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'''simple docstring'''
from __future__ import annotations
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
_lowerCAmelCase =len(a__ ) // 2
# choose the middle 3 elements
_lowerCAmelCase =lst[m - 1 : m + 2]
# if middle element is peak
if three[1] > three[0] and three[1] > three[2]:
return three[1]
# if increasing, recurse on right
elif three[0] < three[2]:
if len(lst[:m] ) == 2:
m -= 1
return peak(lst[m:] )
# decreasing
else:
if len(lst[:m] ) == 2:
m += 1
return peak(lst[:m] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 58
|
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
'''facebook/data2vec-text-base''': '''https://huggingface.co/data2vec/resolve/main/config.json''',
}
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : List[str] = 'data2vec-text'
def __init__( self , __A=3_0522 , __A=768 , __A=12 , __A=12 , __A=3072 , __A="gelu" , __A=0.1 , __A=0.1 , __A=512 , __A=2 , __A=0.02 , __A=1E-12 , __A=1 , __A=0 , __A=2 , __A="absolute" , __A=True , __A=None , **__A , ) -> List[Any]:
super().__init__(pad_token_id=__A , bos_token_id=__A , eos_token_id=__A , **__A )
_lowerCAmelCase =vocab_size
_lowerCAmelCase =hidden_size
_lowerCAmelCase =num_hidden_layers
_lowerCAmelCase =num_attention_heads
_lowerCAmelCase =hidden_act
_lowerCAmelCase =intermediate_size
_lowerCAmelCase =hidden_dropout_prob
_lowerCAmelCase =attention_probs_dropout_prob
_lowerCAmelCase =max_position_embeddings
_lowerCAmelCase =type_vocab_size
_lowerCAmelCase =initializer_range
_lowerCAmelCase =layer_norm_eps
_lowerCAmelCase =position_embedding_type
_lowerCAmelCase =use_cache
_lowerCAmelCase =classifier_dropout
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
@property
def UpperCamelCase__ ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_lowerCAmelCase ={0: 'batch', 1: 'choice', 2: 'sequence'}
else:
_lowerCAmelCase ={0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
] )
| 58
| 1
|
'''simple docstring'''
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def UpperCamelCase__ ( a__ , a__ , a__ ):
'''simple docstring'''
_lowerCAmelCase =1.5
_lowerCAmelCase =int(factor * num_class_images )
_lowerCAmelCase =ClipClient(
url='https://knn.laion.ai/knn-service' , indice_name='laion_400m' , num_images=a__ , aesthetic_weight=0.1 )
os.makedirs(F'''{class_data_dir}/images''' , exist_ok=a__ )
if len(list(Path(F'''{class_data_dir}/images''' ).iterdir() ) ) >= num_class_images:
return
while True:
_lowerCAmelCase =client.query(text=a__ )
if len(a__ ) >= factor * num_class_images or num_images > 1E4:
break
else:
_lowerCAmelCase =int(factor * num_images )
_lowerCAmelCase =ClipClient(
url='https://knn.laion.ai/knn-service' , indice_name='laion_400m' , num_images=a__ , aesthetic_weight=0.1 , )
_lowerCAmelCase =0
_lowerCAmelCase =0
_lowerCAmelCase =tqdm(desc='downloading real regularization images' , total=a__ )
with open(F'''{class_data_dir}/caption.txt''' , 'w' ) as fa, open(F'''{class_data_dir}/urls.txt''' , 'w' ) as fa, open(
F'''{class_data_dir}/images.txt''' , 'w' ) as fa:
while total < num_class_images:
_lowerCAmelCase =class_images[count]
count += 1
try:
_lowerCAmelCase =requests.get(images['url'] )
if img.status_code == 2_0_0:
_lowerCAmelCase =Image.open(BytesIO(img.content ) )
with open(F'''{class_data_dir}/images/{total}.jpg''' , 'wb' ) as f:
f.write(img.content )
fa.write(images['caption'] + '\n' )
fa.write(images['url'] + '\n' )
fa.write(F'''{class_data_dir}/images/{total}.jpg''' + '\n' )
total += 1
pbar.update(1 )
else:
continue
except Exception:
continue
return
def UpperCamelCase__ ( ):
'''simple docstring'''
_lowerCAmelCase =argparse.ArgumentParser('' , add_help=a__ )
parser.add_argument('--class_prompt' , help='text prompt to retrieve images' , required=a__ , type=a__ )
parser.add_argument('--class_data_dir' , help='path to save images' , required=a__ , type=a__ )
parser.add_argument('--num_class_images' , help='number of images to download' , default=2_0_0 , type=a__ )
return parser.parse_args()
if __name__ == "__main__":
lowercase_ = parse_args()
retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)
| 58
|
'''simple docstring'''
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin
@skip_mps
class SCREAMING_SNAKE_CASE ( __lowercase , __lowercase , unittest.TestCase):
"""simple docstring"""
lowercase : List[Any] = IFPipeline
lowercase : Tuple = TEXT_TO_IMAGE_PARAMS - {'width', 'height', 'latents'}
lowercase : Union[str, Any] = TEXT_TO_IMAGE_BATCH_PARAMS
lowercase : int = PipelineTesterMixin.required_optional_params - {'latents'}
def UpperCamelCase__ ( self ) -> str:
return self._get_dummy_components()
def UpperCamelCase__ ( self , __A , __A=0 ) -> int:
if str(__A ).startswith('mps' ):
_lowerCAmelCase =torch.manual_seed(__A )
else:
_lowerCAmelCase =torch.Generator(device=__A ).manual_seed(__A )
_lowerCAmelCase ={
'prompt': 'A painting of a squirrel eating a burger',
'generator': generator,
'num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
def UpperCamelCase__ ( self ) -> Optional[Any]:
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != 'cuda' , reason='float16 requires CUDA' )
def UpperCamelCase__ ( self ) -> Tuple:
# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
super().test_save_load_floataa(expected_max_diff=1E-1 )
def UpperCamelCase__ ( self ) -> List[Any]:
self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )
def UpperCamelCase__ ( self ) -> str:
self._test_save_load_local()
def UpperCamelCase__ ( self ) -> Union[str, Any]:
self._test_inference_batch_single_identical(
expected_max_diff=1E-2 , )
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def UpperCamelCase__ ( self ) -> List[str]:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE ( unittest.TestCase):
"""simple docstring"""
def UpperCamelCase__ ( self ) -> Optional[int]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase__ ( self ) -> Optional[Any]:
# if
_lowerCAmelCase =IFPipeline.from_pretrained('DeepFloyd/IF-I-XL-v1.0' , variant='fp16' , torch_dtype=torch.floataa )
_lowerCAmelCase =IFSuperResolutionPipeline.from_pretrained(
'DeepFloyd/IF-II-L-v1.0' , variant='fp16' , torch_dtype=torch.floataa , text_encoder=__A , tokenizer=__A )
# pre compute text embeddings and remove T5 to save memory
pipe_a.text_encoder.to('cuda' )
_lowerCAmelCase , _lowerCAmelCase =pipe_a.encode_prompt('anime turtle' , device='cuda' )
del pipe_a.tokenizer
del pipe_a.text_encoder
gc.collect()
_lowerCAmelCase =None
_lowerCAmelCase =None
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if(__A , __A , __A , __A )
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# img2img
_lowerCAmelCase =IFImgaImgPipeline(**pipe_a.components )
_lowerCAmelCase =IFImgaImgSuperResolutionPipeline(**pipe_a.components )
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if_imgaimg(__A , __A , __A , __A )
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# inpainting
_lowerCAmelCase =IFInpaintingPipeline(**pipe_a.components )
_lowerCAmelCase =IFInpaintingSuperResolutionPipeline(**pipe_a.components )
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if_inpainting(__A , __A , __A , __A )
def UpperCamelCase__ ( self , __A , __A , __A , __A ) -> str:
# pipeline 1
_start_torch_memory_measurement()
_lowerCAmelCase =torch.Generator(device='cpu' ).manual_seed(0 )
_lowerCAmelCase =pipe_a(
prompt_embeds=__A , negative_prompt_embeds=__A , num_inference_steps=2 , generator=__A , output_type='np' , )
_lowerCAmelCase =output.images[0]
assert image.shape == (64, 64, 3)
_lowerCAmelCase =torch.cuda.max_memory_allocated()
assert mem_bytes < 13 * 10**9
_lowerCAmelCase =load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy' )
assert_mean_pixel_difference(__A , __A )
# pipeline 2
_start_torch_memory_measurement()
_lowerCAmelCase =torch.Generator(device='cpu' ).manual_seed(0 )
_lowerCAmelCase =floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(__A )
_lowerCAmelCase =pipe_a(
prompt_embeds=__A , negative_prompt_embeds=__A , image=__A , generator=__A , num_inference_steps=2 , output_type='np' , )
_lowerCAmelCase =output.images[0]
assert image.shape == (256, 256, 3)
_lowerCAmelCase =torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
_lowerCAmelCase =load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy' )
assert_mean_pixel_difference(__A , __A )
def UpperCamelCase__ ( self , __A , __A , __A , __A ) -> Optional[int]:
# pipeline 1
_start_torch_memory_measurement()
_lowerCAmelCase =floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(__A )
_lowerCAmelCase =torch.Generator(device='cpu' ).manual_seed(0 )
_lowerCAmelCase =pipe_a(
prompt_embeds=__A , negative_prompt_embeds=__A , image=__A , num_inference_steps=2 , generator=__A , output_type='np' , )
_lowerCAmelCase =output.images[0]
assert image.shape == (64, 64, 3)
_lowerCAmelCase =torch.cuda.max_memory_allocated()
