





'''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   	=    {
    "/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     __magic_name__							(				__UpperCAmelCase   )    ->       Optional[int]:

   '''simple docstring'''







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

      __SCREAMING_SNAKE_CASE   				=							R"""(encoder|decoder)\/"""

      if re.match(__UpperCAmelCase				,						__UpperCAmelCase   ):
         __SCREAMING_SNAKE_CASE   				=							re.match(__UpperCAmelCase				,						__UpperCAmelCase   ).groups()
         if groups[0] == "encoder":
            __SCREAMING_SNAKE_CASE   				=							re.sub(R"""/mlp/"""				,						R"""/1/mlp/"""				,						__UpperCAmelCase   )
            __SCREAMING_SNAKE_CASE   				=							re.sub(R"""/pre_mlp_layer_norm/"""				,						R"""/1/layer_norm/"""				,						__UpperCAmelCase   )

         elif groups[0] == "decoder":
            __SCREAMING_SNAKE_CASE   				=							re.sub(R"""/mlp/"""				,						R"""/2/mlp/"""				,						__UpperCAmelCase   )
            __SCREAMING_SNAKE_CASE   				=							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:
            __SCREAMING_SNAKE_CASE   				=							new_key.replace(__UpperCAmelCase				,						__UpperCAmelCase   )

      print(f"""{key} -> {new_key}"""   )
      __SCREAMING_SNAKE_CASE   				=							s_dict.pop(__UpperCAmelCase   )

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

         s_dict.pop(__UpperCAmelCase   )

   return s_dict


a   	=    {
    "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     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       str:

   '''simple docstring'''







   import regex as re

   with open(__UpperCAmelCase				,						"""r"""   ) as f:
      __SCREAMING_SNAKE_CASE   				=							f.read()

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

   __SCREAMING_SNAKE_CASE   				=							re.findall(R"""(.*activations) = \(\'(.*)\',\)"""				,						__UpperCAmelCase   )[0]
   __SCREAMING_SNAKE_CASE   				=							str(activation[1]   )

   __SCREAMING_SNAKE_CASE   				=							num_experts
   __SCREAMING_SNAKE_CASE   				=							SwitchTransformersConfig(**__UpperCAmelCase   )
   return config

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase=None				,						__UpperCAmelCase="./"				,						__UpperCAmelCase=8   )    ->       str:

   '''simple docstring'''







   print(f"""Loading flax weights from : {flax_checkpoint_path}"""   )
   __SCREAMING_SNAKE_CASE   				=							checkpoints.load_tax_checkpoint(__UpperCAmelCase   )

   if gin_file is not None:
      __SCREAMING_SNAKE_CASE   				=							convert_gin_to_config(__UpperCAmelCase				,						__UpperCAmelCase   )
   else:
      __SCREAMING_SNAKE_CASE   				=							SwitchTransformersConfig.from_pretrained(__UpperCAmelCase   )

   __SCREAMING_SNAKE_CASE   				=							SwitchTransformersForConditionalGeneration(__UpperCAmelCase   )

   __SCREAMING_SNAKE_CASE   				=							flax_params["""target"""]
   __SCREAMING_SNAKE_CASE   				=							flatten_dict(__UpperCAmelCase				,						sep="""/"""   )
   __SCREAMING_SNAKE_CASE   				=							rename_keys(__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							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   	=    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   	=    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 gc
import unittest

from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
        CTRLForSequenceClassification,
        CTRLLMHeadModel,
        CTRLModel,
    )







class __a			:





