






from manim import *



class 				_A			(     UpperCamelCase				):
    """simple docstring"""

    def _a							(				self      :     int      )      ->							Dict:
           __UpperCAmelCase     				=Rectangle(height=0.5 ,     width=0.5      )
           __UpperCAmelCase     				=Rectangle(height=0.46 ,     width=0.46      ).set_stroke(width=0      )

           __UpperCAmelCase     				=Rectangle(height=0.25 ,     width=0.25      )

           __UpperCAmelCase     				=[mem.copy() for i in range(6      )]
           __UpperCAmelCase     				=[mem.copy() for i in range(6      )]
           __UpperCAmelCase     				=VGroup(*__SCREAMING_SNAKE_CASE      ).arrange(__SCREAMING_SNAKE_CASE ,     buff=0      )
           __UpperCAmelCase     				=VGroup(*__SCREAMING_SNAKE_CASE      ).arrange(__SCREAMING_SNAKE_CASE ,     buff=0      )
           __UpperCAmelCase     				=VGroup(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      ).arrange(__SCREAMING_SNAKE_CASE ,     buff=0      )
           __UpperCAmelCase     				=Text("""CPU""" ,     font_size=24      )
           __UpperCAmelCase     				=Group(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      ).arrange(__SCREAMING_SNAKE_CASE ,     buff=0.5 ,     aligned_edge=__SCREAMING_SNAKE_CASE      )
           cpu.move_to([-2.5, -0.5, 0]      )
           self.add(__SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				=[mem.copy() for i in range(4      )]
           __UpperCAmelCase     				=VGroup(*__SCREAMING_SNAKE_CASE      ).arrange(__SCREAMING_SNAKE_CASE ,     buff=0      )
           __UpperCAmelCase     				=Text("""GPU""" ,     font_size=24      )
           __UpperCAmelCase     				=Group(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      ).arrange(__SCREAMING_SNAKE_CASE ,     buff=0.5 ,     aligned_edge=__SCREAMING_SNAKE_CASE      )
           gpu.move_to([-1, -1, 0]      )
           self.add(__SCREAMING_SNAKE_CASE      )

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

           __UpperCAmelCase     				=Text("""Model""" ,     font_size=24      )
           __UpperCAmelCase     				=Group(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      ).arrange(__SCREAMING_SNAKE_CASE ,     buff=0.5 ,     aligned_edge=__SCREAMING_SNAKE_CASE      )
           model.move_to([3, -1.0, 0]      )
           self.add(__SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				=[]
           __UpperCAmelCase     				=[]

           for i, rect in enumerate(__SCREAMING_SNAKE_CASE      ):
                  __UpperCAmelCase     				=fill.copy().set_fill(__SCREAMING_SNAKE_CASE ,     opacity=0.8      )
                  target.move_to(__SCREAMING_SNAKE_CASE      )
                  model_arr.append(__SCREAMING_SNAKE_CASE      )

                  __UpperCAmelCase     				=Rectangle(height=0.46 ,     width=0.46      ).set_stroke(width=0.0      ).set_fill(__SCREAMING_SNAKE_CASE ,     opacity=0.8      )
                  cpu_target.move_to(cpu_left_col_base[i]      )
                  model_cpu_arr.append(__SCREAMING_SNAKE_CASE      )

           self.add(*__SCREAMING_SNAKE_CASE ,     *__SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				=[meta_mem.copy() for i in range(6      )]
           __UpperCAmelCase     				=[meta_mem.copy() for i in range(6      )]
           __UpperCAmelCase     				=VGroup(*__SCREAMING_SNAKE_CASE      ).arrange(__SCREAMING_SNAKE_CASE ,     buff=0      )
           __UpperCAmelCase     				=VGroup(*__SCREAMING_SNAKE_CASE      ).arrange(__SCREAMING_SNAKE_CASE ,     buff=0      )
           __UpperCAmelCase     				=VGroup(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      ).arrange(__SCREAMING_SNAKE_CASE ,     buff=0      )
           __UpperCAmelCase     				=Text("""Disk""" ,     font_size=24      )
           __UpperCAmelCase     				=Group(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      ).arrange(__SCREAMING_SNAKE_CASE ,     buff=0.5 ,     aligned_edge=__SCREAMING_SNAKE_CASE      )
           disk.move_to([-4, -1.25, 0]      )
           self.add(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )

