



import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import 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 BridgeTowerImageProcessor






class SCREAMING_SNAKE_CASE__     (		unittest.TestCase    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__ = True,		lowerCamelCase__ = None,		lowerCamelCase__ = 32,		lowerCamelCase__ = True,		lowerCamelCase__ = 1 / 255,		lowerCamelCase__ = True,		lowerCamelCase__ = True,		lowerCamelCase__ = [0.4814_5466, 0.457_8275, 0.4082_1073],		lowerCamelCase__ = [0.2686_2954, 0.2613_0258, 0.2757_7711],		lowerCamelCase__ = True,		lowerCamelCase__=7,		lowerCamelCase__=30,		lowerCamelCase__=400,		lowerCamelCase__=3,		):
        A :       Union[str, Any]          =   parent
        A :       Dict          =   do_resize
        A :       int          =   size if size is not None else {"""shortest_edge""": 288}
        A :       Optional[Any]          =   size_divisor
        A :       int          =   do_rescale
        A :       Dict          =   rescale_factor
        A :       Optional[int]          =   do_normalize
        A :       Dict          =   do_center_crop
        A :       Optional[Any]          =   image_mean
        A :       int          =   image_std
        A :       str          =   do_pad
        A :       List[str]          =   batch_size
        A :       List[str]          =   num_channels
        A :       Tuple          =   min_resolution
        A :       Union[str, Any]          =   max_resolution






    def 			_lowerCAmelCase					(       self		):
        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
            "size_divisor": self.size_divisor,
        }






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__=False		):
        if not batched:
            A :       Union[str, Any]          =   self.size["""shortest_edge"""]
            A :       Dict          =   image_inputs[0]
            if isinstance(lowerCamelCase__,		Image.Image		):
                A     ,							A :       Union[str, Any]          =   image.size
            else:
                A     ,							A :       int          =   image.shape[1], image.shape[2]
            A :       Optional[int]          =   size / min(lowerCamelCase__,		lowerCamelCase__		)
            if h < w:
                A     ,							A :       List[str]          =   size, scale * w
            else:
                A     ,							A :       int          =   scale * h, size

            A :       Optional[int]          =   int((1333 / 800) * size		)
            if max(lowerCamelCase__,		lowerCamelCase__		) > max_size:
                A :       Union[str, Any]          =   max_size / max(lowerCamelCase__,		lowerCamelCase__		)
                A :       List[Any]          =   newh * scale
                A :       List[Any]          =   neww * scale

            A     ,							A :       Dict          =   int(newh + 0.5		), int(neww + 0.5		)
            A     ,							A :       str          =   (
                newh // self.size_divisor * self.size_divisor,
                neww // self.size_divisor * self.size_divisor,
            )

        else:
            A :       Union[str, Any]          =   []
            for image in image_inputs:
                A     ,							A :       str          =   self.get_expected_values([image]		)
                expected_values.append((expected_height, expected_width)		)
            A :       List[Any]          =   max(lowerCamelCase__,		key=lambda lowerCamelCase__		: item[0]		)[0]
            A :       Optional[Any]          =   max(lowerCamelCase__,		key=lambda lowerCamelCase__		: item[1]		)[1]

        return expected_height, expected_width






@require_torch
@require_vision
class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__ ,						unittest.TestCase    ):




    '''simple docstring'''






    __lowerCamelCase			: str					=      BridgeTowerImageProcessor if is_vision_available() else None






    def 			_lowerCAmelCase					(       self		):
        A :       Optional[int]          =   BridgeTowerImageProcessingTester(self		)