   def __init__(       self					:      Union[str, Any]					,lowerCamelCase					:      Optional[int]					,lowerCamelCase					:      Tuple=14					,lowerCamelCase					:      Optional[Any]=7					,lowerCamelCase					:      str=True					,lowerCamelCase					:      List[str]=True					,lowerCamelCase					:      Dict=True					,lowerCamelCase					:      Any=True					,lowerCamelCase					:      int=True					,lowerCamelCase					:      Dict=99					,lowerCamelCase					:      Dict=32					,lowerCamelCase					:      Optional[Any]=5					,lowerCamelCase					:      Tuple=4					,lowerCamelCase					:      Optional[int]=37					,lowerCamelCase					:      Optional[int]="gelu"					,lowerCamelCase					:      Optional[Any]=0.1					,lowerCamelCase					:      Tuple=0.1					,lowerCamelCase					:      Dict=512					,lowerCamelCase					:      int=16					,lowerCamelCase					:      Union[str, Any]=2					,lowerCamelCase					:      Tuple=0.02					,lowerCamelCase					:      str=3					,lowerCamelCase					:      Union[str, Any]=4					,lowerCamelCase					:      Any=None					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							parent
      __SCREAMING_SNAKE_CASE   				=							batch_size
      __SCREAMING_SNAKE_CASE   				=							seq_length
      __SCREAMING_SNAKE_CASE   				=							is_training
      __SCREAMING_SNAKE_CASE   				=							use_token_type_ids
      __SCREAMING_SNAKE_CASE   				=							use_input_mask
      __SCREAMING_SNAKE_CASE   				=							use_labels
      __SCREAMING_SNAKE_CASE   				=							use_mc_token_ids
      __SCREAMING_SNAKE_CASE   				=							vocab_size
      __SCREAMING_SNAKE_CASE   				=							hidden_size
      __SCREAMING_SNAKE_CASE   				=							num_hidden_layers
      __SCREAMING_SNAKE_CASE   				=							num_attention_heads
      __SCREAMING_SNAKE_CASE   				=							intermediate_size
      __SCREAMING_SNAKE_CASE   				=							hidden_act
      __SCREAMING_SNAKE_CASE   				=							hidden_dropout_prob
      __SCREAMING_SNAKE_CASE   				=							attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE   				=							max_position_embeddings
      __SCREAMING_SNAKE_CASE   				=							type_vocab_size
      __SCREAMING_SNAKE_CASE   				=							type_sequence_label_size
      __SCREAMING_SNAKE_CASE   				=							initializer_range
      __SCREAMING_SNAKE_CASE   				=							num_labels
      __SCREAMING_SNAKE_CASE   				=							num_choices
      __SCREAMING_SNAKE_CASE   				=							scope
      __SCREAMING_SNAKE_CASE   				=							self.vocab_size - 1





   def   UpperCAmelCase__  (       self					:      Dict     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							ids_tensor([self.batch_size, self.seq_length]					,self.vocab_size     )

      __SCREAMING_SNAKE_CASE   				=							None
      if self.use_input_mask:
         __SCREAMING_SNAKE_CASE   				=							random_attention_mask([self.batch_size, self.seq_length]     )

      __SCREAMING_SNAKE_CASE   				=							None
      if self.use_token_type_ids:
         __SCREAMING_SNAKE_CASE   				=							ids_tensor([self.batch_size, self.seq_length]					,self.type_vocab_size     )

      __SCREAMING_SNAKE_CASE   				=							None
      if self.use_mc_token_ids:
         __SCREAMING_SNAKE_CASE   				=							ids_tensor([self.batch_size, self.num_choices]					,self.seq_length     )

      __SCREAMING_SNAKE_CASE   				=							None
      __SCREAMING_SNAKE_CASE   				=							None
      __SCREAMING_SNAKE_CASE   				=							None
      if self.use_labels:
         __SCREAMING_SNAKE_CASE   				=							ids_tensor([self.batch_size]					,self.type_sequence_label_size     )
         __SCREAMING_SNAKE_CASE   				=							ids_tensor([self.batch_size, self.seq_length]					,self.num_labels     )
         __SCREAMING_SNAKE_CASE   				=							ids_tensor([self.batch_size]					,self.num_choices     )

      __SCREAMING_SNAKE_CASE   				=							self.get_config()

      __SCREAMING_SNAKE_CASE   				=							ids_tensor([self.num_hidden_layers, self.num_attention_heads]					,2     )

      return (
          config,
          input_ids,
          input_mask,
          head_mask,
          token_type_ids,
          mc_token_ids,
          sequence_labels,
          token_labels,
          choice_labels,
      )





   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      return CTRLConfig(
          vocab_size=self.vocab_size					,n_embd=self.hidden_size					,n_layer=self.num_hidden_layers					,n_head=self.num_attention_heads					,n_positions=self.max_position_embeddings					,pad_token_id=self.pad_token_id					,)





   def   UpperCAmelCase__  (       self					:      Tuple					,lowerCamelCase					:      Optional[Any]					,lowerCamelCase					:      Tuple					,lowerCamelCase					:      Any					,lowerCamelCase					:      List[str]					,lowerCamelCase					:      str					,*lowerCamelCase					:      Tuple     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							CTRLModel(config=lowerCamelCase     )
      model.to(lowerCamelCase     )
      model.eval()

      model(lowerCamelCase					,token_type_ids=lowerCamelCase					,head_mask=lowerCamelCase     )
      model(lowerCamelCase					,token_type_ids=lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							model(lowerCamelCase     )
      self.parent.assertEqual(result.last_hidden_state.shape					,(self.batch_size, self.seq_length, self.hidden_size)     )
      self.parent.assertEqual(len(result.past_key_values     )					,config.n_layer     )





   def   UpperCAmelCase__  (       self					:      Optional[int]					,lowerCamelCase					:      int					,lowerCamelCase					:      Optional[Any]					,lowerCamelCase					:      Optional[Any]					,lowerCamelCase					:      int					,lowerCamelCase					:      Dict					,*lowerCamelCase					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							CTRLLMHeadModel(lowerCamelCase     )
      model.to(lowerCamelCase     )
      model.eval()

