






"""simple docstring"""





from io import BytesIO
from typing import List, Union

import requests

from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_decord_available():
  import numpy as np
  from decord import VideoReader


if is_torch_available():
  from ..models.auto.modeling_auto import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING

UpperCamelCase_	:				Dict										= logging.get_logger(__name__)


@add_end_docstrings(_snake_case						)
class      _lowercase (     _snake_case						):


      def __init__(    self				:	List[str]							,   *a				:	Any							,   **a				:	Tuple						):





            """simple docstring"""







            super().__init__(*snake_case_							,   **snake_case_						)
            requires_backends(self							,   '''decord'''						)
            self.check_model_type(snake_case_						)


      def _UpperCamelCase						(    self				:	Optional[Any]							,   a				:	Union[str, Any]=None							,   a				:	List[Any]=None							,   a				:	Union[str, Any]=None						):





            """simple docstring"""







            __snake_case      :    Optional[Any]  			={}
            if frame_sampling_rate is not None:
                  __snake_case      :    Dict  			=frame_sampling_rate
            if num_frames is not None:
                  __snake_case      :    List[str]  			=num_frames

            __snake_case      :    Dict  			={}
            if top_k is not None:
                  __snake_case      :    Any  			=top_k
            return preprocess_params, {}, postprocess_params


      def __call__(    self				:	List[Any]							,   a				:	List[Any]							,   **a				:	Optional[Any]						):





            """simple docstring"""







            return super().__call__(snake_case_							,   **snake_case_						)


      def _UpperCamelCase						(    self				:	Union[str, Any]							,   a				:	Dict							,   a				:	int=None							,   a				:	Union[str, Any]=1						):





            """simple docstring"""







            if num_frames is None:
                  __snake_case      :    Optional[int]  			=self.model.config.num_frames

            if video.startswith('''http://'''						) or video.startswith('''https://'''						):
                  __snake_case      :    str  			=BytesIO(requests.get(snake_case_						).content						)

            __snake_case      :    Optional[int]  			=VideoReader(snake_case_						)
            videoreader.seek(0						)

            __snake_case      :    List[Any]  			=0
            __snake_case      :    Optional[int]  			=num_frames * frame_sampling_rate - 1
            __snake_case      :    Union[str, Any]  			=np.linspace(snake_case_							,   snake_case_							,   num=snake_case_							,   dtype=np.intaa						)

            __snake_case      :    Optional[int]  			=videoreader.get_batch(snake_case_						).asnumpy()
            __snake_case      :    Union[str, Any]  			=list(snake_case_						)

            __snake_case      :    str  			=self.image_processor(snake_case_							,   return_tensors=self.framework						)
            return model_inputs


      def _UpperCamelCase						(    self				:	Union[str, Any]							,   a				:	Dict						):





            """simple docstring"""







            __snake_case      :    List[Any]  			=self.model(**snake_case_						)
            return model_outputs







      def _UpperCamelCase						(    self				:	Dict							,   a				:	List[str]							,   a				:	List[str]=5						):





            """simple docstring"""







            if top_k > self.model.config.num_labels:
                  __snake_case      :    Tuple  			=self.model.config.num_labels

            if self.framework == "pt":
                  __snake_case      :    List[str]  			=model_outputs.logits.softmax(-1						)[0]
                  __snake_case      :    Optional[Any]  			=probs.topk(snake_case_						)
            else:
                  raise ValueError(f'''Unsupported framework: {self.framework}'''						)

            __snake_case      :    List[Any]  			=scores.tolist()
            __snake_case      :    Any  			=ids.tolist()
            return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(snake_case_							,   snake_case_						)]










"""simple docstring"""





import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import DonutImageProcessor


class      _lowercase (     unittest.TestCase						):


      def __init__(    self				:	List[Any]							,   a				:	int							,   a				:	Dict=7							,   a				:	int=3							,   a				:	int=1_8							,   a				:	Dict=3_0							,   a				:	Dict=4_0_0							,   a				:	Optional[Any]=True							,   a				:	Dict=None							,   a				:	int=True							,   a				:	Dict=False							,   a				:	int=True							,   a				:	str=True							,   a				:	List[str]=[0.5, 0.5, 0.5]							,   a				:	Optional[Any]=[0.5, 0.5, 0.5]							,   ):





            """simple docstring"""







            __snake_case      :    List[Any]  			=parent
            __snake_case      :    List[Any]  			=batch_size
            __snake_case      :    str  			=num_channels
            __snake_case      :    Dict  			=image_size
            __snake_case      :    str  			=min_resolution
            __snake_case      :    Tuple  			=max_resolution
            __snake_case      :    str  			=do_resize
            __snake_case      :    Any  			=size if size is not None else {'''height''': 1_8, '''width''': 2_0}
            __snake_case      :    List[Any]  			=do_thumbnail
            __snake_case      :    Tuple  			=do_align_axis
            __snake_case      :    Any  			=do_pad
            __snake_case      :    Dict  			=do_normalize
            __snake_case      :    List[Any]  			=image_mean
            __snake_case      :    Any  			=image_std







      def _UpperCamelCase						(    self				:	str						):





            """simple docstring"""







            return {
                "do_resize": self.do_resize,
                "size": self.size,
                "do_thumbnail": self.do_thumbnail,
                "do_align_long_axis": self.do_align_axis,
                "do_pad": self.do_pad,
                "do_normalize": self.do_normalize,
                "image_mean": self.image_mean,
                "image_std": self.image_std,
            }



