
import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
UpperCamelCase__      =logging.get_logger('transformers.models.speecht5')

UpperCamelCase__      ={
    'speech_encoder_prenet.layer_norm': 'speecht5.encoder.prenet.feature_projection.layer_norm',
    'speech_encoder_prenet.post_extract_proj': 'speecht5.encoder.prenet.feature_projection.projection',
    'speech_encoder_prenet.pos_conv.0': 'speecht5.encoder.prenet.pos_conv_embed.conv',
    'speech_encoder_prenet.mask_emb': 'speecht5.encoder.prenet.masked_spec_embed',
}
UpperCamelCase__      ={
    'text_encoder_prenet.encoder_prenet.0': 'speecht5.encoder.prenet.embed_tokens',
    'text_encoder_prenet.encoder_prenet.1.alpha': 'speecht5.encoder.prenet.encode_positions.alpha',
}
UpperCamelCase__      ={
    'speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0': 'speecht5.decoder.prenet.layers.0',
    'speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0': 'speecht5.decoder.prenet.layers.1',
    'speech_decoder_prenet.decoder_prenet.0.1': 'speecht5.decoder.prenet.final_layer',
    'speech_decoder_prenet.decoder_prenet.1.alpha': 'speecht5.decoder.prenet.encode_positions.alpha',
    'speech_decoder_prenet.spkembs_layer.0': 'speecht5.decoder.prenet.speaker_embeds_layer',
}
UpperCamelCase__      ={
    'speech_decoder_postnet.feat_out': 'speech_decoder_postnet.feat_out',
    'speech_decoder_postnet.prob_out': 'speech_decoder_postnet.prob_out',
    'speech_decoder_postnet.postnet.postnet.0.0': 'speech_decoder_postnet.layers.0.conv',
    'speech_decoder_postnet.postnet.postnet.0.1': 'speech_decoder_postnet.layers.0.batch_norm',
    'speech_decoder_postnet.postnet.postnet.1.0': 'speech_decoder_postnet.layers.1.conv',
    'speech_decoder_postnet.postnet.postnet.1.1': 'speech_decoder_postnet.layers.1.batch_norm',
    'speech_decoder_postnet.postnet.postnet.2.0': 'speech_decoder_postnet.layers.2.conv',
    'speech_decoder_postnet.postnet.postnet.2.1': 'speech_decoder_postnet.layers.2.batch_norm',
    'speech_decoder_postnet.postnet.postnet.3.0': 'speech_decoder_postnet.layers.3.conv',
    'speech_decoder_postnet.postnet.postnet.3.1': 'speech_decoder_postnet.layers.3.batch_norm',
    'speech_decoder_postnet.postnet.postnet.4.0': 'speech_decoder_postnet.layers.4.conv',
    'speech_decoder_postnet.postnet.postnet.4.1': 'speech_decoder_postnet.layers.4.batch_norm',
}
UpperCamelCase__      ={
    'text_decoder_prenet.embed_tokens': 'speecht5.decoder.prenet.embed_tokens',
}
UpperCamelCase__      ={
    'text_decoder_postnet.output_projection': 'text_decoder_postnet.lm_head',
}
UpperCamelCase__      ={
    'encoder.layers.*.self_attn.k_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj',
    'encoder.layers.*.self_attn.v_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj',
    'encoder.layers.*.self_attn.q_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj',
    'encoder.layers.*.self_attn.out_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj',
    'encoder.layers.*.self_attn_layer_norm': 'speecht5.encoder.wrapped_encoder.layers.*.layer_norm',
    'encoder.layers.*.fc1': 'speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense',
    'encoder.layers.*.fc2': 'speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense',
    'encoder.layers.*.final_layer_norm': 'speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'speecht5.encoder.wrapped_encoder.layer_norm',
    'encoder.pos_emb.pe_k': 'speecht5.encoder.wrapped_encoder.embed_positions.pe_k',
}
UpperCamelCase__      ={
    'decoder.layers.*.self_attn.k_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj',
    'decoder.layers.*.self_attn.v_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj',
    'decoder.layers.*.self_attn.q_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj',
    'decoder.layers.*.self_attn.out_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj',
    'decoder.layers.*.self_attn_layer_norm': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm',
    'decoder.layers.*.encoder_attn.k_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj',
    'decoder.layers.*.encoder_attn.v_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj',
    'decoder.layers.*.encoder_attn.q_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj',
    'decoder.layers.*.encoder_attn.out_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj',
    'decoder.layers.*.encoder_attn_layer_norm': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm',
    'decoder.layers.*.fc1': 'speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense',
    'decoder.layers.*.fc2': 'speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense',
    'decoder.layers.*.final_layer_norm': 'speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm',
}
UpperCamelCase__      ={
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
UpperCamelCase__      ={
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
UpperCamelCase__      ={
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
UpperCamelCase__      =[]
UpperCamelCase__      =[
    'encoder.version',
    'encoder.layers.*.norm_k.weight',
    'encoder.layers.*.norm_k.bias',
    'decoder.version',
    'decoder.layers.*.norm_k.weight',
    'decoder.layers.*.norm_k.bias',
    'decoder.pos_emb.pe_k',
    'speech_encoder_prenet.embed_positions._float_tensor',
    'text_decoder_prenet.embed_positions._float_tensor',
]
UpperCamelCase__      =IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'speech_decoder_prenet.*',
    'speech_decoder_postnet.*',
]
UpperCamelCase__      =IGNORE_KEYS + [
    'encoder.proj',
    'speech_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]
UpperCamelCase__      =IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase	):







