

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

lowerCAmelCase__						 =			logging.get_logger(__name__)

@add_end_docstrings(snake_case      )
class        a__    (       snake_case      ):
			"""simple docstring"""







			def __init__(     self   ,   **lowercase							)	->    Any:
							'''simple docstring'''






							super().__init__(**lowercase							)
							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(lowercase							)


			def   UpperCamelCase		(     self   ,   **lowercase							)	->    str:
							'''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   ,   lowercase   ,   *lowercase   ,   lowercase=None   ,   lowercase=None   ,   **lowercase							)	->    List[str]:
							'''simple docstring'''






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


			def   UpperCamelCase		(     self   ,   lowercase   ,   lowercase=64   ,   lowercase = 0   ,   lowercase = 512 / 1500   ,   lowercase = 32   ,   lowercase = 1   ,   )	->    Union[str, Any]:
							'''simple docstring'''






							A__          =      load_image(lowercase							)
							A__          =      self.image_processor.size["longest_edge"]
							A__     ,					A__     ,					A__     ,					A__          =      self.image_processor.generate_crop_boxes(
							    lowercase   ,   lowercase   ,   lowercase   ,   lowercase   ,   lowercase   ,   lowercase							)
							A__          =      self.image_processor(images=lowercase   ,   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(lowercase   ,   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   ,   lowercase   ,   lowercase							):
											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   ,   lowercase   ,   lowercase=0.88   ,   lowercase=0.95   ,   lowercase=0   ,   lowercase=1   ,   )	->    List[str]:
							'''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(**lowercase							)

							# 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(
							    lowercase   ,   lowercase   ,   lowercase   ,   lowercase   ,   binarize=lowercase							)
							A__          =      model_outputs["iou_scores"]
							A__     ,					A__     ,					A__          =      self.image_processor.filter_masks(
							    masks[0]   ,   iou_scores[0]   ,   original_sizes[0]   ,   input_boxes[0]   ,   lowercase   ,   lowercase   ,   lowercase   ,   lowercase   ,   )
							return {
							    "masks": masks,
							    "is_last": is_last,
							    "boxes": boxes,
							    "iou_scores": iou_scores,
							}


			def   UpperCamelCase		(     self   ,   lowercase   ,   lowercase=False   ,   lowercase=False   ,   lowercase=0.7   ,   )	->    int:
							'''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(lowercase							)
							A__          =      torch.cat(lowercase							)
							A__     ,					A__     ,					A__     ,					A__          =      self.image_processor.post_process_for_mask_generation(
							    lowercase   ,   lowercase   ,   lowercase   ,   lowercase							)

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

							A__          =      {}
							if output_rle_mask:
											A__          =      rle_mask

							if output_bboxes_mask:
											A__          =      bounding_boxes

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





'''simple docstring'''
from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


__SCREAMING_SNAKE_CASE					:				Tuple									=		logging.get_logger(__name__)







def       a_				(	UpperCamelCase_ ):
							if isinstance(UpperCamelCase_						,  np.ndarray ):
														return list(tensor.shape )

							A_			    =		tf.shape(UpperCamelCase_ )

							if tensor.shape == tf.TensorShape(UpperCamelCase_ ):
														return dynamic

							A_			    =		tensor.shape.as_list()

							return [dynamic[i] if s is None else s for i, s in enumerate(UpperCamelCase_ )]







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ = None						,  UpperCamelCase_ = None ):
							return tf.nn.softmax(logits=logits + 1e-9						,  axis=UpperCamelCase_						,  name=UpperCamelCase_ )







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_=1e-5						,  UpperCamelCase_=-1 ):
							# This is a very simplified functional layernorm, designed to duplicate
							# the functionality of PyTorch nn.functional.layer_norm when this is needed to port
							# models in Transformers.

							if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(UpperCamelCase_						,  UpperCamelCase_ ):
														raise NotImplementedError("Only 1D weight and bias tensors are supported for now, with only a single axis." )

							# Get mean and variance on the axis to be normalized
							A_    ,					A_			    =		tf.nn.moments(UpperCamelCase_						,  axes=[axis]						,  keepdims=UpperCamelCase_ )

							if axis != -1:
														# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
														# on every dimension except axis
														A_			    =		[1] * inputs.shape.rank
														A_			    =		shape_list(UpperCamelCase_ )[axis]
														A_			    =		tf.reshape(UpperCamelCase_						,  UpperCamelCase_ )
														A_			    =		tf.reshape(UpperCamelCase_						,  UpperCamelCase_ )

