






'''simple docstring'''

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
UpperCamelCase      					=logging.get_logger(__name__)

UpperCamelCase      					={
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "ctc_proj",
    "mask_emb": "masked_spec_embed",
}
UpperCamelCase      					=[
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]



def 				snake_case       (  a_				:			int     , a_				:			Optional[Any]     , a_				:			List[Any]     , a_				:			Tuple     , a_				:			Optional[int]     , a_				:			List[str]						)							->					Optional[Any]:





      """simple docstring"""

      for attribute in key.split("""."""						):
            if is_finetuned:
                  if attribute in ["quantizer", "project_q", "project_hid"]:
                        # those layers are only relevant for pretraining and should be dropped
                        return

                  if attribute == "ctc_proj":
                        # we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
                        UpperCamelCase_					: int            =				"""lm_head"""

            UpperCamelCase_					: Dict            =				getattr(a_     , a_						)

      if weight_type is not None:
            UpperCamelCase_					: Optional[int]            =				getattr(a_     , a_						).shape
      else:
            UpperCamelCase_					: List[Any]            =				hf_pointer.shape

      assert hf_shape == value.shape, (
          f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
          f" {value.shape} for {full_name}"
      )

      if weight_type == "weight":
            UpperCamelCase_					: Dict            =				value
      elif weight_type == "weight_g":
            UpperCamelCase_					: str            =				value
      elif weight_type == "weight_v":
            UpperCamelCase_					: Optional[int]            =				value
      elif weight_type == "bias":
            UpperCamelCase_					: List[str]            =				value
      else:
            UpperCamelCase_					: Optional[Any]            =				value

      logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."						)



def 				snake_case       (  a_				:			Tuple     , a_				:			int     , a_				:			Dict						)							->					Optional[Any]:





      """simple docstring"""

      UpperCamelCase_					: Optional[int]            =				[]
      UpperCamelCase_					: Optional[Any]            =				fairseq_model.state_dict()

      UpperCamelCase_					: Any            =				hf_model.unispeech.feature_extractor

      for name, value in fairseq_dict.items():
            UpperCamelCase_					: Tuple            =				False
            if "conv_layers" in name:
                  load_conv_layer(
                      a_     , a_     , a_     , a_     , hf_model.config.feat_extract_norm == """group"""     , )
                  UpperCamelCase_					: List[Any]            =				True
            else:
                  for key, mapped_key in MAPPING.items():
                        UpperCamelCase_					: Dict            =				"""unispeech.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
                        if key in name or key.split("""w2v_model."""						)[-1] == name.split("""."""						)[0]:
                              UpperCamelCase_					: int            =				True
                              if "*" in mapped_key:
                                    UpperCamelCase_					: str            =				name.split(a_						)[0].split("""."""						)[-2]
                                    UpperCamelCase_					: int            =				mapped_key.replace("""*"""     , a_						)
                              if "weight_g" in name:
                                    UpperCamelCase_					: Optional[int]            =				"""weight_g"""
                              elif "weight_v" in name:
                                    UpperCamelCase_					: Tuple            =				"""weight_v"""
                              elif "bias" in name:
                                    UpperCamelCase_					: List[Any]            =				"""bias"""
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    UpperCamelCase_					: str            =				"""weight"""
                              else:
                                    UpperCamelCase_					: Optional[Any]            =				None
                              set_recursively(a_     , a_     , a_     , a_     , a_     , a_						)
                        continue
            if not is_used:
                  unused_weights.append(a_						)

      logger.warning(f"Unused weights: {unused_weights}"						)



def 				snake_case       (  a_				:			List[Any]     , a_				:			Optional[Any]     , a_				:			Optional[int]     , a_				:			List[str]     , a_				:			Tuple						)							->					Union[str, Any]:





      """simple docstring"""

