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											=      logging.get_logger(__name__)


@add_end_docstrings(UpperCAmelCase_				)
class 			lowerCamelCase_  (     UpperCAmelCase_				):






    '''simple docstring'''






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







    def __call__(			self				,				__lowercase				,				*__lowercase				,				__lowercase=None				,				__lowercase=None				,				**__lowercase) ->			Tuple:
        return super().__call__(__lowercase				,				*__lowercase				,				num_workers=__lowercase				,				batch_size=__lowercase				,				**__lowercase)







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase=64				,				__lowercase = 0				,				__lowercase = 512 / 1_500				,				__lowercase = 32				,				__lowercase = 1				,				) ->			List[str]:
        __UpperCamelCase  :List[str]    						=	load_image(__lowercase)
        __UpperCamelCase  :Optional[Any]    						=	self.image_processor.size['''longest_edge''']
        __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase  :List[Any]    						=	self.image_processor.generate_crop_boxes(
            __lowercase				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase				,				__lowercase)
        __UpperCamelCase  :Tuple    						=	self.image_processor(images=__lowercase				,				return_tensors='''pt''')

        with self.device_placement():
            if self.framework == "pt":
                __UpperCamelCase  :Dict    						=	self.get_inference_context()
                with inference_context():
                    __UpperCamelCase  :Any    						=	self._ensure_tensor_on_device(__lowercase				,				device=self.device)
                    __UpperCamelCase  :List[str]    						=	self.model.get_image_embeddings(model_inputs.pop('''pixel_values'''))
                    __UpperCamelCase  :str    						=	image_embeddings

        __UpperCamelCase  :Any    						=	grid_points.shape[1]
        __UpperCamelCase  :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				,				__lowercase				,				__lowercase):
            __UpperCamelCase  :Optional[int]    						=	grid_points[:, i : i + points_per_batch, :, :]
            __UpperCamelCase  :Dict    						=	input_labels[:, i : i + points_per_batch]
            __UpperCamelCase  :Optional[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     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase=0.88				,				__lowercase=0.95				,				__lowercase=0				,				__lowercase=1				,				) ->			Optional[Any]:
        __UpperCamelCase  :int    						=	model_inputs.pop('''input_boxes''')
        __UpperCamelCase  :Union[str, Any]    						=	model_inputs.pop('''is_last''')
        __UpperCamelCase  :Tuple    						=	model_inputs.pop('''original_sizes''').tolist()
        __UpperCamelCase  :Union[str, Any]    						=	model_inputs.pop('''reshaped_input_sizes''').tolist()

        __UpperCamelCase  :Optional[Any]    						=	self.model(**__lowercase)

        # post processing happens here in order to avoid CPU GPU copies of ALL the masks
        __UpperCamelCase  :Optional[Any]    						=	model_outputs['''pred_masks''']
        __UpperCamelCase  :Union[str, Any]    						=	self.image_processor.post_process_masks(
            __lowercase				,				__lowercase				,				__lowercase				,				__lowercase				,				binarize=__lowercase)
        __UpperCamelCase  :Optional[int]    						=	model_outputs['''iou_scores''']
        __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase  :Any    						=	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				,				) ->			Union[str, Any]:
        __UpperCamelCase  :List[str]    						=	[]
        __UpperCamelCase  :Union[str, Any]    						=	[]
        __UpperCamelCase  :Union[str, 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'''))

        __UpperCamelCase  :Tuple    						=	torch.cat(__lowercase)
        __UpperCamelCase  :Optional[int]    						=	torch.cat(__lowercase)
        __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase  :List[str]    						=	self.image_processor.post_process_for_mask_generation(
            __lowercase				,				__lowercase				,				__lowercase				,				__lowercase)

        __UpperCamelCase  :Any    						=	defaultdict(__lowercase)
        for output in model_outputs:
            for k, v in output.items():
                extra[k].append(__lowercase)

        __UpperCamelCase  :Tuple    						=	{}
        if output_rle_mask:
            __UpperCamelCase  :Dict    						=	rle_mask

        if output_bboxes_mask:
            __UpperCamelCase  :List[str]    						=	bounding_boxes

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






import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
    import optax
    from flax.training.common_utils import onehot

    from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
    from transformers.models.ta.modeling_flax_ta import shift_tokens_right


@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 			lowerCamelCase_  (     unittest.TestCase				):






    '''simple docstring'''






    @slow
    def     UpperCamelCase__     (			self) ->			List[str]:
        __UpperCamelCase  :List[str]    						=	FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small''')
        __UpperCamelCase  :Dict    						=	AutoTokenizer.from_pretrained('''google/mt5-small''')

        __UpperCamelCase  :Optional[Any]    						=	tokenizer('''Hello there'''				,				return_tensors='''np''').input_ids
        __UpperCamelCase  :List[str]    						=	tokenizer('''Hi I am'''				,				return_tensors='''np''').input_ids

        __UpperCamelCase  :Optional[int]    						=	shift_tokens_right(__lowercase				,				model.config.pad_token_id				,				model.config.decoder_start_token_id)

        __UpperCamelCase  :Tuple    						=	model(__lowercase				,				decoder_input_ids=__lowercase).logits
        __UpperCamelCase  :Any    						=	optax.softmax_cross_entropy(__lowercase				,				onehot(__lowercase				,				logits.shape[-1])).mean()

        __UpperCamelCase  :str    						=	-(labels.shape[-1] * loss.item())

        __UpperCamelCase  :Optional[Any]    						=	-84.91_27
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1E-4)