							# Compute layer normalization using the batch_normalization
							# function.
							A_			    =		tf.nn.batch_normalization(
							    UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  offset=UpperCamelCase_						,  scale=UpperCamelCase_						,  variance_epsilon=UpperCamelCase_						,  )
							return outputs







def       a_				(	UpperCamelCase_						,  UpperCamelCase_=0						,  UpperCamelCase_=-1 ):
							# Replicates the behavior of torch.flatten in TF

							# If end_dim or start_dim is negative, count them from the end
							if end_dim < 0:
														end_dim += input.shape.rank
							if start_dim < 0:
														start_dim += input.shape.rank

							if start_dim == end_dim:
														return input

							A_			    =		tf.shape(UpperCamelCase_ )
							A_			    =		tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
							A_			    =		tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]						,  axis=0 )
							return tf.reshape(UpperCamelCase_						,  UpperCamelCase_ )







def       a_				(	UpperCamelCase_ ):
							if not isinstance(UpperCamelCase_						,  tf.Tensor ):
														A_			    =		tf.convert_to_tensor(UpperCamelCase_ )  # Catches stray NumPy inputs
							if encoder_attention_mask.shape.rank == 3:
														A_			    =		encoder_attention_mask[:, None, :, :]
							if encoder_attention_mask.shape.rank == 2:
														A_			    =		encoder_attention_mask[:, None, None, :]
							# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
							# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
							# /transformer/transformer_layers.py#L270
							# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
							# encoder_extended_attention_mask.transpose(-1, -2))
							A_			    =		(
							    tf.cast(1						,  encoder_attention_mask.dtype ) - encoder_extended_attention_mask
							) * encoder_extended_attention_mask.dtype.min

							return encoder_extended_attention_mask







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ = "input_ids" ):
							tf.debugging.assert_less(
							    UpperCamelCase_						,  tf.cast(UpperCamelCase_						,  dtype=tensor.dtype )						,  message=(
							        f"The maximum value of {tensor_name} ({tf.math.reduce_max(UpperCamelCase_ )}) must be smaller than the embedding "
							        f"layer's input dimension ({embed_dim}). The likely cause is some problem at tokenization time."
							    )						,  )







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		6_4_5_1_2
							# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
							# because in that case even chunking the array would not make the saving
							# possible.
							A_			    =		[x for x in data if len(UpperCamelCase_ ) > HDF5_OBJECT_HEADER_LIMIT]

							# Expecting this to never be true.
							if bad_attributes:
														raise RuntimeError(
														    "The following attributes cannot be saved to HDF5 file because "
														    f"they are larger than {HDF5_OBJECT_HEADER_LIMIT} "
														    f"bytes: {bad_attributes}" )

							A_			    =		np.asarray(UpperCamelCase_ )

							A_			    =		1
							A_			    =		np.array_split(UpperCamelCase_						,  UpperCamelCase_ )

							# This will never loop forever thanks to the test above.
							while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data ):
														num_chunks += 1
														A_			    =		np.array_split(UpperCamelCase_						,  UpperCamelCase_ )

							if num_chunks > 1:
														for chunk_id, chunk_data in enumerate(UpperCamelCase_ ):
																					A_			    =		chunk_data
							else:
														A_			    =		data







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							if name in group.attrs:
														A_			    =		[n.decode("utf8" ) if hasattr(UpperCamelCase_						,  "decode" ) else n for n in group.attrs[name]]
							else:
														A_			    =		[]
														A_			    =		0
														while "%s%d" % (name, chunk_id) in group.attrs:
																					data.extend(
																					    [n.decode("utf8" ) if hasattr(UpperCamelCase_						,  "decode" ) else n for n in group.attrs["%s%d" % (name, chunk_id)]] )
																					chunk_id += 1
							return data







def       a_				(	UpperCamelCase_ ):

							def _expand_single_ad_tensor(UpperCamelCase_ ):
														if isinstance(UpperCamelCase_						,  tf.Tensor ) and t.shape.rank == 1:
																					return tf.expand_dims(UpperCamelCase_						,  axis=-1 )
														return t

							return tf.nest.map_structure(_expand_single_ad_tensor						,  UpperCamelCase_ )





