


import colorsys
import io

import cva
import matplotlib as mpl
import matplotlib.colors as mplc
import matplotlib.figure as mplfigure
import numpy as np
import torch
from matplotlib.backends.backend_agg import FigureCanvasAgg

from utils import img_tensorize


lowerCAmelCase__						=			10_00







class 					lowerCAmelCase__	:




     '''simple docstring'''


     def __init__(						self			,						__lowerCamelCase			,						__lowerCamelCase=1.2			,						__lowerCamelCase="g"			,						__lowerCamelCase=0.5			,						__lowerCamelCase="-"			,						__lowerCamelCase="test_out.jpg"			,						__lowerCamelCase=True			,						__lowerCamelCase=False			,						__lowerCamelCase=None			,						__lowerCamelCase=None			,						__lowerCamelCase=0.7			,						)      ->    List[str]:
            if isinstance(__lowerCamelCase			,						torch.Tensor):
                   _A				:	Union[str, Any]       =			img.numpy().astype("np.uint8")
            if isinstance(__lowerCamelCase			,						__lowerCamelCase):
                   _A				:	Optional[int]       =			img_tensorize(__lowerCamelCase)
            assert isinstance(__lowerCamelCase			,						np.ndarray)

            _A   ,      _A				:	int       =			img.shape[1], img.shape[0]
            _A				:	Optional[int]       =			mplfigure.Figure(frameon=__lowerCamelCase)
            _A				:	List[str]       =			fig.get_dpi()
            _A				:	str       =			(width * scale + 1e-2) / dpi
            _A				:	Tuple       =			(height * scale + 1e-2) / dpi
            fig.set_size_inches(__lowerCamelCase			,						__lowerCamelCase)
            _A				:	Optional[int]       =			fig.add_axes([0.0, 0.0, 1.0, 1.0])
            ax.axis("off")
            ax.set_xlim(0.0			,						__lowerCamelCase)
            ax.set_ylim(__lowerCamelCase)

            _A				:	Optional[int]       =			saveas
            _A				:	Any       =			rgb
            _A				:	Optional[int]       =			pynb
            _A				:	Union[str, Any]       =			img
            _A				:	Any       =			edgecolor
            _A				:	List[str]       =			0.5
            _A				:	Dict       =			linestyle
            _A				:	str       =			int(np.sqrt(min(__lowerCamelCase			,						__lowerCamelCase)) * scale // 3)
            _A				:	Union[str, Any]       =			width
            _A				:	List[str]       =			height
            _A				:	Dict       =			scale
            _A				:	int       =			fig
            _A				:	Optional[int]       =			ax
            _A				:	Any       =			pad
            _A				:	Optional[int]       =			idaobj
            _A				:	Tuple       =			idaattr
            _A				:	Dict       =			FigureCanvasAgg(__lowerCamelCase)


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase=None)      ->    List[Any]:
            if color is None:
                   _A				:	List[str]       =			self.edgecolor
            ((_A)   ,      (_A)   ,      (_A)   ,      (_A))				:	Dict       =			box
            _A				:	List[Any]       =			xa - xa
            _A				:	Union[str, Any]       =			ya - ya
            self.ax.add_patch(
                mpl.patches.Rectangle(
                    (xa, ya)			,						__lowerCamelCase			,						__lowerCamelCase			,						fill=__lowerCamelCase			,						edgecolor=__lowerCamelCase			,						linewidth=self.font_size // 3			,						alpha=self.alpha			,						linestyle=self.linestyle			,						))


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase=None			,						__lowerCamelCase=None			,						__lowerCamelCase=None			,						__lowerCamelCase=None)      ->    Optional[Any]:
            if len(boxes.shape) > 2:
                   _A				:	str       =			boxes[0]
            if len(obj_ids.shape) > 1:
                   _A				:	Union[str, Any]       =			obj_ids[0]
            if len(obj_scores.shape) > 1:
                   _A				:	List[str]       =			obj_scores[0]
            if len(attr_ids.shape) > 1:
                   _A				:	Any       =			attr_ids[0]
            if len(attr_scores.shape) > 1:
                   _A				:	List[str]       =			attr_scores[0]
            if isinstance(__lowerCamelCase			,						torch.Tensor):
                   _A				:	List[str]       =			boxes.numpy()
            if isinstance(__lowerCamelCase			,						__lowerCamelCase):
                   _A				:	Dict       =			np.array(__lowerCamelCase)
            assert isinstance(__lowerCamelCase			,						np.ndarray)
            _A				:	Dict       =			np.prod(boxes[:, 2:] - boxes[:, :2]			,						axis=1)
            _A				:	Any       =			np.argsort(-areas).tolist()
            _A				:	Union[str, Any]       =			boxes[sorted_idxs] if boxes is not None else None
            _A				:	int       =			obj_ids[sorted_idxs] if obj_ids is not None else None
            _A				:	List[Any]       =			obj_scores[sorted_idxs] if obj_scores is not None else None
            _A				:	str       =			attr_ids[sorted_idxs] if attr_ids is not None else None
            _A				:	str       =			attr_scores[sorted_idxs] if attr_scores is not None else None

