




"""simple docstring"""

import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


a							      =     get_tests_dir('''fixtures''')
a							      =     get_tests_dir('''fixtures/dummy_feature_extractor_config.json''')
a							      =     get_tests_dir('''fixtures/dummy-config.json''')





class 							lowercase_			(	unittest.TestCase				):



 '''simple docstring'''

 def      lowerCAmelCase_				(					self			:			Union[str, Any]						):
       _A     =  0

 def      lowerCAmelCase_				(					self			:			Any						):
       _A     =  AutoFeatureExtractor.from_pretrained('facebook/wav2vec2-base-960h'						)
       self.assertIsInstance(_UpperCAmelCase       ,				_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			int						):
       _A     =  AutoFeatureExtractor.from_pretrained(_UpperCAmelCase						)
       self.assertIsInstance(_UpperCAmelCase       ,				_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Tuple						):
       with tempfile.TemporaryDirectory() as tmpdirname:
             _A     =  WavaVecaConfig()

             # remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
             _A     =  AutoFeatureExtractor.from_pretrained(_UpperCAmelCase						).to_dict()

             config_dict.pop('feature_extractor_type'						)
             _A     =  WavaVecaFeatureExtractor(**_UpperCAmelCase						)

             # save in new folder
             model_config.save_pretrained(_UpperCAmelCase						)
             config.save_pretrained(_UpperCAmelCase						)

             _A     =  AutoFeatureExtractor.from_pretrained(_UpperCAmelCase						)

             # make sure private variable is not incorrectly saved
             _A     =  json.loads(config.to_json_string()						)
             self.assertTrue('_processor_class' not in dict_as_saved						)

       self.assertIsInstance(_UpperCAmelCase       ,				_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Union[str, Any]						):
       _A     =  AutoFeatureExtractor.from_pretrained(_UpperCAmelCase						)
       self.assertIsInstance(_UpperCAmelCase       ,				_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			int						):
       with self.assertRaisesRegex(
           _UpperCAmelCase       ,				'bert-base is not a local folder and is not a valid model identifier'						):
             _A     =  AutoFeatureExtractor.from_pretrained('bert-base'						)

 def      lowerCAmelCase_				(					self			:			List[str]						):
       with self.assertRaisesRegex(
           _UpperCAmelCase       ,				r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'						):
             _A     =  AutoFeatureExtractor.from_pretrained(_UpperCAmelCase       ,				revision='aaaaaa'						)

 def      lowerCAmelCase_				(					self			:			str						):
       with self.assertRaisesRegex(
           _UpperCAmelCase       ,				'hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'       ,				):
             _A     =  AutoFeatureExtractor.from_pretrained('hf-internal-testing/config-no-model'						)

 def      lowerCAmelCase_				(					self			:			Any						):
       # If remote code is not set, we will time out when asking whether to load the model.
       with self.assertRaises(_UpperCAmelCase						):
             _A     =  AutoFeatureExtractor.from_pretrained(
                 'hf-internal-testing/test_dynamic_feature_extractor'						)
       # If remote code is disabled, we can't load this config.
       with self.assertRaises(_UpperCAmelCase						):
             _A     =  AutoFeatureExtractor.from_pretrained(
                 'hf-internal-testing/test_dynamic_feature_extractor'       ,				trust_remote_code=_UpperCAmelCase						)

       _A     =  AutoFeatureExtractor.from_pretrained(
           'hf-internal-testing/test_dynamic_feature_extractor'       ,				trust_remote_code=_UpperCAmelCase						)
       self.assertEqual(feature_extractor.__class__.__name__       ,				'NewFeatureExtractor'						)

       # Test feature extractor can be reloaded.
       with tempfile.TemporaryDirectory() as tmp_dir:
             feature_extractor.save_pretrained(_UpperCAmelCase						)
             _A     =  AutoFeatureExtractor.from_pretrained(_UpperCAmelCase       ,				trust_remote_code=_UpperCAmelCase						)
       self.assertEqual(reloaded_feature_extractor.__class__.__name__       ,				'NewFeatureExtractor'						)

 def      lowerCAmelCase_				(					self			:			List[Any]						):
       try:
             AutoConfig.register('custom'       ,				_UpperCAmelCase						)
             AutoFeatureExtractor.register(_UpperCAmelCase       ,				_UpperCAmelCase						)
             # Trying to register something existing in the Transformers library will raise an error
             with self.assertRaises(_UpperCAmelCase						):
                   AutoFeatureExtractor.register(_UpperCAmelCase       ,				_UpperCAmelCase						)