    @property
    def 			_lowerCAmelCase					(       self		):
        return self.image_processor_tester.prepare_image_processor_dict()






    def 			_lowerCAmelCase					(       self		):
        A :       Optional[Any]          =   self.image_processing_class(**self.image_processor_dict		)
        self.assertTrue(hasattr(lowerCamelCase__,		"""image_mean"""		)		)
        self.assertTrue(hasattr(lowerCamelCase__,		"""image_std"""		)		)
        self.assertTrue(hasattr(lowerCamelCase__,		"""do_normalize"""		)		)
        self.assertTrue(hasattr(lowerCamelCase__,		"""do_resize"""		)		)
        self.assertTrue(hasattr(lowerCamelCase__,		"""size"""		)		)
        self.assertTrue(hasattr(lowerCamelCase__,		"""size_divisor"""		)		)






    def 			_lowerCAmelCase					(       self		):
        pass






    def 			_lowerCAmelCase					(       self		):
        # Initialize image processor
        A :       Dict          =   self.image_processing_class(**self.image_processor_dict		)
        # create random PIL images
        A :       Tuple          =   prepare_image_inputs(self.image_processor_tester,		equal_resolution=lowerCamelCase__		)
        for image in image_inputs:
            self.assertIsInstance(lowerCamelCase__,		Image.Image		)

        # Test not batched input
        A :       Tuple          =   image_processing(image_inputs[0],		return_tensors="""pt"""		).pixel_values

        A     ,							A :       Optional[Any]          =   self.image_processor_tester.get_expected_values(lowerCamelCase__		)
        self.assertEqual(
            encoded_images.shape,		(1, self.image_processor_tester.num_channels, expected_height, expected_width),		)

        # Test batched
        A :       Dict          =   image_processing(lowerCamelCase__,		return_tensors="""pt"""		).pixel_values

        A     ,							A :       Any          =   self.image_processor_tester.get_expected_values(lowerCamelCase__,		batched=lowerCamelCase__		)
        self.assertEqual(
            encoded_images.shape,		(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            ),		)






    def 			_lowerCAmelCase					(       self		):
        # Initialize image processor
        A :       str          =   self.image_processing_class(**self.image_processor_dict		)
        # create random numpy tensors
        A :       Optional[int]          =   prepare_image_inputs(self.image_processor_tester,		equal_resolution=lowerCamelCase__,		numpify=lowerCamelCase__		)
        for image in image_inputs:
            self.assertIsInstance(lowerCamelCase__,		np.ndarray		)

        # Test not batched input
        A :       Tuple          =   image_processing(image_inputs[0],		return_tensors="""pt"""		).pixel_values

        A     ,							A :       Tuple          =   self.image_processor_tester.get_expected_values(lowerCamelCase__		)
        self.assertEqual(
            encoded_images.shape,		(1, self.image_processor_tester.num_channels, expected_height, expected_width),		)

        # Test batched
        A :       Tuple          =   image_processing(lowerCamelCase__,		return_tensors="""pt"""		).pixel_values

        A     ,							A :       List[str]          =   self.image_processor_tester.get_expected_values(lowerCamelCase__,		batched=lowerCamelCase__		)
        self.assertEqual(
            encoded_images.shape,		(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            ),		)






    def 			_lowerCAmelCase					(       self		):
        # Initialize image processor
        A :       Optional[Any]          =   self.image_processing_class(**self.image_processor_dict		)
        # create random PyTorch tensors
        A :       List[Any]          =   prepare_image_inputs(self.image_processor_tester,		equal_resolution=lowerCamelCase__,		torchify=lowerCamelCase__		)
        for image in image_inputs:
            self.assertIsInstance(lowerCamelCase__,		torch.Tensor		)

        # Test not batched input
        A :       Dict          =   image_processing(image_inputs[0],		return_tensors="""pt"""		).pixel_values

        A     ,							A :       Optional[int]          =   self.image_processor_tester.get_expected_values(lowerCamelCase__		)
        self.assertEqual(
            encoded_images.shape,		(1, self.image_processor_tester.num_channels, expected_height, expected_width),		)

        # Test batched
        A :       List[Any]          =   image_processing(lowerCamelCase__,		return_tensors="""pt"""		).pixel_values