      UpperCamelCase_					: List[Any]            =				full_name.split("""conv_layers."""						)[-1]
      UpperCamelCase_					: int            =				name.split("""."""						)
      UpperCamelCase_					: str            =				int(items[0]						)
      UpperCamelCase_					: Tuple            =				int(items[1]						)

      if type_id == 0:
            if "bias" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                      f"{full_name} has size {value.shape}, but"
                      f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
                  )
                  UpperCamelCase_					: Union[str, Any]            =				value
                  logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."						)
            elif "weight" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                      f"{full_name} has size {value.shape}, but"
                      f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
                  )
                  UpperCamelCase_					: Union[str, Any]            =				value
                  logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."						)
      elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
            if "bias" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                      f"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
                      " found."
                  )
                  UpperCamelCase_					: Union[str, Any]            =				value
                  logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."						)
            elif "weight" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                      f"{full_name} has size {value.shape}, but"
                      f" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
                  )
                  UpperCamelCase_					: Optional[int]            =				value
                  logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."						)
      else:
            unused_weights.append(a_						)



@torch.no_grad()
def 				snake_case       (  a_				:			List[Any]     , a_				:			Optional[Any]     , a_				:			Any=None     , a_				:			List[str]=None     , a_				:			Tuple=True						)							->					Any:





      """simple docstring"""

      if config_path is not None:
            UpperCamelCase_					: Tuple            =				UniSpeechConfig.from_pretrained(a_						)
      else:
            UpperCamelCase_					: Optional[Any]            =				UniSpeechConfig()

      if is_finetuned:
            if dict_path:
                  UpperCamelCase_					: int            =				Dictionary.load_from_json(a_						)

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  UpperCamelCase_					: int            =				target_dict.pad_index
                  UpperCamelCase_					: Any            =				target_dict.bos_index
                  UpperCamelCase_					: Optional[Any]            =				target_dict.eos_index
                  UpperCamelCase_					: Any            =				len(target_dict.symbols						)
                  UpperCamelCase_					: Union[str, Any]            =				os.path.join(a_     , """vocab.json"""						)
                  if not os.path.isdir(a_						):
                        logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(a_						)						)
                        return
                  os.makedirs(a_     , exist_ok=a_						)
                  UpperCamelCase_					: Any            =				target_dict.indices

                  # fairseq has the <pad> and <s> switched
                  UpperCamelCase_					: Optional[int]            =				42
                  UpperCamelCase_					: List[str]            =				43
                  with open(a_     , """w"""     , encoding="""utf-8"""						) as vocab_handle:
                        json.dump(a_     , a_						)
                  UpperCamelCase_					: Optional[int]            =				WavaVecaPhonemeCTCTokenizer(
                      a_     , unk_token=target_dict.unk_word     , pad_token=target_dict.pad_word     , bos_token=target_dict.bos_word     , eos_token=target_dict.eos_word     , word_delimiter_token="""|"""     , do_lower_case=a_     , )
                  UpperCamelCase_					: str            =				True if config.feat_extract_norm == """layer""" else False
                  UpperCamelCase_					: Tuple            =				WavaVecaFeatureExtractor(
                      feature_size=1     , sampling_rate=16_000     , padding_value=0     , do_normalize=a_     , return_attention_mask=a_     , )
                  UpperCamelCase_					: Tuple            =				WavaVecaProcessor(feature_extractor=a_     , tokenizer=a_						)
                  processor.save_pretrained(a_						)

            UpperCamelCase_					: Union[str, Any]            =				UniSpeechForCTC(a_						)
      else:
            UpperCamelCase_					: Union[str, Any]            =				UniSpeechForPreTraining(a_						)

      if is_finetuned:
            UpperCamelCase_     ,      UpperCamelCase_     ,      UpperCamelCase_					: List[str]            =				fairseq.checkpoint_utils.load_model_ensemble_and_task(
                [checkpoint_path]     , arg_overrides={"""data""": """/""".join(dict_path.split("""/"""						)[:-1]						), """w2v_path""": checkpoint_path}						)
      else:
            UpperCamelCase_     ,      UpperCamelCase_     ,      UpperCamelCase_					: Tuple            =				fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]						)

      UpperCamelCase_					: int            =				model[0].eval()

      recursively_load_weights(a_     , a_     , a_						)

      hf_unispeech.save_pretrained(a_						)


if __name__ == "__main__":
      UpperCamelCase      					=argparse.ArgumentParser()
      parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
      parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
      parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
      parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
      parser.add_argument(
          "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
      )
      UpperCamelCase      					=parser.parse_args()
      convert_unispeech_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
      )














