Delete ts_generation_mixin.py

ts_generation_mixin.py

@@ -1,72 +0,0 @@
-
-from typing import Any, Dict, List, Optional, Union, Callable
-import torch
-from transformers import GenerationMixin, LogitsProcessorList, StoppingCriteriaList
-from transformers.generation.utils import GenerationConfig, GenerateOutput
-from transformers.utils import ModelOutput
-
-
-class TSGenerationMixin(GenerationMixin):
-    @torch.no_grad()
-    def generate(self,
-        inputs: Optional[torch.Tensor] = None,
-        generation_config: Optional[GenerationConfig] = None,
-        logits_processor: Optional[LogitsProcessorList] = None,
-        stopping_criteria: Optional[StoppingCriteriaList] = None,
-        prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None,
-        synced_gpus: Optional[bool] = None,
-        assistant_model: Optional["PreTrainedModel"] = None,
-        streamer: Optional["BaseStreamer"] = None,
-        negative_prompt_ids: Optional[torch.Tensor] = None,
-        negative_prompt_attention_mask: Optional[torch.Tensor] = None,
-        revin: Optional[bool] = True,
-        patch_len:Optional[int] = 48,
-        stride_len:Optional[int]= 48,
-        max_output_length:Optional[int] = 96,
-        inference_patch_len: Optional[int] = 48,
-
-        **kwargs,
-    ) -> Union[GenerateOutput, torch.Tensor]:
-        if len(inputs.shape) != 3:
-            raise ValueError('Input shape must be: [batch_size, seq_len, n_vars]')
-        
-        if revin:
-            means = inputs.mean(dim=1, keepdim=True)
-            stdev = inputs.std(dim=1, keepdim=True, unbiased=False) + 1e-5
-            inputs = (inputs - means) / stdev
-
-        batch_size,seq_len,n_vars = inputs.shape
-        num_patch = (max(seq_len, patch_len)-patch_len) // stride_len + 1
-        outputs = inputs.view(batch_size, num_patch, patch_len, n_vars)
-        outputs = outputs.transpose(2, 3)
-
-
-        model_inputs = {
-            "input" : outputs,
-        }
-
-
-        outputs = self(**model_inputs) #[batch_size,target_dim,n_vars]
-
-        outputs = outputs["prediction"]
-
-
-        if revin:
-
-            outputs = (outputs * stdev) + means
-
-        return outputs
-    
-    
-    def _update_model_kwargs_for_generation(
-            self,
-            outputs: ModelOutput,
-            model_kwargs: Dict[str, Any],
-            horizon_length: int = 1,
-            is_encoder_decoder: bool = False,
-            standardize_cache_format: bool = False,
-    ) -> Dict[str, Any]:
-        
-        return model_kwargs
-    
-