        A     ,							A :       Dict          =   self.image_processor_tester.get_expected_values(lowerCamelCase__,		batched=lowerCamelCase__		)
        self.assertEqual(
            encoded_images.shape,		(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            ),		)







from manim import *






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    def 			_lowerCAmelCase					(       self		):
        A :       Union[str, Any]          =   Rectangle(height=0.5,		width=0.5		)
        A :       Optional[int]          =   Rectangle(height=0.25,		width=0.25		)
        A :       Optional[Any]          =   Rectangle(height=0.46,		width=0.46		).set_stroke(width=0		)

        A :       List[str]          =   [mem.copy() for i in range(6		)]
        A :       Any          =   [mem.copy() for i in range(6		)]
        A :       int          =   VGroup(*lowerCamelCase__		).arrange(lowerCamelCase__,		buff=0		)
        A :       Tuple          =   VGroup(*lowerCamelCase__		).arrange(lowerCamelCase__,		buff=0		)
        A :       str          =   VGroup(lowerCamelCase__,		lowerCamelCase__		).arrange(lowerCamelCase__,		buff=0		)
        A :       List[Any]          =   Text("""CPU""",		font_size=24		)
        A :       Optional[int]          =   Group(lowerCamelCase__,		lowerCamelCase__		).arrange(lowerCamelCase__,		buff=0.5,		aligned_edge=lowerCamelCase__		)
        cpu.move_to([-2.5, -0.5, 0]		)
        self.add(lowerCamelCase__		)

        A :       List[Any]          =   [mem.copy() for i in range(4		)]
        A :       Optional[Any]          =   VGroup(*lowerCamelCase__		).arrange(lowerCamelCase__,		buff=0		)
        A :       Dict          =   Text("""GPU""",		font_size=24		)
        A :       Any          =   Group(lowerCamelCase__,		lowerCamelCase__		).arrange(lowerCamelCase__,		buff=0.5,		aligned_edge=lowerCamelCase__		)
        gpu.move_to([-1, -1, 0]		)
        self.add(lowerCamelCase__		)

        A :       Optional[int]          =   [mem.copy() for i in range(6		)]
        A :       List[str]          =   VGroup(*lowerCamelCase__		).arrange(lowerCamelCase__,		buff=0		)

        A :       Optional[int]          =   Text("""Model""",		font_size=24		)
        A :       List[Any]          =   Group(lowerCamelCase__,		lowerCamelCase__		).arrange(lowerCamelCase__,		buff=0.5,		aligned_edge=lowerCamelCase__		)
        model.move_to([3, -1.0, 0]		)
        self.add(lowerCamelCase__		)

        A :       Tuple          =   []
        A :       Tuple          =   []
        A :       Any          =   []

        for i, rect in enumerate(lowerCamelCase__		):
            rect.set_stroke(lowerCamelCase__		)

            A :       Any          =   Rectangle(height=0.46 / 4,		width=0.46 / 3		).set_stroke(width=0.0		).set_fill(lowerCamelCase__,		opacity=0.7		)

            if i == 0:
                cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT		),		buff=0.02,		direction=lowerCamelCase__		)
                cpu_target.set_x(cpu_target.get_x() + 0.1		)
            elif i == 3:
                cpu_target.next_to(model_cpu_arr[0],		direction=lowerCamelCase__,		buff=0.0		)
            else:
                cpu_target.next_to(model_cpu_arr[i - 1],		direction=lowerCamelCase__,		buff=0.0		)
            self.add(lowerCamelCase__		)
            model_cpu_arr.append(lowerCamelCase__		)

        self.add(*lowerCamelCase__,		*lowerCamelCase__,		*lowerCamelCase__		)

        A :       int          =   [mem.copy() for i in range(6		)]
        A :       List[Any]          =   VGroup(*lowerCamelCase__		).arrange(lowerCamelCase__,		buff=0		)

