


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
snake_case    :   Union[str, 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     __lowerCamelCase						(			UpperCAmelCase_						:		Union[str, Any]						):




      """simple docstring"""





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

            a						:List[str]										=	R'''(encoder|decoder)\/'''

            if re.match(lowerCAmelCase__			,  lowerCAmelCase__						):
                  a						:int										=	re.match(lowerCAmelCase__			,  lowerCAmelCase__						).groups()
                  if groups[0] == "encoder":
                        a						:Union[str, Any]										=	re.sub(R'''/mlp/'''			,  R'''/1/mlp/'''			,  lowerCAmelCase__						)
                        a						:List[Any]										=	re.sub(R'''/pre_mlp_layer_norm/'''			,  R'''/1/layer_norm/'''			,  lowerCAmelCase__						)

                  elif groups[0] == "decoder":
                        a						:str										=	re.sub(R'''/mlp/'''			,  R'''/2/mlp/'''			,  lowerCAmelCase__						)
                        a						:str										=	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:
                        a						:Union[str, Any]										=	new_key.replace(lowerCAmelCase__			,  lowerCAmelCase__						)

            print(F'''{key} -> {new_key}'''						)
            a						:List[str]										=	s_dict.pop(lowerCAmelCase__						)

      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						:List[Any]										=	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						:Tuple										=	s_dict[key].shape[0]
                  a						:int										=	s_dict[key]
                  for idx in range(lowerCAmelCase__						):
                        a						:Dict										=	expert_weihts[idx]
                        print(F'''{key} -> {key.replace('expert/'			,  'nested fstring'						)}'''						)

                  s_dict.pop(lowerCAmelCase__						)

      return s_dict


snake_case    :   Dict	    =					{
    "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     __lowerCamelCase						(			UpperCAmelCase_						:		List[Any]			,  UpperCAmelCase_						:		Optional[Any]						):




      """simple docstring"""





      import regex as re

      with open(lowerCAmelCase__			,  '''r'''						) as f:
            a						:Optional[Any]										=	f.read()

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

      a						:Dict										=	re.findall(R'''(.*activations) = \(\'(.*)\',\)'''			,  lowerCAmelCase__						)[0]
      a						:Any										=	str(activation[1]						)

      a						:Optional[Any]										=	num_experts
      a						:Any										=	SwitchTransformersConfig(**lowerCAmelCase__						)
      return config
def     __lowerCamelCase						(			UpperCAmelCase_						:		Union[str, Any]			,  UpperCAmelCase_						:		Any			,  UpperCAmelCase_						:		Optional[Any]=None			,  UpperCAmelCase_						:		Any="./"			,  UpperCAmelCase_						:		Tuple=8						):




      """simple docstring"""





      print(F'''Loading flax weights from : {flax_checkpoint_path}'''						)
      a						:Tuple										=	checkpoints.load_tax_checkpoint(lowerCAmelCase__						)

      if gin_file is not None:
            a						:Dict										=	convert_gin_to_config(lowerCAmelCase__			,  lowerCAmelCase__						)
      else:
            a						:Optional[int]										=	SwitchTransformersConfig.from_pretrained(lowerCAmelCase__						)

      a						:List[Any]										=	SwitchTransformersForConditionalGeneration(lowerCAmelCase__						)

      a						:Optional[int]										=	flax_params['''target''']
      a						:Optional[Any]										=	flatten_dict(lowerCAmelCase__			,  sep='''/'''						)
      a						:str										=	rename_keys(lowerCAmelCase__						)
      a						:Optional[int]										=	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__":
     snake_case    :   str	    =					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''')
     snake_case    :   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,
     )









from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
     from PIL import Image

     from ..image_utils import load_image

if is_tf_available():
     import tensorflow as tf

     from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
     from ..tf_utils import stable_softmax

if is_torch_available():
     from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

snake_case    :   str	    =					logging.get_logger(__name__)







@add_end_docstrings(_snake_case							)
class 		_snake_case  (				_snake_case							):




 def __init__(		self ,  *_lowerCamelCase ,  **_lowerCamelCase   ):
       super().__init__(*_lowerCamelCase ,  **_lowerCamelCase   )
       requires_backends(self ,  '''vision'''   )
       self.check_model_type(
           TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
           if self.framework == '''tf'''
           else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase=None   ):
       a						:Tuple										=	{}
       if top_k is not None:
             a						:int										=	top_k
       return {}, {}, postprocess_params




 def __call__(		self ,  _lowerCamelCase ,  **_lowerCamelCase   ):
       return super().__call__(_lowerCamelCase ,  **_lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       a						:str										=	load_image(_lowerCamelCase   )
       a						:Any										=	self.image_processor(images=_lowerCamelCase ,  return_tensors=self.framework   )
       return model_inputs




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       a						:List[str]										=	self.model(**_lowerCamelCase   )
       return model_outputs




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase=5   ):
       if top_k > self.model.config.num_labels:
             a						:List[Any]										=	self.model.config.num_labels

       if self.framework == "pt":
             a						:int										=	model_outputs.logits.softmax(-1   )[0]
             a							,    a						:Union[str, Any]										=	probs.topk(_lowerCamelCase   )
       elif self.framework == "tf":
             a						:Optional[Any]										=	stable_softmax(model_outputs.logits ,  axis=-1   )[0]
             a						:Union[str, Any]										=	tf.math.top_k(_lowerCamelCase ,  k=_lowerCamelCase   )
             a							,    a						:Optional[int]										=	topk.values.numpy(), topk.indices.numpy()
       else:
             raise ValueError(F'''Unsupported framework: {self.framework}'''   )

       a						:Optional[int]										=	scores.tolist()
       a						:str										=	ids.tolist()
       return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(_lowerCamelCase ,  _lowerCamelCase   )]





