



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
a_    :  List[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      __lowercase(						UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase             =		list(s_dict.keys()			)
      for key in keys:
            lowerCamelCase             =		r".*/layers_(\d+)"
            lowerCamelCase             =		key
            if re.match(_a , _a			):
                  lowerCamelCase             =		re.sub(r"layers_(\d+)" , r"block/\1/layer" , _a			)

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

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

                  elif groups[0] == "decoder":
                        lowerCamelCase             =		re.sub(r"/mlp/" , r"/2/mlp/" , _a			)
                        lowerCamelCase             =		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:
                        lowerCamelCase             =		new_key.replace(_a , _a			)

            print(F"""{key} -> {new_key}"""			)
            lowerCamelCase             =		s_dict.pop(_a			)

      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(_a			):
                        lowerCamelCase             =		expert_weihts[idx]
                        print(F"""{key} -> {key.replace("expert/" , "nested fstring"			)}"""			)

                  s_dict.pop(_a			)

      return s_dict


a_    :  List[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      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      import regex as re

      with open(_a , "r"			) as f:
            lowerCamelCase             =		f.read()

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

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

      lowerCamelCase             =		num_experts
      lowerCamelCase             =		SwitchTransformersConfig(**_a			)
      return config

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__=None , UpperCAmelCase__="./" , UpperCAmelCase__=8			):


      """simple docstring"""



      print(F"""Loading flax weights from : {flax_checkpoint_path}"""			)
      lowerCamelCase             =		checkpoints.load_tax_checkpoint(_a			)

      if gin_file is not None:
            lowerCamelCase             =		convert_gin_to_config(_a , _a			)
      else:
            lowerCamelCase             =		SwitchTransformersConfig.from_pretrained(_a			)

      lowerCamelCase             =		SwitchTransformersForConditionalGeneration(_a			)

      lowerCamelCase             =		flax_params["target"]
      lowerCamelCase             =		flatten_dict(_a , sep="/"			)
      lowerCamelCase             =		rename_keys(_a			)
      lowerCamelCase             =		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__":
     a_    :  List[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')
     a_    :  List[str]						  =		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"""


from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
  from PIL import Image

  from ..image_utils import load_image

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

UpperCAmelCase													=logging.get_logger(__name__)





@add_end_docstrings(SCREAMING_SNAKE_CASE    )
class lowerCamelCase__   (				SCREAMING_SNAKE_CASE    ):



    '''simple docstring'''




    def __init__( self ,*lowerCamelCase_ ,**lowerCamelCase_    )    ->      str:
          super().__init__(*lowerCamelCase_ ,**lowerCamelCase_    )
          self.check_model_type(lowerCamelCase_    )




    def UpperCamelCase__		( self ,lowerCamelCase_=None ,lowerCamelCase_=None ,lowerCamelCase_=None ,**lowerCamelCase_    )    ->      List[Any]:
          A  ,			A				  =		{}, {}
          if padding is not None:
                A				  =		padding
          if truncation is not None:
                A				  =		truncation
          if top_k is not None:
                A				  =		top_k
          return preprocess_params, {}, postprocess_params




    def __call__( self ,lowerCamelCase_ ,lowerCamelCase_ = None ,**lowerCamelCase_    )    ->      Dict:
          if isinstance(lowerCamelCase_ ,(Image.Image, str)    ) and isinstance(lowerCamelCase_ ,lowerCamelCase_    ):
                A				  =		{"""image""": image, """question""": question}
          else:
                A				  =		image
          A				  =		super().__call__(lowerCamelCase_ ,**lowerCamelCase_    )
          return results




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_=False ,lowerCamelCase_=False    )    ->      List[Any]:
          A				  =		load_image(inputs["""image"""]    )
          A				  =		self.tokenizer(
              inputs["""question"""] ,return_tensors=self.framework ,padding=lowerCamelCase_ ,truncation=lowerCamelCase_    )
          A				  =		self.image_processor(images=lowerCamelCase_ ,return_tensors=self.framework    )
          model_inputs.update(lowerCamelCase_    )
          return model_inputs




    def UpperCamelCase__		( self ,lowerCamelCase_    )    ->      Union[str, Any]:
          A				  =		self.model(**lowerCamelCase_    )
          return model_outputs




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_=5    )    ->      int:
          if top_k > self.model.config.num_labels:
                A				  =		self.model.config.num_labels

          if self.framework == "pt":
                A				  =		model_outputs.logits.sigmoid()[0]
                A  ,			A				  =		probs.topk(lowerCamelCase_    )
          else:
                raise ValueError(f'Unsupported framework: {self.framework}'    )

          A				  =		scores.tolist()
          A				  =		ids.tolist()
          return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(lowerCamelCase_ ,lowerCamelCase_    )]




