





"""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


lowerCamelCase_	:		Dict         =   10_00

class 					_UpperCAmelCase    :



      '''simple docstring'''

      def __init__(	self   , snake_case_   , snake_case_=1.2   , snake_case_="g"   , snake_case_=0.5   , snake_case_="-"   , snake_case_="test_out.jpg"   , snake_case_=True   , snake_case_=False   , snake_case_=None   , snake_case_=None   , snake_case_=0.7   , ):

        """simple docstring"""

        if isinstance(__UpperCamelCase   , torch.Tensor    ):
          A_    :			List[Any]									=    img.numpy().astype('np.uint8'    )
        if isinstance(__UpperCamelCase   , __UpperCamelCase    ):
          A_    :			Optional[int]									=    img_tensorize(__UpperCamelCase    )
        assert isinstance(__UpperCamelCase   , np.ndarray    )

        A_		,			A_    :			List[str]									=    img.shape[1], img.shape[0]
        A_    :			Optional[int]									=    mplfigure.Figure(frameon=__UpperCamelCase    )
        A_    :			List[str]									=    fig.get_dpi()
        A_    :			Dict									=    (width * scale + 1E-2) / dpi
        A_    :			Union[str, Any]									=    (height * scale + 1E-2) / dpi
        fig.set_size_inches(__UpperCamelCase   , __UpperCamelCase    )
        A_    :			str									=    fig.add_axes([0.0, 0.0, 1.0, 1.0]    )
        ax.axis('off'    )
        ax.set_xlim(0.0   , __UpperCamelCase    )
        ax.set_ylim(__UpperCamelCase    )

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

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_=None    ):

        """simple docstring"""

        if color is None:
          A_    :			List[Any]									=    self.edgecolor
        ((A_)		,			(A_)		,			(A_)		,			(A_))    :			Tuple									=    box
        A_    :			Any									=    xa - xa
        A_    :			List[Any]									=    ya - ya
        self.ax.add_patch(
            mpl.patches.Rectangle(
                (xa, ya)   , __UpperCamelCase   , __UpperCamelCase   , fill=__UpperCamelCase   , edgecolor=__UpperCamelCase   , linewidth=self.font_size // 3   , alpha=self.alpha   , linestyle=self.linestyle   , )    )

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_=None   , snake_case_=None   , snake_case_=None   , snake_case_=None    ):

        """simple docstring"""

        if len(boxes.shape    ) > 2:
          A_    :			int									=    boxes[0]
        if len(obj_ids.shape    ) > 1:
          A_    :			Dict									=    obj_ids[0]
        if len(obj_scores.shape    ) > 1:
          A_    :			int									=    obj_scores[0]
        if len(attr_ids.shape    ) > 1:
          A_    :			Union[str, Any]									=    attr_ids[0]
        if len(attr_scores.shape    ) > 1:
          A_    :			str									=    attr_scores[0]
        if isinstance(__UpperCamelCase   , torch.Tensor    ):
          A_    :			int									=    boxes.numpy()
        if isinstance(__UpperCamelCase   , __UpperCamelCase    ):
          A_    :			Optional[int]									=    np.array(__UpperCamelCase    )
        assert isinstance(__UpperCamelCase   , np.ndarray    )
        A_    :			Tuple									=    np.prod(boxes[:, 2:] - boxes[:, :2]   , axis=1    )
        A_    :			int									=    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_    :			List[Any]									=    attr_scores[sorted_idxs] if attr_scores is not None else None

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

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , snake_case_    ):

        """simple docstring"""

        A_		,			A_		,			A_		,			A_    :			Any									=    box
        A_    :			str									=    (xa, ya)
        A_    :			str									=    (ya - ya) * (xa - xa)
        A_    :			Any									=    _SMALL_OBJ * self.scale
        if instance_area < small or ya - ya < 4_0 * self.scale:
          if ya >= self.height - 5:
            A_    :			Union[str, Any]									=    (xa, ya)
          else:
            A_    :			Dict									=    (xa, ya)

        A_    :			Any									=    (ya - ya) / np.sqrt(self.height * self.width    )
        A_    :			Optional[Any]									=    self._change_color_brightness(__UpperCamelCase   , brightness_factor=0.7    )
        A_    :			Any									=    np.clip((height_ratio - 0.02) / 0.08 + 1   , 1.2   , 2    )
        font_size *= 0.75 * self.font_size

