

"""simple docstring"""





from manim import *




class 					_lowerCAmelCase  (	lowercase   ):







 """simple docstring"""







 def 					_lowercase (			self							:			Any	):
    __lowercase								=						Rectangle(height=0.5,					width=0.5	)
    __lowercase								=						Rectangle(height=0.25,					width=0.25	)
    __lowercase								=						Rectangle(height=0.46,					width=0.46	).set_stroke(width=0	)

    __lowercase								=						[mem.copy() for i in range(6	)]
    __lowercase								=						[mem.copy() for i in range(6	)]
    __lowercase								=						VGroup(*UpperCAmelCase__	).arrange(UpperCAmelCase__,					buff=0	)
    __lowercase								=						VGroup(*UpperCAmelCase__	).arrange(UpperCAmelCase__,					buff=0	)
    __lowercase								=						VGroup(UpperCAmelCase__,					UpperCAmelCase__	).arrange(UpperCAmelCase__,					buff=0	)
    __lowercase								=						Text("CPU",					font_size=2_4	)
    __lowercase								=						Group(UpperCAmelCase__,					UpperCAmelCase__	).arrange(UpperCAmelCase__,					buff=0.5,					aligned_edge=UpperCAmelCase__	)
    cpu.move_to([-2.5, -0.5, 0]	)
    self.add(UpperCAmelCase__	)

    __lowercase								=						[mem.copy() for i in range(4	)]
    __lowercase								=						VGroup(*UpperCAmelCase__	).arrange(UpperCAmelCase__,					buff=0	)
    __lowercase								=						Text("GPU",					font_size=2_4	)
    __lowercase								=						Group(UpperCAmelCase__,					UpperCAmelCase__	).arrange(UpperCAmelCase__,					buff=0.5,					aligned_edge=UpperCAmelCase__	)
    gpu.move_to([-1, -1, 0]	)
    self.add(UpperCAmelCase__	)

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

    __lowercase								=						Text("Model",					font_size=2_4	)
    __lowercase								=						Group(UpperCAmelCase__,					UpperCAmelCase__	).arrange(UpperCAmelCase__,					buff=0.5,					aligned_edge=UpperCAmelCase__	)
    model.move_to([3, -1.0, 0]	)
    self.add(UpperCAmelCase__	)

    __lowercase								=						[]
    __lowercase								=						[]
    __lowercase								=						[]

    for i, rect in enumerate(UpperCAmelCase__	):
       rect.set_stroke(UpperCAmelCase__	)

       __lowercase								=						Rectangle(height=0.46 / 4,					width=0.46 / 3	).set_stroke(width=0.0	).set_fill(UpperCAmelCase__,					opacity=0.7	)

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

    self.add(*UpperCAmelCase__,					*UpperCAmelCase__,					*UpperCAmelCase__	)

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

    __lowercase								=						Text("Loaded Checkpoint",					font_size=2_4	)
    __lowercase								=						Group(UpperCAmelCase__,					UpperCAmelCase__	).arrange(UpperCAmelCase__,					buff=0.5,					aligned_edge=UpperCAmelCase__	)
    checkpoint.move_to([3, 0.5, 0]	)

    self.add(UpperCAmelCase__	)

    __lowercase								=						[]
    __lowercase								=						[]

    for i, rect in enumerate(UpperCAmelCase__	):
       __lowercase								=						fill.copy().set_fill(UpperCAmelCase__,					opacity=0.7	)
       target.move_to(UpperCAmelCase__	)
       ckpt_arr.append(UpperCAmelCase__	)

       __lowercase								=						target.copy()
       if i < 5:
          cpu_target.move_to(cpu_left_col_base[i + 1]	)
       else:
          cpu_target.move_to(cpu_right_col_base[i - 5]	)
       ckpt_cpu_arr.append(UpperCAmelCase__	)
    self.add(*UpperCAmelCase__,					*UpperCAmelCase__	)

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

    __lowercase								=						MarkupText(
        F"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model""",					font_size=1_8,					)

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

    self.add(UpperCAmelCase__,					UpperCAmelCase__	)

    __lowercase								=						MarkupText(
        F"""<span fgcolor='{BLUE}'>●</span> Checkpoint""",					font_size=1_8,					)

