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()

SCREAMING_SNAKE_CASE :	Tuple							     =		[
    "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",
]

SCREAMING_SNAKE_CASE :	Dict							     =		[
    "mlp.dense_4h_to_h.weight",
    "self_attention.dense.weight",
]



def   __A		(			_A							,				_A					):



 """simple docstring"""


 __a       =  {
     '''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
 __a       =  int(re.match(r".*layer_(\d*).*"							,				a_					)[1]					)
 layer_number -= 3
 return f"""h.{layer_number}.""" + key



def   __A		(			_A					):



 """simple docstring"""


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



def   __A		(			_A							,				_A							,				_A							,				_A							,				_A					):



 """simple docstring"""


 if bloom_config_file == "":
  __a       =  BloomConfig()
 else:
  __a       =  BloomConfig.from_json_file(a_					)

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

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

  __a       =  None

  __a       =  BloomConfig()

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

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

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

    if tensors is None:
     __a       =  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
       __a       =  1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN					) else 0
       # We concatenate these weights accross TP ranks
       __a       =  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					):
     __a       =  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():
    __a       =  tensors[key]
    total_size += value.numel() * get_dtype_size(value.dtype					)
    if key not in index_dict["weight_map"]:
     __a       =  '''pytorch_model_{}-of-{}.bin'''.format(
         str(j + 1					).zfill(5					)							,				str(len(a_					)					).zfill(5					)					)

  __a       =  BloomConfig()
  __a       =  pytorch_dump_folder_path + '''/''' + CONFIG_NAME
  __a       =  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:
   __a       =  json.dumps(a_							,				indent=2							,				sort_keys=a_					) + '''\n'''
   f.write(a_					)
 else:
  __a       =  BloomModel(a_					)

  __a       =  os.listdir(a_					)
  __a       =  sorted(filter(lambda _A					: s.startswith("layer"					) and "model_00" in s							,				a_					)					)

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

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

    if tensors is None:
     __a       =  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
       __a       =  1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN					) else 0
       # We concatenate these weights accross TP ranks
       __a       =  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					):
     __a       =  tensors[key] / pretraining_tp

   __a       =  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:
    __a       =  set(other_keys.missing_keys					)
   else:
    __a       =  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_					)
  __a       =  pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
  __a       =  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:
   __a       =  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__":
  SCREAMING_SNAKE_CASE :	Any							     =		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""",
  )
  SCREAMING_SNAKE_CASE :	str							     =		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,
  )



import unittest

from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow


if is_flax_available():
  import jax.numpy as jnp

  from transformers import FlaxXLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_flax
class    A_ (       unittest.TestCase ):

  @slow
  def 		_UpperCAmelCase   (		self :      Dict       ):
   __a       =  FlaxXLMRobertaModel.from_pretrained("xlm-roberta-base"       )
   __a       =  AutoTokenizer.from_pretrained("xlm-roberta-base"       )
   __a       =  "The dog is cute and lives in the garden house"
   __a       =  jnp.array([tokenizer.encode(__SCREAMING_SNAKE_CASE       )]       )

   __a       =  (1, 12, 7_68)  # batch_size, sequence_length, embedding_vector_dim
   __a       =  jnp.array(
       [[-0.01_01, 0.12_18, -0.08_03, 0.08_01, 0.13_27, 0.07_76, -0.12_15, 0.23_83, 0.33_38, 0.31_06, 0.03_00, 0.02_52]]       )

   __a       =  model(__SCREAMING_SNAKE_CASE       )["last_hidden_state"]
   self.assertEqual(output.shape     ,							__SCREAMING_SNAKE_CASE       )
   # compare the actual values for a slice of last dim
   self.assertTrue(jnp.allclose(output[:, :, -1]     ,							__SCREAMING_SNAKE_CASE     ,							atol=1E-3       )       )


