"""simple docstring"""


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


__UpperCamelCase										=     1000




class 					UpperCamelCase   :







       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,				) -> Dict:
            if isinstance(lowerCAmelCase__,				torch.Tensor):
                 snake_case_   			=   img.numpy().astype('np.uint8')
            if isinstance(lowerCAmelCase__,				lowerCAmelCase__):
                 snake_case_   			=   img_tensorize(lowerCAmelCase__)
            assert isinstance(lowerCAmelCase__,				np.ndarray)

            snake_case_			,      snake_case_   			=   img.shape[1], img.shape[0]
            snake_case_   			=   mplfigure.Figure(frameon=lowerCAmelCase__)
            snake_case_   			=   fig.get_dpi()
            snake_case_   			=   (width * scale + 1e-2) / dpi
            snake_case_   			=   (height * scale + 1e-2) / dpi
            fig.set_size_inches(lowerCAmelCase__,				lowerCAmelCase__)
            snake_case_   			=   fig.add_axes([0.0, 0.0, 1.0, 1.0])
            ax.axis('off')
            ax.set_xlim(0.0,				lowerCAmelCase__)
            ax.set_ylim(lowerCAmelCase__)

            snake_case_   			=   saveas
            snake_case_   			=   rgb
            snake_case_   			=   pynb
            snake_case_   			=   img
            snake_case_   			=   edgecolor
            snake_case_   			=   0.5
            snake_case_   			=   linestyle
            snake_case_   			=   int(np.sqrt(min(lowerCAmelCase__,				lowerCAmelCase__)) * scale // 3)
            snake_case_   			=   width
            snake_case_   			=   height
            snake_case_   			=   scale
            snake_case_   			=   fig
            snake_case_   			=   ax
            snake_case_   			=   pad
            snake_case_   			=   idaobj
            snake_case_   			=   idaattr
            snake_case_   			=   FigureCanvasAgg(lowerCAmelCase__)







       def 						a_		(					self,				lowerCAmelCase__,				lowerCAmelCase__=None) -> List[str]:
            if color is None:
                 snake_case_   			=   self.edgecolor
            ((snake_case_)			,      (snake_case_)			,      (snake_case_)			,      (snake_case_))   			=   box
            snake_case_   			=   xa - xa
            snake_case_   			=   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 						a_		(					self,				lowerCAmelCase__,				lowerCAmelCase__=None,				lowerCAmelCase__=None,				lowerCAmelCase__=None,				lowerCAmelCase__=None) -> List[Any]:
            if len(boxes.shape) > 2:
                 snake_case_   			=   boxes[0]
            if len(obj_ids.shape) > 1:
                 snake_case_   			=   obj_ids[0]
            if len(obj_scores.shape) > 1:
                 snake_case_   			=   obj_scores[0]
            if len(attr_ids.shape) > 1:
                 snake_case_   			=   attr_ids[0]
            if len(attr_scores.shape) > 1:
                 snake_case_   			=   attr_scores[0]
            if isinstance(lowerCAmelCase__,				torch.Tensor):
                 snake_case_   			=   boxes.numpy()
            if isinstance(lowerCAmelCase__,				lowerCAmelCase__):
                 snake_case_   			=   np.array(lowerCAmelCase__)
            assert isinstance(lowerCAmelCase__,				np.ndarray)
            snake_case_   			=   np.prod(boxes[:, 2:] - boxes[:, :2],				axis=1)
            snake_case_   			=   np.argsort(-areas).tolist()
            snake_case_   			=   boxes[sorted_idxs] if boxes is not None else None
            snake_case_   			=   obj_ids[sorted_idxs] if obj_ids is not None else None
            snake_case_   			=   obj_scores[sorted_idxs] if obj_scores is not None else None
            snake_case_   			=   attr_ids[sorted_idxs] if attr_ids is not None else None
            snake_case_   			=   attr_scores[sorted_idxs] if attr_scores is not None else None

