

from manim import *


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    def 						snake_case__     (      self :       Dict		)->    List[Any]:





        '''simple docstring'''







        A__          =   Rectangle(height=0.5,width=0.5		)
        A__          =   Rectangle(height=0.46,width=0.46		).set_stroke(width=0		)

        A__          =   [mem.copy() for i in range(6		)]
        A__          =   [mem.copy() for i in range(6		)]
        A__          =   VGroup(*lowercase_		).arrange(lowercase_,buff=0		)
        A__          =   VGroup(*lowercase_		).arrange(lowercase_,buff=0		)
        A__          =   VGroup(lowercase_,lowercase_		).arrange(lowercase_,buff=0		)
        A__          =   Text('CPU',font_size=2_4		)
        A__          =   Group(lowercase_,lowercase_		).arrange(lowercase_,buff=0.5,aligned_edge=lowercase_		)
        cpu.move_to([-2.5, -0.5, 0]		)
        self.add(lowercase_		)

        A__          =   [mem.copy() for i in range(4		)]
        A__          =   VGroup(*lowercase_		).arrange(lowercase_,buff=0		)
        A__          =   Text('GPU',font_size=2_4		)
        A__          =   Group(lowercase_,lowercase_		).arrange(lowercase_,buff=0.5,aligned_edge=lowercase_		)
        gpu.move_to([-1, -1, 0]		)
        self.add(lowercase_		)

        A__          =   [mem.copy() for i in range(6		)]
        A__          =   VGroup(*lowercase_		).arrange(lowercase_,buff=0		)

        A__          =   Text('Model',font_size=2_4		)
        A__          =   Group(lowercase_,lowercase_		).arrange(lowercase_,buff=0.5,aligned_edge=lowercase_		)
        model.move_to([3, -1.0, 0]		)
        self.add(lowercase_		)

        A__          =   []
        for i, rect in enumerate(lowercase_		):
            rect.set_stroke(lowercase_		)
            # target = fill.copy().set_fill(YELLOW, opacity=0.7)
            # target.move_to(rect)
            # self.add(target)

            A__          =   Rectangle(height=0.46 / 4,width=0.46 / 3		).set_stroke(width=0.0		).set_fill(lowercase_,opacity=0.7		)

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

        A__          =   [mem.copy() for i in range(6		)]
        A__          =   VGroup(*lowercase_		).arrange(lowercase_,buff=0		)

        A__          =   Text('Loaded Checkpoint',font_size=2_4		)
        A__          =   Group(lowercase_,lowercase_		).arrange(lowercase_,aligned_edge=lowercase_,buff=0.4		)
        checkpoint.move_to([3, 0.5, 0]		)

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

        A__          =   MarkupText(
            F'<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model',font_size=1_8,)

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

        self.add(lowercase_,lowercase_		)

        A__          =   MarkupText(
            F'<span fgcolor=\'{BLUE}\'>●</span> Checkpoint',font_size=1_8,)

        blue_text.next_to(lowercase_,DOWN * 2.4,aligned_edge=key_text.get_left()		)

        A__          =   MarkupText(
            F'Next, a <i><span fgcolor="{BLUE}">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor="{BLUE}">single shard</span>.',font_size=2_4,)
        step_a.move_to([2, 2, 0]		)
        self.play(Write(lowercase_		),Write(lowercase_		)		)

        self.play(Write(lowercase_,run_time=1		),Create(lowercase_,run_time=1		)		)

        A__          =   []
        A__          =   []
        for i, rect in enumerate(lowercase_		):
            A__          =   fill.copy().set_fill(lowercase_,opacity=0.7		)
            target.move_to(lowercase_		)
            first_animations.append(GrowFromCenter(lowercase_,run_time=1		)		)

            A__          =   target.copy()
            cpu_target.generate_target()
            if i < 5:
                cpu_target.target.move_to(cpu_left_col_base[i + 1]		)
            else:
                cpu_target.target.move_to(cpu_right_col_base[i - 5]		)
            second_animations.append(MoveToTarget(lowercase_,run_time=1.5		)		)

        self.play(*lowercase_		)
        self.play(*lowercase_		)
        self.wait()




from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


lowercase_       		=  logging.get_logger(__name__)

lowercase_       		=  {
    "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
    "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
    "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
    "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
    "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							't5'
    lowerCamelCase			=							['past_key_values']
    lowerCamelCase			=							{'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}


    def __init__(      self :       Union[str, Any],lowercase_ :       int=3_2_1_2_8,lowercase_ :       int=5_1_2,lowercase_ :       List[str]=6_4,lowercase_ :       Tuple=2_0_4_8,lowercase_ :       Any=6,lowercase_ :       List[str]=None,lowercase_ :       Union[str, Any]=8,lowercase_ :       int=3_2,lowercase_ :       Dict=1_2_8,lowercase_ :       Optional[int]=0.1,lowercase_ :       List[str]=1E-6,lowercase_ :       Tuple=1.0,lowercase_ :       Any="relu",lowercase_ :       Union[str, Any]=True,lowercase_ :       Optional[Any]=True,lowercase_ :       int=0,lowercase_ :       str=1,**lowercase_ :       str,)->    Any:





        '''simple docstring'''







        A__          =   vocab_size
        A__          =   d_model
        A__          =   d_kv
        A__          =   d_ff
        A__          =   num_layers
        A__          =   (
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        A__          =   num_heads
        A__          =   relative_attention_num_buckets
        A__          =   relative_attention_max_distance
        A__          =   dropout_rate
        A__          =   layer_norm_epsilon
        A__          =   initializer_factor
        A__          =   feed_forward_proj
        A__          =   use_cache

        A__          =   self.feed_forward_proj.split('-'		)
        A__          =   act_info[-1]
        A__          =   act_info[0] == 'gated'

        if len(lowercase_		) > 1 and act_info[0] != "gated" or len(lowercase_		) > 2:
            raise ValueError(
                F'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
                'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
                '\'gated-gelu\' or \'relu\''		)

        # for backwards compatibility
        if feed_forward_proj == "gated-gelu":
            A__          =   'gelu_new'

        super().__init__(
            pad_token_id=lowercase_,eos_token_id=lowercase_,is_encoder_decoder=lowercase_,**lowercase_,)




class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    @property
    def 						snake_case__     (      self :       Tuple		)->    Mapping[str, Mapping[int, str]]:





        '''simple docstring'''







        A__          =   {
            'input_ids': {0: 'batch', 1: 'encoder_sequence'},
            'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
        }
        if self.use_past:
            A__          =   'past_encoder_sequence + sequence'
            A__          =   {0: 'batch'}
            A__          =   {0: 'batch', 1: 'past_decoder_sequence + sequence'}
        else:
            A__          =   {0: 'batch', 1: 'decoder_sequence'}
            A__          =   {0: 'batch', 1: 'decoder_sequence'}

        if self.use_past:
            self.fill_with_past_key_values_(lowercase_,direction='inputs'		)

        return common_inputs


    @property
    def 						snake_case__     (      self :       Any		)->    int:





        '''simple docstring'''







        return 1_3

