoint.copy().keys():
														lowerCAmelCase__	       =     checkpoint.pop(A	)
														lowerCAmelCase__	       =     val

							return checkpoint


def  _snake_case       (					)						->   Any:
							lowerCAmelCase__	       =     '''http://images.cocodataset.org/val2017/000000039769.jpg'''
							lowerCAmelCase__	       =     Image.open(requests.get(A			,   stream=A	).raw	)

							return image


def  _snake_case       (					A			,   A			,   A			,   A	)						->   List[str]:
							lowerCAmelCase__	       =     torch.load(A			,   map_location='''cpu'''	)['''model''']
							lowerCAmelCase__	       =     EfficientFormerConfig.from_json_file(A	)
							lowerCAmelCase__	       =     EfficientFormerForImageClassificationWithTeacher(A	)
							lowerCAmelCase__	       =     '''_'''.join(checkpoint_path.split('''/'''	)[-1].split('''.'''	)[0].split('''_'''	)[:-1]	)

							lowerCAmelCase__	       =     config.depths[-1] - config.num_metaad_blocks + 1
							lowerCAmelCase__	       =     convert_torch_checkpoint(A			,   A	)

							model.load_state_dict(A	)
							model.eval()

							lowerCAmelCase__	       =     {
							    '''bilinear''': PILImageResampling.BILINEAR,
							    '''bicubic''': PILImageResampling.BICUBIC,
							    '''nearest''': PILImageResampling.NEAREST,
							}

							# prepare image
							lowerCAmelCase__	       =     prepare_img()
							lowerCAmelCase__	       =     256
							lowerCAmelCase__	       =     224
							lowerCAmelCase__	       =     EfficientFormerImageProcessor(
							    size={'''shortest_edge''': image_size}			,   crop_size={'''height''': crop_size, '''width''': crop_size}			,   resample=pillow_resamplings['''bicubic''']			,   )
							lowerCAmelCase__	       =     processor(images=A			,   return_tensors='''pt'''	).pixel_values

							# original processing pipeline
							lowerCAmelCase__	       =     Compose(
							    [
							        Resize(A			,   interpolation=pillow_resamplings['''bicubic''']	),
							        CenterCrop(A	),
							        ToTensor(),
							        Normalize(A			,   A	),
							    ]	)
							lowerCAmelCase__	       =     image_transforms(A	).unsqueeze(0	)

							assert torch.allclose(A			,   A	)

							lowerCAmelCase__	       =     model(A	)
							lowerCAmelCase__	       =     outputs.logits

							lowerCAmelCase__	       =     (1, 1000)

							if "l1" in model_name:
														lowerCAmelCase__	       =     torch.Tensor(
														    [-0.1_312, 0.4_353, -1.0_499, -0.5_124, 0.4_183, -0.6_793, -1.3_777, -0.0_893, -0.7_358, -2.4_328]	)
														assert torch.allclose(logits[0, :10]			,   A			,   atol=1E-3	)
														assert logits.shape == expected_shape
							elif "l3" in model_name:
														lowerCAmelCase__	       =     torch.Tensor(
														    [-1.3_150, -1.5_456, -1.2_556, -0.8_496, -0.7_127, -0.7_897, -0.9_728, -0.3_052, 0.3_751, -0.3_127]	)
														assert torch.allclose(logits[0, :10]			,   A			,   atol=1E-3	)
														assert logits.shape == expected_shape
							elif "l7" in model_name:
														lowerCAmelCase__	       =     torch.Tensor(
														    [-1.0_283, -1.4_131, -0.5_644, -1.3_115, -0.5_785, -1.2_049, -0.7_528, 0.1_992, -0.3_822, -0.0_878]	)
														assert logits.shape == expected_shape
							else:
														raise ValueError(
														    F"""Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7"""	)

							# Save Checkpoints
							Path(A	).mkdir(exist_ok=A	)
							model.save_pretrained(A	)
							print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""	)
							processor.save_pretrained(A	)
							print(F"""Processor successfuly saved at {pytorch_dump_path}"""	)

							if push_to_hub:
														print('''Pushing model to the hub...'''	)

														model.push_to_hub(
														    repo_id=F"""Bearnardd/{pytorch_dump_path}"""			,   commit_message='''Add model'''			,   use_temp_dir=A			,   )
														processor.push_to_hub(
														    repo_id=F"""Bearnardd/{pytorch_dump_path}"""			,   commit_message='''Add image processor'''			,   use_temp_dir=A			,   )


if __name__ == "__main__":
		__UpperCAmelCase       =							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--pytorch_model_path''',
		    default=None,
		    type=str,
		    required=True,
		    help='''Path to EfficientFormer pytorch checkpoint.''',
		)
		parser.add_argument(
		    '''--config_file''',
		    default=None,
		    type=str,
		    required=True,
		    help='''The json file for EfficientFormer model config.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)

		parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')
		parser.add_argument(
		    '''--no-push_to_hub''',
		    dest='''push_to_hub''',
		    action='''store_false''',
		    help='''Do not push model and image processor to the hub''',
		)
		parser.set_defaults(push_to_hub=True)