            _A				:	Dict       =			[self._random_color(maximum=1) for _ in range(len(__lowerCamelCase))]
            _A				:	Any       =			[assigned_colors[idx] for idx in sorted_idxs]
            if obj_ids is not None:
                   _A				:	List[str]       =			self._create_text_labels_attr(__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase)
                   for i in range(len(__lowerCamelCase)):
                          _A				:	Optional[Any]       =			assigned_colors[i]
                          self.add_box(boxes[i]			,						__lowerCamelCase)
                          self.draw_labels(labels[i]			,						boxes[i]			,						__lowerCamelCase)


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase)      ->    Any:
            _A   ,      _A   ,      _A   ,      _A				:	Optional[int]       =			box
            _A				:	str       =			(xa, ya)
            _A				:	int       =			(ya - ya) * (xa - xa)
            _A				:	str       =			_SMALL_OBJ * self.scale
            if instance_area < small or ya - ya < 4_0 * self.scale:
                   if ya >= self.height - 5:
                          _A				:	Tuple       =			(xa, ya)
                   else:
                          _A				:	int       =			(xa, ya)

            _A				:	Any       =			(ya - ya) / np.sqrt(self.height * self.width)
            _A				:	Optional[int]       =			self._change_color_brightness(__lowerCamelCase			,						brightness_factor=0.7)
            _A				:	List[str]       =			np.clip((height_ratio - 0.0_2) / 0.0_8 + 1			,						1.2			,						2)
            font_size *= 0.7_5 * self.font_size

            self.draw_text(
                text=__lowerCamelCase			,						position=__lowerCamelCase			,						color=__lowerCamelCase			,						)


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase="g"			,						__lowerCamelCase="left"			,						)      ->    Any:
            _A				:	Optional[int]       =			0
            _A				:	Tuple       =			self.font_size
            _A				:	List[Any]       =			np.maximum(list(mplc.to_rgb(__lowerCamelCase))			,						0.2)
            _A				:	List[str]       =			max(0.8			,						np.max(__lowerCamelCase))
            _A				:	Optional[Any]       =			{
                "facecolor": "black",
                "alpha": self.alpha,
                "pad": self.pad,
                "edgecolor": "none",
            }
            _A   ,      _A				:	Optional[int]       =			position
            self.ax.text(
                __lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						size=font_size * self.scale			,						family="sans-serif"			,						bbox=__lowerCamelCase			,						verticalalignment="top"			,						horizontalalignment=__lowerCamelCase			,						color=__lowerCamelCase			,						zorder=1_0			,						rotation=__lowerCamelCase			,						)


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase=None)      ->    Dict:
            if saveas is None:
                   _A				:	Union[str, Any]       =			self.saveas
            if saveas.lower().endswith(".jpg") or saveas.lower().endswith(".png"):
                   cva.imwrite(
                       __lowerCamelCase			,						self._get_buffer()[:, :, ::-1]			,						)
            else:
                   self.fig.savefig(__lowerCamelCase)


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase)      ->    str:
            _A				:	List[str]       =			[self.idaobj[i] for i in classes]
            _A				:	Any       =			[self.idaattr[i] for i in attr_classes]
            _A				:	Optional[int]       =			[
                F"{label} {score:.2f} {attr} {attr_score:.2f}"
                for label, score, attr, attr_score in zip(__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase)
            ]
            return labels


