


'''simple docstring'''

import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available


class       a__				( __A      ):







				"""simple docstring"""



				__UpperCamelCase    :		Any	=	['image_processor', 'tokenizer']
				__UpperCamelCase    :		str	=	'OwlViTImageProcessor'
				__UpperCamelCase    :		Tuple	=	('CLIPTokenizer', 'CLIPTokenizerFast')




				def __init__(self       ,			__lowercase=None       ,			__lowercase=None       ,			**__lowercase		):
										__lowerCAmelCase							       =				None
										if "feature_extractor" in kwargs:
																warnings.warn(
																    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																    ''' instead.'''       ,			__lowercase       ,			)
																__lowerCAmelCase							       =				kwargs.pop('''feature_extractor'''		)

										__lowerCAmelCase							       =				image_processor if image_processor is not None else feature_extractor
										if image_processor is None:
																raise ValueError('''You need to specify an `image_processor`.'''		)
										if tokenizer is None:
																raise ValueError('''You need to specify a `tokenizer`.'''		)

										super().__init__(__lowercase       ,			__lowercase		)




				def __call__(self       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase="max_length"       ,			__lowercase="np"       ,			**__lowercase		):

										if text is None and query_images is None and images is None:
																raise ValueError(
																    '''You have to specify at least one text or query image or image. All three cannot be none.'''		)

										if text is not None:
																if isinstance(__lowercase       ,			__lowercase		) or (isinstance(__lowercase       ,			__lowercase		) and not isinstance(text[0]       ,			__lowercase		)):
																						__lowerCAmelCase							       =				[self.tokenizer(__lowercase       ,			padding=__lowercase       ,			return_tensors=__lowercase       ,			**__lowercase		)]

																elif isinstance(__lowercase       ,			__lowercase		) and isinstance(text[0]       ,			__lowercase		):
																						__lowerCAmelCase							       =				[]

																						# Maximum number of queries across batch
																						__lowerCAmelCase							       =				max([len(__lowercase		) for t in text]		)

																						# Pad all batch samples to max number of text queries
																						for t in text:
																												if len(__lowercase		) != max_num_queries:
																																		__lowerCAmelCase							       =				t + [''' '''] * (max_num_queries - len(__lowercase		))

																												__lowerCAmelCase							       =				self.tokenizer(__lowercase       ,			padding=__lowercase       ,			return_tensors=__lowercase       ,			**__lowercase		)
																												encodings.append(__lowercase		)
																else:
																						raise TypeError('''Input text should be a string, a list of strings or a nested list of strings'''		)

																if return_tensors == "np":
																						__lowerCAmelCase							       =				np.concatenate([encoding['''input_ids'''] for encoding in encodings]       ,			axis=0		)
																						__lowerCAmelCase							       =				np.concatenate([encoding['''attention_mask'''] for encoding in encodings]       ,			axis=0		)

																elif return_tensors == "jax" and is_flax_available():
																						import jax.numpy as jnp

																						__lowerCAmelCase							       =				jnp.concatenate([encoding['''input_ids'''] for encoding in encodings]       ,			axis=0		)
																						__lowerCAmelCase							       =				jnp.concatenate([encoding['''attention_mask'''] for encoding in encodings]       ,			axis=0		)

																elif return_tensors == "pt" and is_torch_available():
																						import torch

																						__lowerCAmelCase							       =				torch.cat([encoding['''input_ids'''] for encoding in encodings]       ,			dim=0		)
																						__lowerCAmelCase							       =				torch.cat([encoding['''attention_mask'''] for encoding in encodings]       ,			dim=0		)

																elif return_tensors == "tf" and is_tf_available():
																						import tensorflow as tf

																						__lowerCAmelCase							       =				tf.stack([encoding['''input_ids'''] for encoding in encodings]       ,			axis=0		)
																						__lowerCAmelCase							       =				tf.stack([encoding['''attention_mask'''] for encoding in encodings]       ,			axis=0		)

																else:
																						raise ValueError('''Target return tensor type could not be returned'''		)

