
'''simple docstring'''






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		:
					def __init__(     self,    A,    A=12,    A=7,    A=True,    A=True,    A=True,    A=99,    A=32,    A=32,    A=2,    A=4,    A=37,    A=0.1,    A=0.1,    A=512,    A=0.02,    A=0,    A=None,    ):

									"""simple docstring"""


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

									"""simple docstring"""


									lowerCamelCase					:			int									= ids_tensor([self.batch_size, self.seq_length],    self.vocab_size      )

									lowerCamelCase					:			List[Any]									= None
									if self.use_input_mask:
													lowerCamelCase					:			str									= random_attention_mask([self.batch_size, self.seq_length]      )

									if input_mask is not None:
													lowerCamelCase					:			str									= input_mask.numpy()
													lowerCamelCase			, lowerCamelCase					:			List[Any]									= input_mask.shape
													lowerCamelCase					:			List[str]									= np.random.randint(1,    seq_length - 1,    size=(batch_size,)      )
													for batch_idx, start_index in enumerate(A      ):
																	lowerCamelCase					:			Union[str, Any]									= 1
																	lowerCamelCase					:			int									= 0

									lowerCamelCase					:			Any									= self.get_config()

									return config, input_ids, tf.convert_to_tensor(A      )
					def     UpperCAmelCase_							(     self      ):

									"""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,    A,    A,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Tuple									= TFBlipTextModel(config=A      )
									lowerCamelCase					:			List[str]									= model(A,    attention_mask=A,    training=A      )
									lowerCamelCase					:			Optional[int]									= model(A,    training=A      )
									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      ):

									"""simple docstring"""


									lowerCamelCase					:			Tuple									= self.prepare_config_and_inputs()
									lowerCamelCase			, lowerCamelCase			, lowerCamelCase					:			int									= config_and_inputs
									lowerCamelCase					:			List[str]									= {'input_ids': input_ids, 'attention_mask': input_mask}
									return config, inputs_dict





@require_tf
class     __snake_case		(   a__    ,   unittest.TestCase):
					_lowerCAmelCase			     =	(TFBlipTextModel,) if is_tf_available() else ()
					_lowerCAmelCase			     =	False
					_lowerCAmelCase			     =	False
					_lowerCAmelCase			     =	False
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[int]									= BlipTextModelTester(self      )
									lowerCamelCase					:			Union[str, Any]									= ConfigTester(self,    config_class=A,    hidden_size=37      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									self.config_tester.run_common_tests()
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Any									= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									pass
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									pass
					@unittest.skip(reason='Blip does not use inputs_embeds'      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									pass
					@unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									pass
					@unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									pass
					@slow
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowerCamelCase					:			Any									= TFBlipTextModel.from_pretrained(A      )
													self.assertIsNotNone(A      )




					def     UpperCAmelCase_							(     self,    A=True      ):

									"""simple docstring"""


									super().test_pt_tf_model_equivalence(allow_missing_keys=A      )


'''simple docstring'''






from manim import *




class     __snake_case		(   a__):
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[int]									= Rectangle(height=0.5,    width=0.5      )
									lowerCamelCase					:			List[Any]									= Rectangle(height=0.46,    width=0.46      ).set_stroke(width=0      )

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

									lowerCamelCase					:			Optional[int]									= [mem.copy() for i in range(1      )]
									lowerCamelCase					:			Union[str, Any]									= VGroup(*A      ).arrange(A,    buff=0      )
									lowerCamelCase					:			Optional[Any]									= Text('GPU',    font_size=24      )
									lowerCamelCase					:			Tuple									= Group(A,    A      ).arrange(A,    buff=0.5,    aligned_edge=A      )
									gpu.align_to(A,    A      )
									gpu.set_x(gpu.get_x() - 1      )

									self.add(A      )

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

									lowerCamelCase					:			Any									= Text('Model',    font_size=24      )
									lowerCamelCase					:			Tuple									= Group(A,    A      ).arrange(A,    buff=0.5,    aligned_edge=A      )
									model.move_to([3, -1.0, 0]      )

									self.play(
									    Create(A,    run_time=1      ),    Create(A,    run_time=1      ),    Create(A,    run_time=1      ),    )

									lowerCamelCase					:			str									= MarkupText(
									    F'''First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.''',    font_size=24,    )

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

									lowerCamelCase					:			Tuple									= MarkupText(
									    F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model''',    font_size=18,    )

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

									step_a.move_to([2, 2, 0]      )
									self.play(Write(A,    run_time=2.5      ),    Write(A      ),    Write(A      )      )

									self.add(A      )

									lowerCamelCase					:			str									= []
									lowerCamelCase					:			Optional[int]									= []
									lowerCamelCase					:			Optional[Any]									= []
									for i, rect in enumerate(A      ):
													lowerCamelCase					:			List[str]									= Rectangle(height=0.46,    width=0.46      ).set_stroke(width=0.0      ).set_fill(A,    opacity=0.7      )
													cpu_target.move_to(A      )
													cpu_target.generate_target()
													lowerCamelCase					:			int									= 0.46 / 4
													lowerCamelCase					:			Optional[int]									= 0.46 / 3

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

													first_animations.append(rect.animate(run_time=0.5      ).set_stroke(A      )      )
													second_animations.append(MoveToTarget(A,    run_time=1.5      )      )

									self.play(*A      )
									self.play(*A      )

									self.wait()
