



from typing import List, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase: int      			=		logging.get_logger(__name__)

_lowercase: List[Any]      			=		{
    "huggingface/time-series-transformer-tourism-monthly": (
        "https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json"
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}



class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""
	__A							=							"time_series_transformer"
	__A							=							{
	    "hidden_size": "d_model",
	    "num_attention_heads": "encoder_attention_heads",
	    "num_hidden_layers": "encoder_layers",
	}




	def __init__(self		,       lowerCamelCase_ = None		,       lowerCamelCase_ = None		,       lowerCamelCase_ = "student_t"		,       lowerCamelCase_ = "nll"		,       lowerCamelCase_ = 1		,       lowerCamelCase_ = [1, 2, 3, 4, 5, 6, 7]		,       lowerCamelCase_ = "mean"		,       lowerCamelCase_ = 0		,       lowerCamelCase_ = 0		,       lowerCamelCase_ = 0		,       lowerCamelCase_ = 0		,       lowerCamelCase_ = None		,       lowerCamelCase_ = None		,       lowerCamelCase_ = 32		,       lowerCamelCase_ = 32		,       lowerCamelCase_ = 2		,       lowerCamelCase_ = 2		,       lowerCamelCase_ = 2		,       lowerCamelCase_ = 2		,       lowerCamelCase_ = True		,       lowerCamelCase_ = "gelu"		,       lowerCamelCase_ = 64		,       lowerCamelCase_ = 0.1		,       lowerCamelCase_ = 0.1		,       lowerCamelCase_ = 0.1		,       lowerCamelCase_ = 0.1		,       lowerCamelCase_ = 0.1		,       lowerCamelCase_ = 100		,       lowerCamelCase_ = 0.02		,       lowerCamelCase_=True		,       **lowerCamelCase_		,       ):
						"""simple docstring"""

						a					     =   prediction_length
						a					     =   context_length or prediction_length
						a					     =   distribution_output
						a					     =   loss
						a					     =   input_size
						a					     =   num_time_features
						a					     =   lags_sequence
						a					     =   scaling
						a					     =   num_dynamic_real_features
						a					     =   num_static_real_features
						a					     =   num_static_categorical_features
						if cardinality and num_static_categorical_features > 0:
											if len(lowerCamelCase_		) != num_static_categorical_features:
																raise ValueError(
																    "The cardinality should be a list of the same length as `num_static_categorical_features`"		)
											a					     =   cardinality
						else:
											a					     =   [0]
						if embedding_dimension and num_static_categorical_features > 0:
											if len(lowerCamelCase_		) != num_static_categorical_features:
																raise ValueError(
																    "The embedding dimension should be a list of the same length as `num_static_categorical_features`"		)
											a					     =   embedding_dimension
						else:
											a					     =   [min(50		,       (cat + 1) // 2		) for cat in self.cardinality]
						a					     =   num_parallel_samples

						# Transformer architecture configuration
						a					     =   input_size * len(lowerCamelCase_		) + self._number_of_features
						a					     =   d_model
						a					     =   encoder_attention_heads
						a					     =   decoder_attention_heads
						a					     =   encoder_ffn_dim
						a					     =   decoder_ffn_dim
						a					     =   encoder_layers
						a					     =   decoder_layers

						a					     =   dropout
						a					     =   attention_dropout
						a					     =   activation_dropout
						a					     =   encoder_layerdrop
						a					     =   decoder_layerdrop

						a					     =   activation_function
						a					     =   init_std

						a					     =   use_cache

						super().__init__(is_encoder_decoder=lowerCamelCase_		,       **lowerCamelCase_		)




	@property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return (
						    sum(self.embedding_dimension		)
						    + self.num_dynamic_real_features
						    + self.num_time_features
						    + self.num_static_real_features
						    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
						)





from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
				import torch

				from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

_lowercase: Any      			=		logging.get_logger(__name__)



@add_end_docstrings(lowerCAmelCase	)
class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def __init__(self		,       **lowerCamelCase_		):
						"""simple docstring"""

						super().__init__(**lowerCamelCase_		)
						requires_backends(self		,       "vision"		)
						requires_backends(self		,       "torch"		)

						if self.framework != "pt":
											raise ValueError(F'''The {self.__class__} is only available in PyTorch.'''		)

						self.check_model_type(lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       **lowerCamelCase_		):
						"""simple docstring"""