        A :       str          =   Text("""Loaded Checkpoint""",		font_size=24		)
        A :       List[str]          =   Group(lowerCamelCase__,		lowerCamelCase__		).arrange(lowerCamelCase__,		buff=0.5,		aligned_edge=lowerCamelCase__		)
        checkpoint.move_to([3, 0.5, 0]		)

        self.add(lowerCamelCase__		)

        A :       Optional[int]          =   []
        A :       List[Any]          =   []

        for i, rect in enumerate(lowerCamelCase__		):
            A :       int          =   fill.copy().set_fill(lowerCamelCase__,		opacity=0.7		)
            target.move_to(lowerCamelCase__		)
            ckpt_arr.append(lowerCamelCase__		)

            A :       List[Any]          =   target.copy()
            if i < 5:
                cpu_target.move_to(cpu_left_col_base[i + 1]		)
            else:
                cpu_target.move_to(cpu_right_col_base[i - 5]		)
            ckpt_cpu_arr.append(lowerCamelCase__		)
        self.add(*lowerCamelCase__,		*lowerCamelCase__		)

        A :       str          =   Square(side_length=2.2		)
        key.move_to([-5, 2, 0]		)

        A :       List[Any]          =   MarkupText(
            f'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model''',		font_size=18,		)

        key_text.move_to([-5, 2.4, 0]		)

        self.add(lowerCamelCase__,		lowerCamelCase__		)

        A :       Union[str, Any]          =   MarkupText(
            f'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint''',		font_size=18,		)

        blue_text.next_to(lowerCamelCase__,		DOWN * 2.4,		aligned_edge=key_text.get_left()		)
        self.add(lowerCamelCase__		)

        A :       List[str]          =   MarkupText(
            f'''Based on the passed in configuration, weights are stored in\na variety of np.memmaps on disk or to a particular device.''',		font_size=24,		)
        step_a.move_to([2, 2, 0]		)

        A :       List[str]          =   [meta_mem.copy() for i in range(6		)]
        A :       List[Any]          =   [meta_mem.copy() for i in range(6		)]
        A :       List[Any]          =   VGroup(*lowerCamelCase__		).arrange(lowerCamelCase__,		buff=0		)
        A :       List[str]          =   VGroup(*lowerCamelCase__		).arrange(lowerCamelCase__,		buff=0		)
        A :       Dict          =   VGroup(lowerCamelCase__,		lowerCamelCase__		).arrange(lowerCamelCase__,		buff=0		)
        A :       Optional[Any]          =   Text("""Disk""",		font_size=24		)
        A :       List[str]          =   Group(lowerCamelCase__,		lowerCamelCase__		).arrange(lowerCamelCase__,		buff=0.5,		aligned_edge=lowerCamelCase__		)
        disk.move_to([-4.0, -1.25, 0]		)
        self.play(Write(lowerCamelCase__,		run_time=3		),		Write(lowerCamelCase__,		run_time=1		),		Create(lowerCamelCase__,		run_time=1		)		)

        A :       str          =   []
        for i, rect in enumerate(lowerCamelCase__		):
            A :       Optional[Any]          =   rect.copy()
            target.generate_target()
            target.target.move_to(disk_left_col_base[i]		).scale(0.5		)
            animations.append(MoveToTarget(lowerCamelCase__,		run_time=1.5		)		)
        self.play(*lowerCamelCase__		)

        self.play(FadeOut(lowerCamelCase__		)		)

        A :       List[str]          =   MarkupText(f'''Then, the checkpoint is removed from memory\nthrough garbage collection.''',		font_size=24		)
        step_a.move_to([2, 2, 0]		)

        self.play(Write(lowerCamelCase__,		run_time=3		)		)

        self.play(
            FadeOut(lowerCamelCase__,		lowerCamelCase__,		*lowerCamelCase__,		*lowerCamelCase__		),		)

        self.wait()


