
"""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
SCREAMING_SNAKE_CASE_ 			=     {
    """/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       lowercase			(_lowerCAmelCase		):
						__lowerCAmelCase   					=   list(s_dict.keys()		)
						for key in keys:
												__lowerCAmelCase   					=   r""".*/layers_(\d+)"""
												__lowerCAmelCase   					=   key
												if re.match(lowercase_      ,  lowercase_		):
																		__lowerCAmelCase   					=   re.sub(r"""layers_(\d+)"""      ,  r"""block/\1/layer"""      ,  lowercase_		)

												__lowerCAmelCase   					=   r"""(encoder|decoder)\/"""

												if re.match(lowercase_      ,  lowercase_		):
																		__lowerCAmelCase   					=   re.match(lowercase_      ,  lowercase_		).groups()
																		if groups[0] == "encoder":
																								__lowerCAmelCase   					=   re.sub(r"""/mlp/"""      ,  r"""/1/mlp/"""      ,  lowercase_		)
																								__lowerCAmelCase   					=   re.sub(r"""/pre_mlp_layer_norm/"""      ,  r"""/1/layer_norm/"""      ,  lowercase_		)

																		elif groups[0] == "decoder":
																								__lowerCAmelCase   					=   re.sub(r"""/mlp/"""      ,  r"""/2/mlp/"""      ,  lowercase_		)
																								__lowerCAmelCase   					=   re.sub(r"""/pre_mlp_layer_norm/"""      ,  r"""/2/layer_norm/"""      ,  lowercase_		)

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

												print(f"""{key} -> {new_key}"""		)
												__lowerCAmelCase   					=   s_dict.pop(lowercase_		)

						if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
												__lowerCAmelCase   					=   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:
												__lowerCAmelCase   					=   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:
																		__lowerCAmelCase   					=   s_dict[key].shape[0]
																		__lowerCAmelCase   					=   s_dict[key]
																		for idx in range(lowercase_		):
																								__lowerCAmelCase   					=   expert_weihts[idx]
																								print(f"""{key} -> {key.replace("expert/"      ,  "nested fstring"		)}"""		)

																		s_dict.pop(lowercase_		)

						return s_dict


SCREAMING_SNAKE_CASE_ 			=     {
    """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       lowercase			(_lowerCAmelCase      ,  _lowerCAmelCase		):
						import regex as re

						with open(lowercase_      ,  """r"""		) as f:
												__lowerCAmelCase   					=   f.read()

						__lowerCAmelCase   					=   re.findall(r"""(.*) = ([0-9.]*)"""      ,  lowercase_		)
						__lowerCAmelCase   					=   {}
						for param, value in regex_match:
												if param in GIN_TO_CONFIG_MAPPING and value != "":
																		__lowerCAmelCase   					=   float(lowercase_		) if """.""" in value else int(lowercase_		)

						__lowerCAmelCase   					=   re.findall(r"""(.*activations) = \(\'(.*)\',\)"""      ,  lowercase_		)[0]
						__lowerCAmelCase   					=   str(activation[1]		)

						__lowerCAmelCase   					=   num_experts
						__lowerCAmelCase   					=   SwitchTransformersConfig(**lowercase_		)
						return config

def       lowercase			(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase=None      ,  _lowerCAmelCase="./"      ,  _lowerCAmelCase=8		):
						print(f"""Loading flax weights from : {flax_checkpoint_path}"""		)
						__lowerCAmelCase   					=   checkpoints.load_tax_checkpoint(lowercase_		)

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

						__lowerCAmelCase   					=   SwitchTransformersForConditionalGeneration(lowercase_		)

						__lowerCAmelCase   					=   flax_params["""target"""]
						__lowerCAmelCase   					=   flatten_dict(lowercase_      ,  sep="""/"""		)
						__lowerCAmelCase   					=   rename_keys(lowercase_		)
						__lowerCAmelCase   					=   unflatten_dict(lowercase_      ,  sep="""/"""		)

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

						print(f"""Save PyTorch model to {pytorch_dump_path}"""		)
						pt_model.save_pretrained(lowercase_		)


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






from __future__ import annotations







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_						)   ->       tuple[float, list[float]]:



       """simple docstring"""

       A__       =    list(range(len(lowercase_						)						)						)
       A__       =    [v / w for v, w in zip(lowercase_ ,				lowercase_						)]
       index.sort(key=lambda lowercase_						: ratio[i] ,				reverse=lowercase_						)

       A__       =    0
       A__       =    [0] * len(lowercase_						)
       for i in index:
              if weight[i] <= capacity:
                     A__       =    1
                     max_value += value[i]
                     capacity -= weight[i]
              else:
                     A__       =    capacity / weight[i]
                     max_value += value[i] * capacity / weight[i]
                     break

       return max_value, fractions


if __name__ == "__main__":
  import doctest

  doctest.testmod()