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

           __UpperCAmelCase     				=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(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				=MarkupText(
               f'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint''' ,     font_size=18 ,     )

           blue_text.next_to(__SCREAMING_SNAKE_CASE ,     DOWN * 2.4 ,     aligned_edge=key_text.get_left()      )
           self.add(__SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				=MarkupText(
               f'''Now watch as an input is passed through the model\nand how the memory is utilized and handled.''' ,     font_size=24 ,     )
           step_a.move_to([2, 2, 0]      )

           self.play(Write(__SCREAMING_SNAKE_CASE      )      )

           __UpperCAmelCase     				=Square(0.3      )
           input.set_fill(__SCREAMING_SNAKE_CASE ,     opacity=1.0      )
           input.set_stroke(width=0.0      )
           input.next_to(model_base[0] ,     __SCREAMING_SNAKE_CASE ,     buff=0.5      )

           self.play(Write(__SCREAMING_SNAKE_CASE      )      )

           input.generate_target()
           input.target.next_to(model_arr[0] ,     direction=__SCREAMING_SNAKE_CASE ,     buff=0.02      )
           self.play(MoveToTarget(__SCREAMING_SNAKE_CASE      )      )

           self.play(FadeOut(__SCREAMING_SNAKE_CASE      )      )

           __UpperCAmelCase     				=Arrow(start=__SCREAMING_SNAKE_CASE ,     end=__SCREAMING_SNAKE_CASE ,     color=__SCREAMING_SNAKE_CASE ,     buff=0.5      )
           a.next_to(model_arr[0].get_left() ,     __SCREAMING_SNAKE_CASE ,     buff=0.2      )

           model_cpu_arr[0].generate_target()
           model_cpu_arr[0].target.move_to(gpu_rect[0]      )

           __UpperCAmelCase     				=MarkupText(
               f'''As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back.''' ,     font_size=24 ,     )
           step_a.move_to([2, 2, 0]      )

           self.play(Write(__SCREAMING_SNAKE_CASE ,     run_time=3      )      )

           __UpperCAmelCase     				={"""run_time""": 1, """fade_in""": True, """fade_out""": True, """buff""": 0.02}

           self.play(
               Write(__SCREAMING_SNAKE_CASE      ) ,     Circumscribe(model_arr[0] ,     color=__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      ) ,     Circumscribe(model_cpu_arr[0] ,     color=__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      ) ,     Circumscribe(gpu_rect[0] ,     color=__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      ) ,     )
           self.play(MoveToTarget(model_cpu_arr[0]      )      )

           __UpperCAmelCase     				=a.copy()
           for i in range(6      ):
                  a_c.next_to(model_arr[i].get_right() + 0.02 ,     __SCREAMING_SNAKE_CASE ,     buff=0.2      )

                  input.generate_target()
                  input.target.move_to(model_arr[i].get_right() + 0.02      )

                  __UpperCAmelCase     				=AnimationGroup(
                      FadeOut(__SCREAMING_SNAKE_CASE ,     run_time=0.5      ) ,     MoveToTarget(__SCREAMING_SNAKE_CASE ,     run_time=0.5      ) ,     FadeIn(__SCREAMING_SNAKE_CASE ,     run_time=0.5      ) ,     lag_ratio=0.2      )

                  self.play(__SCREAMING_SNAKE_CASE      )

                  model_cpu_arr[i].generate_target()
                  model_cpu_arr[i].target.move_to(cpu_left_col_base[i]      )

                  if i < 5:
                         model_cpu_arr[i + 1].generate_target()
                         model_cpu_arr[i + 1].target.move_to(gpu_rect[0]      )
                         if i >= 1:
                                __UpperCAmelCase     				=0.7

                         self.play(
                             Circumscribe(model_arr[i] ,     **__SCREAMING_SNAKE_CASE      ) ,     Circumscribe(cpu_left_col_base[i] ,     **__SCREAMING_SNAKE_CASE      ) ,     Circumscribe(cpu_left_col_base[i + 1] ,     color=__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      ) ,     Circumscribe(gpu_rect[0] ,     color=__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      ) ,     Circumscribe(model_arr[i + 1] ,     color=__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      ) ,     )
                         if i < 1:
                                self.play(
                                    MoveToTarget(model_cpu_arr[i]      ) ,     MoveToTarget(model_cpu_arr[i + 1]      ) ,     )
                         else:
                                self.play(
                                    MoveToTarget(model_cpu_arr[i] ,     run_time=0.7      ) ,     MoveToTarget(model_cpu_arr[i + 1] ,     run_time=0.7      ) ,     )
                  else:
                         model_cpu_arr[i].generate_target()
                         model_cpu_arr[i].target.move_to(cpu_left_col_base[-1]      )
                         input.generate_target()
                         input.target.next_to(model_arr[-1].get_right() ,     RIGHT + 0.02 ,     buff=0.2      )

