




"""simple docstring"""

import copy
import tempfile
import unittest

from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError

from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test





class 							lowercase_			(	unittest.TestCase				):



 '''simple docstring'''

 @parameterized.expand([(None,), ('foo.json',)]						)
 def      lowerCAmelCase_				(					self			:			Union[str, Any]       ,				_UpperCAmelCase			:			str						):
       _A     =  GenerationConfig(
           do_sample=_UpperCAmelCase       ,				temperature=0.7       ,				length_penalty=1.0       ,				bad_words_ids=[[1, 2, 3], [4, 5]]       ,				)
       with tempfile.TemporaryDirectory() as tmp_dir:
             config.save_pretrained(_UpperCAmelCase       ,				config_name=_UpperCAmelCase						)
             _A     =  GenerationConfig.from_pretrained(_UpperCAmelCase       ,				config_name=_UpperCAmelCase						)

       # Checks parameters that were specified
       self.assertEqual(loaded_config.do_sample       ,				_UpperCAmelCase						)
       self.assertEqual(loaded_config.temperature       ,				0.7						)
       self.assertEqual(loaded_config.length_penalty       ,				1.0						)
       self.assertEqual(loaded_config.bad_words_ids       ,				[[1, 2, 3], [4, 5]]						)

       # Checks parameters that were not specified (defaults)
       self.assertEqual(loaded_config.top_k       ,				50						)
       self.assertEqual(loaded_config.max_length       ,				20						)
       self.assertEqual(loaded_config.max_time       ,				_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Union[str, Any]						):
       _A     =  AutoConfig.from_pretrained('gpt2'						)
       _A     =  GenerationConfig.from_model_config(_UpperCAmelCase						)
       _A     =  GenerationConfig()

       # The generation config has loaded a few non-default parameters from the model config
       self.assertNotEqual(_UpperCAmelCase       ,				_UpperCAmelCase						)

       # One of those parameters is eos_token_id -- check if it matches
       self.assertNotEqual(generation_config_from_model.eos_token_id       ,				default_generation_config.eos_token_id						)
       self.assertEqual(generation_config_from_model.eos_token_id       ,				model_config.eos_token_id						)

 def      lowerCAmelCase_				(					self			:			Dict						):
       _A     =  GenerationConfig()
       _A     =  {
           'max_new_tokens': 1_024,
           'foo': 'bar',
       }
       _A     =  copy.deepcopy(_UpperCAmelCase						)
       _A     =  generation_config.update(**_UpperCAmelCase						)

       # update_kwargs was not modified (no side effects)
       self.assertEqual(_UpperCAmelCase       ,				_UpperCAmelCase						)

       # update_kwargs was used to update the config on valid attributes
       self.assertEqual(generation_config.max_new_tokens       ,				1_024						)

       # `.update()` returns a dictionary of unused kwargs
       self.assertEqual(_UpperCAmelCase       ,				{'foo': 'bar'}						)

 def      lowerCAmelCase_				(					self			:			str						):
       _A     =  GenerationConfig()
       _A     =  'bar'

       with tempfile.TemporaryDirectory('test-generation-config'						) as tmp_dir:
             generation_config.save_pretrained(_UpperCAmelCase						)

             _A     =  GenerationConfig.from_pretrained(_UpperCAmelCase						)
       # update_kwargs was used to update the config on valid attributes
       self.assertEqual(new_config.foo       ,				'bar'						)

       _A     =  GenerationConfig.from_model_config(_UpperCAmelCase						)
       assert not hasattr(_UpperCAmelCase       ,				'foo'						)  # no new kwargs should be initialized if from config



 def      lowerCAmelCase_				(					self			:			Any						):
       _A     =  GenerationConfig()
       self.assertEqual(default_config.temperature       ,				1.0						)
       self.assertEqual(default_config.do_sample       ,				_UpperCAmelCase						)
       self.assertEqual(default_config.num_beams       ,				1						)

