
'''simple docstring'''







import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
_lowercase			:  str   							=	{
    "/attention/": "/0/SelfAttention/",
    "/self_attention/": "/0/SelfAttention/",
    "/encoder_decoder_attention/": "/1/EncDecAttention/",
    "value": "v",
    "query": "q",
    "key": "k",
    "out": "o",
    "pre_self_attention_layer_norm": "0/layer_norm",
    "pre_cross_attention_layer_norm": "1/layer_norm",
    "pre_attention_layer_norm": "0/layer_norm",  # previously 1, but seems wrong
    "token_embedder": "shared",
    "encoder_norm": "final_layer_norm",
    "decoder_norm": "final_layer_norm",
    "relpos_bias/rel_embedding": "block/0/layer/0/SelfAttention/relative_attention_bias/weight",
    "router/router_weights/w/": "router/classifier/",
    "roer/roer_weights/w/": "router/classifier/",
    "logits_dense": "lm_head",
}
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			int							):



						"""simple docstring"""





						lowercase_     :					List[str]													=	list(s_dict.keys()							)
						for key in keys:
												lowercase_     :					List[Any]													=	r'.*/layers_(\d+)'
												lowercase_     :					Union[str, Any]													=	key
												if re.match(lowerCAmelCase_				,				lowerCAmelCase_							):
																		lowercase_     :					int													=	re.sub(R'''layers_(\d+)'''				,				R'''block/\1/layer'''				,				lowerCAmelCase_							)

												lowercase_     :					Dict													=	r'(encoder|decoder)\/'

												if re.match(lowerCAmelCase_				,				lowerCAmelCase_							):
																		lowercase_     :					Union[str, Any]													=	re.match(lowerCAmelCase_				,				lowerCAmelCase_							).groups()
																		if groups[0] == "encoder":
																								lowercase_     :					Dict													=	re.sub(R'''/mlp/'''				,				R'''/1/mlp/'''				,				lowerCAmelCase_							)
																								lowercase_     :					Union[str, Any]													=	re.sub(R'''/pre_mlp_layer_norm/'''				,				R'''/1/layer_norm/'''				,				lowerCAmelCase_							)

																		elif groups[0] == "decoder":
																								lowercase_     :					Dict													=	re.sub(R'''/mlp/'''				,				R'''/2/mlp/'''				,				lowerCAmelCase_							)
																								lowercase_     :					Dict													=	re.sub(R'''/pre_mlp_layer_norm/'''				,				R'''/2/layer_norm/'''				,				lowerCAmelCase_							)

        # 2. Convert other classic mappings
												for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
																		if old_key in new_key:
																								lowercase_     :					int													=	new_key.replace(lowerCAmelCase_				,				lowerCAmelCase_							)

												print(F'''{key} -> {new_key}'''							)
												lowercase_     :					Optional[int]													=	s_dict.pop(lowerCAmelCase_							)

						if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
												lowercase_     :					int													=	s_dict[
												    'encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'
												].T
						if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
												lowercase_     :					Optional[int]													=	s_dict[
												    'decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'
												].T

						# 3. Take extra care of the EXPERTS layer
						for key in list(s_dict.keys()							):
												if "expert" in key:
																		lowercase_     :					Any													=	s_dict[key].shape[0]
																		lowercase_     :					str													=	s_dict[key]
																		for idx in range(lowerCAmelCase_							):
																								lowercase_     :					Any													=	expert_weihts[idx]
																								print(F'''{key} -> {key.replace('expert/'				,				'nested fstring'							)}'''							)

																		s_dict.pop(lowerCAmelCase_							)

						return s_dict


_lowercase			:  List[str]   							=	{
    "NUM_ENCODER_LAYERS": "num_layers",
    "NUM_DECODER_LAYERS": "num_decoder_layers",
    "NUM_HEADS": "num_heads",
    "HEAD_DIM": "d_kv",
    "EMBED_DIM": "d_model",
    "MLP_DIM": "d_ff",
    "NUM_SELECTED_EXPERTS": "num_selected_experts",
    "NUM_ENCODER_SPARSE_LAYERS": "num_sparse_encoder_layers",
    "NUM_DECODER_SPARSE_LAYERS": "num_sparse_decoder_layers",
    "dense.MlpBlock.activations": "feed_forward_proj",
}
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Optional[int]				,				__SCREAMING_SNAKE_CASE :			Union[str, Any]							):