assert mem_bytes < 10 * 10**9
_lowerCAmelCase =load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy' )
assert_mean_pixel_difference(__A , __A )
# pipeline 2
_start_torch_memory_measurement()
_lowerCAmelCase =torch.Generator(device='cpu' ).manual_seed(0 )
_lowerCAmelCase =floats_tensor((1, 3, 256, 256) , rng=random.Random(0 ) ).to(__A )
_lowerCAmelCase =floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(__A )
_lowerCAmelCase =pipe_a(
prompt_embeds=__A , negative_prompt_embeds=__A , image=__A , original_image=__A , generator=__A , num_inference_steps=2 , output_type='np' , )
_lowerCAmelCase =output.images[0]
assert image.shape == (256, 256, 3)
_lowerCAmelCase =torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
_lowerCAmelCase =load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy' )
assert_mean_pixel_difference(__A , __A )
def UpperCamelCase__ ( self , __A , __A , __A , __A ) -> Dict:
# pipeline 1
_start_torch_memory_measurement()
_lowerCAmelCase =floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(__A )
_lowerCAmelCase =floats_tensor((1, 3, 64, 64) , rng=random.Random(1 ) ).to(__A )
_lowerCAmelCase =torch.Generator(device='cpu' ).manual_seed(0 )
_lowerCAmelCase =pipe_a(
prompt_embeds=__A , negative_prompt_embeds=__A , image=__A , mask_image=__A , num_inference_steps=2 , generator=__A , output_type='np' , )
_lowerCAmelCase =output.images[0]
assert image.shape == (64, 64, 3)
_lowerCAmelCase =torch.cuda.max_memory_allocated()
assert mem_bytes < 10 * 10**9
_lowerCAmelCase =load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy' )
assert_mean_pixel_difference(__A , __A )
# pipeline 2
_start_torch_memory_measurement()
_lowerCAmelCase =torch.Generator(device='cpu' ).manual_seed(0 )
_lowerCAmelCase =floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(__A )
_lowerCAmelCase =floats_tensor((1, 3, 256, 256) , rng=random.Random(0 ) ).to(__A )
_lowerCAmelCase =floats_tensor((1, 3, 256, 256) , rng=random.Random(1 ) ).to(__A )
_lowerCAmelCase =pipe_a(
prompt_embeds=__A , negative_prompt_embeds=__A , image=__A , mask_image=__A , original_image=__A , generator=__A , num_inference_steps=2 , output_type='np' , )
_lowerCAmelCase =output.images[0]
assert image.shape == (256, 256, 3)
_lowerCAmelCase =torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
_lowerCAmelCase =load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy' )
assert_mean_pixel_difference(__A , __A )
def UpperCamelCase__ ( ):
'''simple docstring'''
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
| 58
| 1
|
'''simple docstring'''
def UpperCamelCase__ ( a__ , a__ ):
'''simple docstring'''
_lowerCAmelCase =len(a__ )
_lowerCAmelCase =[]
for i in range(len(a__ ) - pat_len + 1 ):
_lowerCAmelCase =True
for j in range(a__ ):
if s[i + j] != pattern[j]:
_lowerCAmelCase =False
break
if match_found:
position.append(a__ )
return position
if __name__ == "__main__":
assert naive_pattern_search('''ABCDEFG''', '''DE''') == [3]
print(naive_pattern_search('''ABAAABCDBBABCDDEBCABC''', '''ABC'''))
| 58
|
'''simple docstring'''
import unittest
from knapsack import knapsack as k
class SCREAMING_SNAKE_CASE ( unittest.TestCase):
"""simple docstring"""
def UpperCamelCase__ ( self ) -> Optional[Any]:
_lowerCAmelCase =0
_lowerCAmelCase =[0]
_lowerCAmelCase =[0]
_lowerCAmelCase =len(__A )
self.assertEqual(k.knapsack(__A , __A , __A , __A ) , 0 )
_lowerCAmelCase =[60]
_lowerCAmelCase =[10]
_lowerCAmelCase =len(__A )
self.assertEqual(k.knapsack(__A , __A , __A , __A ) , 0 )
def UpperCamelCase__ ( self ) -> Tuple:
_lowerCAmelCase =3
_lowerCAmelCase =[1, 2, 3]
_lowerCAmelCase =[3, 2, 1]
_lowerCAmelCase =len(__A )
self.assertEqual(k.knapsack(__A , __A , __A , __A ) , 5 )
def UpperCamelCase__ ( self ) -> Union[str, Any]:
_lowerCAmelCase =50
_lowerCAmelCase =[60, 100, 120]
_lowerCAmelCase =[10, 20, 30]
_lowerCAmelCase =len(__A )
self.assertEqual(k.knapsack(__A , __A , __A , __A ) , 220 )
if __name__ == "__main__":
unittest.main()
| 58
| 1
|
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase_ = {
'''configuration_vivit''': ['''VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VivitConfig'''],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = ['''VivitImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
'''VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''VivitModel''',
'''VivitPreTrainedModel''',
'''VivitForVideoClassification''',
]
if TYPE_CHECKING:
from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_vivit import VivitImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vivit import (
VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
VivitForVideoClassification,
VivitModel,
VivitPreTrainedModel,
)
else:
import sys
lowercase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 58
|
'''simple docstring'''
lowercase_ = '''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
lowercase_ = [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
lowercase_ = {
'''{processor_class}''': '''FakeProcessorClass''',
'''{model_class}''': '''FakeModelClass''',
'''{object_class}''': '''FakeObjectClass''',
}
| 58
| 1
|
'''simple docstring'''
import unittest
from knapsack import knapsack as k
class SCREAMING_SNAKE_CASE ( unittest.TestCase):
"""simple docstring"""
def UpperCamelCase__ ( self ) -> Optional[Any]:
_lowerCAmelCase =0
_lowerCAmelCase =[0]
_lowerCAmelCase =[0]
_lowerCAmelCase =len(__A )
self.assertEqual(k.knapsack(__A , __A , __A , __A ) , 0 )
_lowerCAmelCase =[60]
_lowerCAmelCase =[10]
_lowerCAmelCase =len(__A )
self.assertEqual(k.knapsack(__A , __A , __A , __A ) , 0 )
def UpperCamelCase__ ( self ) -> Tuple:
_lowerCAmelCase =3
_lowerCAmelCase =[1, 2, 3]
_lowerCAmelCase =[3, 2, 1]
_lowerCAmelCase =len(__A )
self.assertEqual(k.knapsack(__A , __A , __A , __A ) , 5 )
def UpperCamelCase__ ( self ) -> Union[str, Any]:
_lowerCAmelCase =50
_lowerCAmelCase =[60, 100, 120]
_lowerCAmelCase =[10, 20, 30]
_lowerCAmelCase =len(__A )
self.assertEqual(k.knapsack(__A , __A , __A , __A ) , 220 )
if __name__ == "__main__":
unittest.main()
| 58
|
'''simple docstring'''
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json
lowercase_ = '''sshleifer/mar_enro_6_3_student'''
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
def UpperCamelCase__ ( self ) -> Optional[Any]:
super().setUp()
_lowerCAmelCase =cached_path(
'https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz' , extract_compressed_file=__A , )
_lowerCAmelCase =F'''{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'''
@slow
@require_torch_gpu
def UpperCamelCase__ ( self ) -> Union[str, Any]:
MarianMTModel.from_pretrained(__A )
@slow
@require_torch_gpu
def UpperCamelCase__ ( self ) -> Union[str, Any]:
_lowerCAmelCase ={
'$MAX_LEN': 64,
'$BS': 64,
'$GAS': 1,
'$ENRO_DIR': self.data_dir,
'facebook/mbart-large-cc25': MARIAN_MODEL,
# "val_check_interval=0.25": "val_check_interval=1.0",
'--learning_rate=3e-5': '--learning_rate 3e-4',
'--num_train_epochs 6': '--num_train_epochs 1',
}
# Clean up bash script
_lowerCAmelCase =(self.test_file_dir / 'train_mbart_cc25_enro.sh').open().read().split('finetune.py' )[1].strip()
_lowerCAmelCase =bash_script.replace('\\\n' , '' ).strip().replace('"$@"' , '' )
for k, v in env_vars_to_replace.items():
_lowerCAmelCase =bash_script.replace(__A , str(__A ) )
_lowerCAmelCase =self.get_auto_remove_tmp_dir()
# bash_script = bash_script.replace("--fp16 ", "")
_lowerCAmelCase =F'''
--output_dir {output_dir}
--tokenizer_name Helsinki-NLP/opus-mt-en-ro
--sortish_sampler
--do_predict
--gpus 1
--freeze_encoder
--n_train 40000
--n_val 500
--n_test 500
--fp16_opt_level O1
--num_sanity_val_steps 0
--eval_beams 2
'''.split()
# XXX: args.gpus > 1 : handle multi_gpu in the future
_lowerCAmelCase =['finetune.py'] + bash_script.split() + args
with patch.object(__A , 'argv' , __A ):
_lowerCAmelCase =argparse.ArgumentParser()
_lowerCAmelCase =pl.Trainer.add_argparse_args(__A )
_lowerCAmelCase =SummarizationModule.add_model_specific_args(__A , os.getcwd() )
_lowerCAmelCase =parser.parse_args()
_lowerCAmelCase =main(__A )
# Check metrics
_lowerCAmelCase =load_json(model.metrics_save_path )
_lowerCAmelCase =metrics['val'][0]
_lowerCAmelCase =metrics['val'][-1]
self.assertEqual(len(metrics['val'] ) , (args.max_epochs / args.val_check_interval) )
assert isinstance(last_step_stats[F'''val_avg_{model.val_metric}'''] , __A )
self.assertGreater(last_step_stats['val_avg_gen_time'] , 0.01 )
# model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
self.assertLessEqual(last_step_stats['val_avg_gen_time'] , 1.0 )
# test learning requirements:
# 1. BLEU improves over the course of training by more than 2 pts
self.assertGreater(last_step_stats['val_avg_bleu'] - first_step_stats['val_avg_bleu'] , 2 )
# 2. BLEU finishes above 17
self.assertGreater(last_step_stats['val_avg_bleu'] , 17 )