            snake_case_   			=   [self._random_color(maximum=1) for _ in range(len(lowerCAmelCase__))]
            snake_case_   			=   [assigned_colors[idx] for idx in sorted_idxs]
            if obj_ids is not None:
                 snake_case_   			=   self._create_text_labels_attr(lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__)
                 for i in range(len(lowerCAmelCase__)):
                      snake_case_   			=   assigned_colors[i]
                      self.add_box(boxes[i],				lowerCAmelCase__)
                      self.draw_labels(labels[i],				boxes[i],				lowerCAmelCase__)







       def 						a_		(					self,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__) -> int:
            snake_case_			,      snake_case_			,      snake_case_			,      snake_case_   			=   box
            snake_case_   			=   (xa, ya)
            snake_case_   			=   (ya - ya) * (xa - xa)
            snake_case_   			=   _SMALL_OBJ * self.scale
            if instance_area < small or ya - ya < 40 * self.scale:
                 if ya >= self.height - 5:
                      snake_case_   			=   (xa, ya)
                 else:
                      snake_case_   			=   (xa, ya)

            snake_case_   			=   (ya - ya) / np.sqrt(self.height * self.width)
            snake_case_   			=   self._change_color_brightness(lowerCAmelCase__,				brightness_factor=0.7)
            snake_case_   			=   np.clip((height_ratio - 0.02) / 0.08 + 1,				1.2,				2)
            font_size *= 0.75 * self.font_size

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







       def 						a_		(					self,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__="g",				lowerCAmelCase__="left",				) -> Optional[int]:
            snake_case_   			=   0
            snake_case_   			=   self.font_size
            snake_case_   			=   np.maximum(list(mplc.to_rgb(lowerCAmelCase__)),				0.2)
            snake_case_   			=   max(0.8,				np.max(lowerCAmelCase__))
            snake_case_   			=   {
                'facecolor': 'black',
                'alpha': self.alpha,
                'pad': self.pad,
                'edgecolor': 'none',
            }
            snake_case_			,      snake_case_   			=   position
            self.ax.text(
                lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				size=font_size * self.scale,				family='sans-serif',				bbox=lowerCAmelCase__,				verticalalignment='top',				horizontalalignment=lowerCAmelCase__,				color=lowerCAmelCase__,				zorder=10,				rotation=lowerCAmelCase__,				)







       def 						a_		(					self,				lowerCAmelCase__=None) -> Tuple:
            if saveas is None:
                 snake_case_   			=   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 						a_		(					self,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__) -> List[Any]:
            snake_case_   			=   [self.idaobj[i] for i in classes]
            snake_case_   			=   [self.idaattr[i] for i in attr_classes]
            snake_case_   			=   [
                f'{label} {score:.2f} {attr} {attr_score:.2f}'
                for label, score, attr, attr_score in zip(lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__)
            ]
            return labels







       def 						a_		(					self,				lowerCAmelCase__,				lowerCAmelCase__) -> Optional[int]:
            snake_case_   			=   [self.idaobj[i] for i in classes]
            if scores is not None:
                 if labels is None:
                      snake_case_   			=   ['{:.0f}%'.format(s * 100) for s in scores]
                 else:
                      snake_case_   			=   ['{} {:.0f}%'.format(lowerCAmelCase__,				s * 100) for li, s in zip(lowerCAmelCase__,				lowerCAmelCase__)]
            return labels







       def 						a_		(					self,				lowerCAmelCase__=255) -> Union[str, Any]:
            snake_case_   			=   np.random.randint(0,				len(_COLORS))
            snake_case_   			=   _COLORS[idx] * maximum
            if not self.rgb:
                 snake_case_   			=   ret[::-1]
            return ret







