"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


_a				:		Union[str, Any]	   =       logging.get_logger(__name__)

_a				:		List[Any]	   =       {
    'facebook/s2t-small-librispeech-asr': (
        'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json'
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}
class 						__A							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Union[str, Any]		 =		"speech_to_text"
     _UpperCamelCase							:      List[Any]		 =		["past_key_values"]
     _UpperCamelCase							:      Optional[int]		 =		{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






     def __init__(		self    ,					a__=10000    ,					a__=12    ,					a__=2048    ,					a__=4    ,					a__=6    ,					a__=2048    ,					a__=4    ,					a__=0.0    ,					a__=0.0    ,					a__=True    ,					a__=True    ,					a__="relu"    ,					a__=256    ,					a__=0.1    ,					a__=0.0    ,					a__=0.0    ,					a__=0.0_2    ,					a__=2    ,					a__=True    ,					a__=1    ,					a__=0    ,					a__=2    ,					a__=6000    ,					a__=1024    ,					a__=2    ,					a__=(5, 5)    ,					a__=1024    ,					a__=80    ,					a__=1    ,					**a__    ,					):
       _lowerCAmelCase						:	int        =					vocab_size
       _lowerCAmelCase						:	str        =					d_model
       _lowerCAmelCase						:	List[str]        =					encoder_ffn_dim
       _lowerCAmelCase						:	Optional[int]        =					encoder_layers
       _lowerCAmelCase						:	List[str]        =					encoder_attention_heads
       _lowerCAmelCase						:	Tuple        =					decoder_ffn_dim
       _lowerCAmelCase						:	Optional[Any]        =					decoder_layers
       _lowerCAmelCase						:	Tuple        =					decoder_attention_heads
       _lowerCAmelCase						:	Dict        =					dropout
       _lowerCAmelCase						:	Union[str, Any]        =					attention_dropout
       _lowerCAmelCase						:	List[str]        =					activation_dropout
       _lowerCAmelCase						:	Any        =					activation_function
       _lowerCAmelCase						:	Any        =					init_std
       _lowerCAmelCase						:	Union[str, Any]        =					encoder_layerdrop
       _lowerCAmelCase						:	str        =					decoder_layerdrop
       _lowerCAmelCase						:	int        =					use_cache
       _lowerCAmelCase						:	Optional[Any]        =					encoder_layers
       _lowerCAmelCase						:	Union[str, Any]        =					scale_embedding  # scale factor will be sqrt(d_model) if True
       _lowerCAmelCase						:	str        =					max_source_positions
       _lowerCAmelCase						:	List[Any]        =					max_target_positions
       _lowerCAmelCase						:	List[str]        =					num_conv_layers
       _lowerCAmelCase						:	int        =					list(a__  )
       _lowerCAmelCase						:	Any        =					conv_channels
       _lowerCAmelCase						:	Union[str, Any]        =					input_feat_per_channel
       _lowerCAmelCase						:	Tuple        =					input_channels

       if len(self.conv_kernel_sizes  ) != self.num_conv_layers:
         raise ValueError(
             """Configuration for convolutional module is incorrect. """
             """It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` """
             F"but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes  )}`, "
             F"`config.num_conv_layers = {self.num_conv_layers}`."  )

       super().__init__(
           pad_token_id=a__    ,					bos_token_id=a__    ,					eos_token_id=a__    ,					is_encoder_decoder=a__    ,					decoder_start_token_id=a__    ,					**a__    ,					)





"""simple docstring"""




from manim import *
class 						__A							(			SCREAMING_SNAKE_CASE_		):






     def    __A  (		self  ):
       _lowerCAmelCase						:	Any        =					Rectangle(height=0.5    ,					width=0.5  )
       _lowerCAmelCase						:	List[Any]        =					Rectangle(height=0.4_6    ,					width=0.4_6  ).set_stroke(width=0  )

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

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

       _lowerCAmelCase						:	Optional[int]        =					[mem.copy() for i in range(6  )]
       _lowerCAmelCase						:	Any        =					VGroup(*a__  ).arrange(a__    ,					buff=0  )

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

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

         _lowerCAmelCase						:	List[str]        =					Rectangle(height=0.4_6 / 4    ,					width=0.4_6 / 3  ).set_stroke(width=0.0  ).set_fill(a__    ,					opacity=0.7  )

         if i == 0:
           cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT  )    ,					buff=0.0_2    ,					direction=a__  )
           cpu_target.set_x(cpu_target.get_x() + 0.1  )
         elif i == 3:
           cpu_target.next_to(cpu_targs[0]    ,					direction=a__    ,					buff=0.0  )
         else:
           cpu_target.next_to(cpu_targs[i - 1]    ,					direction=a__    ,					buff=0.0  )
         self.add(a__  )
         cpu_targs.append(a__  )

       _lowerCAmelCase						:	Any        =					[mem.copy() for i in range(6  )]
       _lowerCAmelCase						:	List[str]        =					VGroup(*a__  ).arrange(a__    ,					buff=0  )

       _lowerCAmelCase						:	int        =					Text("""Loaded Checkpoint"""    ,					font_size=24  )
       _lowerCAmelCase						:	Optional[int]        =					Group(a__    ,					a__  ).arrange(a__    ,					aligned_edge=a__    ,					buff=0.4  )
       checkpoint.move_to([3, 0.5, 0]  )

       _lowerCAmelCase						:	List[Any]        =					Square(side_length=2.2  )
       key.move_to([-5, 2, 0]  )

       _lowerCAmelCase						:	List[str]        =					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(a__    ,					a__  )

       _lowerCAmelCase						:	int        =					MarkupText(
           F"<span fgcolor='{BLUE}'>●</span> Checkpoint"    ,					font_size=18    ,					)

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

       _lowerCAmelCase						:	List[Any]        =					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=24    ,					)
       step_a.move_to([2, 2, 0]  )
       self.play(Write(a__  )    ,					Write(a__  )  )

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

       _lowerCAmelCase						:	int        =					[]
       _lowerCAmelCase						:	List[Any]        =					[]
       for i, rect in enumerate(a__  ):
         _lowerCAmelCase						:	Tuple        =					fill.copy().set_fill(a__    ,					opacity=0.7  )
         target.move_to(a__  )
         first_animations.append(GrowFromCenter(a__    ,					run_time=1  )  )

         _lowerCAmelCase						:	Optional[Any]        =					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(a__    ,					run_time=1.5  )  )

       self.play(*a__  )
       self.play(*a__  )
       self.wait()




