
'''simple docstring'''
import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class       _lowerCAmelCase   (   __snake_case				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						["image_processor", "tokenizer"]
							lowerCAmelCase_ 						=						"LayoutLMv3ImageProcessor"
							lowerCAmelCase_ 						=						("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")

							def __init__(self  ,  UpperCAmelCase=None  ,  UpperCAmelCase=None  ,  **UpperCAmelCase  )					->       int:
														_snake_case       =  None
														if "feature_extractor" in kwargs:
																					warnings.warn(
																					    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
																					    """ instead."""  ,  UpperCAmelCase  ,  )
																					_snake_case       =  kwargs.pop("""feature_extractor"""  )

														_snake_case       =  image_processor if image_processor is not None else feature_extractor
														if image_processor is None:
																					raise ValueError("""You need to specify an `image_processor`."""  )
														if tokenizer is None:
																					raise ValueError("""You need to specify a `tokenizer`."""  )

														super().__init__(UpperCAmelCase  ,  UpperCAmelCase  )

							def __call__(self  ,  UpperCAmelCase  ,  UpperCAmelCase = None  ,  UpperCAmelCase = None  ,  UpperCAmelCase = None  ,  UpperCAmelCase = None  ,  UpperCAmelCase = True  ,  UpperCAmelCase = False  ,  UpperCAmelCase = None  ,  UpperCAmelCase = None  ,  UpperCAmelCase = 0  ,  UpperCAmelCase = None  ,  UpperCAmelCase = None  ,  UpperCAmelCase = None  ,  UpperCAmelCase = False  ,  UpperCAmelCase = False  ,  UpperCAmelCase = False  ,  UpperCAmelCase = False  ,  UpperCAmelCase = True  ,  UpperCAmelCase = None  ,  **UpperCAmelCase  ,  )					->       BatchEncoding:
														# verify input
														if self.image_processor.apply_ocr and (boxes is not None):
																					raise ValueError(
																					    """You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."""  )

														if self.image_processor.apply_ocr and (word_labels is not None):
																					raise ValueError(
																					    """You cannot provide word labels if you initialized the image processor with apply_ocr set to True."""  )

														# first, apply the image processor
														_snake_case       =  self.image_processor(images=UpperCAmelCase  ,  return_tensors=UpperCAmelCase  )

														# second, apply the tokenizer
														if text is not None and self.image_processor.apply_ocr and text_pair is None:
																					if isinstance(UpperCAmelCase  ,  UpperCAmelCase  ):
																												_snake_case       =  [text]  # add batch dimension (as the image processor always adds a batch dimension)
																					_snake_case       =  features["""words"""]

														_snake_case       =  self.tokenizer(
														    text=text if text is not None else features["""words"""]  ,  text_pair=text_pair if text_pair is not None else None  ,  boxes=boxes if boxes is not None else features["""boxes"""]  ,  word_labels=UpperCAmelCase  ,  add_special_tokens=UpperCAmelCase  ,  padding=UpperCAmelCase  ,  truncation=UpperCAmelCase  ,  max_length=UpperCAmelCase  ,  stride=UpperCAmelCase  ,  pad_to_multiple_of=UpperCAmelCase  ,  return_token_type_ids=UpperCAmelCase  ,  return_attention_mask=UpperCAmelCase  ,  return_overflowing_tokens=UpperCAmelCase  ,  return_special_tokens_mask=UpperCAmelCase  ,  return_offsets_mapping=UpperCAmelCase  ,  return_length=UpperCAmelCase  ,  verbose=UpperCAmelCase  ,  return_tensors=UpperCAmelCase  ,  **UpperCAmelCase  ,  )