                         self.play(
                             Circumscribe(model_arr[-1] ,     color=__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      ) ,     Circumscribe(cpu_left_col_base[-1] ,     color=__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      ) ,     Circumscribe(gpu_rect[0] ,     color=__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      ) ,     )

                         self.play(MoveToTarget(model_cpu_arr[i]      )      )

                  __UpperCAmelCase     				=a_c
                  __UpperCAmelCase     				=a_c.copy()

           input.generate_target()
           input.target.next_to(model_base[-1] ,     RIGHT + 0.02 ,     buff=0.5      )
           self.play(
               FadeOut(__SCREAMING_SNAKE_CASE      ) ,     FadeOut(__SCREAMING_SNAKE_CASE ,     run_time=0.5      ) ,     )

           __UpperCAmelCase     				=MarkupText(f'''Inference on a model too large for GPU memory\nis successfully completed.''' ,     font_size=24      )
           step_a.move_to([2, 2, 0]      )

           self.play(Write(__SCREAMING_SNAKE_CASE ,     run_time=3      ) ,     MoveToTarget(__SCREAMING_SNAKE_CASE      )      )

           self.wait()












from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
			import tensorflow as tf

			from transformers import TFBlipTextModel
			from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST




class 							snake_case_       :

						"""simple docstring"""



						def __init__(self:					Optional[Any] ,					__UpperCAmelCase:					List[str] ,					__UpperCAmelCase:					int=12 ,					__UpperCAmelCase:					Dict=7 ,					__UpperCAmelCase:					str=True ,					__UpperCAmelCase:					Tuple=True ,					__UpperCAmelCase:					Tuple=True ,					__UpperCAmelCase:					Optional[int]=99 ,					__UpperCAmelCase:					str=32 ,					__UpperCAmelCase:					Any=32 ,					__UpperCAmelCase:					int=2 ,					__UpperCAmelCase:					List[str]=4 ,					__UpperCAmelCase:					Tuple=37 ,					__UpperCAmelCase:					Optional[int]=0.1 ,					__UpperCAmelCase:					List[Any]=0.1 ,					__UpperCAmelCase:					Dict=512 ,					__UpperCAmelCase:					Union[str, Any]=0.02 ,					__UpperCAmelCase:					List[Any]=0 ,					__UpperCAmelCase:					str=None ,					)    ->     Tuple:




											'''simple docstring'''



											__a					:						Union[str, Any]      =					parent
											__a					:						Dict      =					batch_size
											__a					:						Optional[Any]      =					seq_length
											__a					:						int      =					is_training
											__a					:						Union[str, Any]      =					use_input_mask
											__a					:						str      =					use_labels
											__a					:						Tuple      =					vocab_size
											__a					:						Dict      =					hidden_size
											__a					:						Optional[int]      =					projection_dim
											__a					:						Tuple      =					num_hidden_layers
											__a					:						Dict      =					num_attention_heads
											__a					:						Any      =					intermediate_size
											__a					:						Optional[int]      =					dropout
											__a					:						Dict      =					attention_dropout
											__a					:						List[Any]      =					max_position_embeddings
											__a					:						Any      =					initializer_range
											__a					:						Tuple      =					scope
											__a					:						Dict      =					bos_token_id



						def        UpperCAmelCase__					(self:					str   )    ->     Union[str, Any]:




											'''simple docstring'''



											__a					:						Optional[Any]      =					ids_tensor([self.batch_size, self.seq_length] ,					self.vocab_size   )

											__a					:						Any      =					None
											if self.use_input_mask:
																__a					:						Tuple      =					random_attention_mask([self.batch_size, self.seq_length]   )

											if input_mask is not None:
																__a					:						Tuple      =					input_mask.numpy()
																__a      ,			__a					:						int      =					input_mask.shape
																__a					:						Dict      =					np.random.randint(1 ,					seq_length - 1 ,					size=(batch_size,)   )
																for batch_idx, start_index in enumerate(__UpperCAmelCase   ):
																					__a					:						Any      =					1
																					__a					:						int      =					0