																__lowerCAmelCase							       =				BatchEncoding()
																__lowerCAmelCase							       =				input_ids
																__lowerCAmelCase							       =				attention_mask

										if query_images is not None:
																__lowerCAmelCase							       =				BatchEncoding()
																__lowerCAmelCase							       =				self.image_processor(
																    __lowercase       ,			return_tensors=__lowercase       ,			**__lowercase		).pixel_values
																__lowerCAmelCase							       =				query_pixel_values

										if images is not None:
																__lowerCAmelCase							       =				self.image_processor(__lowercase       ,			return_tensors=__lowercase       ,			**__lowercase		)

										if text is not None and images is not None:
																__lowerCAmelCase							       =				image_features.pixel_values
																return encoding
										elif query_images is not None and images is not None:
																__lowerCAmelCase							       =				image_features.pixel_values
																return encoding
										elif text is not None or query_images is not None:
																return encoding
										else:
																return BatchEncoding(data=dict(**__lowercase		)       ,			tensor_type=__lowercase		)




				def 					_snake_case  (self       ,			*__lowercase       ,			**__lowercase		):
										return self.image_processor.post_process(*__lowercase       ,			**__lowercase		)




				def 					_snake_case  (self       ,			*__lowercase       ,			**__lowercase		):
										return self.image_processor.post_process_object_detection(*__lowercase       ,			**__lowercase		)




				def 					_snake_case  (self       ,			*__lowercase       ,			**__lowercase		):
										return self.image_processor.post_process_image_guided_detection(*__lowercase       ,			**__lowercase		)




				def 					_snake_case  (self       ,			*__lowercase       ,			**__lowercase		):
										return self.tokenizer.batch_decode(*__lowercase       ,			**__lowercase		)




				def 					_snake_case  (self       ,			*__lowercase       ,			**__lowercase		):
										return self.tokenizer.decode(*__lowercase       ,			**__lowercase		)




				@property
				def 					_snake_case  (self		):
										warnings.warn(
										    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''       ,			__lowercase       ,			)
										return self.image_processor_class




				@property
				def 					_snake_case  (self		):
										warnings.warn(
										    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''       ,			__lowercase       ,			)
										return self.image_processor





'''simple docstring'''

from manim import *


class       a__				( __A      ):







				"""simple docstring"""



				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				Rectangle(height=0.5       ,			width=0.5		)
										__lowerCAmelCase							       =				Rectangle(height=0.4_6       ,			width=0.4_6		).set_stroke(width=0		)

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

										__lowerCAmelCase							       =				[mem.copy() for i in range(1		)]
										__lowerCAmelCase							       =				VGroup(*__lowercase		).arrange(__lowercase       ,			buff=0		)
										__lowerCAmelCase							       =				Text('''GPU'''       ,			font_size=24		)
										__lowerCAmelCase							       =				Group(__lowercase       ,			__lowercase		).arrange(__lowercase       ,			buff=0.5       ,			aligned_edge=__lowercase		)
										gpu.align_to(__lowercase       ,			__lowercase		)
										gpu.set_x(gpu.get_x() - 1		)

										self.add(__lowercase		)

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

										__lowerCAmelCase							       =				Text('''Model'''       ,			font_size=24		)
										__lowerCAmelCase							       =				Group(__lowercase       ,			__lowercase		).arrange(__lowercase       ,			buff=0.5       ,			aligned_edge=__lowercase		)
										model.move_to([3, -1.0, 0]		)

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

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

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

										__lowerCAmelCase							       =				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(__lowercase       ,			run_time=2.5		)       ,			Write(__lowercase		)       ,			Write(__lowercase		)		)

										self.add(__lowercase		)

										__lowerCAmelCase							       =				[]
										__lowerCAmelCase							       =				[]
										__lowerCAmelCase							       =				[]
										for i, rect in enumerate(__lowercase		):
																__lowerCAmelCase							       =				Rectangle(height=0.4_6       ,			width=0.4_6		).set_stroke(width=0.0		).set_fill(__lowercase       ,			opacity=0.7		)
																cpu_target.move_to(__lowercase		)
																cpu_target.generate_target()
																__lowerCAmelCase							       =				0.4_6 / 4
																__lowerCAmelCase							       =				0.4_6 / 3

																if i == 0:
																						cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT		)       ,			buff=0.0_2       ,			direction=__lowercase		)
																						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=__lowercase       ,			buff=0.0		)
																else:
																						cpu_target.target.next_to(cpu_targs[i - 1].target       ,			direction=__lowercase       ,			buff=0.0		)
																cpu_targs.append(__lowercase		)

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

										self.play(*__lowercase		)
										self.play(*__lowercase		)

										self.wait()