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

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , snake_case_="g"   , snake_case_="left"   , ):

        """simple docstring"""

        A_    :			Union[str, Any]									=    0
        A_    :			Optional[Any]									=    self.font_size
        A_    :			Union[str, Any]									=    np.maximum(list(mplc.to_rgb(__UpperCamelCase    )    )   , 0.2    )
        A_    :			List[Any]									=    max(0.8   , np.max(__UpperCamelCase    )    )
        A_    :			List[str]									=    {
            'facecolor': 'black',
            'alpha': self.alpha,
            'pad': self.pad,
            'edgecolor': 'none',
        }
        A_		,			A_    :			int									=    position
        self.ax.text(
            __UpperCamelCase   , __UpperCamelCase   , __UpperCamelCase   , size=font_size * self.scale   , family='sans-serif'   , bbox=__UpperCamelCase   , verticalalignment='top'   , horizontalalignment=__UpperCamelCase   , color=__UpperCamelCase   , zorder=1_0   , rotation=__UpperCamelCase   , )

      def    lowerCamelCase_     (	self   , snake_case_=None    ):

        """simple docstring"""

        if saveas is None:
          A_    :			str									=    self.saveas
        if saveas.lower().endswith('.jpg'    ) or saveas.lower().endswith('.png'    ):
          cva.imwrite(
              __UpperCamelCase   , self._get_buffer()[:, :, ::-1]   , )
        else:
          self.fig.savefig(__UpperCamelCase    )

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , snake_case_   , snake_case_    ):

        """simple docstring"""

        A_    :			List[Any]									=    [self.idaobj[i] for i in classes]
        A_    :			Tuple									=    [self.idaattr[i] for i in attr_classes]
        A_    :			List[Any]									=    [
            F"""{label} {score:.2f} {attr} {attr_score:.2f}"""
            for label, score, attr, attr_score in zip(__UpperCamelCase   , __UpperCamelCase   , __UpperCamelCase   , __UpperCamelCase    )
        ]
        return labels

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_    ):

        """simple docstring"""

        A_    :			Optional[int]									=    [self.idaobj[i] for i in classes]
        if scores is not None:
          if labels is None:
            A_    :			Optional[Any]									=    ['{:.0f}%'.format(s * 1_0_0    ) for s in scores]
          else:
            A_    :			Optional[Any]									=    ['{} {:.0f}%'.format(__UpperCamelCase   , s * 1_0_0    ) for li, s in zip(__UpperCamelCase   , __UpperCamelCase    )]
        return labels

      def    lowerCamelCase_     (	self   , snake_case_=2_5_5    ):

        """simple docstring"""

        A_    :			Dict									=    np.random.randint(0   , len(_COLORS    )    )
        A_    :			Any									=    _COLORS[idx] * maximum
        if not self.rgb:
          A_    :			Optional[Any]									=    ret[::-1]
        return ret

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        if not self.pynb:
          A_		,			A_    :			Dict									=    self.canvas.print_to_buffer()
          if (width, height) != (self.width, self.height):
            A_    :			Tuple									=    cva.resize(self.img   , (width, height)    )
          else:
            A_    :			List[Any]									=    self.img
        else:
          A_    :			Any									=    io.BytesIO()  # works for cairo backend
          self.canvas.print_rgba(__UpperCamelCase    )
          A_		,			A_    :			List[Any]									=    self.width, self.height
          A_    :			List[str]									=    buf.getvalue()
          A_    :			Optional[Any]									=    self.img

        A_    :			List[str]									=    np.frombuffer(__UpperCamelCase   , dtype='uint8'    )
        A_    :			Any									=    buffer.reshape(__UpperCamelCase   , __UpperCamelCase   , 4    )
        A_		,			A_    :			Tuple									=    np.split(__UpperCamelCase   , [3]   , axis=2    )

        try:
          import numexpr as ne  # fuse them with numexpr

          A_    :			Tuple									=    ne.evaluate('img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)'    )
        except ImportError:
          A_    :			Union[str, Any]									=    alpha.astype('float32'    ) / 2_55.0
          A_    :			List[str]									=    img * (1 - alpha) + rgb * alpha

        return visualized_image.astype('uint8'    )

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_    ):

        """simple docstring"""