											__a					:						Union[str, Any]      =					self.get_config()

											return config, input_ids, tf.convert_to_tensor(__UpperCAmelCase   )



						def        UpperCAmelCase__					(self:					List[str]   )    ->     Optional[int]:




											'''simple docstring'''



											return BlipTextConfig(
											    vocab_size=self.vocab_size ,					hidden_size=self.hidden_size ,					projection_dim=self.projection_dim ,					num_hidden_layers=self.num_hidden_layers ,					num_attention_heads=self.num_attention_heads ,					intermediate_size=self.intermediate_size ,					dropout=self.dropout ,					attention_dropout=self.attention_dropout ,					max_position_embeddings=self.max_position_embeddings ,					initializer_range=self.initializer_range ,					bos_token_id=self.bos_token_id ,					)



						def        UpperCAmelCase__					(self:					List[str] ,					__UpperCAmelCase:					Any ,					__UpperCAmelCase:					Dict ,					__UpperCAmelCase:					str   )    ->     List[str]:




											'''simple docstring'''



											__a					:						int      =					TFBlipTextModel(config=__UpperCAmelCase   )
											__a					:						List[str]      =					model(__UpperCAmelCase ,					attention_mask=__UpperCAmelCase ,					training=__UpperCAmelCase   )
											__a					:						Any      =					model(__UpperCAmelCase ,					training=__UpperCAmelCase   )
											self.parent.assertEqual(result.last_hidden_state.shape ,					(self.batch_size, self.seq_length, self.hidden_size)   )
											self.parent.assertEqual(result.pooler_output.shape ,					(self.batch_size, self.hidden_size)   )



						def        UpperCAmelCase__					(self:					List[str]   )    ->     str:




											'''simple docstring'''



											__a					:						Tuple      =					self.prepare_config_and_inputs()
											__a      ,			__a      ,			__a					:						Dict      =					config_and_inputs
											__a					:						Union[str, Any]      =					{"input_ids": input_ids, "attention_mask": input_mask}
											return config, inputs_dict




@require_tf
class 							snake_case_       (    __UpperCamelCase		,							unittest.TestCase      ):

						"""simple docstring"""



						snake_case__        =      (TFBlipTextModel,) if is_tf_available() else ()
						snake_case__        =      False
						snake_case__        =      False
						snake_case__        =      False



						def        UpperCAmelCase__					(self:					List[str]   )    ->     Optional[int]:




											'''simple docstring'''



											__a					:						str      =					BlipTextModelTester(self   )
											__a					:						str      =					ConfigTester(self ,					config_class=__UpperCAmelCase ,					hidden_size=37   )



						def        UpperCAmelCase__					(self:					str   )    ->     str:




											'''simple docstring'''



											self.config_tester.run_common_tests()



						def        UpperCAmelCase__					(self:					Any   )    ->     Union[str, Any]:




											'''simple docstring'''



											__a					:						int      =					self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__UpperCAmelCase   )



						def        UpperCAmelCase__					(self:					Tuple   )    ->     Dict:




											'''simple docstring'''



											pass



						def        UpperCAmelCase__					(self:					Union[str, Any]   )    ->     Optional[int]:




											'''simple docstring'''



											pass



						@unittest.skip(reason="Blip does not use inputs_embeds"   )
						def        UpperCAmelCase__					(self:					Any   )    ->     Dict:




											'''simple docstring'''



											pass



						@unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING"   )
						def        UpperCAmelCase__					(self:					List[Any]   )    ->     List[str]:




											'''simple docstring'''



											pass



						@unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING"   )
						def        UpperCAmelCase__					(self:					Union[str, Any]   )    ->     int:




											'''simple docstring'''



											pass



						@slow
						def        UpperCAmelCase__					(self:					Optional[Any]   )    ->     Tuple:




											'''simple docstring'''



											for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__a					:						List[str]      =					TFBlipTextModel.from_pretrained(__UpperCAmelCase   )
																self.assertIsNotNone(__UpperCAmelCase   )



						def        UpperCAmelCase__					(self:					Dict ,					__UpperCAmelCase:					Optional[Any]=True   )    ->     Any:




											'''simple docstring'''



											super().test_pt_tf_model_equivalence(allow_missing_keys=__UpperCAmelCase   )





