

'''simple docstring'''







import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase		       =    logging.get_logger(__name__)

__lowerCamelCase		       =    {
    '''google/pix2struct-textcaps-base''': (
        '''https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'''
    ),
}


class 					A__      (      _snake_case			):
    lowercase						=		"pix2struct_text_model"
    lowercase						=		["past_key_values"]
    lowercase						=		{
        "hidden_size": "hidden_size",
        "num_attention_heads": "num_heads",
        "num_hidden_layers": "num_layers",
    }



    def __init__(					self		,				UpperCamelCase__=50244		,				UpperCamelCase__=768		,				UpperCamelCase__=64		,				UpperCamelCase__=2048		,				UpperCamelCase__=12		,				UpperCamelCase__=12		,				UpperCamelCase__=32		,				UpperCamelCase__=128		,				UpperCamelCase__=0.1		,				UpperCamelCase__=1e-6		,				UpperCamelCase__=1.0		,				UpperCamelCase__="gelu_new"		,				UpperCamelCase__=0		,				UpperCamelCase__=False		,				UpperCamelCase__=0		,				UpperCamelCase__=1		,				UpperCamelCase__=False		,				UpperCamelCase__=True		,				**UpperCamelCase__		,				)						->      Optional[Any]:





           '''simple docstring'''
           A_						=      vocab_size
           A_						=      hidden_size
           A_						=      d_kv
           A_						=      d_ff
           A_						=      num_layers
           A_						=      num_heads
           A_						=      relative_attention_num_buckets
           A_						=      relative_attention_max_distance
           A_						=      dropout_rate
           A_						=      layer_norm_epsilon
           A_						=      initializer_factor
           A_						=      use_cache

           A_						=      eos_token_id
           A_						=      decoder_start_token_id

           # for backwards compatibility
           A_						=      dense_act_fn

           super().__init__(
               pad_token_id=UpperCamelCase__		,				eos_token_id=UpperCamelCase__		,				decoder_start_token_id=UpperCamelCase__		,				tie_word_embeddings=UpperCamelCase__		,				is_decoder=UpperCamelCase__		,				**UpperCamelCase__		,				)







    @classmethod
    def 					snake_case_			(					cls		,				UpperCamelCase__		,				**UpperCamelCase__       )						->      "PretrainedConfig":





           '''simple docstring'''
           cls._set_token_in_kwargs(UpperCamelCase__       )

           A_					,			A_						=      cls.get_config_dict(UpperCamelCase__		,				**UpperCamelCase__       )

           # get the text config dict if we are loading from Pix2StructConfig
           if config_dict.get("""model_type"""       ) == "pix2struct":
                  A_						=      config_dict["""text_config"""]

           if "model_type" in config_dict and hasattr(cls		,				"""model_type"""       ) and config_dict["model_type"] != cls.model_type:
                  logger.warning(
                      f'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
                      f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''       )

           return cls.from_dict(UpperCamelCase__		,				**UpperCamelCase__       )


class 					A__      (      _snake_case			):
    lowercase						=		"pix2struct_vision_model"



    def __init__(					self		,				UpperCamelCase__=768		,				UpperCamelCase__=768		,				UpperCamelCase__=2048		,				UpperCamelCase__=64		,				UpperCamelCase__=12		,				UpperCamelCase__=12		,				UpperCamelCase__="gelu_new"		,				UpperCamelCase__=1e-6		,				UpperCamelCase__=0.0		,				UpperCamelCase__=0.0		,				UpperCamelCase__=1e-1_0		,				UpperCamelCase__=1.0		,				UpperCamelCase__=4096		,				UpperCamelCase__=32		,				UpperCamelCase__=128		,				**UpperCamelCase__		,				)						->      List[Any]:





           '''simple docstring'''
           super().__init__(**UpperCamelCase__       )

           A_						=      hidden_size
           A_						=      patch_embed_hidden_size
           A_						=      d_ff
           A_						=      dropout_rate
           A_						=      num_hidden_layers
           A_						=      num_attention_heads
           A_						=      initializer_range
           A_						=      initializer_factor
           A_						=      attention_dropout
           A_						=      layer_norm_eps
           A_						=      dense_act_fn
           A_						=      seq_len
           A_						=      relative_attention_num_buckets
           A_						=      relative_attention_max_distance
           A_						=      d_kv







    @classmethod
    def 					snake_case_			(					cls		,				UpperCamelCase__		,				**UpperCamelCase__       )						->      "PretrainedConfig":





           '''simple docstring'''
           cls._set_token_in_kwargs(UpperCamelCase__       )