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

    __lowercase								=						MarkupText(
        F"""Based on the passed in configuration, weights are stored in\na variety of np.memmaps on disk or to a particular device.""",					font_size=2_4,					)
    step_a.move_to([2, 2, 0]	)

    __lowercase								=						[meta_mem.copy() for i in range(6	)]
    __lowercase								=						[meta_mem.copy() for i in range(6	)]
    __lowercase								=						VGroup(*UpperCAmelCase__	).arrange(UpperCAmelCase__,					buff=0	)
    __lowercase								=						VGroup(*UpperCAmelCase__	).arrange(UpperCAmelCase__,					buff=0	)
    __lowercase								=						VGroup(UpperCAmelCase__,					UpperCAmelCase__	).arrange(UpperCAmelCase__,					buff=0	)
    __lowercase								=						Text("Disk",					font_size=2_4	)
    __lowercase								=						Group(UpperCAmelCase__,					UpperCAmelCase__	).arrange(UpperCAmelCase__,					buff=0.5,					aligned_edge=UpperCAmelCase__	)
    disk.move_to([-4.0, -1.25, 0]	)
    self.play(Write(UpperCAmelCase__,					run_time=3	),					Write(UpperCAmelCase__,					run_time=1	),					Create(UpperCAmelCase__,					run_time=1	)	)

    __lowercase								=						[]
    for i, rect in enumerate(UpperCAmelCase__	):
       __lowercase								=						rect.copy()
       target.generate_target()
       target.target.move_to(disk_left_col_base[i]	).scale(0.5	)
       animations.append(MoveToTarget(UpperCAmelCase__,					run_time=1.5	)	)
    self.play(*UpperCAmelCase__	)

    self.play(FadeOut(UpperCAmelCase__	)	)

    __lowercase								=						MarkupText(F"""Then, the checkpoint is removed from memory\nthrough garbage collection.""",					font_size=2_4	)
    step_a.move_to([2, 2, 0]	)

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

    self.play(
        FadeOut(UpperCAmelCase__,					UpperCAmelCase__,					*UpperCAmelCase__,					*UpperCAmelCase__	),					)

    self.wait()












from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import TFResNetForImageClassification, TFResNetModel
		from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor





class __lowerCamelCase :





	"""simple docstring"""






	def __init__(							self       ,    UpperCAmelCase       ,    UpperCAmelCase=3       ,    UpperCAmelCase=32       ,    UpperCAmelCase=3       ,    UpperCAmelCase=10       ,    UpperCAmelCase=[10, 20, 30, 40]       ,    UpperCAmelCase=[1, 1, 2, 1]       ,    UpperCAmelCase=True       ,    UpperCAmelCase=True       ,    UpperCAmelCase="relu"       ,    UpperCAmelCase=3       ,    UpperCAmelCase=None       ,    ):
							"""simple docstring"""







							_UpperCAmelCase									=			parent
							_UpperCAmelCase									=			batch_size
							_UpperCAmelCase									=			image_size
							_UpperCAmelCase									=			num_channels
							_UpperCAmelCase									=			embeddings_size
							_UpperCAmelCase									=			hidden_sizes
							_UpperCAmelCase									=			depths
							_UpperCAmelCase									=			is_training
							_UpperCAmelCase									=			use_labels
							_UpperCAmelCase									=			hidden_act
							_UpperCAmelCase									=			num_labels
							_UpperCAmelCase									=			scope
							_UpperCAmelCase									=			len(UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

							_UpperCAmelCase									=			None
							if self.use_labels:
													_UpperCAmelCase									=			ids_tensor([self.batch_size]       ,    self.num_labels    )

							_UpperCAmelCase									=			self.get_config()

							return config, pixel_values, labels

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							return ResNetConfig(
							    num_channels=self.num_channels       ,    embeddings_size=self.embeddings_size       ,    hidden_sizes=self.hidden_sizes       ,    depths=self.depths       ,    hidden_act=self.hidden_act       ,    num_labels=self.num_labels       ,    image_size=self.image_size       ,    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			TFResNetModel(config=UpperCAmelCase    )
							_UpperCAmelCase									=			model(UpperCAmelCase    )
							# expected last hidden states: B, C, H // 32, W // 32
							self.parent.assertEqual(
							    result.last_hidden_state.shape       ,    (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)       ,    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.num_labels
							_UpperCAmelCase									=			TFResNetForImageClassification(UpperCAmelCase    )
							_UpperCAmelCase									=			model(UpperCAmelCase       ,    labels=UpperCAmelCase    )
							self.parent.assertEqual(result.logits.shape       ,    (self.batch_size, self.num_labels)    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.prepare_config_and_inputs()
							_UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase									=			config_and_inputs
							_UpperCAmelCase									=			{'pixel_values': pixel_values}
							return config, inputs_dict





