



'''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
lowercase_									=    {
    "/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 lowerCAmelCase    (__A):



     """simple docstring"""



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

          _a           =    r'''(encoder|decoder)\/'''

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

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

          print(F'''{key} -> {new_key}''')
          _a           =    s_dict.pop(__A)

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

               s_dict.pop(__A)

     return s_dict


lowercase_									=    {
    "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 lowerCAmelCase    (__A    ,      __A):



     """simple docstring"""



     import regex as re

     with open(__A    ,      '''r''') as f:
          _a           =    f.read()

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

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

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




def lowerCAmelCase    (__A    ,      __A    ,      __A=None    ,      __A="./"    ,      __A=8):



     """simple docstring"""



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

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

     _a           =    SwitchTransformersForConditionalGeneration(__A)

     _a           =    flax_params['''target''']
     _a           =    flatten_dict(__A    ,      sep='''/''')
     _a           =    rename_keys(__A)
     _a           =    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__":
  lowercase_									=    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")
  lowercase_									=    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 inspect
import unittest

from transformers import DecisionTransformerConfig, 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, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import DecisionTransformerModel
  from transformers.models.decision_transformer.modeling_decision_transformer import (
      DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
  )






class __A     :



 '''simple docstring'''







 def __init__(self					,  A					,  A=13					,  A=7					,  A=6					,  A=17					,  A=23					,  A=11					,  A=True					,  )       ->	Tuple:


      """simple docstring"""



      _a           =    parent
      _a           =    batch_size
      _a           =    seq_length
      _a           =    act_dim
      _a           =    state_dim
      _a           =    hidden_size
      _a           =    max_length
      _a           =    is_training



 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      _a           =    floats_tensor((self.batch_size, self.seq_length, self.state_dim)			)
      _a           =    floats_tensor((self.batch_size, self.seq_length, self.act_dim)			)
      _a           =    floats_tensor((self.batch_size, self.seq_length, 1)			)
      _a           =    floats_tensor((self.batch_size, self.seq_length, 1)			)
      _a           =    ids_tensor((self.batch_size, self.seq_length)					,  vocab_size=1_000			)
      _a           =    random_attention_mask((self.batch_size, self.seq_length)			)

      _a           =    self.get_config()

      return (
          config,
          states,
          actions,
          rewards,
          returns_to_go,
          timesteps,
          attention_mask,
      )



 def    a__    (self			)       ->	str:


      """simple docstring"""



      return DecisionTransformerConfig(
          batch_size=self.batch_size					,  seq_length=self.seq_length					,  act_dim=self.act_dim					,  state_dim=self.state_dim					,  hidden_size=self.hidden_size					,  max_length=self.max_length					,  )



 def    a__    (self					,  A					,  A					,  A					,  A					,  A					,  A					,  A					,  )       ->	List[Any]:


      """simple docstring"""



      _a           =    DecisionTransformerModel(config=A			)
      model.to(A			)
      model.eval()
      _a           =    model(A					,  A					,  A					,  A					,  A					,  A			)

      self.parent.assertEqual(result.state_preds.shape					,  states.shape			)
      self.parent.assertEqual(result.action_preds.shape					,  actions.shape			)
      self.parent.assertEqual(result.return_preds.shape					,  returns_to_go.shape			)
      self.parent.assertEqual(
          result.last_hidden_state.shape					,  (self.batch_size, self.seq_length * 3, self.hidden_size)			)  # seq length *3 as there are 3 modelities: states, returns and actions



 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      _a           =    self.prepare_config_and_inputs()
      (
          (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       
      )           =    config_and_inputs
      _a           =    {
          '''states''': states,
          '''actions''': actions,
          '''rewards''': rewards,
          '''returns_to_go''': returns_to_go,
          '''timesteps''': timesteps,
          '''attention_mask''': attention_mask,
      }
      return config, inputs_dict






@require_torch
class __A     (    A  ,  A  ,  A  ,  unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase       :			Optional[Any]		=			(DecisionTransformerModel,) if is_torch_available() else ()
 __lowerCamelCase       :			List[str]		=			()
 __lowerCamelCase       :			Tuple		=			{'feature-extraction': DecisionTransformerModel} if is_torch_available() else {}

 # Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
 __lowerCamelCase       :			str		=			False

 # Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
 __lowerCamelCase       :			List[str]		=			False
 __lowerCamelCase       :			List[str]		=			False
 __lowerCamelCase       :			Tuple		=			False
 __lowerCamelCase       :			str		=			False
 __lowerCamelCase       :			Dict		=			False
 __lowerCamelCase       :			Tuple		=			False
 __lowerCamelCase       :			Tuple		=			False
 __lowerCamelCase       :			Dict		=			False
 __lowerCamelCase       :			List[str]		=			False



 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      _a           =    DecisionTransformerModelTester(self			)
      _a           =    ConfigTester(self					,  config_class=A					,  hidden_size=37			)



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


      """simple docstring"""



      self.config_tester.run_common_tests()



 def    a__    (self			)       ->	List[Any]:


      """simple docstring"""



      _a           =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*A			)



 @slow
 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _a           =    DecisionTransformerModel.from_pretrained(A			)
           self.assertIsNotNone(A			)



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


      """simple docstring"""



      _a    ,       _a           =    self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           _a           =    model_class(A			)
           _a           =    inspect.signature(model.forward			)
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           _a           =    [*signature.parameters.keys()]

           _a           =    [
               '''states''',
               '''actions''',
               '''rewards''',
               '''returns_to_go''',
               '''timesteps''',
               '''attention_mask''',
           ]

           self.assertListEqual(arg_names[: len(A			)]					,  A			)






@require_torch
class __A     (    unittest.TestCase		):



 '''simple docstring'''







 @slow
 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      _a           =    2  # number of steps of autoregressive prediction we will perform
      _a           =    10  # defined by the RL environment, may be normalized
      _a           =    DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-expert'''			)
      _a           =    model.to(A			)
      _a           =    model.config
      torch.manual_seed(0			)
      _a           =    torch.randn(1					,  1					,  config.state_dim			).to(device=A					,  dtype=torch.floataa			)  # env.reset()

      _a           =    torch.tensor(
          [[0.242793, -0.28693074, 0.8742613], [0.67815274, -0.08101085, -0.12952147]]					,  device=A			)

      _a           =    torch.tensor(A					,  device=A					,  dtype=torch.floataa			).reshape(1					,  1					,  1			)
      _a           =    state
      _a           =    torch.zeros(1					,  0					,  config.act_dim					,  device=A					,  dtype=torch.floataa			)
      _a           =    torch.zeros(1					,  0					,  device=A					,  dtype=torch.floataa			)
      _a           =    torch.tensor(0					,  device=A					,  dtype=torch.long			).reshape(1					,  1			)

      for step in range(A			):
           _a           =    torch.cat([actions, torch.zeros(1					,  1					,  config.act_dim					,  device=A			)]					,  dim=1			)
           _a           =    torch.cat([rewards, torch.zeros(1					,  1					,  device=A			)]					,  dim=1			)

           _a           =    torch.ones(1					,  states.shape[1]			).to(dtype=torch.long					,  device=states.device			)

           with torch.no_grad():
                _a    ,       _a    ,       _a           =    model(
                    states=A					,  actions=A					,  rewards=A					,  returns_to_go=A					,  timesteps=A					,  attention_mask=A					,  return_dict=A					,  )

           self.assertEqual(action_pred.shape					,  actions.shape			)
           self.assertTrue(torch.allclose(action_pred[0, -1]					,  expected_outputs[step]					,  atol=1E-4			)			)
           _a    ,       _a    ,       _a    ,       _a           =    (  # env.step(action)
               torch.randn(1					,  1					,  config.state_dim			).to(device=A					,  dtype=torch.floataa			),
               1.0,
               False,
               {},
           )

           _a           =    action_pred[0, -1]
           _a           =    torch.cat([states, state]					,  dim=1			)
           _a           =    returns_to_go[0, -1] - reward
           _a           =    torch.cat([returns_to_go, pred_return.reshape(1					,  1					,  1			)]					,  dim=1			)
           _a           =    torch.cat(
               [timesteps, torch.ones((1, 1)					,  device=A					,  dtype=torch.long			) * (step + 1)]					,  dim=1			)