# 3. test BLEU and val BLEU within ~1.1 pt.
self.assertLess(abs(metrics['val'][-1]['val_avg_bleu'] - metrics['test'][-1]['test_avg_bleu'] ) , 1.1 )
# check lightning ckpt can be loaded and has a reasonable statedict
_lowerCAmelCase =os.listdir(__A )
_lowerCAmelCase =[x for x in contents if x.endswith('.ckpt' )][0]
_lowerCAmelCase =os.path.join(args.output_dir , __A )
_lowerCAmelCase =torch.load(__A , map_location='cpu' )
_lowerCAmelCase ='model.model.decoder.layers.0.encoder_attn_layer_norm.weight'
assert expected_key in ckpt["state_dict"]
assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa
# TODO: turn on args.do_predict when PL bug fixed.
if args.do_predict:
_lowerCAmelCase ={os.path.basename(__A ) for p in contents}
assert "test_generations.txt" in contents
assert "test_results.txt" in contents
# assert len(metrics["val"]) == desired_n_evals
assert len(metrics['test'] ) == 1
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
@timeout_decorator.timeout(600 )
@slow
@require_torch_gpu
def UpperCamelCase__ ( self ) -> Tuple:
_lowerCAmelCase =F'''{self.test_file_dir_str}/test_data/wmt_en_ro'''
_lowerCAmelCase ={
'--fp16_opt_level=O1': '',
'$MAX_LEN': 128,
'$BS': 16,
'$GAS': 1,
'$ENRO_DIR': data_dir,
'$m': 'sshleifer/student_marian_en_ro_6_1',
'val_check_interval=0.25': 'val_check_interval=1.0',
}
# Clean up bash script
_lowerCAmelCase =(
(self.test_file_dir / 'distil_marian_no_teacher.sh').open().read().split('distillation.py' )[1].strip()
)
_lowerCAmelCase =bash_script.replace('\\\n' , '' ).strip().replace('"$@"' , '' )
_lowerCAmelCase =bash_script.replace('--fp16 ' , ' ' )
for k, v in env_vars_to_replace.items():
_lowerCAmelCase =bash_script.replace(__A , str(__A ) )
_lowerCAmelCase =self.get_auto_remove_tmp_dir()
_lowerCAmelCase =bash_script.replace('--fp16' , '' )
_lowerCAmelCase =6
_lowerCAmelCase =(
['distillation.py']
+ bash_script.split()
+ [
F'''--output_dir={output_dir}''',
'--gpus=1',
'--learning_rate=1e-3',
F'''--num_train_epochs={epochs}''',
'--warmup_steps=10',
'--val_check_interval=1.0',
'--do_predict',
]
)
with patch.object(__A , 'argv' , __A ):
_lowerCAmelCase =argparse.ArgumentParser()
_lowerCAmelCase =pl.Trainer.add_argparse_args(__A )
_lowerCAmelCase =SummarizationDistiller.add_model_specific_args(__A , os.getcwd() )
_lowerCAmelCase =parser.parse_args()
# assert args.gpus == gpus THIS BREAKS for multi_gpu
_lowerCAmelCase =distill_main(__A )
# Check metrics
_lowerCAmelCase =load_json(model.metrics_save_path )
_lowerCAmelCase =metrics['val'][0]
_lowerCAmelCase =metrics['val'][-1]
assert len(metrics['val'] ) >= (args.max_epochs / args.val_check_interval) # +1 accounts for val_sanity_check
assert last_step_stats["val_avg_gen_time"] >= 0.01
assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"] # model learned nothing
assert 1.0 >= last_step_stats["val_avg_gen_time"] # model hanging on generate. Maybe bad config was saved.
assert isinstance(last_step_stats[F'''val_avg_{model.val_metric}'''] , __A )
# check lightning ckpt can be loaded and has a reasonable statedict
_lowerCAmelCase =os.listdir(__A )
_lowerCAmelCase =[x for x in contents if x.endswith('.ckpt' )][0]
_lowerCAmelCase =os.path.join(args.output_dir , __A )
_lowerCAmelCase =torch.load(__A , map_location='cpu' )
_lowerCAmelCase ='model.model.decoder.layers.0.encoder_attn_layer_norm.weight'
assert expected_key in ckpt["state_dict"]
assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa
# TODO: turn on args.do_predict when PL bug fixed.
if args.do_predict:
_lowerCAmelCase ={os.path.basename(__A ) for p in contents}
assert "test_generations.txt" in contents
assert "test_results.txt" in contents
# assert len(metrics["val"]) == desired_n_evals
assert len(metrics['test'] ) == 1
| 58
| 1
|
'''simple docstring'''
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __A ) -> None:
_lowerCAmelCase =set_counts
_lowerCAmelCase =max(__A )
_lowerCAmelCase =len(__A )
_lowerCAmelCase =[1] * num_sets
_lowerCAmelCase =list(range(__A ) )
def UpperCamelCase__ ( self , __A , __A ) -> bool:
_lowerCAmelCase =self.get_parent(__A )
_lowerCAmelCase =self.get_parent(__A )
if src_parent == dst_parent:
return False
if self.ranks[dst_parent] >= self.ranks[src_parent]:
self.set_counts[dst_parent] += self.set_counts[src_parent]
_lowerCAmelCase =0
_lowerCAmelCase =dst_parent
if self.ranks[dst_parent] == self.ranks[src_parent]:
self.ranks[dst_parent] += 1
_lowerCAmelCase =self.set_counts[dst_parent]
else:
self.set_counts[src_parent] += self.set_counts[dst_parent]
_lowerCAmelCase =0
_lowerCAmelCase =src_parent
_lowerCAmelCase =self.set_counts[src_parent]
_lowerCAmelCase =max(self.max_set , __A )
return True
def UpperCamelCase__ ( self , __A ) -> int:
if self.parents[disj_set] == disj_set:
return disj_set
_lowerCAmelCase =self.get_parent(self.parents[disj_set] )
return self.parents[disj_set]
| 58
|
'''simple docstring'''
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors
lowercase_ = logging.getLogger(__name__)
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : int = 'sequence-classification'
def __init__( self , __A ) -> List[Any]:
if type(__A ) == dict:
_lowerCAmelCase =Namespace(**__A )
_lowerCAmelCase =glue_output_modes[hparams.task]
_lowerCAmelCase =glue_tasks_num_labels[hparams.task]
super().__init__(__A , __A , self.mode )
def UpperCamelCase__ ( self , **__A ) -> Any:
return self.model(**__A )
def UpperCamelCase__ ( self , __A , __A ) -> Union[str, Any]:
_lowerCAmelCase ={'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
_lowerCAmelCase =batch[2] if self.config.model_type in ['bert', 'xlnet', 'albert'] else None
_lowerCAmelCase =self(**__A )
_lowerCAmelCase =outputs[0]
_lowerCAmelCase =self.trainer.lr_schedulers[0]['scheduler']
_lowerCAmelCase ={'loss': loss, 'rate': lr_scheduler.get_last_lr()[-1]}
return {"loss": loss, "log": tensorboard_logs}
def UpperCamelCase__ ( self ) -> Any:
_lowerCAmelCase =self.hparams
_lowerCAmelCase =processors[args.task]()
_lowerCAmelCase =processor.get_labels()
for mode in ["train", "dev"]:
_lowerCAmelCase =self._feature_file(__A )
if os.path.exists(__A ) and not args.overwrite_cache:
logger.info('Loading features from cached file %s' , __A )
else:
logger.info('Creating features from dataset file at %s' , args.data_dir )
_lowerCAmelCase =(
processor.get_dev_examples(args.data_dir )
if mode == 'dev'
else processor.get_train_examples(args.data_dir )
)
_lowerCAmelCase =convert_examples_to_features(
__A , self.tokenizer , max_length=args.max_seq_length , label_list=self.labels , output_mode=args.glue_output_mode , )
logger.info('Saving features into cached file %s' , __A )
torch.save(__A , __A )
def UpperCamelCase__ ( self , __A , __A , __A = False ) -> DataLoader:
_lowerCAmelCase ='dev' if mode == 'test' else mode
_lowerCAmelCase =self._feature_file(__A )
logger.info('Loading features from cached file %s' , __A )
_lowerCAmelCase =torch.load(__A )
_lowerCAmelCase =torch.tensor([f.input_ids for f in features] , dtype=torch.long )
_lowerCAmelCase =torch.tensor([f.attention_mask for f in features] , dtype=torch.long )
_lowerCAmelCase =torch.tensor([f.token_type_ids for f in features] , dtype=torch.long )
if self.hparams.glue_output_mode == "classification":
_lowerCAmelCase =torch.tensor([f.label for f in features] , dtype=torch.long )
elif self.hparams.glue_output_mode == "regression":
_lowerCAmelCase =torch.tensor([f.label for f in features] , dtype=torch.float )
return DataLoader(
TensorDataset(__A , __A , __A , __A ) , batch_size=__A , shuffle=__A , )
def UpperCamelCase__ ( self , __A , __A ) -> List[str]:
_lowerCAmelCase ={'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
_lowerCAmelCase =batch[2] if self.config.model_type in ['bert', 'xlnet', 'albert'] else None
_lowerCAmelCase =self(**__A )
_lowerCAmelCase , _lowerCAmelCase =outputs[:2]
_lowerCAmelCase =logits.detach().cpu().numpy()
_lowerCAmelCase =inputs['labels'].detach().cpu().numpy()
return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
def UpperCamelCase__ ( self , __A ) -> tuple:
_lowerCAmelCase =torch.stack([x['val_loss'] for x in outputs] ).mean().detach().cpu().item()
_lowerCAmelCase =np.concatenate([x['pred'] for x in outputs] , axis=0 )
if self.hparams.glue_output_mode == "classification":
_lowerCAmelCase =np.argmax(__A , axis=1 )
elif self.hparams.glue_output_mode == "regression":
_lowerCAmelCase =np.squeeze(__A )