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


lowerCamelCase_	:		List[str]         =   (
    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)
)

import argparse
import logging

import numpy as np
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
    datefmt='%m/%d/%Y %H:%M:%S',
    level=logging.INFO,
)
__magic_name__  :  List[str]     			=   logging.getLogger(__name__)





def        lowerCAmelCase       (				snake_case__   :      Union[str, Any] )->			Optional[Any]:
    np.random.seed(args.seed )
    torch.manual_seed(args.seed )
    if args.n_gpu > 0:
        torch.cuda.manual_seed_all(args.seed )





def        lowerCAmelCase       (				)->			int:
    A_								=					argparse.ArgumentParser()
    parser.add_argument(
        "--model_name_or_path"				,					default=snake_case__				,					type=snake_case__				,					required=snake_case__				,					)
    parser.add_argument("--prompt"				,					type=snake_case__				,					default="" )
    parser.add_argument("--length"				,					type=snake_case__				,					default=20 )
    parser.add_argument("--stop_token"				,					type=snake_case__				,					default=snake_case__				,					help="Token at which text generation is stopped" )
    parser.add_argument(
        "--temperature"				,					type=snake_case__				,					default=1.0				,					help="temperature of 1.0 has no effect, lower tend toward greedy sampling"				,					)
    parser.add_argument(
        "--repetition_penalty"				,					type=snake_case__				,					default=1.0				,					help="primarily useful for CTRL model; in that case, use 1.2" )
    parser.add_argument("--k"				,					type=snake_case__				,					default=0 )
    parser.add_argument("--penalty_alpha"				,					type=snake_case__				,					default=0.0 )
    parser.add_argument("--p"				,					type=snake_case__				,					default=0.9 )

    parser.add_argument("--prefix"				,					type=snake_case__				,					default=""				,					help="Text added prior to input." )
    parser.add_argument("--padding_text"				,					type=snake_case__				,					default=""				,					help="Deprecated, the use of `--prefix` is preferred." )
    parser.add_argument("--xlm_language"				,					type=snake_case__				,					default=""				,					help="Optional language when used with the XLM model." )

    parser.add_argument("--seed"				,					type=snake_case__				,					default=42				,					help="random seed for initialization" )
    parser.add_argument("--no_cuda"				,					action="store_true"				,					help="Avoid using CUDA when available" )
    parser.add_argument(
        "--fp16"				,					action="store_true"				,					help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"				,					)
    A_								=					parser.parse_args()

    A_								=					torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu" )
    A_								=					0 if args.no_cuda else torch.cuda.device_count()

    logger.warning(f'device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}' )

    set_seed(snake_case__ )

    # Initialize the model and tokenizer
    A_								=					AutoTokenizer.from_pretrained(args.model_name_or_path )
    A_								=					AutoModelForCausalLM.from_pretrained(args.model_name_or_path )

    # tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
    # model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
    model.to(args.device )

    if args.fpaa:
        model.half()

    logger.info(snake_case__ )
    A_								=					args.prompt if args.prompt else input("Model prompt >>> " )

    A_								=					tokenizer(snake_case__				,					return_tensors="pt"				,					add_special_tokens=snake_case__ )
    A_								=					{key: value.to(args.device ) for key, value in inputs.items()}

    A_								=					model.generate(
        **snake_case__				,					max_length=args.length + len(inputs["input_ids"][0] )				,					penalty_alpha=args.penalty_alpha				,					top_k=args.k				,					)

    A_								=					[]
    for generated_sequence_idx, generated_sequence in enumerate(snake_case__ ):
        print(f'=== GENERATED SEQUENCE {generated_sequence_idx + 1} ===' )
        A_								=					generated_sequence.tolist()

        # Decode text
        A_								=					tokenizer.decode(snake_case__				,					clean_up_tokenization_spaces=snake_case__				,					add_special_tokens=snake_case__ )

        # Remove all text after the stop token
        A_								=					text[: text.find(args.stop_token ) if args.stop_token else None]

        # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
        A_								=					(
            prompt_text + text[len(tokenizer.decode(inputs["input_ids"][0]				,					clean_up_tokenization_spaces=snake_case__ ) ) :]
        )

        generated_sequences.append(snake_case__ )
        print(snake_case__ )

    return generated_sequences


if __name__ == "__main__":
     main()