'''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      ,  __lowerCAmelCase      ,  __lowerCAmelCase=13      ,  __lowerCAmelCase=7      ,  __lowerCAmelCase=6      ,  __lowerCAmelCase=17      ,  __lowerCAmelCase=23      ,  __lowerCAmelCase=11      ,  __lowerCAmelCase=True      ,  ):
       UpperCamelCase_					: Tuple            =				parent
       UpperCamelCase_					: List[Any]            =				batch_size
       UpperCamelCase_					: List[str]            =				seq_length
       UpperCamelCase_					: Dict            =				act_dim
       UpperCamelCase_					: Optional[int]            =				state_dim
       UpperCamelCase_					: Dict            =				hidden_size
       UpperCamelCase_					: str            =				max_length
       UpperCamelCase_					: int            =				is_training
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: List[str]            =				floats_tensor((self.batch_size, self.seq_length, self.state_dim)			)
       UpperCamelCase_					: Dict            =				floats_tensor((self.batch_size, self.seq_length, self.act_dim)			)
       UpperCamelCase_					: Union[str, Any]            =				floats_tensor((self.batch_size, self.seq_length, 1)			)
       UpperCamelCase_					: List[str]            =				floats_tensor((self.batch_size, self.seq_length, 1)			)
       UpperCamelCase_					: Optional[Any]            =				ids_tensor((self.batch_size, self.seq_length)      ,  vocab_size=10_00			)
       UpperCamelCase_					: Optional[Any]            =				random_attention_mask((self.batch_size, self.seq_length)			)

       UpperCamelCase_					: Tuple            =				self.get_config()

       return (
           config,
           states,
           actions,
           rewards,
           returns_to_go,
           timesteps,
           attention_mask,
       )
 def 	_UpperCAmelCase							(				self			):
       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 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  ):
       UpperCamelCase_					: Tuple            =				DecisionTransformerModel(config=__lowerCAmelCase			)
       model.to(__lowerCAmelCase			)
       model.eval()
       UpperCamelCase_					: str            =				model(__lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase			)

       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 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Dict            =				self.prepare_config_and_inputs()
       (
           (
           UpperCamelCase_
       )     ,      (
           UpperCamelCase_
       )     ,      (
           UpperCamelCase_
       )     ,      (
           UpperCamelCase_
       )     ,      (
           UpperCamelCase_
       )     ,      (
           UpperCamelCase_
       )     ,      (
           UpperCamelCase_
       )     ,      
       )					: str            =				config_and_inputs
       UpperCamelCase_					: int            =				{
           """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     (   SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, unittest.TestCase    ):






 """simple docstring"""





 __a      :    Tuple			  =    (DecisionTransformerModel,) if is_torch_available() else ()
 __a      :    Union[str, Any]			  =    ()
 __a      :    int			  =    {'''feature-extraction''': DecisionTransformerModel} if is_torch_available() else {}

 # Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
 __a      :    Optional[Any]			  =    False

 # Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
 __a      :    int			  =    False
 __a      :    Tuple			  =    False
 __a      :    Dict			  =    False
 __a      :    Union[str, Any]			  =    False
 __a      :    int			  =    False
 __a      :    List[Any]			  =    False
 __a      :    List[str]			  =    False
 __a      :    Dict			  =    False
 __a      :    Optional[Any]			  =    False
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Dict            =				DecisionTransformerModelTester(self			)
       UpperCamelCase_					: Dict            =				ConfigTester(self      ,  config_class=__lowerCAmelCase      ,  hidden_size=37			)
 def 	_UpperCAmelCase							(				self			):
       self.config_tester.run_common_tests()
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Optional[int]            =				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__lowerCAmelCase			)
 @slow
 def 	_UpperCAmelCase							(				self			):
       for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCamelCase_					: Union[str, Any]            =				DecisionTransformerModel.from_pretrained(__lowerCAmelCase			)
             self.assertIsNotNone(__lowerCAmelCase			)