_lowerCAmelCase =np.concatenate([x['target'] for x in outputs] , axis=0 )
_lowerCAmelCase =[[] for _ in range(out_label_ids.shape[0] )]
_lowerCAmelCase =[[] for _ in range(out_label_ids.shape[0] )]
_lowerCAmelCase ={**{'val_loss': val_loss_mean}, **compute_metrics(self.hparams.task , __A , __A )}
_lowerCAmelCase =dict(results.items() )
_lowerCAmelCase =results
return ret, preds_list, out_label_list
def UpperCamelCase__ ( self , __A ) -> dict:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase =self._eval_end(__A )
_lowerCAmelCase =ret['log']
return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
def UpperCamelCase__ ( self , __A ) -> dict:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase =self._eval_end(__A )
_lowerCAmelCase =ret['log']
# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
@staticmethod
def UpperCamelCase__ ( __A , __A ) -> Any:
BaseTransformer.add_model_specific_args(__A , __A )
parser.add_argument(
'--max_seq_length' , default=128 , type=__A , help=(
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
) , )
parser.add_argument(
'--task' , default='' , type=__A , required=__A , help='The GLUE task to run' , )
parser.add_argument(
'--gpus' , default=0 , type=__A , help='The number of GPUs allocated for this, it is by default 0 meaning none' , )
parser.add_argument(
'--overwrite_cache' , action='store_true' , help='Overwrite the cached training and evaluation sets' )
return parser
def UpperCamelCase__ ( ):
'''simple docstring'''
_lowerCAmelCase =argparse.ArgumentParser()
add_generic_args(a__ , os.getcwd() )
_lowerCAmelCase =GLUETransformer.add_model_specific_args(a__ , os.getcwd() )
_lowerCAmelCase =parser.parse_args()
# If output_dir not provided, a folder will be generated in pwd
if args.output_dir is None:
_lowerCAmelCase =os.path.join(
'./results' , F'''{args.task}_{time.strftime('%Y%m%d_%H%M%S' )}''' , )
os.makedirs(args.output_dir )
_lowerCAmelCase =GLUETransformer(a__ )
_lowerCAmelCase =generic_train(a__ , a__ )
# Optionally, predict on dev set and write to output_dir
if args.do_predict:
_lowerCAmelCase =sorted(glob.glob(os.path.join(args.output_dir , 'checkpoint-epoch=*.ckpt' ) , recursive=a__ ) )
_lowerCAmelCase =model.load_from_checkpoint(checkpoints[-1] )
return trainer.test(a__ )
if __name__ == "__main__":
main()
| 58
| 1
|
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
'''microsoft/resnet-50''': '''https://huggingface.co/microsoft/resnet-50/blob/main/config.json''',
}
class SCREAMING_SNAKE_CASE ( __lowercase , __lowercase):
"""simple docstring"""
lowercase : List[str] = 'resnet'
lowercase : Union[str, Any] = ['basic', 'bottleneck']
def __init__( self , __A=3 , __A=64 , __A=[256, 512, 1024, 2048] , __A=[3, 4, 6, 3] , __A="bottleneck" , __A="relu" , __A=False , __A=None , __A=None , **__A , ) -> Optional[int]:
super().__init__(**__A )
if layer_type not in self.layer_types:
raise ValueError(F'''layer_type={layer_type} is not one of {','.join(self.layer_types )}''' )
_lowerCAmelCase =num_channels
_lowerCAmelCase =embedding_size
_lowerCAmelCase =hidden_sizes
_lowerCAmelCase =depths
_lowerCAmelCase =layer_type
_lowerCAmelCase =hidden_act
_lowerCAmelCase =downsample_in_first_stage
_lowerCAmelCase =['stem'] + [F'''stage{idx}''' for idx in range(1 , len(__A ) + 1 )]
_lowerCAmelCase , _lowerCAmelCase =get_aligned_output_features_output_indices(
out_features=__A , out_indices=__A , stage_names=self.stage_names )
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : Tuple = version.parse('1.11')
@property
def UpperCamelCase__ ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def UpperCamelCase__ ( self ) -> float:
return 1E-3
| 58
|
'''simple docstring'''
from __future__ import annotations
from typing import Any
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __A ) -> None:
_lowerCAmelCase =num_of_nodes
_lowerCAmelCase =[]
_lowerCAmelCase ={}
def UpperCamelCase__ ( self , __A , __A , __A ) -> None:
self.m_edges.append([u_node, v_node, weight] )
def UpperCamelCase__ ( self , __A ) -> int:
if self.m_component[u_node] == u_node:
return u_node
return self.find_component(self.m_component[u_node] )
def UpperCamelCase__ ( self , __A ) -> None:
if self.m_component[u_node] != u_node:
for k in self.m_component:
_lowerCAmelCase =self.find_component(__A )
def UpperCamelCase__ ( self , __A , __A , __A ) -> None:
if component_size[u_node] <= component_size[v_node]:
_lowerCAmelCase =v_node
component_size[v_node] += component_size[u_node]
self.set_component(__A )
elif component_size[u_node] >= component_size[v_node]:
_lowerCAmelCase =self.find_component(__A )
component_size[u_node] += component_size[v_node]
self.set_component(__A )
def UpperCamelCase__ ( self ) -> None:
_lowerCAmelCase =[]
_lowerCAmelCase =0
_lowerCAmelCase =[-1] * self.m_num_of_nodes
# A list of components (initialized to all of the nodes)
for node in range(self.m_num_of_nodes ):
self.m_component.update({node: node} )
component_size.append(1 )
_lowerCAmelCase =self.m_num_of_nodes
while num_of_components > 1:
for edge in self.m_edges:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase =edge
_lowerCAmelCase =self.m_component[u]
_lowerCAmelCase =self.m_component[v]
if u_component != v_component:
for component in (u_component, v_component):
if (
minimum_weight_edge[component] == -1
or minimum_weight_edge[component][2] > w
):
_lowerCAmelCase =[u, v, w]
for edge in minimum_weight_edge:
if isinstance(__A , __A ):
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase =edge
_lowerCAmelCase =self.m_component[u]
_lowerCAmelCase =self.m_component[v]
if u_component != v_component:
mst_weight += w
self.union(__A , __A , __A )
print(F'''Added edge [{u} - {v}]\nAdded weight: {w}\n''' )
num_of_components -= 1
_lowerCAmelCase =[-1] * self.m_num_of_nodes
print(F'''The total weight of the minimal spanning tree is: {mst_weight}''' )
def UpperCamelCase__ ( ):
'''simple docstring'''
if __name__ == "__main__":
import doctest
doctest.testmod()
| 58
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowercase_ = {'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
'''VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ViTMAEForPreTraining''',
'''ViTMAELayer''',
'''ViTMAEModel''',
'''ViTMAEPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
'''TFViTMAEForPreTraining''',
'''TFViTMAEModel''',
'''TFViTMAEPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit_mae import (
VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTMAEForPreTraining,
ViTMAELayer,
ViTMAEModel,
ViTMAEPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel
else:
import sys
lowercase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 58
|
'''simple docstring'''
from PIL import Image
def UpperCamelCase__ ( a__ , a__ ):
'''simple docstring'''
def brightness(a__ ) -> float:
return 1_2_8 + level + (c - 1_2_8)
if not -255.0 <= level <= 255.0:
raise ValueError('level must be between -255.0 (black) and 255.0 (white)' )
return img.point(a__ )
if __name__ == "__main__":
# Load image
with Image.open('''image_data/lena.jpg''') as img:
# Change brightness to 100
lowercase_ = change_brightness(img, 100)
brigt_img.save('''image_data/lena_brightness.png''', format='''png''')
| 58
| 1
|
'''simple docstring'''
from jiwer import compute_measures
import datasets
lowercase_ = '''\
@inproceedings{inproceedings,
author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
year = {2004},
month = {01},
pages = {},
title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}
}
'''
lowercase_ = '''\
Word error rate (WER) is a common metric of the performance of an automatic speech recognition system.
The general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.
This problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.
Word error rate can then be computed as:
WER = (S + D + I) / N = (S + D + I) / (S + D + C)
where
S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct words,
N is the number of words in the reference (N=S+D+C).
This value indicates the average number of errors per reference word. The lower the value, the better the
performance of the ASR system with a WER of 0 being a perfect score.
'''
lowercase_ = '''
Compute WER score of transcribed segments against references.
Args:
references: List of references for each speech input.
predictions: List of transcriptions to score.
concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.