		__UpperCAmelCase       =							parser.parse_args()
		convert_efficientformer_checkpoint(
		    checkpoint_path=args.pytorch_model_path,
		    efficientformer_config_file=args.config_file,
		    pytorch_dump_path=args.pytorch_dump_path,
		    push_to_hub=args.push_to_hub,
		)



'''simple docstring'''






import argparse
import json
import os
import re

import torch

from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging


logging.set_verbosity_info()

__UpperCAmelCase       =							[
    '''word_embeddings_layernorm.weight''',
    '''word_embeddings_layernorm.bias''',
    '''input_layernorm.weight''',
    '''input_layernorm.bias''',
    '''post_attention_layernorm.weight''',
    '''post_attention_layernorm.bias''',
    '''self_attention.dense.bias''',
    '''mlp.dense_4h_to_h.bias''',
    '''ln_f.weight''',
    '''ln_f.bias''',
]

__UpperCAmelCase       =							[
    '''mlp.dense_4h_to_h.weight''',
    '''self_attention.dense.weight''',
]


def  _snake_case       (					A			,   A	)						->   Optional[Any]:
							lowerCAmelCase__	       =     {
							    '''word_embeddings.weight''': '''word_embeddings.weight''',
							    '''word_embeddings.norm.weight''': '''word_embeddings_layernorm.weight''',
							    '''word_embeddings.norm.bias''': '''word_embeddings_layernorm.bias''',
							    '''weight''': '''ln_f.weight''',
							    '''bias''': '''ln_f.bias''',
							}

							if key in layer_rename_map:
														return layer_rename_map[key]

							# Handle transformer blocks
							lowerCAmelCase__	       =     int(re.match(R'''.*layer_(\d*).*'''			,   A	)[1]	)
							layer_number -= 3
							return F"""h.{layer_number}.""" + key


def  _snake_case       (					A	)						->   Optional[int]:
							if dtype == torch.bool:
														return 1 / 8
							lowerCAmelCase__	       =     re.search(R'''[^\d](\d+)$'''			,   str(A	)	)
							if bit_search is None:
														raise ValueError(F"""`dtype` is not a valid dtype: {dtype}."""	)
							lowerCAmelCase__	       =     int(bit_search.groups()[0]	)
							return bit_size // 8


def  _snake_case       (					A			,   A			,   A			,   A			,   A	)						->   Dict:
							# Construct model
							if bloom_config_file == "":
														lowerCAmelCase__	       =     BloomConfig()
							else:
														lowerCAmelCase__	       =     BloomConfig.from_json_file(A	)

							if shard_model:
														lowerCAmelCase__	       =     os.listdir(A	)
														lowerCAmelCase__	       =     sorted(filter(lambda A	: s.startswith('''layer'''	) and "model_00" in s			,   A	)	)

														lowerCAmelCase__	       =     {'''weight_map''': {}, '''metadata''': {}}
														lowerCAmelCase__	       =     0

														lowerCAmelCase__	       =     None

														lowerCAmelCase__	       =     BloomConfig()

														for j, file in enumerate(A	):
																					print('''Processing file: {}'''.format(A	)	)
																					lowerCAmelCase__	       =     None

																					for i in range(A	):
																												# load all TP files
																												lowerCAmelCase__	       =     file.replace('''model_00'''			,   F"""model_0{i}"""	)
																												lowerCAmelCase__	       =     torch.load(os.path.join(A			,   A	)			,   map_location='''cpu'''	)

																												# Rename keys in the transformers names
																												lowerCAmelCase__	       =     list(temp.keys()	)
																												for key in keys:
																																			lowerCAmelCase__	       =     temp.pop(A	)

																												if tensors is None:
																																			lowerCAmelCase__	       =     temp
																												else:
																																			for key in tensors.keys():
																																										if any(key.endswith(A	) for end in WEIGHTS_TO_AVERAGE_ENDSWITH	):
																																																	# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
																																																	tensors[key] += temp[key]
																																										else:
																																																	# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
																																																	lowerCAmelCase__	       =     1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN	) else 0
																																																	# We concatenate these weights accross TP ranks
																																																	lowerCAmelCase__	       =     torch.cat([tensors[key], temp[key]]			,   dim=A	)