     for attribute in key.split("."	):
          _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							getattr(__snake_case,       __snake_case	)

     if weight_type is not None:
          _SCREAMING_SNAKE_CASE       :	List[str]						    =							getattr(__snake_case,       __snake_case	).shape
     else:
          _SCREAMING_SNAKE_CASE       :	int						    =							hf_pointer.shape

     if hf_shape != value.shape:
          raise ValueError(
              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":
          _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							value
     elif weight_type == "weight_g":
          _SCREAMING_SNAKE_CASE       :	Tuple						    =							value
     elif weight_type == "weight_v":
          _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							value
     elif weight_type == "bias":
          _SCREAMING_SNAKE_CASE       :	int						    =							value
     elif weight_type == "running_mean":
          _SCREAMING_SNAKE_CASE       :	Any						    =							value
     elif weight_type == "running_var":
          _SCREAMING_SNAKE_CASE       :	List[Any]						    =							value
     elif weight_type == "num_batches_tracked":
          _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							value
     else:
          _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							value

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






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase	):







     for key in ignore_keys:
          if key.endswith(".*"	):
               if name.startswith(key[:-1]	):
                    return True
          elif ".*." in key:
               _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	int						    =							key.split(".*."	)
               if prefix in name and suffix in name:
                    return True
          elif key in name:
               return True
     return False






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase	):
     _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							[]

     if task == "s2t":
          _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							hf_model.speechta.encoder.prenet.feature_encoder
          _SCREAMING_SNAKE_CASE       :	Any						    =							MAPPING_S2T
          _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							IGNORE_KEYS_S2T
     elif task == "t2s":
          _SCREAMING_SNAKE_CASE       :	Any						    =							None
          _SCREAMING_SNAKE_CASE       :	Tuple						    =							MAPPING_T2S
          _SCREAMING_SNAKE_CASE       :	List[str]						    =							IGNORE_KEYS_T2S
     elif task == "s2s":
          _SCREAMING_SNAKE_CASE       :	List[Any]						    =							hf_model.speechta.encoder.prenet.feature_encoder
          _SCREAMING_SNAKE_CASE       :	List[str]						    =							MAPPING_S2S
          _SCREAMING_SNAKE_CASE       :	Any						    =							IGNORE_KEYS_S2S
     else:
          raise ValueError(f"""Unsupported task: {task}"""	)

     for name, value in fairseq_dict.items():
          if should_ignore(__snake_case,       __snake_case	):
               logger.info(f"""{name} was ignored"""	)
               continue