Returns:
(float): the word error rate
Examples:
>>> predictions = ["this is the prediction", "there is an other sample"]
>>> references = ["this is the reference", "there is another one"]
>>> wer = datasets.load_metric("wer")
>>> wer_score = wer.compute(predictions=predictions, references=references)
>>> print(wer_score)
0.5
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class SCREAMING_SNAKE_CASE ( datasets.Metric):
"""simple docstring"""
def UpperCamelCase__ ( self ) -> List[str]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('string' , id='sequence' ),
'references': datasets.Value('string' , id='sequence' ),
} ) , codebase_urls=['https://github.com/jitsi/jiwer/'] , reference_urls=[
'https://en.wikipedia.org/wiki/Word_error_rate',
] , )
def UpperCamelCase__ ( self , __A=None , __A=None , __A=False ) -> str:
if concatenate_texts:
return compute_measures(__A , __A )["wer"]
else:
_lowerCAmelCase =0
_lowerCAmelCase =0
for prediction, reference in zip(__A , __A ):
_lowerCAmelCase =compute_measures(__A , __A )
incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
total += measures["substitutions"] + measures["deletions"] + measures["hits"]
return incorrect / total
| 58
|
'''simple docstring'''
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / '''utils'''))
from test_module.custom_configuration import CustomConfig # noqa E402
lowercase_ = {
'''return_dict''': False,
'''output_hidden_states''': True,
'''output_attentions''': True,
'''torchscript''': True,
'''torch_dtype''': '''float16''',
'''use_bfloat16''': True,
'''tf_legacy_loss''': True,
'''pruned_heads''': {'''a''': 1},
'''tie_word_embeddings''': False,
'''is_decoder''': True,
'''cross_attention_hidden_size''': 128,
'''add_cross_attention''': True,
'''tie_encoder_decoder''': True,
'''max_length''': 50,
'''min_length''': 3,
'''do_sample''': True,
'''early_stopping''': True,
'''num_beams''': 3,
'''num_beam_groups''': 3,
'''diversity_penalty''': 0.5,
'''temperature''': 2.0,
'''top_k''': 10,
'''top_p''': 0.7,
'''typical_p''': 0.2,
'''repetition_penalty''': 0.8,
'''length_penalty''': 0.8,
'''no_repeat_ngram_size''': 5,
'''encoder_no_repeat_ngram_size''': 5,
'''bad_words_ids''': [1, 2, 3],
'''num_return_sequences''': 3,
'''chunk_size_feed_forward''': 5,
'''output_scores''': True,
'''return_dict_in_generate''': True,
'''forced_bos_token_id''': 2,
'''forced_eos_token_id''': 3,
'''remove_invalid_values''': True,
'''architectures''': ['''BertModel'''],
'''finetuning_task''': '''translation''',
'''id2label''': {0: '''label'''},
'''label2id''': {'''label''': '''0'''},
'''tokenizer_class''': '''BertTokenizerFast''',
'''prefix''': '''prefix''',
'''bos_token_id''': 6,
'''pad_token_id''': 7,
'''eos_token_id''': 8,
'''sep_token_id''': 9,
'''decoder_start_token_id''': 10,
'''exponential_decay_length_penalty''': (5, 1.01),
'''suppress_tokens''': [0, 1],
'''begin_suppress_tokens''': 2,
'''task_specific_params''': {'''translation''': '''some_params'''},
'''problem_type''': '''regression''',
}
@is_staging_test
class SCREAMING_SNAKE_CASE ( unittest.TestCase):
"""simple docstring"""
@classmethod
def UpperCamelCase__ ( cls ) -> Optional[Any]:
_lowerCAmelCase =TOKEN
HfFolder.save_token(__A )
@classmethod
def UpperCamelCase__ ( cls ) -> List[str]:
try:
delete_repo(token=cls._token , repo_id='test-config' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='valid_org/test-config-org' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='test-dynamic-config' )
except HTTPError:
pass
def UpperCamelCase__ ( self ) -> str:
_lowerCAmelCase =BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
config.push_to_hub('test-config' , use_auth_token=self._token )
_lowerCAmelCase =BertConfig.from_pretrained(F'''{USER}/test-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__A , getattr(__A , __A ) )
# Reset repo
delete_repo(token=self._token , repo_id='test-config' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(__A , repo_id='test-config' , push_to_hub=__A , use_auth_token=self._token )
_lowerCAmelCase =BertConfig.from_pretrained(F'''{USER}/test-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__A , getattr(__A , __A ) )
def UpperCamelCase__ ( self ) -> Dict:
_lowerCAmelCase =BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
config.push_to_hub('valid_org/test-config-org' , use_auth_token=self._token )
_lowerCAmelCase =BertConfig.from_pretrained('valid_org/test-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__A , getattr(__A , __A ) )
# Reset repo
delete_repo(token=self._token , repo_id='valid_org/test-config-org' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
__A , repo_id='valid_org/test-config-org' , push_to_hub=__A , use_auth_token=self._token )
_lowerCAmelCase =BertConfig.from_pretrained('valid_org/test-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__A , getattr(__A , __A ) )
def UpperCamelCase__ ( self ) -> List[str]:
CustomConfig.register_for_auto_class()
_lowerCAmelCase =CustomConfig(attribute=42 )
config.push_to_hub('test-dynamic-config' , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(config.auto_map , {'AutoConfig': 'custom_configuration.CustomConfig'} )
_lowerCAmelCase =AutoConfig.from_pretrained(F'''{USER}/test-dynamic-config''' , trust_remote_code=__A )
# Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
self.assertEqual(new_config.__class__.__name__ , 'CustomConfig' )
self.assertEqual(new_config.attribute , 42 )
class SCREAMING_SNAKE_CASE ( unittest.TestCase):
"""simple docstring"""
def UpperCamelCase__ ( self ) -> List[Any]:
_lowerCAmelCase =GPTaConfig()
# attempt to modify each of int/float/bool/str config records and verify they were updated
_lowerCAmelCase =c.n_embd + 1 # int
_lowerCAmelCase =c.resid_pdrop + 1.0 # float
_lowerCAmelCase =not c.scale_attn_weights # bool
_lowerCAmelCase =c.summary_type + 'foo' # str
c.update_from_string(
F'''n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}''' )
self.assertEqual(__A , c.n_embd , 'mismatch for key: n_embd' )
self.assertEqual(__A , c.resid_pdrop , 'mismatch for key: resid_pdrop' )
self.assertEqual(__A , c.scale_attn_weights , 'mismatch for key: scale_attn_weights' )
self.assertEqual(__A , c.summary_type , 'mismatch for key: summary_type' )
def UpperCamelCase__ ( self ) -> List[str]:
_lowerCAmelCase =PretrainedConfig()
_lowerCAmelCase =[key for key in base_config.__dict__ if key not in config_common_kwargs]
# If this part of the test fails, you have arguments to addin config_common_kwargs above.
self.assertListEqual(
__A , ['is_encoder_decoder', '_name_or_path', '_commit_hash', 'transformers_version'] )
_lowerCAmelCase =[key for key, value in config_common_kwargs.items() if value == getattr(__A , __A )]
if len(__A ) > 0:
raise ValueError(
'The following keys are set with the default values in'
' `test_configuration_common.config_common_kwargs` pick another value for them:'
F''' {', '.join(__A )}.''' )
def UpperCamelCase__ ( self ) -> Optional[int]:
with self.assertRaises(__A ):
# config is in subfolder, the following should not work without specifying the subfolder
_lowerCAmelCase =BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder' )
_lowerCAmelCase =BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder' , subfolder='bert' )
self.assertIsNotNone(__A )
def UpperCamelCase__ ( self ) -> List[str]:
# A mock response for an HTTP head request to emulate server down
_lowerCAmelCase =mock.Mock()
_lowerCAmelCase =500
_lowerCAmelCase ={}
_lowerCAmelCase =HTTPError
_lowerCAmelCase ={}
# Download this model to make sure it's in the cache.
_lowerCAmelCase =BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert' )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch('requests.Session.request' , return_value=__A ) as mock_head:
_lowerCAmelCase =BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert' )
# This check we did call the fake head request
mock_head.assert_called()
def UpperCamelCase__ ( self ) -> Optional[int]:
# This test is for deprecated behavior and can be removed in v5
_lowerCAmelCase =BertConfig.from_pretrained(
'https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json' )
def UpperCamelCase__ ( self ) -> Any:
_lowerCAmelCase =AutoConfig.from_pretrained('bert-base-cased' )
_lowerCAmelCase =['config.4.0.0.json']
with tempfile.TemporaryDirectory() as tmp_dir:
configuration.save_pretrained(__A )
_lowerCAmelCase =2
json.dump(configuration.to_dict() , open(os.path.join(__A , 'config.4.0.0.json' ) , 'w' ) )
# This should pick the new configuration file as the version of Transformers is > 4.0.0
_lowerCAmelCase =AutoConfig.from_pretrained(__A )
self.assertEqual(new_configuration.hidden_size , 2 )
