"""simple docstring"""






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

__A			   = logging.get_logger(__name__)


@add_end_docstrings(_UpperCAmelCase  )
class     UpperCAmelCase						(_UpperCAmelCase  ):



    """simple docstring"""

    def __init__(   self    ,			**_UpperCAmelCase							):
       super().__init__(**_UpperCAmelCase							)
       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(_UpperCAmelCase							)






    def _snake_case			(   self    ,			**_UpperCAmelCase							):
       lowercase__:   List[Any]					=      {}
       lowercase__:   List[Any]					=      {}
       lowercase__:   Dict					=      {}
       # preprocess args
       if "points_per_batch" in kwargs:
          lowercase__:   Dict					=      kwargs['''points_per_batch''']
       if "points_per_crop" in kwargs:
          lowercase__:   Any					=      kwargs['''points_per_crop''']
       if "crops_n_layers" in kwargs:
          lowercase__:   Union[str, Any]					=      kwargs['''crops_n_layers''']
       if "crop_overlap_ratio" in kwargs:
          lowercase__:   Optional[Any]					=      kwargs['''crop_overlap_ratio''']
       if "crop_n_points_downscale_factor" in kwargs:
          lowercase__:   Union[str, Any]					=      kwargs['''crop_n_points_downscale_factor''']
       # postprocess args
       if "pred_iou_thresh" in kwargs:
          lowercase__:   Any					=      kwargs['''pred_iou_thresh''']
       if "stability_score_offset" in kwargs:
          lowercase__:   Tuple					=      kwargs['''stability_score_offset''']
       if "mask_threshold" in kwargs:
          lowercase__:   List[str]					=      kwargs['''mask_threshold''']
       if "stability_score_thresh" in kwargs:
          lowercase__:   str					=      kwargs['''stability_score_thresh''']
       if "crops_nms_thresh" in kwargs:
          lowercase__:   List[str]					=      kwargs['''crops_nms_thresh''']
       if "output_rle_mask" in kwargs:
          lowercase__:   Dict					=      kwargs['''output_rle_mask''']
       if "output_bboxes_mask" in kwargs:
          lowercase__:   int					=      kwargs['''output_bboxes_mask''']
       return preprocess_kwargs, forward_params, postprocess_kwargs






    def __call__(   self    ,			_UpperCAmelCase    ,			*_UpperCAmelCase    ,			_UpperCAmelCase=None    ,			_UpperCAmelCase=None    ,			**_UpperCAmelCase							):
       return super().__call__(_UpperCAmelCase    ,			*_UpperCAmelCase    ,			num_workers=_UpperCAmelCase    ,			batch_size=_UpperCAmelCase    ,			**_UpperCAmelCase							)






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase=64    ,			_UpperCAmelCase = 0    ,			_UpperCAmelCase = 512 / 1500    ,			_UpperCAmelCase = 32    ,			_UpperCAmelCase = 1    ,			):
       lowercase__:   Union[str, Any]					=      load_image(_UpperCAmelCase							)
       lowercase__:   Dict					=      self.image_processor.size['''longest_edge''']
       lowercase__, lowercase__, lowercase__, lowercase__:   Optional[Any]					=      self.image_processor.generate_crop_boxes(
           _UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase							)
       lowercase__:   List[Any]					=      self.image_processor(images=_UpperCAmelCase    ,			return_tensors='''pt'''							)

       with self.device_placement():
          if self.framework == "pt":
             lowercase__:   Tuple					=      self.get_inference_context()
             with inference_context():
                lowercase__:   Optional[Any]					=      self._ensure_tensor_on_device(_UpperCAmelCase    ,			device=self.device							)
                lowercase__:   Any					=      self.model.get_image_embeddings(model_inputs.pop('''pixel_values'''							)							)
                lowercase__:   Tuple					=      image_embeddings

       lowercase__:   Optional[Any]					=      grid_points.shape[1]
       lowercase__:   Tuple					=      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    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
          lowercase__:   Dict					=      grid_points[:, i : i + points_per_batch, :, :]
          lowercase__:   int					=      input_labels[:, i : i + points_per_batch]
          lowercase__:   Any					=      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 _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase=0.88    ,			_UpperCAmelCase=0.95    ,			_UpperCAmelCase=0    ,			_UpperCAmelCase=1    ,			):
       lowercase__:   List[Any]					=      model_inputs.pop('''input_boxes'''							)
       lowercase__:   List[Any]					=      model_inputs.pop('''is_last'''							)
       lowercase__:   Any					=      model_inputs.pop('''original_sizes'''							).tolist()
       lowercase__:   Union[str, Any]					=      model_inputs.pop('''reshaped_input_sizes'''							).tolist()

       lowercase__:   List[Any]					=      self.model(**_UpperCAmelCase							)

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






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase=False    ,			_UpperCAmelCase=False    ,			_UpperCAmelCase=0.7    ,			):
       lowercase__:   int					=      []
       lowercase__:   str					=      []
       lowercase__:   List[Any]					=      []
       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'''							)							)

       lowercase__:   Any					=      torch.cat(_UpperCAmelCase							)
       lowercase__:   Dict					=      torch.cat(_UpperCAmelCase							)
       lowercase__, lowercase__, lowercase__, lowercase__:   Any					=      self.image_processor.post_process_for_mask_generation(
           _UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase							)

       lowercase__:   Union[str, Any]					=      defaultdict(_UpperCAmelCase							)
       for output in model_outputs:
          for k, v in output.items():
             extra[k].append(_UpperCAmelCase							)

       lowercase__:   Any					=      {}
       if output_rle_mask:
          lowercase__:   Optional[Any]					=      rle_mask

       if output_bboxes_mask:
          lowercase__:   Optional[int]					=      bounding_boxes