           A_					,			A_						=      cls.get_config_dict(UpperCamelCase__		,				**UpperCamelCase__       )

           # get the vision config dict if we are loading from Pix2StructConfig
           if config_dict.get("""model_type"""       ) == "pix2struct":
                  A_						=      config_dict["""vision_config"""]

           if "model_type" in config_dict and hasattr(cls		,				"""model_type"""       ) and config_dict["model_type"] != cls.model_type:
                  logger.warning(
                      f'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
                      f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''       )

           return cls.from_dict(UpperCamelCase__		,				**UpperCamelCase__       )


class 					A__      (      _snake_case			):
    lowercase						=		"pix2struct"
    lowercase						=		True



    def __init__(					self		,				UpperCamelCase__=None		,				UpperCamelCase__=None		,				UpperCamelCase__=1.0		,				UpperCamelCase__=0.02		,				UpperCamelCase__=False		,				UpperCamelCase__=False		,				UpperCamelCase__=True		,				**UpperCamelCase__		,				)						->      Dict:





           '''simple docstring'''
           super().__init__(tie_word_embeddings=UpperCamelCase__		,				is_encoder_decoder=UpperCamelCase__		,				**UpperCamelCase__       )

           if text_config is None:
                  A_						=      {}
                  logger.info("""text_config is None. Initializing the Pix2StructTextConfig with default values."""       )

           if vision_config is None:
                  A_						=      {}
                  logger.info("""vision_config is None. Initializing the Pix2StructVisionConfig with default values."""       )

           A_						=      PixaStructTextConfig(**UpperCamelCase__       )
           A_						=      PixaStructVisionConfig(**UpperCamelCase__       )

           A_						=      self.text_config.decoder_start_token_id
           A_						=      self.text_config.pad_token_id
           A_						=      self.text_config.eos_token_id

           A_						=      initializer_factor
           A_						=      initializer_range

           A_						=      self.initializer_range
           A_						=      self.initializer_range

           A_						=      is_vqa



    @classmethod
    def 					snake_case_			(					cls		,				UpperCamelCase__		,				UpperCamelCase__		,				**UpperCamelCase__       )						->      Union[str, Any]:





           '''simple docstring'''
           return cls(text_config=text_config.to_dict()		,				vision_config=vision_config.to_dict()		,				**UpperCamelCase__       )







    def 					snake_case_			(					self       )						->      Union[str, Any]:





           '''simple docstring'''
           A_						=      copy.deepcopy(self.__dict__       )
           A_						=      self.text_config.to_dict()
           A_						=      self.vision_config.to_dict()
           A_						=      self.__class__.model_type
           return output



'''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
__lowerCamelCase		       =    {
    '''/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       UpperCAmelCase__    (      UpperCAmelCase__				)			->			Tuple:
       # 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
       # the original model
       A_						=      list(s_dict.keys()				)
       for key in keys:
              A_						=      r""".*/layers_(\d+)"""
              A_						=      key
              if re.match(UpperCAmelCase__,       UpperCAmelCase__				):
                     A_						=      re.sub(r"""layers_(\d+)""",       r"""block/\1/layer""",       UpperCAmelCase__				)

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

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

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

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

              print(F'''{key} -> {new_key}'''				)
              A_						=      s_dict.pop(UpperCAmelCase__				)

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

                     s_dict.pop(UpperCAmelCase__				)

       return s_dict


__lowerCamelCase		       =    {
    '''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       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__				)			->			List[Any]:
       # Convert a google style config to the hugging face fromat
       import regex as re

       with open(UpperCAmelCase__,       """r"""				) as f:
              A_						=      f.read()

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

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

       A_						=      num_experts
       A_						=      SwitchTransformersConfig(**UpperCAmelCase__				)
       return config
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__=None,       UpperCAmelCase__="./",       UpperCAmelCase__=8				)			->			List[str]:
       # Initialise PyTorch model

       print(F'''Loading flax weights from : {flax_checkpoint_path}'''				)
       A_						=      checkpoints.load_tax_checkpoint(UpperCAmelCase__				)

       if gin_file is not None:
              A_						=      convert_gin_to_config(UpperCAmelCase__,       UpperCAmelCase__				)
       else:
              A_						=      SwitchTransformersConfig.from_pretrained(UpperCAmelCase__				)

       A_						=      SwitchTransformersForConditionalGeneration(UpperCAmelCase__				)

       A_						=      flax_params["""target"""]
       A_						=      flatten_dict(UpperCAmelCase__,       sep="""/"""				)
       A_						=      rename_keys(UpperCAmelCase__				)
       A_						=      unflatten_dict(UpperCAmelCase__,       sep="""/"""				)

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

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


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