# Will need to be adjusted if we reach v42 and this test is still here.
# Should pick the old configuration file as the version of Transformers is < 4.42.0
_lowerCAmelCase =['config.42.0.0.json']
_lowerCAmelCase =768
configuration.save_pretrained(__A )
shutil.move(os.path.join(__A , 'config.4.0.0.json' ) , os.path.join(__A , 'config.42.0.0.json' ) )
_lowerCAmelCase =AutoConfig.from_pretrained(__A )
self.assertEqual(new_configuration.hidden_size , 768 )
def UpperCamelCase__ ( self ) -> Any:
# This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
_lowerCAmelCase ='hf-internal-testing/test-two-configs'
import transformers as new_transformers
_lowerCAmelCase ='v4.0.0'
_lowerCAmelCase , _lowerCAmelCase =new_transformers.models.auto.AutoConfig.from_pretrained(
__A , return_unused_kwargs=__A )
self.assertEqual(new_configuration.hidden_size , 2 )
# This checks `_configuration_file` ia not kept in the kwargs by mistake.
self.assertDictEqual(__A , {} )
# Testing an older version by monkey-patching the version in the module it's used.
import transformers as old_transformers
_lowerCAmelCase ='v3.0.0'
_lowerCAmelCase =old_transformers.models.auto.AutoConfig.from_pretrained(__A )
self.assertEqual(old_configuration.hidden_size , 768 )
| 58
| 1
|
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
'''BridgeTower/bridgetower-base''': '''https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json''',
'''BridgeTower/bridgetower-base-itm-mlm''': (
'''https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json'''
),
}
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : List[Any] = 'bridgetower_vision_model'
def __init__( self , __A=768 , __A=12 , __A=3 , __A=16 , __A=288 , __A=1 , __A=1E-05 , __A=False , __A=True , __A=False , **__A , ) -> Optional[int]:
super().__init__(**__A )
_lowerCAmelCase =hidden_size
_lowerCAmelCase =num_hidden_layers
_lowerCAmelCase =num_channels
_lowerCAmelCase =patch_size
_lowerCAmelCase =image_size
_lowerCAmelCase =initializer_factor
_lowerCAmelCase =layer_norm_eps
_lowerCAmelCase =stop_gradient
_lowerCAmelCase =share_layernorm
_lowerCAmelCase =remove_last_layer
@classmethod
def UpperCamelCase__ ( cls , __A , **__A ) -> "PretrainedConfig":
_lowerCAmelCase , _lowerCAmelCase =cls.get_config_dict(__A , **__A )
if config_dict.get('model_type' ) == "bridgetower":
_lowerCAmelCase =config_dict['text_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(__A , **__A )
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : Optional[int] = 'bridgetower_text_model'
def __init__( self , __A=5_0265 , __A=768 , __A=12 , __A=12 , __A=1 , __A=3072 , __A="gelu" , __A=0.1 , __A=0.1 , __A=514 , __A=1 , __A=1E-05 , __A=1 , __A=0 , __A=2 , __A="absolute" , __A=True , **__A , ) -> Dict:
super().__init__(**__A )
_lowerCAmelCase =vocab_size
_lowerCAmelCase =hidden_size
_lowerCAmelCase =num_hidden_layers
_lowerCAmelCase =num_attention_heads
_lowerCAmelCase =hidden_act
_lowerCAmelCase =initializer_factor
_lowerCAmelCase =intermediate_size
_lowerCAmelCase =hidden_dropout_prob
_lowerCAmelCase =attention_probs_dropout_prob
_lowerCAmelCase =max_position_embeddings
_lowerCAmelCase =type_vocab_size
_lowerCAmelCase =layer_norm_eps
_lowerCAmelCase =position_embedding_type
_lowerCAmelCase =use_cache
_lowerCAmelCase =pad_token_id
_lowerCAmelCase =bos_token_id
_lowerCAmelCase =eos_token_id
@classmethod
def UpperCamelCase__ ( cls , __A , **__A ) -> "PretrainedConfig":
_lowerCAmelCase , _lowerCAmelCase =cls.get_config_dict(__A , **__A )
if config_dict.get('model_type' ) == "bridgetower":
_lowerCAmelCase =config_dict['text_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(__A , **__A )
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : Dict = 'bridgetower'
def __init__( self , __A=True , __A="gelu" , __A=768 , __A=1 , __A=1E-05 , __A=False , __A="add" , __A=12 , __A=6 , __A=False , __A=False , __A=None , __A=None , **__A , ) -> Any:
# TODO: remove this once the Hub files are updated.
_lowerCAmelCase =kwargs.pop('text_config_dict' , __A )
_lowerCAmelCase =kwargs.pop('vision_config_dict' , __A )
super().__init__(**__A )
_lowerCAmelCase =share_cross_modal_transformer_layers
_lowerCAmelCase =hidden_act
_lowerCAmelCase =hidden_size
_lowerCAmelCase =initializer_factor
_lowerCAmelCase =layer_norm_eps
_lowerCAmelCase =share_link_tower_layers
_lowerCAmelCase =link_tower_type
_lowerCAmelCase =num_attention_heads
_lowerCAmelCase =num_hidden_layers
_lowerCAmelCase =tie_word_embeddings
_lowerCAmelCase =init_layernorm_from_vision_encoder
if text_config is None:
_lowerCAmelCase ={}
logger.info('`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values.' )
if vision_config is None:
_lowerCAmelCase ={}
logger.info('`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values.' )
_lowerCAmelCase =BridgeTowerTextConfig(**__A )
_lowerCAmelCase =BridgeTowerVisionConfig(**__A )
@classmethod
def UpperCamelCase__ ( cls , __A , __A , **__A ) -> List[Any]:
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **__A )
def UpperCamelCase__ ( self ) -> Optional[Any]:
_lowerCAmelCase =copy.deepcopy(self.__dict__ )
_lowerCAmelCase =self.text_config.to_dict()
_lowerCAmelCase =self.vision_config.to_dict()
_lowerCAmelCase =self.__class__.model_type
return output
| 58
|
'''simple docstring'''
from __future__ import annotations
lowercase_ = 10
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
_lowerCAmelCase =1
_lowerCAmelCase =max(a__ )
while placement <= max_digit:
# declare and initialize empty buckets
_lowerCAmelCase =[[] for _ in range(a__ )]
# split list_of_ints between the buckets
for i in list_of_ints:
_lowerCAmelCase =int((i / placement) % RADIX )
buckets[tmp].append(a__ )
# put each buckets' contents into list_of_ints
_lowerCAmelCase =0
for b in range(a__ ):
for i in buckets[b]:
_lowerCAmelCase =i
a += 1
# move to next
placement *= RADIX
return list_of_ints
if __name__ == "__main__":
import doctest
doctest.testmod()
| 58
| 1
|
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class SCREAMING_SNAKE_CASE ( unittest.TestCase):
"""simple docstring"""
def __init__( self , __A , __A=13 , __A=3 , __A=224 , __A=30 , __A=400 , __A=True , __A=None , __A=True , __A=[0.5, 0.5, 0.5] , __A=[0.5, 0.5, 0.5] , ) -> Any:
_lowerCAmelCase =size if size is not None else {'height': 18, 'width': 18}
_lowerCAmelCase =parent
_lowerCAmelCase =batch_size
_lowerCAmelCase =num_channels
_lowerCAmelCase =image_size
_lowerCAmelCase =min_resolution
_lowerCAmelCase =max_resolution
_lowerCAmelCase =do_resize
_lowerCAmelCase =size
_lowerCAmelCase =do_normalize
_lowerCAmelCase =image_mean
_lowerCAmelCase =image_std
def UpperCamelCase__ ( self ) -> Optional[Any]:
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
}
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE ( __lowercase , unittest.TestCase):
"""simple docstring"""
lowercase : Dict = ViTImageProcessor if is_vision_available() else None
def UpperCamelCase__ ( self ) -> Any:
_lowerCAmelCase =EfficientFormerImageProcessorTester(self )
@property
def UpperCamelCase__ ( self ) -> int:
return self.image_proc_tester.prepare_image_processor_dict()
def UpperCamelCase__ ( self ) -> List[str]:
_lowerCAmelCase =self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__A , 'image_mean' ) )
self.assertTrue(hasattr(__A , 'image_std' ) )
self.assertTrue(hasattr(__A , 'do_normalize' ) )
self.assertTrue(hasattr(__A , 'do_resize' ) )
self.assertTrue(hasattr(__A , 'size' ) )
def UpperCamelCase__ ( self ) -> Union[str, Any]:
pass
def UpperCamelCase__ ( self ) -> List[str]:
# Initialize image_processor
_lowerCAmelCase =self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_lowerCAmelCase =prepare_image_inputs(self.image_proc_tester , equal_resolution=__A )
for image in image_inputs:
self.assertIsInstance(__A , Image.Image )
# Test not batched input
_lowerCAmelCase =image_processor(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['height'],
self.image_proc_tester.size['width'],
) , )