       _A     =  GenerationConfig(
           do_sample=_UpperCAmelCase       ,				temperature=0.7       ,				length_penalty=1.0       ,				bad_words_ids=[[1, 2, 3], [4, 5]]       ,				)
       self.assertEqual(config.temperature       ,				0.7						)
       self.assertEqual(config.do_sample       ,				_UpperCAmelCase						)
       self.assertEqual(config.num_beams       ,				1						)

       with tempfile.TemporaryDirectory() as tmp_dir:
             config.save_pretrained(_UpperCAmelCase						)
             _A     =  GenerationConfig.from_pretrained(_UpperCAmelCase       ,				temperature=1.0						)

       self.assertEqual(loaded_config.temperature       ,				1.0						)
       self.assertEqual(loaded_config.do_sample       ,				_UpperCAmelCase						)
       self.assertEqual(loaded_config.num_beams       ,				1						)  # default value





@is_staging_test
class 							lowercase_			(	unittest.TestCase				):



 '''simple docstring'''

 @classmethod
 def      lowerCAmelCase_				(					cls			:			Any						):
       _A     =  TOKEN
       HfFolder.save_token(_UpperCAmelCase						)

 @classmethod
 def      lowerCAmelCase_				(					cls			:			str						):
       try:
             delete_repo(token=cls._token       ,				repo_id='test-generation-config'						)
       except HTTPError:
             pass

       try:
             delete_repo(token=cls._token       ,				repo_id='valid_org/test-generation-config-org'						)
       except HTTPError:
             pass

 def      lowerCAmelCase_				(					self			:			List[Any]						):
       _A     =  GenerationConfig(
           do_sample=_UpperCAmelCase       ,				temperature=0.7       ,				length_penalty=1.0       ,				)
       config.push_to_hub('test-generation-config'       ,				use_auth_token=self._token						)

       _A     =  GenerationConfig.from_pretrained(F'''{USER}/test-generation-config'''						)
       for k, v in config.to_dict().items():
             if k != "transformers_version":
                   self.assertEqual(_UpperCAmelCase       ,				getattr(_UpperCAmelCase       ,				_UpperCAmelCase						)						)

        # Reset repo
       delete_repo(token=self._token       ,				repo_id='test-generation-config'						)

       # Push to hub via save_pretrained
       with tempfile.TemporaryDirectory() as tmp_dir:
             config.save_pretrained(
                 _UpperCAmelCase       ,				repo_id='test-generation-config'       ,				push_to_hub=_UpperCAmelCase       ,				use_auth_token=self._token						)

       _A     =  GenerationConfig.from_pretrained(F'''{USER}/test-generation-config'''						)
       for k, v in config.to_dict().items():
             if k != "transformers_version":
                   self.assertEqual(_UpperCAmelCase       ,				getattr(_UpperCAmelCase       ,				_UpperCAmelCase						)						)



 def      lowerCAmelCase_				(					self			:			Any						):
       _A     =  GenerationConfig(
           do_sample=_UpperCAmelCase       ,				temperature=0.7       ,				length_penalty=1.0       ,				)
       config.push_to_hub('valid_org/test-generation-config-org'       ,				use_auth_token=self._token						)

       _A     =  GenerationConfig.from_pretrained('valid_org/test-generation-config-org'						)
       for k, v in config.to_dict().items():
             if k != "transformers_version":
                   self.assertEqual(_UpperCAmelCase       ,				getattr(_UpperCAmelCase       ,				_UpperCAmelCase						)						)

        # Reset repo
       delete_repo(token=self._token       ,				repo_id='valid_org/test-generation-config-org'						)

       # Push to hub via save_pretrained
       with tempfile.TemporaryDirectory() as tmp_dir:
             config.save_pretrained(
                 _UpperCAmelCase       ,				repo_id='valid_org/test-generation-config-org'       ,				push_to_hub=_UpperCAmelCase       ,				use_auth_token=self._token						)

       _A     =  GenerationConfig.from_pretrained('valid_org/test-generation-config-org'						)
       for k, v in config.to_dict().items():
             if k != "transformers_version":
                   self.assertEqual(_UpperCAmelCase       ,				getattr(_UpperCAmelCase       ,				_UpperCAmelCase						)						)







"""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()