@require_torch
@require_vision
class      _lowercase (     lowerCAmelCase		,  unittest.TestCase						):
      _a   :     str								=   DonutImageProcessor if is_vision_available() else None


      def _UpperCamelCase						(    self				:	Optional[int]						):





            """simple docstring"""







            __snake_case      :    Optional[Any]  			=DonutImageProcessingTester(self						)


      @property
      def _UpperCamelCase						(    self				:	Optional[int]						):





            """simple docstring"""







            return self.image_processor_tester.prepare_image_processor_dict()


      def _UpperCamelCase						(    self				:	List[str]						):





            """simple docstring"""







            __snake_case      :    Optional[Any]  			=self.image_processing_class(**self.image_processor_dict						)
            self.assertTrue(hasattr(a							,   '''do_resize'''						)						)
            self.assertTrue(hasattr(a							,   '''size'''						)						)
            self.assertTrue(hasattr(a							,   '''do_thumbnail'''						)						)
            self.assertTrue(hasattr(a							,   '''do_align_long_axis'''						)						)
            self.assertTrue(hasattr(a							,   '''do_pad'''						)						)
            self.assertTrue(hasattr(a							,   '''do_normalize'''						)						)
            self.assertTrue(hasattr(a							,   '''image_mean'''						)						)
            self.assertTrue(hasattr(a							,   '''image_std'''						)						)


      def _UpperCamelCase						(    self				:	Tuple						):





            """simple docstring"""







            __snake_case      :    Dict  			=self.image_processing_class.from_dict(self.image_processor_dict						)
            self.assertEqual(image_processor.size							,   {'''height''': 1_8, '''width''': 2_0}						)

            __snake_case      :    List[Any]  			=self.image_processing_class.from_dict(self.image_processor_dict							,   size=4_2						)
            self.assertEqual(image_processor.size							,   {'''height''': 4_2, '''width''': 4_2}						)

            # Previous config had dimensions in (width, height) order
            __snake_case      :    Tuple  			=self.image_processing_class.from_dict(self.image_processor_dict							,   size=(4_2, 8_4)						)
            self.assertEqual(image_processor.size							,   {'''height''': 8_4, '''width''': 4_2}						)


      def _UpperCamelCase						(    self				:	Any						):





            """simple docstring"""







            pass


      @is_flaky()
      def _UpperCamelCase						(    self				:	Union[str, Any]						):





            """simple docstring"""







            __snake_case      :    List[Any]  			=self.image_processing_class(**self.image_processor_dict						)
            # create random PIL images
            __snake_case      :    List[Any]  			=prepare_image_inputs(self.image_processor_tester							,   equal_resolution=a						)
            for image in image_inputs:
                  self.assertIsInstance(a							,   Image.Image						)

            # Test not batched input
            __snake_case      :    str  			=image_processing(image_inputs[0]							,   return_tensors='''pt'''						).pixel_values
            self.assertEqual(
                encoded_images.shape							,   (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size['''height'''],
                    self.image_processor_tester.size['''width'''],
                )							,   )

            # Test batched
            __snake_case      :    Optional[int]  			=image_processing(a							,   return_tensors='''pt'''						).pixel_values
            self.assertEqual(
                encoded_images.shape							,   (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size['''height'''],
                    self.image_processor_tester.size['''width'''],
                )							,   )


      @is_flaky()
      def _UpperCamelCase						(    self				:	Tuple						):





            """simple docstring"""







            __snake_case      :    Tuple  			=self.image_processing_class(**self.image_processor_dict						)
            # create random numpy tensors
            __snake_case      :    Optional[int]  			=prepare_image_inputs(self.image_processor_tester							,   equal_resolution=a							,   numpify=a						)
            for image in image_inputs:
                  self.assertIsInstance(a							,   np.ndarray						)

            # Test not batched input
            __snake_case      :    List[Any]  			=image_processing(image_inputs[0]							,   return_tensors='''pt'''						).pixel_values
            self.assertEqual(
                encoded_images.shape							,   (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size['''height'''],
                    self.image_processor_tester.size['''width'''],
                )							,   )

            # Test batched
            __snake_case      :    int  			=image_processing(a							,   return_tensors='''pt'''						).pixel_values
            self.assertEqual(
                encoded_images.shape							,   (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size['''height'''],
                    self.image_processor_tester.size['''width'''],
                )							,   )







      @is_flaky()
      def _UpperCamelCase						(    self				:	List[str]						):





            """simple docstring"""







            __snake_case      :    Dict  			=self.image_processing_class(**self.image_processor_dict						)
            # create random PyTorch tensors
            __snake_case      :    Dict  			=prepare_image_inputs(self.image_processor_tester							,   equal_resolution=a							,   torchify=a						)
            for image in image_inputs:
                  self.assertIsInstance(a							,   torch.Tensor						)

            # Test not batched input
            __snake_case      :    Dict  			=image_processing(image_inputs[0]							,   return_tensors='''pt'''						).pixel_values
            self.assertEqual(
                encoded_images.shape							,   (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size['''height'''],
                    self.image_processor_tester.size['''width'''],
                )							,   )

            # Test batched
            __snake_case      :    Optional[int]  			=image_processing(a							,   return_tensors='''pt'''						).pixel_values
            self.assertEqual(
                encoded_images.shape							,   (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.size['''height'''],
                    self.image_processor_tester.size['''width'''],
                )							,   )