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase)      ->    Optional[Any]:
            _A				:	List[Any]       =			[self.idaobj[i] for i in classes]
            if scores is not None:
                   if labels is None:
                          _A				:	Any       =			["{:.0f}%".format(s * 1_0_0) for s in scores]
                   else:
                          _A				:	Tuple       =			["{} {:.0f}%".format(__lowerCamelCase			,						s * 1_0_0) for li, s in zip(__lowerCamelCase			,						__lowerCamelCase)]
            return labels


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase=2_5_5)      ->    str:
            _A				:	List[Any]       =			np.random.randint(0			,						len(_COLORS))
            _A				:	Dict       =			_COLORS[idx] * maximum
            if not self.rgb:
                   _A				:	List[str]       =			ret[::-1]
            return ret


     def 			_lowerCamelCase			(						self)      ->    Dict:
            if not self.pynb:
                   _A   ,      _A				:	Any       =			self.canvas.print_to_buffer()
                   if (width, height) != (self.width, self.height):
                          _A				:	Optional[Any]       =			cva.resize(self.img			,						(width, height))
                   else:
                          _A				:	Union[str, Any]       =			self.img
            else:
                   _A				:	Optional[Any]       =			io.BytesIO()  # works for cairo backend
                   self.canvas.print_rgba(__lowerCamelCase)
                   _A   ,      _A				:	Union[str, Any]       =			self.width, self.height
                   _A				:	int       =			buf.getvalue()
                   _A				:	Any       =			self.img

            _A				:	Any       =			np.frombuffer(__lowerCamelCase			,						dtype="uint8")
            _A				:	int       =			buffer.reshape(__lowerCamelCase			,						__lowerCamelCase			,						4)
            _A   ,      _A				:	Optional[Any]       =			np.split(__lowerCamelCase			,						[3]			,						axis=2)

            try:
                   import numexpr as ne  # fuse them with numexpr

                   _A				:	List[Any]       =			ne.evaluate("img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)")
            except ImportError:
                   _A				:	Optional[int]       =			alpha.astype("float32") / 2_5_5.0
                   _A				:	Optional[Any]       =			img * (1 - alpha) + rgb * alpha

            return visualized_image.astype("uint8")


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase)      ->    List[str]:
            assert brightness_factor >= -1.0 and brightness_factor <= 1.0
            _A				:	List[str]       =			mplc.to_rgb(__lowerCamelCase)
            _A				:	Any       =			colorsys.rgb_to_hls(*mplc.to_rgb(__lowerCamelCase))
            _A				:	str       =			polygon_color[1] + (brightness_factor * polygon_color[1])
            _A				:	Optional[Any]       =			0.0 if modified_lightness < 0.0 else modified_lightness
            _A				:	Union[str, Any]       =			1.0 if modified_lightness > 1.0 else modified_lightness
            _A				:	List[str]       =			colorsys.hls_to_rgb(polygon_color[0]			,						__lowerCamelCase			,						polygon_color[2])
            return modified_color