          _SCREAMING_SNAKE_CASE       :	Tuple						    =							False
          if "conv_layers" in name:
               load_conv_layer(
                   __snake_case,       __snake_case,       __snake_case,       __snake_case,       hf_model.config.feat_extract_norm == "group",       )
               _SCREAMING_SNAKE_CASE       :	Dict						    =							True
          else:
               for key, mapped_key in MAPPING.items():
                    # mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

                    if "*" in key:
                         _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							key.split(".*."	)
                         if prefix in name and suffix in name:
                              _SCREAMING_SNAKE_CASE       :	List[Any]						    =							suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
                    if key in name:
                         _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							True
                         if "*" in mapped_key:
                              _SCREAMING_SNAKE_CASE       :	Tuple						    =							name.split(__snake_case	)[0].split("."	)[-2]
                              _SCREAMING_SNAKE_CASE       :	List[Any]						    =							mapped_key.replace("*",       __snake_case	)
                         if "weight_g" in name:
                              _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							"weight_g"
                         elif "weight_v" in name:
                              _SCREAMING_SNAKE_CASE       :	str						    =							"weight_v"
                         elif "bias" in name:
                              _SCREAMING_SNAKE_CASE       :	Tuple						    =							"bias"
                         elif "weight" in name:
                              _SCREAMING_SNAKE_CASE       :	List[str]						    =							"weight"
                         elif "running_mean" in name:
                              _SCREAMING_SNAKE_CASE       :	Any						    =							"running_mean"
                         elif "running_var" in name:
                              _SCREAMING_SNAKE_CASE       :	int						    =							"running_var"
                         elif "num_batches_tracked" in name:
                              _SCREAMING_SNAKE_CASE       :	List[Any]						    =							"num_batches_tracked"
                         else:
                              _SCREAMING_SNAKE_CASE       :	Dict						    =							None
                         set_recursively(__snake_case,       __snake_case,       __snake_case,       __snake_case,       __snake_case	)
                    continue
          if not is_used:
               unused_weights.append(__snake_case	)

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






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase	):
     _SCREAMING_SNAKE_CASE       :	Dict						    =							full_name.split("conv_layers."	)[-1]
     _SCREAMING_SNAKE_CASE       :	str						    =							name.split("."	)
     _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							int(items[0]	)
     _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							int(items[1]	)

     if type_id == 0:
          if "bias" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                    raise ValueError(
                        f"""{full_name} has size {value.shape}, but"""
                        f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""	)
               _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							value
               logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""	)
          elif "weight" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                    raise ValueError(
                        f"""{full_name} has size {value.shape}, but"""
                        f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""	)
               _SCREAMING_SNAKE_CASE       :	Optional[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:
               if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                    raise ValueError(
                        f"""{full_name} has size {value.shape}, but"""
                        f""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."""	)
               _SCREAMING_SNAKE_CASE       :	Dict						    =							value
               logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""	)
          elif "weight" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                    raise ValueError(
                        f"""{full_name} has size {value.shape}, but"""
                        f""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."""	)
               _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							value
               logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""	)
     else:
          unused_weights.append(__snake_case	)






@torch.no_grad()
def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase=None,       __lowerCamelCase=None,       __lowerCamelCase=None,       ):







     if config_path is not None:
          _SCREAMING_SNAKE_CASE       :	int						    =							SpeechTaConfig.from_pretrained(__snake_case	)
     else:
          _SCREAMING_SNAKE_CASE       :	str						    =							SpeechTaConfig()

     if task == "s2t":
          _SCREAMING_SNAKE_CASE       :	Any						    =							config.max_text_positions
          _SCREAMING_SNAKE_CASE       :	Dict						    =							SpeechTaForSpeechToText(__snake_case	)
     elif task == "t2s":
          _SCREAMING_SNAKE_CASE       :	int						    =							1876
          _SCREAMING_SNAKE_CASE       :	Any						    =							600
          _SCREAMING_SNAKE_CASE       :	List[str]						    =							config.max_speech_positions
          _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							SpeechTaForTextToSpeech(__snake_case	)
     elif task == "s2s":
          _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							1876
          _SCREAMING_SNAKE_CASE       :	int						    =							config.max_speech_positions
          _SCREAMING_SNAKE_CASE       :	str						    =							SpeechTaForSpeechToSpeech(__snake_case	)
     else:
          raise ValueError(f"""Unknown task name: {task}"""	)

     if vocab_path:
          _SCREAMING_SNAKE_CASE       :	Tuple						    =							SpeechTaTokenizer(__snake_case,       model_max_length=config.max_text_positions	)

