


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
UpperCAmelCase_      : Optional[Any]  					=       {
    '/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        SCREAMING_SNAKE_CASE_						(			__A :			List[str]				)	->		List[Any]:

       """simple docstring"""

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

              a_					:		int							=  R'(encoder|decoder)\/'

              if re.match(__A  ,    __A				):
                     a_					:		List[str]							=  re.match(__A  ,    __A				).groups()
                     if groups[0] == "encoder":
                            a_					:		int							=  re.sub(R'/mlp/'  ,    R'/1/mlp/'  ,    __A				)
                            a_					:		Optional[int]							=  re.sub(R'/pre_mlp_layer_norm/'  ,    R'/1/layer_norm/'  ,    __A				)

                     elif groups[0] == "decoder":
                            a_					:		List[str]							=  re.sub(R'/mlp/'  ,    R'/2/mlp/'  ,    __A				)
                            a_					:		List[Any]							=  re.sub(R'/pre_mlp_layer_norm/'  ,    R'/2/layer_norm/'  ,    __A				)

                                # 2. Convert other classic mappings
              for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
                     if old_key in new_key:
                            a_					:		List[Any]							=  new_key.replace(__A  ,    __A				)

              print(F"""{key} -> {new_key}"""				)
              a_					:		List[Any]							=  s_dict.pop(__A				)

       if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
              a_					:		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:
              a_					:		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:
                     a_					:		List[Any]							=  s_dict[key].shape[0]
                     a_					:		str							=  s_dict[key]
                     for idx in range(__A				):
                            a_					:		str							=  expert_weihts[idx]
                            print(F"""{key} -> {key.replace("expert/"  ,    "nested fstring"				)}"""				)

                     s_dict.pop(__A				)

       return s_dict


UpperCAmelCase_      : Optional[Any]  					=       {
    '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        SCREAMING_SNAKE_CASE_						(			__A :			Optional[int]  ,    __A :			Tuple				)	->		List[Any]:

       """simple docstring"""

       import regex as re

       with open(__A  ,    'r'				) as f:
              a_					:		int							=  f.read()

       a_					:		Optional[int]							=  re.findall(R'(.*) = ([0-9.]*)'  ,    __A				)
       a_					:		Optional[int]							=  {}
       for param, value in regex_match:
              if param in GIN_TO_CONFIG_MAPPING and value != "":
                     a_					:		int							=  float(__A				) if '.' in value else int(__A				)

       a_					:		int							=  re.findall(R'(.*activations) = \(\'(.*)\',\)'  ,    __A				)[0]
       a_					:		int							=  str(activation[1]				)

       a_					:		Union[str, Any]							=  num_experts
       a_					:		str							=  SwitchTransformersConfig(**__A				)
       return config







def        SCREAMING_SNAKE_CASE_						(			__A :			Union[str, Any]  ,    __A :			Union[str, Any]  ,    __A :			Optional[int]=None  ,    __A :			Dict="./"  ,    __A :			Optional[int]=8				)	->		Union[str, Any]:

       """simple docstring"""

       print(F"""Loading flax weights from : {flax_checkpoint_path}"""				)
       a_					:		Any							=  checkpoints.load_tax_checkpoint(__A				)

       if gin_file is not None:
              a_					:		Tuple							=  convert_gin_to_config(__A  ,    __A				)
       else:
              a_					:		Dict							=  SwitchTransformersConfig.from_pretrained(__A				)

       a_					:		Union[str, Any]							=  SwitchTransformersForConditionalGeneration(__A				)

       a_					:		Tuple							=  flax_params['target']
       a_					:		Union[str, Any]							=  flatten_dict(__A  ,    sep='/'				)
       a_					:		Dict							=  rename_keys(__A				)
       a_					:		List[str]							=  unflatten_dict(__A  ,    sep='/'				)

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

       print(F"""Save PyTorch model to {pytorch_dump_path}"""				)
       pt_model.save_pretrained(__A				)


if __name__ == "__main__":
   UpperCAmelCase_      : Union[str, Any]  					=       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')
   UpperCAmelCase_      : Optional[int]  					=       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,
   )



import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
   a_    					=       argparse.ArgumentParser(
       description=(
           'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'
           ' Distillation'
       )
   )
   parser.add_argument('--model_type', default='roberta', choices=['roberta', 'gpt2'])
   parser.add_argument('--model_name', default='roberta-large', type=str)
   parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_roberta_048131723.pth', type=str)
   parser.add_argument('--vocab_transform', action='store_true')
   a_    					=       parser.parse_args()

   if args.model_type == "roberta":
      a_    					=       RobertaForMaskedLM.from_pretrained(args.model_name)
      a_    					=       'roberta'
   elif args.model_type == "gpt2":
      a_    					=       GPTaLMHeadModel.from_pretrained(args.model_name)
      a_    					=       'transformer'

   a_    					=       model.state_dict()
   a_    					=       {}

   # Embeddings #
   if args.model_type == "gpt2":
      for param_name in ["wte.weight", "wpe.weight"]:
         a_    					=       state_dict[F"""{prefix}.{param_name}"""]
   else:
      for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
         a_    					=       F"""{prefix}.embeddings.{w}.weight"""
         a_    					=       state_dict[param_name]
      for w in ["weight", "bias"]:
         a_    					=       F"""{prefix}.embeddings.LayerNorm.{w}"""
         a_    					=       state_dict[param_name]

    # Transformer Blocks #
   a_    					=       0
   for teacher_idx in [0, 2, 4, 7, 9, 11]:
      if args.model_type == "gpt2":
         for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
            for w in ["weight", "bias"]:
               a_    					=       state_dict[
                   F"""{prefix}.h.{teacher_idx}.{layer}.{w}"""
               ]
         a_    					=       state_dict[F"""{prefix}.h.{teacher_idx}.attn.bias"""]
      else:
         for layer in [
             "attention.self.query",
             "attention.self.key",
             "attention.self.value",
             "attention.output.dense",
             "attention.output.LayerNorm",
             "intermediate.dense",
             "output.dense",
             "output.LayerNorm",
         ]:
            for w in ["weight", "bias"]:
               a_    					=       state_dict[
                   F"""{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}"""
               ]
      std_idx += 1

   # Language Modeling Head ###s
   if args.model_type == "roberta":
      for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
         a_    					=       state_dict[F"""{layer}"""]
      if args.vocab_transform:
         for w in ["weight", "bias"]:
            a_    					=       state_dict[F"""lm_head.dense.{w}"""]
            a_    					=       state_dict[F"""lm_head.layer_norm.{w}"""]
   elif args.model_type == "gpt2":
      for w in ["weight", "bias"]:
         a_    					=       state_dict[F"""{prefix}.ln_f.{w}"""]
      a_    					=       state_dict['lm_head.weight']

   print(F"""N layers selected for distillation: {std_idx}""")
   print(F"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")

   print(F"""Save transferred checkpoint to {args.dump_checkpoint}.""")
   torch.save(compressed_sd, args.dump_checkpoint)