             # Now that the config is registered, it can be used as any other config with the auto-API
             _A     =  CustomFeatureExtractor.from_pretrained(_UpperCAmelCase						)
             with tempfile.TemporaryDirectory() as tmp_dir:
                   feature_extractor.save_pretrained(_UpperCAmelCase						)
                   _A     =  AutoFeatureExtractor.from_pretrained(_UpperCAmelCase						)
                   self.assertIsInstance(_UpperCAmelCase       ,				_UpperCAmelCase						)

       finally:
             if "custom" in CONFIG_MAPPING._extra_content:
                   del CONFIG_MAPPING._extra_content["custom"]
             if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
                   del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]



 def      lowerCAmelCase_				(					self			:			Optional[Any]						):





       class 							lowercase_			(	__lowerCAmelCase				):



        '''simple docstring'''
        UpperCAmelCase       :	int     =      True

       try:
             AutoConfig.register('custom'       ,				_UpperCAmelCase						)
             AutoFeatureExtractor.register(_UpperCAmelCase       ,				_UpperCAmelCase						)
             # If remote code is not set, the default is to use local
             _A     =  AutoFeatureExtractor.from_pretrained(
                 'hf-internal-testing/test_dynamic_feature_extractor'						)
             self.assertEqual(feature_extractor.__class__.__name__       ,				'NewFeatureExtractor'						)
             self.assertTrue(feature_extractor.is_local						)

             # If remote code is disabled, we load the local one.
             _A     =  AutoFeatureExtractor.from_pretrained(
                 'hf-internal-testing/test_dynamic_feature_extractor'       ,				trust_remote_code=_UpperCAmelCase						)
             self.assertEqual(feature_extractor.__class__.__name__       ,				'NewFeatureExtractor'						)
             self.assertTrue(feature_extractor.is_local						)

             # If remote is enabled, we load from the Hub
             _A     =  AutoFeatureExtractor.from_pretrained(
                 'hf-internal-testing/test_dynamic_feature_extractor'       ,				trust_remote_code=_UpperCAmelCase						)
             self.assertEqual(feature_extractor.__class__.__name__       ,				'NewFeatureExtractor'						)
             self.assertTrue(not hasattr(_UpperCAmelCase       ,				'is_local'						)						)

       finally:
             if "custom" in CONFIG_MAPPING._extra_content:
                   del CONFIG_MAPPING._extra_content["custom"]
             if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
                   del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]







"""simple docstring"""

from manim import *





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''

 def      lowerCAmelCase_				(					self			:			Union[str, Any]						):
       _A     =  Rectangle(height=0.5       ,				width=0.5						)
       _A     =  Rectangle(height=0.46       ,				width=0.46						).set_stroke(width=0						)

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

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

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

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

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

       _A     =  []
       _A     =  []

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

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

       self.add(*_UpperCAmelCase       ,				*_UpperCAmelCase						)

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

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

       _A     =  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(_UpperCAmelCase       ,				_UpperCAmelCase						)

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

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

       _A     =  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(_UpperCAmelCase						)						)

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

       self.play(Write(_UpperCAmelCase						)						)

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

       self.play(FadeOut(_UpperCAmelCase						)						)

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

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

       _A     =  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(_UpperCAmelCase       ,				run_time=3						)						)

       _A     =  {'run_time': 1, 'fade_in': True, 'fade_out': True, 'buff': 0.02}

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

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

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

             _A     =  AnimationGroup(
                 FadeOut(_UpperCAmelCase       ,				run_time=0.5						)       ,				MoveToTarget(_UpperCAmelCase       ,				run_time=0.5						)       ,				FadeIn(_UpperCAmelCase       ,				run_time=0.5						)       ,				lag_ratio=0.2						)

             self.play(_UpperCAmelCase						)

             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:
                         _A     =  0.7

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

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

             _A     =  a_c
             _A     =  a_c.copy()

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

       _A     =  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(_UpperCAmelCase       ,				run_time=3						)       ,				MoveToTarget(_UpperCAmelCase						)						)

       self.wait()

