"""simple docstring"""




from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


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

_a				:		int	   =       {
    'EleutherAI/gpt-j-6B': 'https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}
class 						__A							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Optional[int]		 =		"gptj"
     _UpperCamelCase							:      Dict		 =		{
         "max_position_embeddings": "n_positions",
         "hidden_size": "n_embd",
         "num_attention_heads": "n_head",
         "num_hidden_layers": "n_layer",
     }






     def __init__(		self    ,					a__=50400    ,					a__=2048    ,					a__=4096    ,					a__=28    ,					a__=16    ,					a__=64    ,					a__=None    ,					a__="gelu_new"    ,					a__=0.0    ,					a__=0.0    ,					a__=0.0    ,					a__=1e-5    ,					a__=0.0_2    ,					a__=True    ,					a__=50256    ,					a__=50256    ,					a__=False    ,					**a__    ,					):
       _lowerCAmelCase						:	Optional[int]        =					vocab_size
       _lowerCAmelCase						:	List[Any]        =					n_positions
       _lowerCAmelCase						:	Dict        =					n_embd
       _lowerCAmelCase						:	Optional[int]        =					n_layer
       _lowerCAmelCase						:	Dict        =					n_head
       _lowerCAmelCase						:	List[Any]        =					n_inner
       _lowerCAmelCase						:	Tuple        =					rotary_dim
       _lowerCAmelCase						:	Optional[Any]        =					activation_function
       _lowerCAmelCase						:	int        =					resid_pdrop
       _lowerCAmelCase						:	List[str]        =					embd_pdrop
       _lowerCAmelCase						:	List[Any]        =					attn_pdrop
       _lowerCAmelCase						:	Optional[int]        =					layer_norm_epsilon
       _lowerCAmelCase						:	Dict        =					initializer_range
       _lowerCAmelCase						:	Union[str, Any]        =					use_cache

       _lowerCAmelCase						:	str        =					bos_token_id
       _lowerCAmelCase						:	List[str]        =					eos_token_id

       super().__init__(
           bos_token_id=a__    ,					eos_token_id=a__    ,					tie_word_embeddings=a__    ,					**a__  )







class 						__A							(			SCREAMING_SNAKE_CASE_		):






     def __init__(		self    ,					a__    ,					a__ = "default"    ,					a__ = None    ,					a__ = False    ,					):
       super().__init__(a__    ,					task=a__    ,					patching_specs=a__    ,					use_past=a__  )
       if not getattr(self._config    ,					"""pad_token_id"""    ,					a__  ):
         # TODO: how to do that better?
         _lowerCAmelCase						:	List[str]        =					0






     @property
     def    __A  (		self  ):
       _lowerCAmelCase						:	Union[str, Any]        =					OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}  )
       if self.use_past:
         self.fill_with_past_key_values_(a__    ,					direction="""inputs"""  )
         _lowerCAmelCase						:	Union[str, Any]        =					{0: """batch""", 1: """past_sequence + sequence"""}
       else:
         _lowerCAmelCase						:	int        =					{0: """batch""", 1: """sequence"""}

       return common_inputs






     @property
     def    __A  (		self  ):
       return self._config.n_layer






     @property
     def    __A  (		self  ):
       return self._config.n_head






     def    __A  (		self    ,					a__    ,					a__ = -1    ,					a__ = -1    ,					a__ = False    ,					a__ = None    ,					):
       _lowerCAmelCase						:	str        =					super(a__    ,					self  ).generate_dummy_inputs(
           a__    ,					batch_size=a__    ,					seq_length=a__    ,					is_pair=a__    ,					framework=a__  )

       # We need to order the input in the way they appears in the forward()
       _lowerCAmelCase						:	str        =					OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}  )

       # Need to add the past_keys
       if self.use_past:
         if not is_torch_available():
           raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""  )
         else:
           import torch

           _lowerCAmelCase      ,     _lowerCAmelCase						:	List[str]        =					common_inputs["""input_ids"""].shape
           # Not using the same length for past_key_values
           _lowerCAmelCase						:	List[str]        =					seqlen + 2
           _lowerCAmelCase						:	Dict        =					(
               batch,
               self.num_attention_heads,
               past_key_values_length,
               self._config.hidden_size // self.num_attention_heads,
           )
           _lowerCAmelCase						:	Tuple        =					[
               (torch.zeros(a__  ), torch.zeros(a__  )) for _ in range(self.num_layers  )
           ]

       _lowerCAmelCase						:	List[Any]        =					common_inputs["""attention_mask"""]
       if self.use_past:
         _lowerCAmelCase						:	Tuple        =					ordered_inputs["""attention_mask"""].dtype
         _lowerCAmelCase						:	int        =					torch.cat(
             [ordered_inputs["""attention_mask"""], torch.ones(a__    ,					a__    ,					dtype=a__  )]    ,					dim=1  )

       return ordered_inputs






     @property
     def    __A  (		self  ):
       return 13





"""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()