						a					     =   {}
						a					     =   {}
						a					     =   {}
						# preprocess args
						if "points_per_batch" in kwargs:
											a					     =   kwargs["points_per_batch"]
						if "points_per_crop" in kwargs:
											a					     =   kwargs["points_per_crop"]
						if "crops_n_layers" in kwargs:
											a					     =   kwargs["crops_n_layers"]
						if "crop_overlap_ratio" in kwargs:
											a					     =   kwargs["crop_overlap_ratio"]
						if "crop_n_points_downscale_factor" in kwargs:
											a					     =   kwargs["crop_n_points_downscale_factor"]
						# postprocess args
						if "pred_iou_thresh" in kwargs:
											a					     =   kwargs["pred_iou_thresh"]
						if "stability_score_offset" in kwargs:
											a					     =   kwargs["stability_score_offset"]
						if "mask_threshold" in kwargs:
											a					     =   kwargs["mask_threshold"]
						if "stability_score_thresh" in kwargs:
											a					     =   kwargs["stability_score_thresh"]
						if "crops_nms_thresh" in kwargs:
											a					     =   kwargs["crops_nms_thresh"]
						if "output_rle_mask" in kwargs:
											a					     =   kwargs["output_rle_mask"]
						if "output_bboxes_mask" in kwargs:
											a					     =   kwargs["output_bboxes_mask"]
						return preprocess_kwargs, forward_params, postprocess_kwargs




	def __call__(self		,       lowerCamelCase_		,       *lowerCamelCase_		,       lowerCamelCase_=None		,       lowerCamelCase_=None		,       **lowerCamelCase_		):
						"""simple docstring"""

						return super().__call__(lowerCamelCase_		,       *lowerCamelCase_		,       num_workers=lowerCamelCase_		,       batch_size=lowerCamelCase_		,       **lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=64		,       lowerCamelCase_ = 0		,       lowerCamelCase_ = 512 / 1500		,       lowerCamelCase_ = 32		,       lowerCamelCase_ = 1		,       ):
						"""simple docstring"""

						a					     =   load_image(lowerCamelCase_		)
						a					     =   self.image_processor.size["longest_edge"]
						a     ,				a     ,				a     ,				a					     =   self.image_processor.generate_crop_boxes(
						    lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		)
						a					     =   self.image_processor(images=lowerCamelCase_		,       return_tensors="pt"		)

						with self.device_placement():
											if self.framework == "pt":
																a					     =   self.get_inference_context()
																with inference_context():
																					a					     =   self._ensure_tensor_on_device(lowerCamelCase_		,       device=self.device		)
																					a					     =   self.model.get_image_embeddings(model_inputs.pop("pixel_values"		)		)
																					a					     =   image_embeddings

						a					     =   grid_points.shape[1]
						a					     =   points_per_batch if points_per_batch is not None else n_points

						if points_per_batch <= 0:
											raise ValueError(
											    "Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. "
											    "To return all points at once, set points_per_batch to None"		)

						for i in range(0		,       lowerCamelCase_		,       lowerCamelCase_		):
											a					     =   grid_points[:, i : i + points_per_batch, :, :]
											a					     =   input_labels[:, i : i + points_per_batch]
											a					     =   i == n_points - points_per_batch
											yield {
											    "input_points": batched_points,
											    "input_labels": labels,
											    "input_boxes": crop_boxes,
											    "is_last": is_last,
											    **model_inputs,
											}




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=0.88		,       lowerCamelCase_=0.95		,       lowerCamelCase_=0		,       lowerCamelCase_=1		,       ):
						"""simple docstring"""

						a					     =   model_inputs.pop("input_boxes"		)
						a					     =   model_inputs.pop("is_last"		)
						a					     =   model_inputs.pop("original_sizes"		).tolist()
						a					     =   model_inputs.pop("reshaped_input_sizes"		).tolist()

						a					     =   self.model(**lowerCamelCase_		)

						# post processing happens here in order to avoid CPU GPU copies of ALL the masks
						a					     =   model_outputs["pred_masks"]
						a					     =   self.image_processor.post_process_masks(
						    lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       binarize=lowerCamelCase_		)
						a					     =   model_outputs["iou_scores"]
						a     ,				a     ,				a					     =   self.image_processor.filter_masks(
						    masks[0]		,       iou_scores[0]		,       original_sizes[0]		,       input_boxes[0]		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       )
						return {
						    "masks": masks,
						    "is_last": is_last,
						    "boxes": boxes,
						    "iou_scores": iou_scores,
						}




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=False		,       lowerCamelCase_=False		,       lowerCamelCase_=0.7		,       ):
						"""simple docstring"""

						a					     =   []
						a					     =   []
						a					     =   []
						for model_output in model_outputs:
											all_scores.append(model_output.pop("iou_scores"		)		)
											all_masks.extend(model_output.pop("masks"		)		)
											all_boxes.append(model_output.pop("boxes"		)		)

						a					     =   torch.cat(lowerCamelCase_		)
						a					     =   torch.cat(lowerCamelCase_		)
						a     ,				a     ,				a     ,				a					     =   self.image_processor.post_process_for_mask_generation(
						    lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		)

						a					     =   defaultdict(lowerCamelCase_		)
						for output in model_outputs:
											for k, v in output.items():
																extra[k].append(lowerCamelCase_		)

						a					     =   {}
						if output_rle_mask:
											a					     =   rle_mask

						if output_bboxes_mask:
											a					     =   bounding_boxes

						return {"masks": output_masks, "scores": iou_scores, **optional, **extra}
