'''simple docstring'''






import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Union[str, Any]   ,		__snake_case							:			List[Any]   ,		__snake_case							:			int   ,		__snake_case							:			List[str]="attention"				):
     _A    		=  params[F'{prefix}/layers_{i}/{layer_name}/key/kernel']
     _A    		=  params[F'{prefix}/layers_{i}/{layer_name}/out/kernel']
     _A    		=  params[F'{prefix}/layers_{i}/{layer_name}/query/kernel']
     _A    		=  params[F'{prefix}/layers_{i}/{layer_name}/value/kernel']
     return k, o, q, v





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			List[Any]   ,		__snake_case							:			Tuple   ,		__snake_case							:			Optional[int]   ,		__snake_case							:			Dict=False				):
     if split_mlp_wi:
          _A    		=  params[F'{prefix}/layers_{i}/mlp/wi_0/kernel']
          _A    		=  params[F'{prefix}/layers_{i}/mlp/wi_1/kernel']
          _A    		=  (wi_a, wi_a)
     else:
          _A    		=  params[F'{prefix}/layers_{i}/mlp/wi/kernel']

     _A    		=  params[F'{prefix}/layers_{i}/mlp/wo/kernel']
     return wi, wo





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Optional[Any]   ,		__snake_case							:			int   ,		__snake_case							:			Any   ,		__snake_case							:			Any				):
     return params[F'{prefix}/layers_{i}/{layer_name}/scale']





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Dict   ,		*, __snake_case							:			Any   ,		__snake_case							:			Tuple				):
     _A    		=  traverse_util.flatten_dict(variables['target']				)
     _A    		=  {'/'.join(__lowercase				): v for k, v in old.items()}

     # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
     _A    		=  'encoder/layers_0/mlp/wi_0/kernel' in old
     print('Split MLP:'   ,		__lowercase				)

     _A    		=  collections.OrderedDict()

     # Shared embeddings.
     _A    		=  old['token_embedder/embedding']

     # Encoder.
     for i in range(__lowercase				):
          # Block i, layer 0 (Self Attention).
          _A    		=  tax_layer_norm_lookup(__lowercase   ,		__lowercase   ,		'encoder'   ,		'pre_attention_layer_norm'				)
          _A    		=  tax_attention_lookup(__lowercase   ,		__lowercase   ,		'encoder'   ,		'attention'				)
          _A    		=  layer_norm
          _A    		=  k.T
          _A    		=  o.T
          _A    		=  q.T
          _A    		=  v.T

          # Block i, layer 1 (MLP).
          _A    		=  tax_layer_norm_lookup(__lowercase   ,		__lowercase   ,		'encoder'   ,		'pre_mlp_layer_norm'				)
          _A    		=  tax_mlp_lookup(__lowercase   ,		__lowercase   ,		'encoder'   ,		__lowercase				)
          _A    		=  layer_norm
          if split_mlp_wi:
               _A    		=  wi[0].T
               _A    		=  wi[1].T
          else:
               _A    		=  wi.T
          _A    		=  wo.T

     _A    		=  old[
         'encoder/relpos_bias/rel_embedding'
     ].T
     _A    		=  old['encoder/encoder_norm/scale']

     if not is_encoder_only:
          # Decoder.
          for i in range(__lowercase				):
               # Block i, layer 0 (Self Attention).
               _A    		=  tax_layer_norm_lookup(__lowercase   ,		__lowercase   ,		'decoder'   ,		'pre_self_attention_layer_norm'				)
               _A    		=  tax_attention_lookup(__lowercase   ,		__lowercase   ,		'decoder'   ,		'self_attention'				)
               _A    		=  layer_norm
               _A    		=  k.T
               _A    		=  o.T
               _A    		=  q.T
               _A    		=  v.T

               # Block i, layer 1 (Cross Attention).
               _A    		=  tax_layer_norm_lookup(__lowercase   ,		__lowercase   ,		'decoder'   ,		'pre_cross_attention_layer_norm'				)
               _A    		=  tax_attention_lookup(__lowercase   ,		__lowercase   ,		'decoder'   ,		'encoder_decoder_attention'				)
               _A    		=  layer_norm
               _A    		=  k.T
               _A    		=  o.T
               _A    		=  q.T
               _A    		=  v.T

               # Block i, layer 2 (MLP).
               _A    		=  tax_layer_norm_lookup(__lowercase   ,		__lowercase   ,		'decoder'   ,		'pre_mlp_layer_norm'				)
               _A    		=  tax_mlp_lookup(__lowercase   ,		__lowercase   ,		'decoder'   ,		__lowercase				)
               _A    		=  layer_norm
               if split_mlp_wi:
                    _A    		=  wi[0].T
                    _A    		=  wi[1].T
               else:
                    _A    		=  wi.T
               _A    		=  wo.T

          _A    		=  old['decoder/decoder_norm/scale']
          _A    		=  old[
              'decoder/relpos_bias/rel_embedding'
          ].T

          # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
          if "decoder/logits_dense/kernel" in old:
               _A    		=  old['decoder/logits_dense/kernel'].T

     return new





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Union[str, Any]   ,		__snake_case							:			Tuple				):
     _A    		=  collections.OrderedDict([(k, torch.from_numpy(v.copy()				)) for (k, v) in converted_params.items()]				)