lowerCAmelCase__						=			(
    np.array(
        [
            0.0_00,
            0.4_47,
            0.7_41,
            0.8_50,
            0.3_25,
            0.0_98,
            0.9_29,
            0.6_94,
            0.1_25,
            0.4_94,
            0.1_84,
            0.5_56,
            0.4_66,
            0.6_74,
            0.1_88,
            0.3_01,
            0.7_45,
            0.9_33,
            0.6_35,
            0.0_78,
            0.1_84,
            0.3_00,
            0.3_00,
            0.3_00,
            0.6_00,
            0.6_00,
            0.6_00,
            1.0_00,
            0.0_00,
            0.0_00,
            1.0_00,
            0.5_00,
            0.0_00,
            0.7_49,
            0.7_49,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            1.0_00,
            0.6_67,
            0.0_00,
            1.0_00,
            0.3_33,
            0.3_33,
            0.0_00,
            0.3_33,
            0.6_67,
            0.0_00,
            0.3_33,
            1.0_00,
            0.0_00,
            0.6_67,
            0.3_33,
            0.0_00,
            0.6_67,
            0.6_67,
            0.0_00,
            0.6_67,
            1.0_00,
            0.0_00,
            1.0_00,
            0.3_33,
            0.0_00,
            1.0_00,
            0.6_67,
            0.0_00,
            1.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.3_33,
            0.5_00,
            0.0_00,
            0.6_67,
            0.5_00,
            0.0_00,
            1.0_00,
            0.5_00,
            0.3_33,
            0.0_00,
            0.5_00,
            0.3_33,
            0.3_33,
            0.5_00,
            0.3_33,
            0.6_67,
            0.5_00,
            0.3_33,
            1.0_00,
            0.5_00,
            0.6_67,
            0.0_00,
            0.5_00,
            0.6_67,
            0.3_33,
            0.5_00,
            0.6_67,
            0.6_67,
            0.5_00,
            0.6_67,
            1.0_00,
            0.5_00,
            1.0_00,
            0.0_00,
            0.5_00,
            1.0_00,
            0.3_33,
            0.5_00,
            1.0_00,
            0.6_67,
            0.5_00,
            1.0_00,
            1.0_00,
            0.5_00,
            0.0_00,
            0.3_33,
            1.0_00,
            0.0_00,
            0.6_67,
            1.0_00,
            0.0_00,
            1.0_00,
            1.0_00,
            0.3_33,
            0.0_00,
            1.0_00,
            0.3_33,
            0.3_33,
            1.0_00,
            0.3_33,
            0.6_67,
            1.0_00,
            0.3_33,
            1.0_00,
            1.0_00,
            0.6_67,
            0.0_00,
            1.0_00,
            0.6_67,
            0.3_33,
            1.0_00,
            0.6_67,
            0.6_67,
            1.0_00,
            0.6_67,
            1.0_00,
            1.0_00,
            1.0_00,
            0.0_00,
            1.0_00,
            1.0_00,
            0.3_33,
            1.0_00,
            1.0_00,
            0.6_67,
            1.0_00,
            0.3_33,
            0.0_00,
            0.0_00,
            0.5_00,
            0.0_00,
            0.0_00,
            0.6_67,
            0.0_00,
            0.0_00,
            0.8_33,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            0.1_67,
            0.0_00,
            0.0_00,
            0.3_33,
            0.0_00,
            0.0_00,
            0.5_00,
            0.0_00,
            0.0_00,
            0.6_67,
            0.0_00,
            0.0_00,
            0.8_33,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            0.1_67,
            0.0_00,
            0.0_00,
            0.3_33,
            0.0_00,
            0.0_00,
            0.5_00,
            0.0_00,
            0.0_00,
            0.6_67,
            0.0_00,
            0.0_00,
            0.8_33,
            0.0_00,
            0.0_00,
            1.0_00,
            0.0_00,
            0.0_00,
            0.0_00,
            0.1_43,
            0.1_43,
            0.1_43,
            0.8_57,
            0.8_57,
            0.8_57,
            1.0_00,
            1.0_00,
            1.0_00,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)




import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask


if is_torch_available():
    import torch

    from transformers import BlipTextModel
    from transformers.models.blip.modeling_blip import BLIP_PRETRAINED_MODEL_ARCHIVE_LIST







class 					lowerCAmelCase__	:




     '''simple docstring'''


     def __init__(						self			,						__lowerCamelCase			,						__lowerCamelCase=1_2			,						__lowerCamelCase=7			,						__lowerCamelCase=True			,						__lowerCamelCase=True			,						__lowerCamelCase=True			,						__lowerCamelCase=9_9			,						__lowerCamelCase=3_2			,						__lowerCamelCase=3_2			,						__lowerCamelCase=5			,						__lowerCamelCase=4			,						__lowerCamelCase=3_7			,						__lowerCamelCase=0.1			,						__lowerCamelCase=0.1			,						__lowerCamelCase=5_1_2			,						__lowerCamelCase=0.0_2			,						__lowerCamelCase=0			,						__lowerCamelCase=None			,						)      ->    str:
            _A				:	Optional[int]       =			parent
            _A				:	Optional[int]       =			batch_size
            _A				:	Optional[int]       =			seq_length
            _A				:	Dict       =			is_training
            _A				:	Union[str, Any]       =			use_input_mask
            _A				:	Optional[int]       =			use_labels
            _A				:	Any       =			vocab_size
            _A				:	Dict       =			hidden_size
            _A				:	Optional[Any]       =			projection_dim
            _A				:	Union[str, Any]       =			num_hidden_layers
            _A				:	Tuple       =			num_attention_heads
            _A				:	List[str]       =			intermediate_size
            _A				:	Any       =			dropout
            _A				:	List[str]       =			attention_dropout
            _A				:	str       =			max_position_embeddings
            _A				:	List[Any]       =			initializer_range
            _A				:	int       =			scope
            _A				:	List[str]       =			bos_token_id


     def 			_lowerCamelCase			(						self)      ->    List[str]:
            _A				:	Tuple       =			ids_tensor([self.batch_size, self.seq_length]			,						self.vocab_size)