          # Mask token behaves like a normal word, i.e. include the space before it
          _SCREAMING_SNAKE_CASE       :	Dict						    =							AddedToken("<mask>",       lstrip=__snake_case,       rstrip=__snake_case	)
          _SCREAMING_SNAKE_CASE       :	Dict						    =							mask_token
          tokenizer.add_special_tokens({"mask_token": mask_token}	)
          tokenizer.add_tokens(["<ctc_blank>"]	)

     _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							SpeechTaFeatureExtractor()
     _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							SpeechTaProcessor(tokenizer=__snake_case,       feature_extractor=__snake_case	)
     processor.save_pretrained(__snake_case	)

     _SCREAMING_SNAKE_CASE       :	Any						    =							torch.load(__snake_case	)
     recursively_load_weights(fairseq_checkpoint["model"],       __snake_case,       __snake_case	)

     model.save_pretrained(__snake_case	)

     if repo_id:
          print("Pushing to the hub..."	)
          processor.push_to_hub(__snake_case	)
          model.push_to_hub(__snake_case	)


if __name__ == "__main__":
       UpperCamelCase__      =argparse.ArgumentParser()
       parser.add_argument(
           '--task',
           default='s2t',
           type=str,
           help='Type of the SpeechT5 model you\'d like to convert. Should be one of \'s2t\', \'t2s\', \'s2s\'.',
       )
       parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to fairseq checkpoint')
       parser.add_argument('--vocab_path', default=None, type=str, help='Path to SentencePiece model')
       parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
       parser.add_argument(
           '--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
       )
       parser.add_argument(
           '--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
       )

       UpperCamelCase__      =parser.parse_args()
       convert_speechta_checkpoint(
           args.task,
           args.checkpoint_path,
           args.pytorch_dump_folder_path,
           args.config_path,
           args.vocab_path,
           args.push_to_hub,
       )






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 			lowerCAmelCase__:






       '''simple docstring'''


       def __init__(      self    ,						__lowerCamelCase    ,						__lowerCamelCase=1_3    ,						__lowerCamelCase=7    ,						__lowerCamelCase=6    ,						__lowerCamelCase=1_7    ,						__lowerCamelCase=2_3    ,						__lowerCamelCase=1_1    ,						__lowerCamelCase=True    ,						)			->     Optional[int]:
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							parent
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							batch_size
            _SCREAMING_SNAKE_CASE       :	int						    =							seq_length
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							act_dim
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							state_dim
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							hidden_size
            _SCREAMING_SNAKE_CASE       :	Any						    =							max_length
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							is_training




       def     UpperCamelCase_			(      self							)			->     List[Any]:
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							floats_tensor((self.batch_size, self.seq_length, self.state_dim)							)
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							floats_tensor((self.batch_size, self.seq_length, self.act_dim)							)
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							floats_tensor((self.batch_size, self.seq_length, 1)							)
            _SCREAMING_SNAKE_CASE       :	str						    =							floats_tensor((self.batch_size, self.seq_length, 1)							)
            _SCREAMING_SNAKE_CASE       :	str						    =							ids_tensor((self.batch_size, self.seq_length)    ,						vocab_size=1_0_0_0							)
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							random_attention_mask((self.batch_size, self.seq_length)							)

            _SCREAMING_SNAKE_CASE       :	List[str]						    =							self.get_config()

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




       def     UpperCamelCase_			(      self							)			->     List[Any]:
            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    ,						)			->     Any:
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							DecisionTransformerModel(config=__lowerCamelCase							)
            model.to(__lowerCamelCase							)
            model.eval()
            _SCREAMING_SNAKE_CASE       :	List[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							)			->     Dict:
            _SCREAMING_SNAKE_CASE       :	int						    =							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
            )    ,			
            )       :	Optional[Any]						    =							config_and_inputs
            _SCREAMING_SNAKE_CASE       :	Any						    =							{
                "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 			lowerCAmelCase__( __lowercase  ,     __lowercase  ,     __lowercase  ,     unittest.TestCase    ):






       '''simple docstring'''


       __snake_case 			=    (DecisionTransformerModel,) if is_torch_available() else ()
       __snake_case 			=    ()
       __snake_case 			=    {'feature-extraction': DecisionTransformerModel} if is_torch_available() else {}