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

"""simple docstring"""






import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
       import jax.numpy as jnp

       from transformers.models.roformer.modeling_flax_roformer import (
           FlaxRoFormerForMaskedLM,
           FlaxRoFormerForMultipleChoice,
           FlaxRoFormerForQuestionAnswering,
           FlaxRoFormerForSequenceClassification,
           FlaxRoFormerForTokenClassification,
           FlaxRoFormerModel,
       )


class     UpperCAmelCase						(unittest.TestCase  ):



    """simple docstring"""

    def __init__(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase=13    ,			_UpperCAmelCase=7    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=99    ,			_UpperCAmelCase=32    ,			_UpperCAmelCase=5    ,			_UpperCAmelCase=4    ,			_UpperCAmelCase=37    ,			_UpperCAmelCase="gelu"    ,			_UpperCAmelCase=0.1    ,			_UpperCAmelCase=0.1    ,			_UpperCAmelCase=512    ,			_UpperCAmelCase=16    ,			_UpperCAmelCase=2    ,			_UpperCAmelCase=0.02    ,			_UpperCAmelCase=4    ,			):
       lowercase__:   Dict					=      parent
       lowercase__:   List[str]					=      batch_size
       lowercase__:   Optional[Any]					=      seq_length
       lowercase__:   List[Any]					=      is_training
       lowercase__:   int					=      use_attention_mask
       lowercase__:   Tuple					=      use_token_type_ids
       lowercase__:   Union[str, Any]					=      use_labels
       lowercase__:   str					=      vocab_size
       lowercase__:   str					=      hidden_size
       lowercase__:   str					=      num_hidden_layers
       lowercase__:   Optional[int]					=      num_attention_heads
       lowercase__:   List[str]					=      intermediate_size
       lowercase__:   List[str]					=      hidden_act
       lowercase__:   Tuple					=      hidden_dropout_prob
       lowercase__:   int					=      attention_probs_dropout_prob
       lowercase__:   int					=      max_position_embeddings
       lowercase__:   Union[str, Any]					=      type_vocab_size
       lowercase__:   List[Any]					=      type_sequence_label_size
       lowercase__:   Any					=      initializer_range
       lowercase__:   str					=      num_choices






    def _snake_case			(   self							):
       lowercase__:   int					=      ids_tensor([self.batch_size, self.seq_length]    ,			self.vocab_size							)

       lowercase__:   List[Any]					=      None
       if self.use_attention_mask:
          lowercase__:   Tuple					=      random_attention_mask([self.batch_size, self.seq_length]							)

       lowercase__:   List[Any]					=      None
       if self.use_token_type_ids:
          lowercase__:   str					=      ids_tensor([self.batch_size, self.seq_length]    ,			self.type_vocab_size							)

       lowercase__:   Optional[int]					=      RoFormerConfig(
           vocab_size=self.vocab_size    ,			hidden_size=self.hidden_size    ,			num_hidden_layers=self.num_hidden_layers    ,			num_attention_heads=self.num_attention_heads    ,			intermediate_size=self.intermediate_size    ,			hidden_act=self.hidden_act    ,			hidden_dropout_prob=self.hidden_dropout_prob    ,			attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,			max_position_embeddings=self.max_position_embeddings    ,			type_vocab_size=self.type_vocab_size    ,			is_decoder=_UpperCAmelCase    ,			initializer_range=self.initializer_range    ,			)

       return config, input_ids, token_type_ids, attention_mask






    def _snake_case			(   self							):
       lowercase__:   str					=      self.prepare_config_and_inputs()
       lowercase__, lowercase__, lowercase__, lowercase__:   Optional[Any]					=      config_and_inputs
       lowercase__:   Dict					=      {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
       return config, inputs_dict



@require_flax
class     UpperCAmelCase						(_UpperCAmelCase    ,unittest.TestCase  ):



    """simple docstring"""

    _UpperCAmelCase	:List[str]						    = True

    _UpperCAmelCase	:Union[str, Any]						    = (
        (
            FlaxRoFormerModel,
            FlaxRoFormerForMaskedLM,
            FlaxRoFormerForSequenceClassification,
            FlaxRoFormerForTokenClassification,
            FlaxRoFormerForMultipleChoice,
            FlaxRoFormerForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )






    def _snake_case			(   self							):
       lowercase__:   str					=      FlaxRoFormerModelTester(self							)






    @slow
    def _snake_case			(   self							):
       for model_class_name in self.all_model_classes:
          lowercase__:   Dict					=      model_class_name.from_pretrained('''junnyu/roformer_chinese_small'''    ,			from_pt=_UpperCAmelCase							)
          lowercase__:   int					=      model(np.ones((1, 1)							)							)
          self.assertIsNotNone(_UpperCAmelCase							)



@require_flax
class     UpperCAmelCase						(unittest.TestCase  ):



    """simple docstring"""

    @slow
    def _snake_case			(   self							):
       lowercase__:   Any					=      FlaxRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''							)
       lowercase__:   Optional[int]					=      jnp.array([[0, 1, 2, 3, 4, 5]]							)
       lowercase__:   List[Any]					=      model(_UpperCAmelCase							)[0]

       lowercase__:   str					=      50000

       lowercase__:   Tuple					=      (1, 6, vocab_size)
       self.assertEqual(output.shape    ,			_UpperCAmelCase							)

       lowercase__:   List[Any]					=      jnp.array(
           [[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]]							)

       self.assertTrue(jnp.allclose(output[:, :3, :3]    ,			_UpperCAmelCase    ,			atol=1e-4							)							)
