

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()

a_						     =    [
    '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',
]

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

def  lowerCamelCase__ (					_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Dict				     =       {
	    "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
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       int(re.match(r".*layer_(\d*).*"							,			_a)[1])
	layer_number -= 3
	return f"h.{layer_number}." + key

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

def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a							,			_a):
	# Construct model
	if bloom_config_file == "":
		SCREAMING_SNAKE_CASE   :      int				     =       BloomConfig()
	else:
		SCREAMING_SNAKE_CASE   :      Optional[int]				     =       BloomConfig.from_json_file(_a)

	if shard_model:
		SCREAMING_SNAKE_CASE   :      List[Any]				     =       os.listdir(_a)
		SCREAMING_SNAKE_CASE   :      str				     =       sorted(filter(lambda _a: s.startswith("layer") and "model_00" in s							,			_a))

		SCREAMING_SNAKE_CASE   :      str				     =       {"weight_map": {}, "metadata": {}}
		SCREAMING_SNAKE_CASE   :      List[Any]				     =       0

		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       None

		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       BloomConfig()

		for j, file in enumerate(_a):
			print("Processing file: {}".format(_a))
			SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       None

			for i in range(_a):
				# load all TP files
				SCREAMING_SNAKE_CASE   :      Tuple				     =       file.replace("model_00"							,			f"model_0{i}")
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       torch.load(os.path.join(_a							,			_a)							,			map_location="cpu")

				# Rename keys in the transformers names
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       list(temp.keys())
				for key in keys:
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       temp.pop(_a)

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

		SCREAMING_SNAKE_CASE   :      Any				     =       BloomConfig()
		SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       pytorch_dump_folder_path + "/" + CONFIG_NAME
		SCREAMING_SNAKE_CASE   :      int				     =       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:
			SCREAMING_SNAKE_CASE   :      List[Any]				     =       json.dumps(_a							,			indent=2							,			sort_keys=_a) + "\n"
			f.write(_a)
	else:
		SCREAMING_SNAKE_CASE   :      List[Any]				     =       BloomModel(_a)

		SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       os.listdir(_a)
		SCREAMING_SNAKE_CASE   :      Optional[int]				     =       sorted(filter(lambda _a: s.startswith("layer") and "model_00" in s							,			_a))

		SCREAMING_SNAKE_CASE   :      List[Any]				     =       None
		for i, file in enumerate(_a):
			SCREAMING_SNAKE_CASE   :      List[str]				     =       None
			for i in range(_a):
				# load all TP files
				SCREAMING_SNAKE_CASE   :      int				     =       file.replace("model_00"							,			f"model_0{i}")
				SCREAMING_SNAKE_CASE   :      Tuple				     =       torch.load(os.path.join(_a							,			_a)							,			map_location="cpu")

				# Rename keys in the transformers names
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       list(temp.keys())
				for key in keys:
					SCREAMING_SNAKE_CASE   :      Dict				     =       temp.pop(_a)

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

			SCREAMING_SNAKE_CASE   :      int				     =       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:
				SCREAMING_SNAKE_CASE   :      Tuple				     =       set(other_keys.missing_keys)
			else:
				SCREAMING_SNAKE_CASE   :      Dict				     =       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)
		SCREAMING_SNAKE_CASE   :      int				     =       pytorch_dump_folder_path + "/" + WEIGHTS_NAME
		SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       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:
			SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       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__":
			a_						     =    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',
			)
			a_						     =    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 os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property






@unittest.skipUnless(os.path.exists(__A   )							,	'Tatoeba directory does not exist.'   )
class _UpperCamelCase							(							unittest.TestCase   ):
			'''simple docstring'''







			@cached_property
			def 							__UpperCamelCase (			self							: Any	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       tempfile.mkdtemp()
				return TatoebaConverter(save_dir=a	)
			@slow
			def 							__UpperCamelCase (			self							: List[Any]	)							->       int:

				"""simple docstring"""

				self.resolver.convert_models(["heb-eng"]	)






			@slow
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.resolver.write_model_card("opus-mt-he-en"      ,      dry_run=a	)
				assert mmeta["long_pair"] == "heb-eng"