


'''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
A__		:					str												=  {
    """/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_    :				Optional[Any]							)     ->		int:
     # 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
     # the original model
     __lowerCamelCase   :	Union[str, Any]								=		list(s_dict.keys()							)
     for key in keys:
          __lowerCamelCase   :	Any								=		R'.*/layers_(\d+)'
          __lowerCamelCase   :	Union[str, Any]								=		key
          if re.match(UpperCAmelCase_      ,    UpperCAmelCase_							):
               __lowerCamelCase   :	Union[str, Any]								=		re.sub(R'layers_(\d+)'      ,    R'block/\1/layer'      ,    UpperCAmelCase_							)

          __lowerCamelCase   :	Optional[Any]								=		R'(encoder|decoder)\/'

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

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

          print(F'{key} -> {new_key}'							)
          __lowerCamelCase   :	int								=		s_dict.pop(UpperCAmelCase_							)

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

               s_dict.pop(UpperCAmelCase_							)

     return s_dict


A__		:					Union[str, 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 			UpperCAmelCase__					(	UpperCAmelCase_    :				Tuple      ,    UpperCAmelCase_    :				List[str]							)     ->		str:
     # Convert a google style config to the hugging face fromat
     import regex as re

     with open(UpperCAmelCase_      ,    'r'							) as f:
          __lowerCamelCase   :	Optional[int]								=		f.read()

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

     __lowerCamelCase   :	Optional[int]								=		re.findall(R'(.*activations) = \(\'(.*)\',\)'      ,    UpperCAmelCase_							)[0]
     __lowerCamelCase   :	Tuple								=		str(activation[1]							)

     __lowerCamelCase   :	Any								=		num_experts
     __lowerCamelCase   :	List[Any]								=		SwitchTransformersConfig(**UpperCAmelCase_							)
     return config



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				int      ,    UpperCAmelCase_    :				Optional[int]      ,    UpperCAmelCase_    :				Any=None      ,    UpperCAmelCase_    :				Any="./"      ,    UpperCAmelCase_    :				Any=8							)     ->		Optional[int]:
     # Initialise PyTorch model

     print(F'Loading flax weights from : {flax_checkpoint_path}'							)
     __lowerCamelCase   :	List[Any]								=		checkpoints.load_tax_checkpoint(UpperCAmelCase_							)

     if gin_file is not None:
          __lowerCamelCase   :	List[str]								=		convert_gin_to_config(UpperCAmelCase_      ,    UpperCAmelCase_							)
     else:
          __lowerCamelCase   :	List[str]								=		SwitchTransformersConfig.from_pretrained(UpperCAmelCase_							)

     __lowerCamelCase   :	List[str]								=		SwitchTransformersForConditionalGeneration(UpperCAmelCase_							)

     __lowerCamelCase   :	Union[str, Any]								=		flax_params['target']
     __lowerCamelCase   :	str								=		flatten_dict(UpperCAmelCase_      ,    sep='/'							)
     __lowerCamelCase   :	List[str]								=		rename_keys(UpperCAmelCase_							)
     __lowerCamelCase   :	Optional[Any]								=		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__":
      A__		:					Optional[int]												=  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[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,
      )










'''simple docstring'''



import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
      import torch

      from transformers import AutoModelForImageClassification

if is_vision_available():
      from transformers import AutoImageProcessor





@require_torch
@require_vision
class    UpperCAmelCase_  (unittest.TestCase  ):





    """simple docstring"""






    @slow
    def       lowercase_				(							self	)				->     List[str]:
         __lowerCamelCase   :	Any								=		AutoImageProcessor.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'	)
         __lowerCamelCase   :	Tuple								=		AutoModelForImageClassification.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'	)
         model.to(SCREAMING_SNAKE_CASE_	)

         from datasets import load_dataset

         __lowerCamelCase   :	str								=		load_dataset('nielsr/rvlcdip-demo'	)

         __lowerCamelCase   :	List[Any]								=		dataset['train'][0]['image'].convert('RGB'	)

         __lowerCamelCase   :	str								=		image_processor(SCREAMING_SNAKE_CASE_			,     return_tensors='pt'	).to(SCREAMING_SNAKE_CASE_	)

         # forward pass
         with torch.no_grad():
              __lowerCamelCase   :	str								=		model(**SCREAMING_SNAKE_CASE_	)
              __lowerCamelCase   :	int								=		outputs.logits

         __lowerCamelCase   :	List[Any]								=		torch.Size((1, 16)	)
         self.assertEqual(logits.shape			,     SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	Optional[Any]								=		torch.tensor(
             [-0.4_1_5_8, -0.4_0_9_2, -0.4_3_4_7]			,     device=SCREAMING_SNAKE_CASE_			,     dtype=torch.float			,     )
         self.assertTrue(torch.allclose(logits[0, :3]			,     SCREAMING_SNAKE_CASE_			,     atol=1E-4	)	)