     # Add what is missing.
     if "encoder.embed_tokens.weight" not in state_dict:
          _A    		=  state_dict['shared.weight']

     if not is_encoder_only:
          if "decoder.embed_tokens.weight" not in state_dict:
               _A    		=  state_dict['shared.weight']

          if "lm_head.weight" not in state_dict:  # For old 1.0 models.
               print('Using shared word embeddings as lm_head.'				)
               _A    		=  state_dict['shared.weight']

     return state_dict





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			int   ,		__snake_case							:			Any   ,		__snake_case							:			Optional[Any]   ,		__snake_case							:			Any				):
     _A    		=  checkpoints.load_tax_checkpoint(__lowercase				)
     _A    		=  convert_tax_to_pytorch(__lowercase   ,		num_layers=config.num_layers   ,		is_encoder_only=__lowercase				)
     _A    		=  make_state_dict(__lowercase   ,		__lowercase				)
     model.load_state_dict(__lowercase   ,		strict=__lowercase				)





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			List[str]   ,		__snake_case							:			Optional[Any]   ,		__snake_case							:			str   ,		__snake_case							:			List[Any] = False				):
     _A    		=  TaConfig.from_json_file(__lowercase				)
     print(F'Building PyTorch model from configuration: {config}'				)
     # Non-v1.1 checkpoints could also use T5Model, but this works for all.
     # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
     if is_encoder_only:
          _A    		=  TaEncoderModel(__lowercase				)
     else:
          _A    		=  TaForConditionalGeneration(__lowercase				)

     # Load weights from tf checkpoint
     load_tax_weights_in_ta(__lowercase   ,		__lowercase   ,		__lowercase   ,		__lowercase				)

     # Save pytorch-model
     print(F'Save PyTorch model to {pytorch_dump_path}'				)
     model.save_pretrained(__lowercase				)

     # Verify that we can load the checkpoint.
     model.from_pretrained(__lowercase				)
     print('Done'				)


if __name__ == "__main__":
   _UpperCAmelCase    :     str         =       argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
   # Required parameters
   parser.add_argument(
       '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
   )
   parser.add_argument(
       '''--config_file''',
       default=None,
       type=str,
       required=True,
       help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
   )
   parser.add_argument(
       '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
   )
   parser.add_argument(
       '''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
   )
   _UpperCAmelCase    :     Tuple         =       parser.parse_args()
   convert_tax_checkpoint_to_pytorch(
       args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
   )





"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__SCREAMING_SNAKE_CASE							=     logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE							=     {
    'microsoft/focalnet-tiny': 'https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json',
}





class 				a__					(						A__   ,							A__       ):
     UpperCAmelCase__         =      '''focalnet'''



     def __init__(							self :Any      ,  _lowerCamelCase :List[Any]=224      ,  _lowerCamelCase :str=4      ,  _lowerCamelCase :List[str]=3      ,  _lowerCamelCase :Optional[Any]=96      ,  _lowerCamelCase :Dict=False      ,  _lowerCamelCase :Optional[Any]=[192, 384, 768, 768]      ,  _lowerCamelCase :Tuple=[2, 2, 6, 2]      ,  _lowerCamelCase :List[Any]=[2, 2, 2, 2]      ,  _lowerCamelCase :Optional[int]=[3, 3, 3, 3]      ,  _lowerCamelCase :Optional[Any]="gelu"      ,  _lowerCamelCase :Tuple=4.0      ,  _lowerCamelCase :Optional[Any]=0.0      ,  _lowerCamelCase :int=0.1      ,  _lowerCamelCase :List[str]=False      ,  _lowerCamelCase :str=1E-4      ,  _lowerCamelCase :Optional[int]=False      ,  _lowerCamelCase :List[str]=False      ,  _lowerCamelCase :str=False      ,  _lowerCamelCase :Optional[int]=0.02      ,  _lowerCamelCase :int=1E-5      ,  _lowerCamelCase :Tuple=32      ,  _lowerCamelCase :List[str]=None      ,  _lowerCamelCase :Dict=None      ,  **_lowerCamelCase :List[Any]      ,  ):







      '''simple docstring'''



      super().__init__(**_lowerCamelCase   )

      UpperCamelCase_     :					Any   	=image_size
      UpperCamelCase_     :					int   	=patch_size
      UpperCamelCase_     :					int   	=num_channels
      UpperCamelCase_     :					Union[str, Any]   	=embed_dim
      UpperCamelCase_     :					int   	=use_conv_embed
      UpperCamelCase_     :					Optional[int]   	=hidden_sizes
      UpperCamelCase_     :					str   	=depths
      UpperCamelCase_     :					Any   	=focal_levels
      UpperCamelCase_     :					List[str]   	=focal_windows
      UpperCamelCase_     :					str   	=hidden_act
      UpperCamelCase_     :					Dict   	=mlp_ratio
      UpperCamelCase_     :					List[str]   	=hidden_dropout_prob
      UpperCamelCase_     :					Optional[int]   	=drop_path_rate
      UpperCamelCase_     :					str   	=use_layerscale
      UpperCamelCase_     :					List[Any]   	=layerscale_value
      UpperCamelCase_     :					Optional[int]   	=use_post_layernorm
      UpperCamelCase_     :					Dict   	=use_post_layernorm_in_modulation
      UpperCamelCase_     :					Optional[Any]   	=normalize_modulator
      UpperCamelCase_     :					List[Any]   	=initializer_range
      UpperCamelCase_     :					List[str]   	=layer_norm_eps
      UpperCamelCase_     :					List[Any]   	=encoder_stride
      UpperCamelCase_     :					str   	=['stem'] + [f'''stage{idx}''' for idx in range(1      ,  len(self.depths   ) + 1   )]
      UpperCamelCase_	,     UpperCamelCase_     :					int   	=get_aligned_output_features_output_indices(
          out_features=_lowerCamelCase      ,  out_indices=_lowerCamelCase      ,  stage_names=self.stage_names   )
