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()

__magic_name__    		=[
    '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',
]

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







def 			__UpperCamelCase     (    A			,				A				):
     UpperCamelCase__					    =   {
         '''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
     UpperCamelCase__					    =   int(re.match(r'''.*layer_(\d*).*'''			,				__UpperCAmelCase				)[1]				)
     layer_number -= 3
     return f"h.{layer_number}." + key







def 			__UpperCamelCase     (    A				):

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







def 			__UpperCamelCase     (    A			,				A			,				A			,				A			,				A				):

     if bloom_config_file == "":
          UpperCamelCase__					    =   BloomConfig()
     else:
          UpperCamelCase__					    =   BloomConfig.from_json_file(__UpperCAmelCase				)

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

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

          UpperCamelCase__					    =   None

          UpperCamelCase__					    =   BloomConfig()

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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


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









'''simple docstring'''

def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )						->   Optional[Any]:

					'''simple docstring'''

					snake_case_		     =							[False] * len(__UpperCAmelCase      )
					snake_case_		     =							[]
					queue.append(__UpperCAmelCase      )
					snake_case_		     =							True

					while queue:
										snake_case_		     =							queue.pop(0      )
										for ind in range(len(graph[u]      )      ):
															if visited[ind] is False and graph[u][ind] > 0:
																				queue.append(__UpperCAmelCase      )
																				snake_case_		     =							True
																				snake_case_		     =							u

					return visited[t]


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )						->   Tuple:

					'''simple docstring'''

					snake_case_		     =							[-1] * (len(__UpperCAmelCase      ))
					snake_case_		     =							0
					while bfs(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      ):
										snake_case_		     =							float('''Inf'''      )
										snake_case_		     =							sink

										while s != source:
															# Find the minimum value in select path
															snake_case_		     =							min(__UpperCAmelCase, graph[parent[s]][s]      )
															snake_case_		     =							parent[s]

										max_flow += path_flow
										snake_case_		     =							sink

										while v != source:
															snake_case_		     =							parent[v]
															graph[u][v] -= path_flow
															graph[v][u] += path_flow
															snake_case_		     =							parent[v]
					return max_flow


a    :							List[Any]										=    [
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

a						,a    :							int										=    0, 5

print(ford_fulkerson(graph, source, sink))

