





from manim import *

class 		snake_case_      (       UpperCAmelCase_	):







	'''simple docstring'''




	def       UpperCAmelCase (	self       :    Dict      )					->   List[Any]:

			'''simple docstring'''




			__lowercase					=    Rectangle(height=0.5				,			width=0.5      )
			__lowercase					=    Rectangle(height=0.46				,			width=0.46      ).set_stroke(width=0      )
			__lowercase					=    Rectangle(height=0.25				,			width=0.25      )

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

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

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

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

			__lowercase					=    []
			__lowercase					=    []

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

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

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

			self.add(*__lowerCamelCase				,			*__lowerCamelCase      )

			__lowercase					=    [meta_mem.copy() for i in range(6      )]
			__lowercase					=    [meta_mem.copy() for i in range(6      )]
			__lowercase					=    VGroup(*__lowerCamelCase      ).arrange(__lowerCamelCase				,			buff=0      )
			__lowercase					=    VGroup(*__lowerCamelCase      ).arrange(__lowerCamelCase				,			buff=0      )
			__lowercase					=    VGroup(__lowerCamelCase				,			__lowerCamelCase      ).arrange(__lowerCamelCase				,			buff=0      )
			__lowercase					=    Text('Disk'				,			font_size=24      )
			__lowercase					=    Group(__lowerCamelCase				,			__lowerCamelCase      ).arrange(__lowerCamelCase				,			buff=0.5				,			aligned_edge=__lowerCamelCase      )
			disk.move_to([-4.0, -1.25, 0]      )
			self.add(__lowerCamelCase				,			__lowerCamelCase      )

			__lowercase					=    []

			for i in range(6      ):
					__lowercase					=    fill.copy().set_fill(__lowerCamelCase				,			opacity=0.8      )
					target.move_to(disk_left_col_base[i]      ).scale(0.5      )
					cpu_disk_arr.append(__lowerCamelCase      )

			self.add(*__lowerCamelCase      )

			__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=18				,			)

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

			self.add(__lowerCamelCase				,			__lowerCamelCase      )

			__lowercase					=    MarkupText(
			    F"<span fgcolor='{BLUE}'>●</span> Checkpoint"				,			font_size=18				,			)

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

			__lowercase					=    MarkupText(F"The offloaded weights are all sent to the CPU."				,			font_size=24      )
			step_a.move_to([2, 2, 0]      )

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

			for i in range(6      ):
					__lowercase					=    cpu_disk_arr[i]
					__lowercase					=    rect.copy().set_fill(__lowerCamelCase				,			opacity=0.8      ).scale(2.0      )
					cpa.generate_target()
					cpa.target.move_to(model_base[i]      )

					if i == 0:
							rect.set_fill(__lowerCamelCase				,			opacity=0.8      )
							rect.generate_target()
							rect.target.move_to(cpu_left_col_base[0]      ).scale(2.0      )

							self.remove(
							    *__lowerCamelCase				,			*__lowerCamelCase				,			)

					else:
							rect.generate_target()
							rect.target.move_to(cpu_left_col_base[i]      ).scale(2.0      )
					self.play(MoveToTarget(__lowerCamelCase      )				,			MoveToTarget(__lowerCamelCase      )				,			model_base[i].animate.set_stroke(__lowerCamelCase      )      )
			self.play(FadeOut(__lowerCamelCase      )      )

			__lowercase					=    MarkupText(
			    F"Finally, hooks are added to each weight in the model\nto transfer the weights from CPU to GPU\n\t\tand back when needed."				,			font_size=24				,			)
			step_a.move_to([2, 2, 0]      )

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

			__lowercase					=    []
			__lowercase					=    []
			for i in range(6      ):
					__lowercase					=    Arrow(start=__lowerCamelCase				,			end=__lowerCamelCase				,			color=__lowerCamelCase				,			buff=0.5      )
					a.next_to(model_base[i].get_left()				,			__lowerCamelCase				,			buff=0.2      )
					arrows.append(__lowerCamelCase      )
					animations.append(Write(__lowerCamelCase      )      )
			self.play(*__lowerCamelCase      )
			self.wait()











import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


SCREAMING_SNAKE_CASE_      :			Any		  =      '''\
@inproceedings{ramshaw-marcus-1995-text,
    title = "Text Chunking using Transformation-Based Learning",
    author = "Ramshaw, Lance  and
      Marcus, Mitch",
    booktitle = "Third Workshop on Very Large Corpora",
    year = "1995",
    url = "https://www.aclweb.org/anthology/W95-0107",
}
@misc{seqeval,
  title={{seqeval}: A Python framework for sequence labeling evaluation},
  url={https://github.com/chakki-works/seqeval},
  note={Software available from https://github.com/chakki-works/seqeval},
  author={Hiroki Nakayama},
  year={2018},
}
'''

SCREAMING_SNAKE_CASE_      :			Tuple		  =      '''\
seqeval is a Python framework for sequence labeling evaluation.
seqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.