						"""simple docstring"""





						import regex as re

						with open(lowerCAmelCase_				,				'''r'''							) as f:
												lowercase_     :					List[str]													=	f.read()

						lowercase_     :					Union[str, Any]													=	re.findall(R'''(.*) = ([0-9.]*)'''				,				lowerCAmelCase_							)
						lowercase_     :					Tuple													=	{}
						for param, value in regex_match:
												if param in GIN_TO_CONFIG_MAPPING and value != "":
																		lowercase_     :					Union[str, Any]													=	float(lowerCAmelCase_							) if '.' in value else int(lowerCAmelCase_							)

						lowercase_     :					Any													=	re.findall(R'''(.*activations) = \(\'(.*)\',\)'''				,				lowerCAmelCase_							)[0]
						lowercase_     :					Tuple													=	str(activation[1]							)

						lowercase_     :					Optional[Any]													=	num_experts
						lowercase_     :					List[Any]													=	SwitchTransformersConfig(**lowerCAmelCase_							)
						return config
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Any				,				__SCREAMING_SNAKE_CASE :			Optional[int]				,				__SCREAMING_SNAKE_CASE :			Optional[Any]=None				,				__SCREAMING_SNAKE_CASE :			Union[str, Any]="./"				,				__SCREAMING_SNAKE_CASE :			int=8							):



						"""simple docstring"""





						print(F'''Loading flax weights from : {flax_checkpoint_path}'''							)
						lowercase_     :					Union[str, Any]													=	checkpoints.load_tax_checkpoint(lowerCAmelCase_							)

						if gin_file is not None:
												lowercase_     :					int													=	convert_gin_to_config(lowerCAmelCase_				,				lowerCAmelCase_							)
						else:
												lowercase_     :					Dict													=	SwitchTransformersConfig.from_pretrained(lowerCAmelCase_							)

						lowercase_     :					Optional[int]													=	SwitchTransformersForConditionalGeneration(lowerCAmelCase_							)

						lowercase_     :					Union[str, Any]													=	flax_params['target']
						lowercase_     :					Optional[Any]													=	flatten_dict(lowerCAmelCase_				,				sep='''/'''							)
						lowercase_     :					List[Any]													=	rename_keys(lowerCAmelCase_							)
						lowercase_     :					Dict													=	unflatten_dict(lowerCAmelCase_				,				sep='''/'''							)

						# Load the flax params in the PT model
						load_flax_weights_in_pytorch_model(lowerCAmelCase_				,				lowerCAmelCase_							)

						print(F'''Save PyTorch model to {pytorch_dump_path}'''							)
						pt_model.save_pretrained(lowerCAmelCase_							)


if __name__ == "__main__":
							_lowercase			:  Optional[int]   							=	argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--switch_t5x_checkpoint_path",
							    default=None,
							    type=str,
							    required=True,
							    help=(
							        "The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the"
							        " model architecture. If not provided, a `gin_file` has to be provided."
							    ),
							)
							parser.add_argument(
							    "--gin_file",
							    default=None,
							    type=str,
							    required=False,
							    help="Path to the gin config file. If not provided, a `config_file` has to be passed   ",
							)
							parser.add_argument(
							    "--config_name", default=None, type=str, required=False, help="Config name of SwitchTransformers model."
							)
							parser.add_argument(
							    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output pytorch model."
							)
							parser.add_argument("--num_experts", default=8, type=int, required=False, help="Number of experts")
							_lowercase			:  Any   							=	parser.parse_args()
							convert_flax_checkpoint_to_pytorch(
							    args.switch_tax_checkpoint_path,
							    args.config_name,
							    args.gin_file,
							    args.pytorch_dump_folder_path,
							    args.num_experts,
							)





'''simple docstring'''







import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


_lowercase			:  Any   							=	logging.get_logger(__name__)






class 			lowerCAmelCase__       (  lowerCamelCase_					):






					def __init__(       self    ,							*__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											warnings.warn(
											    '''The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
											    ''' use DonutImageProcessor instead.'''    ,							__SCREAMING_SNAKE_CASE    ,							)
											super().__init__(*__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		)