            # Divide by the number of TP the weights we want to average
																					for key in tensors.keys():
																												if any(key.endswith(A	) for end in WEIGHTS_TO_AVERAGE_ENDSWITH	):
																																			lowerCAmelCase__	       =     tensors[key] / pretraining_tp
																					torch.save(
																					    A			,   os.path.join(
																					        A			,   '''pytorch_model_{}-of-{}.bin'''.format(str(j + 1	).zfill(5	)			,   str(len(A	)	).zfill(5	)	)			,   )			,   )

																					for key in tensors.keys():
																												lowerCAmelCase__	       =     tensors[key]
																												total_size += value.numel() * get_dtype_size(value.dtype	)
																												if key not in index_dict["weight_map"]:
																																			lowerCAmelCase__	       =     '''pytorch_model_{}-of-{}.bin'''.format(
																																			    str(j + 1	).zfill(5	)			,   str(len(A	)	).zfill(5	)	)

														lowerCAmelCase__	       =     BloomConfig()
														lowerCAmelCase__	       =     pytorch_dump_folder_path + '''/''' + CONFIG_NAME
														lowerCAmelCase__	       =     total_size
														with open(A			,   '''w'''			,   encoding='''utf-8'''	) as f:
																					f.write(config.to_json_string()	)
														with open(os.path.join(A			,   WEIGHTS_NAME + '''.index.json'''	)			,   '''w'''			,   encoding='''utf-8'''	) as f:
																					lowerCAmelCase__	       =     json.dumps(A			,   indent=2			,   sort_keys=A	) + '''\n'''
																					f.write(A	)
							else:
														lowerCAmelCase__	       =     BloomModel(A	)

														lowerCAmelCase__	       =     os.listdir(A	)
														lowerCAmelCase__	       =     sorted(filter(lambda A	: s.startswith('''layer'''	) and "model_00" in s			,   A	)	)

														lowerCAmelCase__	       =     None
														for i, file in enumerate(A	):
																					lowerCAmelCase__	       =     None
																					for i in range(A	):
																												# load all TP files
																												lowerCAmelCase__	       =     file.replace('''model_00'''			,   F"""model_0{i}"""	)
																												lowerCAmelCase__	       =     torch.load(os.path.join(A			,   A	)			,   map_location='''cpu'''	)

																												# Rename keys in the transformers names
																												lowerCAmelCase__	       =     list(temp.keys()	)
																												for key in keys:
																																			lowerCAmelCase__	       =     temp.pop(A	)

																												if tensors is None:
																																			lowerCAmelCase__	       =     temp
																												else:
																																			for key in tensors.keys():
																																										# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
																																										if any(key.endswith(A	) for end in WEIGHTS_TO_AVERAGE_ENDSWITH	):
																																																	tensors[key] += temp[key]
																																										else:
																																																	# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
																																																	lowerCAmelCase__	       =     1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN	) else 0
																																																	# We concatenate these weights accross TP ranks
																																																	lowerCAmelCase__	       =     torch.cat([tensors[key], temp[key]]			,   dim=A	)

            # Divide by the number of TP the weights we want to average
																					for key in tensors.keys():
																												if any(key.endswith(A	) for end in WEIGHTS_TO_AVERAGE_ENDSWITH	):
																																			lowerCAmelCase__	       =     tensors[key] / pretraining_tp

																					lowerCAmelCase__	       =     model.load_state_dict(A			,   strict=A	)
																					assert not other_keys.unexpected_keys, F"""The keys {other_keys.unexpected_keys} are unexpected"""
																					if missing_keys is None:
																												lowerCAmelCase__	       =     set(other_keys.missing_keys	)
																					else:
																												lowerCAmelCase__	       =     missing_keys.intersection(set(other_keys.missing_keys	)	)

														assert not missing_keys, F"""The keys {missing_keys} are missing"""

														# Save pytorch-model
														os.makedirs(A			,   exist_ok=A	)
														lowerCAmelCase__	       =     pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
														lowerCAmelCase__	       =     pytorch_dump_folder_path + '''/''' + CONFIG_NAME
														print(F"""Save PyTorch model to {pytorch_weights_dump_path} with dtype {config.torch_dtype}"""	)
														if config.torch_dtype is not None:
																					lowerCAmelCase__	       =     model.to(config.torch_dtype	)
														torch.save(model.state_dict()			,   A	)
														print(F"""Save configuration file to {pytorch_config_dump_path}"""	)
														with open(A			,   '''w'''			,   encoding='''utf-8'''	) as f:
																					f.write(config.to_json_string()	)


if __name__ == "__main__":
		__UpperCAmelCase       =							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--bloom_checkpoint_path''',
		    default=None,
		    type=str,
		    required=True,
		    help='''Path to the Megatron-LM checkpoint path.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		parser.add_argument(
		    '''--bloom_config_file''',
		    default='''''',
		    type=str,
		    help=(
		        '''An optional config json file corresponding to the pre-trained model. \n'''
		        '''This specifies the model architecture.'''
		    ),
		)
		parser.add_argument(
		    '''--shard_model''',
		    action='''store_true''',
		    help='''An optional setting to shard the output model \nThis enables sharding the converted checkpoint''',
		)
		parser.add_argument(
		    '''--pretraining_tp''',
		    default=4,
		    type=int,
		    help='''Pretraining TP rank that has been used when training the model in Megatron-LM \n''',
		)
		__UpperCAmelCase       =							parser.parse_args()
		convert_bloom_checkpoint_to_pytorch(
		    args.bloom_checkpoint_path,
		    args.bloom_config_file,
		    args.pytorch_dump_folder_path,
		    args.shard_model,
		    args.pretraining_tp,
		)