       def 						a_		(					self) -> int:
            if not self.pynb:
                 snake_case_			,      snake_case_   			=   self.canvas.print_to_buffer()
                 if (width, height) != (self.width, self.height):
                      snake_case_   			=   cva.resize(self.img,				(width, height))
                 else:
                      snake_case_   			=   self.img
            else:
                 snake_case_   			=   io.BytesIO()  # works for cairo backend
                 self.canvas.print_rgba(lowerCAmelCase__)
                 snake_case_			,      snake_case_   			=   self.width, self.height
                 snake_case_   			=   buf.getvalue()
                 snake_case_   			=   self.img

            snake_case_   			=   np.frombuffer(lowerCAmelCase__,				dtype='uint8')
            snake_case_   			=   buffer.reshape(lowerCAmelCase__,				lowerCAmelCase__,				4)
            snake_case_			,      snake_case_   			=   np.split(lowerCAmelCase__,				[3],				axis=2)

            try:
                 import numexpr as ne  # fuse them with numexpr

                 snake_case_   			=   ne.evaluate('img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)')
            except ImportError:
                 snake_case_   			=   alpha.astype('float32') / 255.0
                 snake_case_   			=   img * (1 - alpha) + rgb * alpha

            return visualized_image.astype('uint8')







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


__UpperCamelCase										=     (
    np.array(
        [
            0.000,
            0.447,
            0.741,
            0.850,
            0.325,
            0.098,
            0.929,
            0.694,
            0.125,
            0.494,
            0.184,
            0.556,
            0.466,
            0.674,
            0.188,
            0.301,
            0.745,
            0.933,
            0.635,
            0.078,
            0.184,
            0.300,
            0.300,
            0.300,
            0.600,
            0.600,
            0.600,
            1.000,
            0.000,
            0.000,
            1.000,
            0.500,
            0.000,
            0.749,
            0.749,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.333,
            0.333,
            0.000,
            0.333,
            0.667,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            0.333,
            0.000,
            0.667,
            0.667,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            1.000,
            0.000,
            0.000,
            0.333,
            0.500,
            0.000,
            0.667,
            0.500,
            0.000,
            1.000,
            0.500,
            0.333,
            0.000,
            0.500,
            0.333,
            0.333,
            0.500,
            0.333,
            0.667,
            0.500,
            0.333,
            1.000,
            0.500,
            0.667,
            0.000,
            0.500,
            0.667,
            0.333,
            0.500,
            0.667,
            0.667,
            0.500,
            0.667,
            1.000,
            0.500,
            1.000,
            0.000,
            0.500,
            1.000,
            0.333,
            0.500,
            1.000,
            0.667,
            0.500,
            1.000,
            1.000,
            0.500,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.333,
            0.333,
            1.000,
            0.333,
            0.667,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.667,
            0.333,
            1.000,
            0.667,
            0.667,
            1.000,
            0.667,
            1.000,
            1.000,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            1.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.143,
            0.143,
            0.143,
            0.857,
            0.857,
            0.857,
            1.000,
            1.000,
            1.000,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)


"""simple docstring"""


import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> Optional[Any]:
     snake_case_			,      snake_case_			,      snake_case_   			=   pt_attn_layer.in_proj_weight.chunk(3		,   dim=0   )
     snake_case_			,      snake_case_			,      snake_case_   			=   pt_attn_layer.in_proj_bias.chunk(3		,   dim=0   )

     snake_case_   			=   pt_attn_layer.out_proj.weight
     snake_case_   			=   pt_attn_layer.out_proj.bias

     snake_case_   			=   q_proj
     snake_case_   			=   q_proj_bias

     snake_case_   			=   k_proj
     snake_case_   			=   k_proj_bias

     snake_case_   			=   v_proj
     snake_case_   			=   v_proj_bias

     snake_case_   			=   out_proj_weights
     snake_case_   			=   out_proj_bias







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> int:
     copy_linear(hf_mlp.fca		,   pt_mlp.c_fc   )
     copy_linear(hf_mlp.fca		,   pt_mlp.c_proj   )