      __SCREAMING_SNAKE_CASE   				=							model(lowerCamelCase					,token_type_ids=lowerCamelCase					,labels=lowerCamelCase     )
      self.parent.assertEqual(result.loss.shape					,()     )
      self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.seq_length, self.vocab_size)     )





   def   UpperCAmelCase__  (       self					:      List[Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.prepare_config_and_inputs()

      (
          (
          __SCREAMING_SNAKE_CASE
      )				,  (
          __SCREAMING_SNAKE_CASE
      )				,  (
          __SCREAMING_SNAKE_CASE
      )				,  (
          __SCREAMING_SNAKE_CASE
      )				,  (
          __SCREAMING_SNAKE_CASE
      )				,  (
          __SCREAMING_SNAKE_CASE
      )				,  (
          __SCREAMING_SNAKE_CASE
      )				,  (
          __SCREAMING_SNAKE_CASE
      )				,  (
          __SCREAMING_SNAKE_CASE
      )				,  
      )   				=							config_and_inputs

      __SCREAMING_SNAKE_CASE   				=							{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """head_mask""": head_mask}

      return config, inputs_dict





   def   UpperCAmelCase__  (       self					:      Dict					,lowerCamelCase					:      int					,lowerCamelCase					:      str					,lowerCamelCase					:      Optional[Any]					,lowerCamelCase					:      Optional[Any]					,*lowerCamelCase					:      Optional[Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.num_labels
      __SCREAMING_SNAKE_CASE   				=							CTRLForSequenceClassification(lowerCamelCase     )
      model.to(lowerCamelCase     )
      model.eval()
      __SCREAMING_SNAKE_CASE   				=							ids_tensor([self.batch_size]					,self.type_sequence_label_size     )
      __SCREAMING_SNAKE_CASE   				=							model(lowerCamelCase					,token_type_ids=lowerCamelCase					,labels=lowerCamelCase     )
      self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.num_labels)     )







@require_torch
class __a			( _snake_case, _snake_case, _snake_case, unittest.TestCase      ):
   __UpperCamelCase :			str						   =						(CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
   __UpperCamelCase :			Dict						   =						(CTRLLMHeadModel,) if is_torch_available() else ()
   __UpperCamelCase :			int						   =						(
       {
           'feature-extraction': CTRLModel,
           'text-classification': CTRLForSequenceClassification,
           'text-generation': CTRLLMHeadModel,
           'zero-shot': CTRLForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   __UpperCamelCase :			List[str]						   =						True
   __UpperCamelCase :			Dict						   =						False
   __UpperCamelCase :			Tuple						   =						False





   def   UpperCAmelCase__  (       self					:      Dict					,lowerCamelCase					:      Any					,lowerCamelCase					:      str					,lowerCamelCase					:      List[str]					,lowerCamelCase					:      int					,lowerCamelCase					:      Dict     ):

      '''simple docstring'''

      if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
         # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
         # `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
         # config could not be created.
         return True

      return False





   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							CTRLModelTester(self     )
      __SCREAMING_SNAKE_CASE   				=							ConfigTester(self					,config_class=lowerCamelCase					,n_embd=37     )





   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      super().tearDown()
      # clean-up as much as possible GPU memory occupied by PyTorch
      gc.collect()
      torch.cuda.empty_cache()





   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

      self.config_tester.run_common_tests()





   def   UpperCAmelCase__  (       self					:      Dict     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_ctrl_model(*lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_lm_head_model(*lowerCamelCase     )





   @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""     )
   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      pass





   @slow
   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         __SCREAMING_SNAKE_CASE   				=							CTRLModel.from_pretrained(lowerCamelCase     )
         self.assertIsNotNone(lowerCamelCase     )





   @unittest.skip("""The model doesn't support left padding"""     )  # and it's not used enough to be worth fixing :)
   def   UpperCAmelCase__  (       self					:      Tuple     ):

      '''simple docstring'''

      pass







@require_torch
class __a			( unittest.TestCase      ):





   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      super().tearDown()
      # clean-up as much as possible GPU memory occupied by PyTorch
      gc.collect()
      torch.cuda.empty_cache()





   @slow
   def   UpperCAmelCase__  (       self					:      Union[str, Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							CTRLLMHeadModel.from_pretrained("""ctrl"""     )
      model.to(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							torch.tensor(
          [[1_1859, 0, 1611, 8]]					,dtype=torch.long					,device=lowerCamelCase     )  # Legal the president is
      __SCREAMING_SNAKE_CASE   				=							[
          1_1859,
          0,
          1611,
          8,
          5,
          150,
          2_6449,
          2,
          19,
          348,
          469,
          3,
          2595,
          48,
          2_0740,
          24_6533,
          24_6533,
          19,
          30,
          5,
      ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

      __SCREAMING_SNAKE_CASE   				=							model.generate(lowerCamelCase					,do_sample=lowerCamelCase     )
      self.assertListEqual(output_ids[0].tolist()					,lowerCamelCase     )