 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_     ,      UpperCamelCase_					: str            =				self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             UpperCamelCase_					: Union[str, Any]            =				model_class(__lowerCAmelCase			)
             UpperCamelCase_					: Optional[int]            =				inspect.signature(model.forward			)
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             UpperCamelCase_					: Any            =				[*signature.parameters.keys()]

             UpperCamelCase_					: List[str]            =				[
                 """states""",
                 """actions""",
                 """rewards""",
                 """returns_to_go""",
                 """timesteps""",
                 """attention_mask""",
             ]

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






@require_torch
class      A     (   unittest.TestCase    ):






 """simple docstring"""





 @slow
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Tuple            =				2  # number of steps of autoregressive prediction we will perform
       UpperCamelCase_					: Dict            =				10  # defined by the RL environment, may be normalized
       UpperCamelCase_					: Any            =				DecisionTransformerModel.from_pretrained("""edbeeching/decision-transformer-gym-hopper-expert"""			)
       UpperCamelCase_					: Tuple            =				model.to(__lowerCAmelCase			)
       UpperCamelCase_					: List[str]            =				model.config
       torch.manual_seed(0			)
       UpperCamelCase_					: str            =				torch.randn(1      ,  1      ,  config.state_dim			).to(device=__lowerCAmelCase      ,  dtype=torch.floataa			)  # env.reset()

       UpperCamelCase_					: List[str]            =				torch.tensor(
           [[0.24_27_93, -0.28_69_30_74, 0.8_74_26_13], [0.67_81_52_74, -0.08_10_10_85, -0.12_95_21_47]]      ,  device=__lowerCAmelCase			)

       UpperCamelCase_					: Tuple            =				torch.tensor(__lowerCAmelCase      ,  device=__lowerCAmelCase      ,  dtype=torch.floataa			).reshape(1      ,  1      ,  1			)
       UpperCamelCase_					: Optional[int]            =				state
       UpperCamelCase_					: int            =				torch.zeros(1      ,  0      ,  config.act_dim      ,  device=__lowerCAmelCase      ,  dtype=torch.floataa			)
       UpperCamelCase_					: Optional[int]            =				torch.zeros(1      ,  0      ,  device=__lowerCAmelCase      ,  dtype=torch.floataa			)
       UpperCamelCase_					: Optional[Any]            =				torch.tensor(0      ,  device=__lowerCAmelCase      ,  dtype=torch.long			).reshape(1      ,  1			)

       for step in range(__lowerCAmelCase			):
             UpperCamelCase_					: List[Any]            =				torch.cat([actions, torch.zeros(1      ,  1      ,  config.act_dim      ,  device=__lowerCAmelCase			)]      ,  dim=1			)
             UpperCamelCase_					: Optional[int]            =				torch.cat([rewards, torch.zeros(1      ,  1      ,  device=__lowerCAmelCase			)]      ,  dim=1			)

             UpperCamelCase_					: Union[str, Any]            =				torch.ones(1      ,  states.shape[1]			).to(dtype=torch.long      ,  device=states.device			)

             with torch.no_grad():
                   UpperCamelCase_     ,      UpperCamelCase_     ,      UpperCamelCase_					: List[Any]            =				model(
                       states=__lowerCAmelCase      ,  actions=__lowerCAmelCase      ,  rewards=__lowerCAmelCase      ,  returns_to_go=__lowerCAmelCase      ,  timesteps=__lowerCAmelCase      ,  attention_mask=__lowerCAmelCase      ,  return_dict=__lowerCAmelCase      ,  )

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

             UpperCamelCase_					: Any            =				action_pred[0, -1]
             UpperCamelCase_					: int            =				torch.cat([states, state]      ,  dim=1			)
             UpperCamelCase_					: str            =				returns_to_go[0, -1] - reward
             UpperCamelCase_					: int            =				torch.cat([returns_to_go, pred_return.reshape(1      ,  1      ,  1			)]      ,  dim=1			)
             UpperCamelCase_					: int            =				torch.cat(
                 [timesteps, torch.ones((1, 1)      ,  device=__lowerCAmelCase      ,  dtype=torch.long			) * (step + 1)]      ,  dim=1			)