This is well-tested by using the Perl script conlleval, which can be used for
measuring the performance of a system that has processed the CoNLL-2000 shared task data.

seqeval supports following formats:
IOB1
IOB2
IOE1
IOE2
IOBES

See the [README.md] file at https://github.com/chakki-works/seqeval for more information.
'''

SCREAMING_SNAKE_CASE_      :			Dict		  =      '''
Produces labelling scores along with its sufficient statistics
from a source against one or more references.

Args:
    predictions: List of List of predicted labels (Estimated targets as returned by a tagger)
    references: List of List of reference labels (Ground truth (correct) target values)
    suffix: True if the IOB prefix is after type, False otherwise. default: False
    scheme: Specify target tagging scheme. Should be one of ["IOB1", "IOB2", "IOE1", "IOE2", "IOBES", "BILOU"].
        default: None
    mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.
        If you want to only count exact matches, pass mode="strict". default: None.
    sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None
    zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,
        "warn". "warn" acts as 0, but the warning is raised.

Returns:
    \'scores\': dict. Summary of the scores for overall and per type
        Overall:
            \'accuracy\': accuracy,
            \'precision\': precision,
            \'recall\': recall,
            \'f1\': F1 score, also known as balanced F-score or F-measure,
        Per type:
            \'precision\': precision,
            \'recall\': recall,
            \'f1\': F1 score, also known as balanced F-score or F-measure
Examples:

    >>> predictions = [[\'O\', \'O\', \'B-MISC\', \'I-MISC\', \'I-MISC\', \'I-MISC\', \'O\'], [\'B-PER\', \'I-PER\', \'O\']]
    >>> references = [[\'O\', \'O\', \'O\', \'B-MISC\', \'I-MISC\', \'I-MISC\', \'O\'], [\'B-PER\', \'I-PER\', \'O\']]
    >>> seqeval = datasets.load_metric("seqeval")
    >>> results = seqeval.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    [\'MISC\', \'PER\', \'overall_precision\', \'overall_recall\', \'overall_f1\', \'overall_accuracy\']
    >>> print(results["overall_f1"])
    0.5
    >>> print(results["PER"]["f1"])
    1.0
'''

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,    _KWARGS_DESCRIPTION	)
class 		snake_case_      (       datasets.Metric	):







	'''simple docstring'''




	def       UpperCAmelCase (	self       :    Any      )					->   str:

			'''simple docstring'''




			return datasets.MetricInfo(
			    description=_DESCRIPTION				,			citation=_CITATION				,			homepage='https://github.com/chakki-works/seqeval'				,			inputs_description=_KWARGS_DESCRIPTION				,			features=datasets.Features(
			        {
			            'predictions': datasets.Sequence(datasets.Value('string'				,			id='label'      )				,			id='sequence'      ),
			            'references': datasets.Sequence(datasets.Value('string'				,			id='label'      )				,			id='sequence'      ),
			        }      )				,			codebase_urls=['https://github.com/chakki-works/seqeval']				,			reference_urls=['https://github.com/chakki-works/seqeval']				,			)






	def       UpperCAmelCase (	self       :    Optional[Any]				,			__lowerCamelCase       :    int				,			__lowerCamelCase       :    str				,			__lowerCamelCase       :    bool = False				,			__lowerCamelCase       :    Optional[str] = None				,			__lowerCamelCase       :    Optional[str] = None				,			__lowerCamelCase       :    Optional[List[int]] = None				,			__lowerCamelCase       :    Union[str, int] = "warn"				,			)					->   Tuple:

			'''simple docstring'''




			if scheme is not None:
					try:
							__lowercase					=    importlib.import_module('seqeval.scheme'      )
							__lowercase					=    getattr(__lowerCamelCase				,			__lowerCamelCase      )
					except AttributeError:
							raise ValueError(F"Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}"      )
			__lowercase					=    classification_report(
			    y_true=__lowerCamelCase				,			y_pred=__lowerCamelCase				,			suffix=__lowerCamelCase				,			output_dict=__lowerCamelCase				,			scheme=__lowerCamelCase				,			mode=__lowerCamelCase				,			sample_weight=__lowerCamelCase				,			zero_division=__lowerCamelCase				,			)
			report.pop('macro avg'      )
			report.pop('weighted avg'      )
			__lowercase					=    report.pop('micro avg'      )

			__lowercase					=    {
			    type_name: {
			        'precision': score['precision'],
			        'recall': score['recall'],
			        'f1': score['f1-score'],
			        'number': score['support'],
			    }
			    for type_name, score in report.items()
			}
			__lowercase					=    overall_score['precision']
			__lowercase					=    overall_score['recall']
			__lowercase					=    overall_score['f1-score']
			__lowercase					=    accuracy_score(y_true=__lowerCamelCase				,			y_pred=__lowerCamelCase      )

			return scores