@require_tf
class __lowerCamelCase (      snake_case__     ,       snake_case__     ,       unittest.TestCase):





	"""simple docstring"""






	UpperCamelCase__								=      (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
	UpperCamelCase__								=      (
	    {"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
	    if is_tf_available()
	    else {}
	)

	UpperCamelCase__								=      False
	UpperCamelCase__								=      False
	UpperCamelCase__								=      False
	UpperCamelCase__								=      False
	UpperCamelCase__								=      False

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			TFResNetModelTester(self    )
							_UpperCAmelCase									=			ConfigTester(self       ,    config_class=UpperCAmelCase       ,    has_text_modality=UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							self.create_and_test_config_common_properties()
							self.config_tester.create_and_test_config_to_json_string()
							self.config_tester.create_and_test_config_to_json_file()
							self.config_tester.create_and_test_config_from_and_save_pretrained()
							self.config_tester.create_and_test_config_with_num_labels()
							self.config_tester.check_config_can_be_init_without_params()
							self.config_tester.check_config_arguments_init()

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							return

	@unittest.skip(reason='ResNet does not use inputs_embeds'    )
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							pass

	@unittest.skip(reason='ResNet does not support input and output embeddings'    )
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							pass

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase					, _UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													_UpperCAmelCase									=			model_class(UpperCAmelCase    )
													_UpperCAmelCase									=			inspect.signature(model.call    )
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													_UpperCAmelCase									=			[*signature.parameters.keys()]

													_UpperCAmelCase									=			['pixel_values']
													self.assertListEqual(arg_names[:1]       ,    UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							def check_hidden_states_output(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    ):
													_UpperCAmelCase									=			model_class(UpperCAmelCase    )
													_UpperCAmelCase									=			model(**self._prepare_for_class(UpperCAmelCase       ,    UpperCAmelCase    )    )

													_UpperCAmelCase									=			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

													_UpperCAmelCase									=			self.model_tester.num_stages
													self.assertEqual(len(UpperCAmelCase    )       ,    expected_num_stages + 1    )

													# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
													self.assertListEqual(
													    list(hidden_states[0].shape[-2:]    )       ,    [self.model_tester.image_size // 4, self.model_tester.image_size // 4]       ,    )

							_UpperCAmelCase					, _UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs_for_common()
							_UpperCAmelCase									=			['basic', 'bottleneck']
							for model_class in self.all_model_classes:
													for layer_type in layers_type:
																			_UpperCAmelCase									=			layer_type
																			_UpperCAmelCase									=			True
																			check_hidden_states_output(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    )

																			# check that output_hidden_states also work using config
																			del inputs_dict["output_hidden_states"]
																			_UpperCAmelCase									=			True

																			check_hidden_states_output(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase    )

	@slow
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													_UpperCAmelCase									=			TFResNetModel.from_pretrained(UpperCAmelCase    )
													self.assertIsNotNone(UpperCAmelCase    )



def __A    (      )->  Optional[Any]:





						"""simple docstring"""



						_UpperCAmelCase									=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
						return image





@require_tf
@require_vision
class __lowerCamelCase (      unittest.TestCase):





	"""simple docstring"""






	@cached_property
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							return (
							    AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]    )
							    if is_vision_available()
							    else None
							)

	@slow
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]    )

							_UpperCAmelCase									=			self.default_image_processor
							_UpperCAmelCase									=			prepare_img()
							_UpperCAmelCase									=			image_processor(images=UpperCAmelCase       ,    return_tensors='tf'    )

							# forward pass
							_UpperCAmelCase									=			model(**UpperCAmelCase    )

							# verify the logits
							_UpperCAmelCase									=			tf.TensorShape((1, 1000)    )
							self.assertEqual(outputs.logits.shape       ,    UpperCAmelCase    )

							_UpperCAmelCase									=			tf.constant([-11.10_69, -9.78_77, -8.37_77]    )

							self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()       ,    UpperCAmelCase       ,    atol=1e-4    )    )