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

       # Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
       __snake_case 			=    False
       __snake_case 			=    False
       __snake_case 			=    False
       __snake_case 			=    False
       __snake_case 			=    False
       __snake_case 			=    False
       __snake_case 			=    False
       __snake_case 			=    False
       __snake_case 			=    False




       def     UpperCamelCase_			(      self							)			->     Union[str, Any]:
            _SCREAMING_SNAKE_CASE       :	int						    =							DecisionTransformerModelTester(self							)
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							ConfigTester(self    ,						config_class=__lowerCamelCase    ,						hidden_size=3_7							)




       def     UpperCamelCase_			(      self							)			->     Optional[int]:
            self.config_tester.run_common_tests()




       def     UpperCamelCase_			(      self							)			->     List[Any]:
            _SCREAMING_SNAKE_CASE       :	str						    =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*__lowerCamelCase							)




       @slow
       def     UpperCamelCase_			(      self							)			->     Dict:
            for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 _SCREAMING_SNAKE_CASE       :	str						    =							DecisionTransformerModel.from_pretrained(__lowerCamelCase							)
                 self.assertIsNotNone(__lowerCamelCase							)







       def     UpperCamelCase_			(      self							)			->     Optional[int]:
            _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Dict						    =							self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 _SCREAMING_SNAKE_CASE       :	Tuple						    =							model_class(__lowerCamelCase							)
                 _SCREAMING_SNAKE_CASE       :	List[str]						    =							inspect.signature(model.forward							)
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 _SCREAMING_SNAKE_CASE       :	int						    =							[*signature.parameters.keys()]

                 _SCREAMING_SNAKE_CASE       :	Tuple						    =							[
                     "states",
                     "actions",
                     "rewards",
                     "returns_to_go",
                     "timesteps",
                     "attention_mask",
                 ]

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





@require_torch
class 			lowerCAmelCase__( unittest.TestCase    ):






       '''simple docstring'''


       @slow
       def     UpperCamelCase_			(      self							)			->     Union[str, Any]:
            _SCREAMING_SNAKE_CASE       :	str						    =							2  # number of steps of autoregressive prediction we will perform
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							1_0  # defined by the RL environment, may be normalized
            _SCREAMING_SNAKE_CASE       :	int						    =							DecisionTransformerModel.from_pretrained("edbeeching/decision-transformer-gym-hopper-expert"							)
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							model.to(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							model.config
            torch.manual_seed(0							)
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							torch.randn(1    ,						1    ,						config.state_dim							).to(device=__lowerCamelCase    ,						dtype=torch.floataa							)  # env.reset()

            _SCREAMING_SNAKE_CASE       :	List[str]						    =							torch.tensor(
                [[0.24_2793, -0.2869_3074, 0.874_2613], [0.6781_5274, -0.0810_1085, -0.1295_2147]]    ,						device=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							torch.tensor(__lowerCamelCase    ,						device=__lowerCamelCase    ,						dtype=torch.floataa							).reshape(1    ,						1    ,						1							)
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							state
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							torch.zeros(1    ,						0    ,						config.act_dim    ,						device=__lowerCamelCase    ,						dtype=torch.floataa							)
            _SCREAMING_SNAKE_CASE       :	Dict						    =							torch.zeros(1    ,						0    ,						device=__lowerCamelCase    ,						dtype=torch.floataa							)
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							torch.tensor(0    ,						device=__lowerCamelCase    ,						dtype=torch.long							).reshape(1    ,						1							)

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

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

                 with torch.no_grad():
                      _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Optional[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							)							)
                 _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Tuple						    =							(  # env.step(action)
                     torch.randn(1    ,						1    ,						config.state_dim							).to(device=__lowerCamelCase    ,						dtype=torch.floataa							),
                     1.0,
                     False,
                     {},
                 )

                 _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							action_pred[0, -1]
                 _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							torch.cat([states, state]    ,						dim=1							)
                 _SCREAMING_SNAKE_CASE       :	List[str]						    =							returns_to_go[0, -1] - reward
                 _SCREAMING_SNAKE_CASE       :	str						    =							torch.cat([returns_to_go, pred_return.reshape(1    ,						1    ,						1							)]    ,						dim=1							)
                 _SCREAMING_SNAKE_CASE       :	Tuple						    =							torch.cat(
                     [timesteps, torch.ones((1, 1)    ,						device=__lowerCamelCase    ,						dtype=torch.long							) * (step + 1)]    ,						dim=1							)