            _A				:	str       =			None
            if self.use_input_mask:
                   _A				:	Union[str, Any]       =			random_attention_mask([self.batch_size, self.seq_length])

            if input_mask is not None:
                   _A   ,      _A				:	int       =			input_mask.shape
                   _A				:	Optional[Any]       =			np.random.randint(1			,						seq_length - 1			,						size=(batch_size,))
                   for batch_idx, start_index in enumerate(__lowerCamelCase):
                          _A				:	Tuple       =			1
                          _A				:	List[str]       =			0

            _A				:	str       =			self.get_config()

            return config, input_ids, input_mask


     def 			_lowerCamelCase			(						self)      ->    Optional[int]:
            return BlipTextConfig(
                vocab_size=self.vocab_size			,						hidden_size=self.hidden_size			,						projection_dim=self.projection_dim			,						num_hidden_layers=self.num_hidden_layers			,						num_attention_heads=self.num_attention_heads			,						intermediate_size=self.intermediate_size			,						dropout=self.dropout			,						attention_dropout=self.attention_dropout			,						max_position_embeddings=self.max_position_embeddings			,						initializer_range=self.initializer_range			,						bos_token_id=self.bos_token_id			,						)


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase)      ->    List[str]:
            _A				:	int       =			BlipTextModel(config=__lowerCamelCase)
            model.to(__lowerCamelCase)
            model.eval()
            with torch.no_grad():
                   _A				:	int       =			model(__lowerCamelCase			,						attention_mask=__lowerCamelCase)
                   _A				:	List[str]       =			model(__lowerCamelCase)
            self.parent.assertEqual(result.last_hidden_state.shape			,						(self.batch_size, self.seq_length, self.hidden_size))
            self.parent.assertEqual(result.pooler_output.shape			,						(self.batch_size, self.hidden_size))


     def 			_lowerCamelCase			(						self)      ->    Any:
            _A				:	Dict       =			self.prepare_config_and_inputs()
            _A   ,      _A   ,      _A				:	str       =			config_and_inputs
            _A				:	List[str]       =			{"input_ids": input_ids, "attention_mask": input_mask}
            return config, inputs_dict







@require_torch
class 					lowerCAmelCase__	(    a					,   unittest.TestCase):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE        =			(BlipTextModel,) if is_torch_available() else ()
     __SCREAMING_SNAKE_CASE        =			False
     __SCREAMING_SNAKE_CASE        =			False
     __SCREAMING_SNAKE_CASE        =			False


     def 			_lowerCamelCase			(						self)      ->    Dict:
            _A				:	Optional[Any]       =			BlipTextModelTester(self)
            _A				:	List[str]       =			ConfigTester(self			,						config_class=__lowerCamelCase			,						hidden_size=3_7)


     def 			_lowerCamelCase			(						self)      ->    List[Any]:
            self.config_tester.run_common_tests()


     def 			_lowerCamelCase			(						self)      ->    Union[str, Any]:
            _A				:	Optional[Any]       =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*__lowerCamelCase)


     def 			_lowerCamelCase			(						self)      ->    Optional[Any]:
            pass


     def 			_lowerCamelCase			(						self)      ->    Dict:
            pass


     @unittest.skip(reason="Blip does not use inputs_embeds")
     def 			_lowerCamelCase			(						self)      ->    str:
            pass


     @unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING")
     def 			_lowerCamelCase			(						self)      ->    Optional[Any]:
            pass


     @unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING")
     def 			_lowerCamelCase			(						self)      ->    str:
            pass


     @slow
     def 			_lowerCamelCase			(						self)      ->    Optional[Any]:
            for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   _A				:	Union[str, Any]       =			BlipTextModel.from_pretrained(__lowerCamelCase)
                   self.assertIsNotNone(__lowerCamelCase)


     def 			_lowerCamelCase			(						self)      ->    Optional[int]:
            super().test_pt_tf_model_equivalence(allow_missing_keys=__lowerCamelCase)