														# add pixel values
														_snake_case       =  features.pop("""pixel_values"""  )
														if return_overflowing_tokens is True:
																					_snake_case       =  self.get_overflowing_images(UpperCAmelCase  ,  encoded_inputs["""overflow_to_sample_mapping"""]  )
														_snake_case       =  images

														return encoded_inputs

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       List[str]:
														# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
														_snake_case       =  []
														for sample_idx in overflow_to_sample_mapping:
																					images_with_overflow.append(images[sample_idx]  )

														if len(UpperCAmelCase  ) != len(UpperCAmelCase  ):
																					raise ValueError(
																					    """Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
																					    f""" {len(UpperCAmelCase  )} and {len(UpperCAmelCase  )}"""  )

														return images_with_overflow

							def 	lowercase							(self  ,  *UpperCAmelCase  ,  **UpperCAmelCase  )					->       Tuple:
														return self.tokenizer.batch_decode(*UpperCAmelCase  ,  **UpperCAmelCase  )

							def 	lowercase							(self  ,  *UpperCAmelCase  ,  **UpperCAmelCase  )					->       Dict:
														return self.tokenizer.decode(*UpperCAmelCase  ,  **UpperCAmelCase  )

							@property
							def 	lowercase							(self  )					->       str:
														return ["input_ids", "bbox", "attention_mask", "pixel_values"]

							@property
							def 	lowercase							(self  )					->       Dict:
														warnings.warn(
														    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""  ,  UpperCAmelCase  ,  )
														return self.image_processor_class





							@property
							def 	lowercase							(self  )					->       Union[str, Any]:
														warnings.warn(
														    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""  ,  UpperCAmelCase  ,  )
														return self.image_processor

'''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()

__lowerCAmelCase												=	[
    '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',
]

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






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							_snake_case       =  {
							    """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
							_snake_case       =  int(re.match(R""".*layer_(\d*).*"""							, _SCREAMING_SNAKE_CASE				)[1]				)
							layer_number -= 3
							return f"""h.{layer_number}.""" + key






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






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							# Construct model
							if bloom_config_file == "":
														_snake_case       =  BloomConfig()
							else:
														_snake_case       =  BloomConfig.from_json_file(_SCREAMING_SNAKE_CASE				)

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

														_snake_case       =  {"""weight_map""": {}, """metadata""": {}}
														_snake_case       =  0

														_snake_case       =  None

														_snake_case       =  BloomConfig()

														for j, file in enumerate(_SCREAMING_SNAKE_CASE				):
																					print("""Processing file: {}""".format(_SCREAMING_SNAKE_CASE				)				)
																					_snake_case       =  None

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

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

																												if tensors is None:
																																			_snake_case       =  temp
																												else:
																																			for key in tensors.keys():
																																										if any(key.endswith(_SCREAMING_SNAKE_CASE				) 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
																																																	_snake_case       =  1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN				) else 0
																																																	# We concatenate these weights accross TP ranks
																																																	_snake_case       =  torch.cat([tensors[key], temp[key]]							, dim=_SCREAMING_SNAKE_CASE				)

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

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

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

														_snake_case       =  os.listdir(_SCREAMING_SNAKE_CASE				)
														_snake_case       =  sorted(filter(lambda _SCREAMING_SNAKE_CASE				: s.startswith("""layer"""				) and "model_00" in s							, _SCREAMING_SNAKE_CASE				)				)

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

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

																												if tensors is None:
																																			_snake_case       =  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(_SCREAMING_SNAKE_CASE				) for end in WEIGHTS_TO_AVERAGE_ENDSWITH				):
																																																	tensors[key] += temp[key]
																																										else:
																																																	# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
																																																	_snake_case       =  1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN				) else 0
																																																	# We concatenate these weights accross TP ranks
																																																	_snake_case       =  torch.cat([tensors[key], temp[key]]							, dim=_SCREAMING_SNAKE_CASE				)

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

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

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

														# Save pytorch-model
														os.makedirs(_SCREAMING_SNAKE_CASE							, exist_ok=_SCREAMING_SNAKE_CASE				)
														_snake_case       =  pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
														_snake_case       =  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:
																					_snake_case       =  model.to(config.torch_dtype				)
														torch.save(model.state_dict()							, _SCREAMING_SNAKE_CASE				)
														print(f"""Save configuration file to {pytorch_config_dump_path}"""				)
														with open(_SCREAMING_SNAKE_CASE							, """w"""							, encoding="""utf-8"""				) as f:
																					f.write(config.to_json_string()				)


if __name__ == "__main__":
				__lowerCAmelCase												=	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',
				)
				__lowerCAmelCase												=	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,
				)