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> int:
     snake_case_   			=   pt_linear.weight
     snake_case_   			=   pt_linear.bias







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> str:
     # copy layer norms
     copy_linear(hf_layer.layer_norma		,   pt_layer.ln_a   )
     copy_linear(hf_layer.layer_norma		,   pt_layer.ln_a   )

     # copy MLP
     copy_mlp(hf_layer.mlp		,   pt_layer.mlp   )

     # copy attn
     copy_attn_layer(hf_layer.self_attn		,   pt_layer.attn   )







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> Optional[Any]:
     for hf_layer, pt_layer in zip(UpperCAmelCase		,   UpperCAmelCase   ):
          copy_layer(UpperCAmelCase		,   UpperCAmelCase   )







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> Tuple:
     # copy  embeds
     snake_case_   			=   pt_model.token_embedding.weight
     snake_case_   			=   pt_model.positional_embedding

     # copy layer norm
     copy_linear(hf_encoder.final_layer_norm		,   pt_model.ln_final   )

     # copy hidden layers
     copy_layers(hf_encoder.encoder.layers		,   pt_model.transformer.resblocks   )







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> int:
     # copy projection
     snake_case_   			=   pt_model.text_projection.data.T

     # copy text encoder
     copy_encoder(hf_model.text_model		,   UpperCAmelCase   )







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase   )							-> List[str]:
     # copy projection
     snake_case_   			=   pt_model.visual.proj.data.T

     # copy layer norms
     copy_linear(hf_model.vision_model.pre_layrnorm		,   pt_model.visual.ln_pre   )
     copy_linear(hf_model.vision_model.post_layernorm		,   pt_model.visual.ln_post   )

     # copy embeds
     snake_case_   			=   pt_model.visual.conva.weight.data
     snake_case_   			=   pt_model.visual.class_embedding
     snake_case_   			=   pt_model.visual.positional_embedding.data

     # copy encoder
     copy_layers(hf_model.vision_model.encoder.layers		,   pt_model.visual.transformer.resblocks   )







@torch.no_grad()
def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase		,   UpperCAmelCase=None   )							-> Optional[Any]:
     if config_path is not None:
          snake_case_   			=   CLIPConfig.from_pretrained(UpperCAmelCase   )
     else:
          snake_case_   			=   CLIPConfig(projection_dim=512		,   text_config={}		,   vision_config={}   )

     snake_case_   			=   CLIPModel(UpperCAmelCase   ).eval()

     snake_case_			,      snake_case_   			=   load(UpperCAmelCase		,   device='cpu'		,   jit=UpperCAmelCase   )
     snake_case_   			=   pt_model.eval()

     copy_text_model_and_projection(UpperCAmelCase		,   UpperCAmelCase   )
     copy_vison_model_and_projection(UpperCAmelCase		,   UpperCAmelCase   )
     snake_case_   			=   pt_model.logit_scale

     snake_case_   			=   torch.arange(0		,   77   ).unsqueeze(0   )
     snake_case_   			=   torch.randn(1		,   3		,   224		,   224   )

     snake_case_   			=   hf_model(input_ids=UpperCAmelCase		,   pixel_values=UpperCAmelCase		,   return_dict=UpperCAmelCase   )
     snake_case_   			=   hf_outputs.logits_per_image
     snake_case_   			=   hf_outputs.logits_per_text
     snake_case_			,      snake_case_   			=   pt_model(UpperCAmelCase		,   UpperCAmelCase   )

     assert torch.allclose(UpperCAmelCase		,   UpperCAmelCase		,   atol=1e-3   )
     assert torch.allclose(UpperCAmelCase		,   UpperCAmelCase		,   atol=1e-3   )

     hf_model.save_pretrained(UpperCAmelCase   )


if __name__ == "__main__":
    __UpperCamelCase										=     argparse.ArgumentParser()
    parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
    parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
    parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
    __UpperCamelCase										=     parser.parse_args()

    convert_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)