# Test batched
_lowerCAmelCase =image_processor(__A , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['height'],
self.image_proc_tester.size['width'],
) , )
def UpperCamelCase__ ( self ) -> int:
# Initialize image_processor
_lowerCAmelCase =self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_lowerCAmelCase =prepare_image_inputs(self.image_proc_tester , equal_resolution=__A , numpify=__A )
for image in image_inputs:
self.assertIsInstance(__A , np.ndarray )
# Test not batched input
_lowerCAmelCase =image_processor(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['height'],
self.image_proc_tester.size['width'],
) , )
# Test batched
_lowerCAmelCase =image_processor(__A , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['height'],
self.image_proc_tester.size['width'],
) , )
def UpperCamelCase__ ( self ) -> List[Any]:
# Initialize image_processor
_lowerCAmelCase =self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_lowerCAmelCase =prepare_image_inputs(self.image_proc_tester , equal_resolution=__A , torchify=__A )
for image in image_inputs:
self.assertIsInstance(__A , torch.Tensor )
# Test not batched input
_lowerCAmelCase =image_processor(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['height'],
self.image_proc_tester.size['width'],
) , )
# Test batched
_lowerCAmelCase =image_processor(__A , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['height'],
self.image_proc_tester.size['width'],
) , )
| 58
|
'''simple docstring'''
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
FEATURE_EXTRACTOR_NAME,
FLAX_WEIGHTS_NAME,
HF_MODULES_CACHE,
HUGGINGFACE_CO_PREFIX,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
MODEL_CARD_NAME,
MULTIPLE_CHOICE_DUMMY_INPUTS,
PYTORCH_PRETRAINED_BERT_CACHE,
PYTORCH_TRANSFORMERS_CACHE,
S3_BUCKET_PREFIX,
SENTENCEPIECE_UNDERLINE,
SPIECE_UNDERLINE,
TF2_WEIGHTS_NAME,
TF_WEIGHTS_NAME,
TORCH_FX_REQUIRED_VERSION,
TRANSFORMERS_CACHE,
TRANSFORMERS_DYNAMIC_MODULE_NAME,
USE_JAX,
USE_TF,
USE_TORCH,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
ContextManagers,
DummyObject,
EntryNotFoundError,
ExplicitEnum,
ModelOutput,
PaddingStrategy,
PushToHubMixin,
RepositoryNotFoundError,
RevisionNotFoundError,
TensorType,
_LazyModule,
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
cached_property,
copy_func,
default_cache_path,
define_sagemaker_information,
get_cached_models,
get_file_from_repo,
get_full_repo_name,
get_torch_version,
has_file,
http_user_agent,
is_apex_available,
is_bsa_available,
is_coloredlogs_available,
is_datasets_available,
is_detectrona_available,
is_faiss_available,
is_flax_available,
is_ftfy_available,
is_in_notebook,
is_ipex_available,
is_librosa_available,
is_offline_mode,
is_onnx_available,
is_pandas_available,
is_phonemizer_available,
is_protobuf_available,
is_psutil_available,
is_pyanvml_available,
is_pyctcdecode_available,
is_pytesseract_available,
is_pytorch_quantization_available,
is_rjieba_available,
is_sagemaker_dp_enabled,
is_sagemaker_mp_enabled,
is_scipy_available,
is_sentencepiece_available,
is_seqio_available,
is_sklearn_available,
is_soundfile_availble,
is_spacy_available,
is_speech_available,
is_tensor,
is_tensorflow_probability_available,
is_tfaonnx_available,
is_tf_available,
is_timm_available,
is_tokenizers_available,
is_torch_available,
is_torch_bfaa_available,
is_torch_cuda_available,
is_torch_fx_available,
is_torch_fx_proxy,
is_torch_mps_available,
is_torch_tfaa_available,
is_torch_tpu_available,
is_torchaudio_available,
is_training_run_on_sagemaker,
is_vision_available,
replace_return_docstrings,
requires_backends,
to_numpy,
to_py_obj,
torch_only_method,
)
| 58
| 1
|
'''simple docstring'''
import gc
import threading
import time
import psutil
import torch
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self ) -> List[Any]:
_lowerCAmelCase =psutil.Process()
_lowerCAmelCase =False
def UpperCamelCase__ ( self ) -> Optional[int]:
_lowerCAmelCase =-1
while True:
_lowerCAmelCase =max(self.process.memory_info().rss , self.cpu_memory_peak )
# can't sleep or will not catch the peak right (this comment is here on purpose)
if not self.peak_monitoring:
break
def UpperCamelCase__ ( self ) -> Optional[int]:
_lowerCAmelCase =True
_lowerCAmelCase =threading.Thread(target=self.peak_monitor )
_lowerCAmelCase =True
self.thread.start()
def UpperCamelCase__ ( self ) -> int:
_lowerCAmelCase =False
self.thread.join()
return self.cpu_memory_peak
lowercase_ = PeakCPUMemory()
def UpperCamelCase__ ( ):
'''simple docstring'''
_lowerCAmelCase ={'time': time.time()}
gc.collect()
torch.cuda.empty_cache()
# CPU mem
_lowerCAmelCase =psutil.Process().memory_info().rss
cpu_peak_tracker.start()
# GPU mem
for i in range(torch.cuda.device_count() ):
_lowerCAmelCase =torch.cuda.memory_allocated(a__ )
torch.cuda.reset_peak_memory_stats()
return measures
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
_lowerCAmelCase ={'time': time.time() - start_measures['time']}
gc.collect()
torch.cuda.empty_cache()
# CPU mem
_lowerCAmelCase =(psutil.Process().memory_info().rss - start_measures['cpu']) / 2**2_0
_lowerCAmelCase =(cpu_peak_tracker.stop() - start_measures['cpu']) / 2**2_0
# GPU mem
for i in range(torch.cuda.device_count() ):
_lowerCAmelCase =(torch.cuda.memory_allocated(a__ ) - start_measures[str(a__ )]) / 2**2_0
_lowerCAmelCase =(torch.cuda.max_memory_allocated(a__ ) - start_measures[str(a__ )]) / 2**2_0
return measures
def UpperCamelCase__ ( a__ , a__ ):
'''simple docstring'''
print(F'''{description}:''' )
print(F'''- Time: {measures['time']:.2f}s''' )
for i in range(torch.cuda.device_count() ):
print(F'''- GPU {i} allocated: {measures[str(a__ )]:.2f}MiB''' )
_lowerCAmelCase =measures[F'''{i}-peak''']
print(F'''- GPU {i} peak: {peak:.2f}MiB''' )
print(F'''- CPU RAM allocated: {measures['cpu']:.2f}MiB''' )
print(F'''- CPU RAM peak: {measures['cpu-peak']:.2f}MiB''' )
| 58
|
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
_lowerCAmelCase =len(a__ ) // 2
# choose the middle 3 elements
_lowerCAmelCase =lst[m - 1 : m + 2]
# if middle element is peak
if three[1] > three[0] and three[1] > three[2]:
return three[1]
# if increasing, recurse on right
elif three[0] < three[2]:
if len(lst[:m] ) == 2:
m -= 1
return peak(lst[m:] )
# decreasing
else:
if len(lst[:m] ) == 2:
m += 1
return peak(lst[:m] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 58
| 1
|
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFFunnelBaseModel,
TFFunnelForMaskedLM,
TFFunnelForMultipleChoice,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForSequenceClassification,
TFFunnelForTokenClassification,
TFFunnelModel,
)
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __A , __A=13 , __A=7 , __A=True , __A=True , __A=True , __A=True , __A=99 , __A=[1, 1, 2] , __A=1 , __A=32 , __A=4 , __A=8 , __A=37 , __A="gelu_new" , __A=0.1 , __A=0.1 , __A=0.0 , __A=512 , __A=3 , __A=0.02 , __A=3 , __A=4 , __A=None , __A=False , ) -> str:
_lowerCAmelCase =parent
_lowerCAmelCase =batch_size
_lowerCAmelCase =seq_length
_lowerCAmelCase =is_training
_lowerCAmelCase =use_input_mask
_lowerCAmelCase =use_token_type_ids
_lowerCAmelCase =use_labels
_lowerCAmelCase =vocab_size
_lowerCAmelCase =block_sizes
_lowerCAmelCase =num_decoder_layers
_lowerCAmelCase =d_model
_lowerCAmelCase =n_head
_lowerCAmelCase =d_head
_lowerCAmelCase =d_inner
_lowerCAmelCase =hidden_act
_lowerCAmelCase =hidden_dropout
_lowerCAmelCase =attention_dropout
_lowerCAmelCase =activation_dropout
_lowerCAmelCase =max_position_embeddings
_lowerCAmelCase =type_vocab_size
_lowerCAmelCase =2
_lowerCAmelCase =num_labels
_lowerCAmelCase =num_choices
_lowerCAmelCase =scope
_lowerCAmelCase =initializer_std
# Used in the tests to check the size of the first attention layer
_lowerCAmelCase =n_head
# Used in the tests to check the size of the first hidden state
_lowerCAmelCase =self.d_model
# Used in the tests to check the number of output hidden states/attentions
_lowerCAmelCase =sum(self.block_sizes ) + (0 if base else self.num_decoder_layers)
# FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
# the last hidden state of the first block (which is the first hidden state of the decoder).
if not base:
_lowerCAmelCase =self.num_hidden_layers + 2
def UpperCamelCase__ ( self ) -> str:
_lowerCAmelCase =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_lowerCAmelCase =None
if self.use_input_mask:
_lowerCAmelCase =random_attention_mask([self.batch_size, self.seq_length] )
_lowerCAmelCase =None
if self.use_token_type_ids:
_lowerCAmelCase =ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_lowerCAmelCase =None
_lowerCAmelCase =None
_lowerCAmelCase =None
if self.use_labels:
_lowerCAmelCase =ids_tensor([self.batch_size] , self.type_sequence_label_size )
_lowerCAmelCase =ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_lowerCAmelCase =ids_tensor([self.batch_size] , self.num_choices )
_lowerCAmelCase =FunnelConfig(
vocab_size=self.vocab_size , block_sizes=self.block_sizes , num_decoder_layers=self.num_decoder_layers , d_model=self.d_model , n_head=self.n_head , d_head=self.d_head , d_inner=self.d_inner , hidden_act=self.hidden_act , hidden_dropout=self.hidden_dropout , attention_dropout=self.attention_dropout , activation_dropout=self.activation_dropout , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_std=self.initializer_std , )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
)
def UpperCamelCase__ ( self , __A , __A , __A , __A , __A , __A , __A , ) -> Optional[Any]:
_lowerCAmelCase =TFFunnelModel(config=__A )
_lowerCAmelCase ={'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
_lowerCAmelCase =model(__A )
_lowerCAmelCase =[input_ids, input_mask]
_lowerCAmelCase =model(__A )
_lowerCAmelCase =model(__A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.d_model) )
_lowerCAmelCase =False
_lowerCAmelCase =TFFunnelModel(config=__A )
_lowerCAmelCase =model(__A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.d_model) )
_lowerCAmelCase =False
_lowerCAmelCase =TFFunnelModel(config=__A )
_lowerCAmelCase =model(__A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.d_model) )
def UpperCamelCase__ ( self , __A , __A , __A , __A , __A , __A , __A , ) -> Any:
_lowerCAmelCase =TFFunnelBaseModel(config=__A )
_lowerCAmelCase ={'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
_lowerCAmelCase =model(__A )
_lowerCAmelCase =[input_ids, input_mask]
_lowerCAmelCase =model(__A )
_lowerCAmelCase =model(__A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, 2, self.d_model) )
_lowerCAmelCase =False
_lowerCAmelCase =TFFunnelBaseModel(config=__A )
_lowerCAmelCase =model(__A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, 3, self.d_model) )
_lowerCAmelCase =False
_lowerCAmelCase =TFFunnelBaseModel(config=__A )
_lowerCAmelCase =model(__A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, 2, self.d_model) )
def UpperCamelCase__ ( self , __A , __A , __A , __A , __A , __A , __A , ) -> Optional[int]:
_lowerCAmelCase =TFFunnelForPreTraining(config=__A )
_lowerCAmelCase ={'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
_lowerCAmelCase =model(__A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length) )
def UpperCamelCase__ ( self , __A , __A , __A , __A , __A , __A , __A , ) -> Union[str, Any]:
_lowerCAmelCase =TFFunnelForMaskedLM(config=__A )
_lowerCAmelCase ={'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
_lowerCAmelCase =model(__A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase__ ( self , __A , __A , __A , __A , __A , __A , __A , ) -> Any:
_lowerCAmelCase =self.num_labels
_lowerCAmelCase =TFFunnelForSequenceClassification(config=__A )
_lowerCAmelCase ={'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
_lowerCAmelCase =model(__A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCamelCase__ ( self , __A , __A , __A , __A , __A , __A , __A , ) -> Tuple:
_lowerCAmelCase =self.num_choices
_lowerCAmelCase =TFFunnelForMultipleChoice(config=__A )
_lowerCAmelCase =tf.tile(tf.expand_dims(__A , 1 ) , (1, self.num_choices, 1) )
_lowerCAmelCase =tf.tile(tf.expand_dims(__A , 1 ) , (1, self.num_choices, 1) )
_lowerCAmelCase =tf.tile(tf.expand_dims(__A , 1 ) , (1, self.num_choices, 1) )
_lowerCAmelCase ={
'input_ids': multiple_choice_inputs_ids,
'attention_mask': multiple_choice_input_mask,
'token_type_ids': multiple_choice_token_type_ids,
}
_lowerCAmelCase =model(__A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def UpperCamelCase__ ( self , __A , __A , __A , __A , __A , __A , __A , ) -> Tuple:
_lowerCAmelCase =self.num_labels
_lowerCAmelCase =TFFunnelForTokenClassification(config=__A )
_lowerCAmelCase ={'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
_lowerCAmelCase =model(__A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCamelCase__ ( self , __A , __A , __A , __A , __A , __A , __A , ) -> Tuple:
_lowerCAmelCase =TFFunnelForQuestionAnswering(config=__A )
_lowerCAmelCase ={'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
_lowerCAmelCase =model(__A )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCamelCase__ ( self ) -> int:
_lowerCAmelCase =self.prepare_config_and_inputs()
(
(
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) ,
) =config_and_inputs
_lowerCAmelCase ={'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_tf
class SCREAMING_SNAKE_CASE ( __lowercase , __lowercase , unittest.TestCase):
"""simple docstring"""
lowercase : Tuple = (
(
TFFunnelModel,
TFFunnelForMaskedLM,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForTokenClassification,
)
if is_tf_available()
else ()
)
lowercase : Union[str, Any] = (
{
'feature-extraction': (TFFunnelBaseModel, TFFunnelModel),
'fill-mask': TFFunnelForMaskedLM,
'question-answering': TFFunnelForQuestionAnswering,
'text-classification': TFFunnelForSequenceClassification,
'token-classification': TFFunnelForTokenClassification,
'zero-shot': TFFunnelForSequenceClassification,
}
if is_tf_available()
else {}
)
lowercase : Tuple = False
lowercase : Union[str, Any] = False
def UpperCamelCase__ ( self ) -> Dict:
_lowerCAmelCase =TFFunnelModelTester(self )
_lowerCAmelCase =ConfigTester(self , config_class=__A )
def UpperCamelCase__ ( self ) -> List[str]:
self.config_tester.run_common_tests()
def UpperCamelCase__ ( self ) -> Optional[Any]:
_lowerCAmelCase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__A )
def UpperCamelCase__ ( self ) -> List[Any]:
_lowerCAmelCase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*__A )
def UpperCamelCase__ ( self ) -> str:
_lowerCAmelCase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__A )
def UpperCamelCase__ ( self ) -> List[str]:
_lowerCAmelCase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__A )
def UpperCamelCase__ ( self ) -> str:
_lowerCAmelCase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__A )
@require_tf
class SCREAMING_SNAKE_CASE ( __lowercase , unittest.TestCase):
"""simple docstring"""
lowercase : Tuple = (
(TFFunnelBaseModel, TFFunnelForMultipleChoice, TFFunnelForSequenceClassification) if is_tf_available() else ()
)
lowercase : List[Any] = False
lowercase : Optional[Any] = False
def UpperCamelCase__ ( self ) -> Optional[int]:
_lowerCAmelCase =TFFunnelModelTester(self , base=__A )
_lowerCAmelCase =ConfigTester(self , config_class=__A )
def UpperCamelCase__ ( self ) -> Optional[Any]:
self.config_tester.run_common_tests()
def UpperCamelCase__ ( self ) -> int:
_lowerCAmelCase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_base_model(*__A )
def UpperCamelCase__ ( self ) -> List[str]:
_lowerCAmelCase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__A )
def UpperCamelCase__ ( self ) -> List[Any]:
_lowerCAmelCase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*__A )
| 58
|
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
lowercase_ = logging.get_logger(__name__)
lowercase_ = {'''vocab_file''': '''vocab.txt'''}
lowercase_ = {
'''vocab_file''': {
'''YituTech/conv-bert-base''': '''https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt''',
'''YituTech/conv-bert-medium-small''': (
'''https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt'''
),
'''YituTech/conv-bert-small''': '''https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt''',
}
}
lowercase_ = {
'''YituTech/conv-bert-base''': 512,
'''YituTech/conv-bert-medium-small''': 512,
'''YituTech/conv-bert-small''': 512,
}
lowercase_ = {
'''YituTech/conv-bert-base''': {'''do_lower_case''': True},
'''YituTech/conv-bert-medium-small''': {'''do_lower_case''': True},
'''YituTech/conv-bert-small''': {'''do_lower_case''': True},
}
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : Union[str, Any] = VOCAB_FILES_NAMES
lowercase : Tuple = PRETRAINED_VOCAB_FILES_MAP
lowercase : Optional[int] = PRETRAINED_INIT_CONFIGURATION
lowercase : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase : List[str] = ConvBertTokenizer
def __init__( self , __A=None , __A=None , __A=True , __A="[UNK]" , __A="[SEP]" , __A="[PAD]" , __A="[CLS]" , __A="[MASK]" , __A=True , __A=None , **__A , ) -> Union[str, Any]:
super().__init__(
__A , tokenizer_file=__A , do_lower_case=__A , unk_token=__A , sep_token=__A , pad_token=__A , cls_token=__A , mask_token=__A , tokenize_chinese_chars=__A , strip_accents=__A , **__A , )
_lowerCAmelCase =json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('lowercase' , __A ) != do_lower_case
or normalizer_state.get('strip_accents' , __A ) != strip_accents
or normalizer_state.get('handle_chinese_chars' , __A ) != tokenize_chinese_chars
):
_lowerCAmelCase =getattr(__A , normalizer_state.pop('type' ) )
_lowerCAmelCase =do_lower_case
_lowerCAmelCase =strip_accents
_lowerCAmelCase =tokenize_chinese_chars
_lowerCAmelCase =normalizer_class(**__A )
_lowerCAmelCase =do_lower_case
def UpperCamelCase__ ( self , __A , __A=None ) -> int:
_lowerCAmelCase =[self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def UpperCamelCase__ ( self , __A , __A = None ) -> List[int]:
_lowerCAmelCase =[self.sep_token_id]
_lowerCAmelCase =[self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def UpperCamelCase__ ( self , __A , __A = None ) -> Tuple[str]:
_lowerCAmelCase =self._tokenizer.model.save(__A , name=__A )
return tuple(__A )
| 58
| 1
|
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : Any = ['image_processor', 'tokenizer']
lowercase : Any = 'CLIPImageProcessor'
lowercase : int = ('CLIPTokenizer', 'CLIPTokenizerFast')
def __init__( self , __A=None , __A=None , **__A ) -> str:
_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.' , __A , )
_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__(__A , __A )
def __call__( self , __A=None , __A=None , __A=None , **__A ) -> Optional[int]:
if text is None and images is None:
raise ValueError('You have to specify either text or images. Both cannot be none.' )
if text is not None:
_lowerCAmelCase =self.tokenizer(__A , return_tensors=__A , **__A )
if images is not None:
_lowerCAmelCase =self.image_processor(__A , return_tensors=__A , **__A )
if text is not None and images is not None:
_lowerCAmelCase =image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**__A ) , tensor_type=__A )
def UpperCamelCase__ ( self , *__A , **__A ) -> Any:
return self.tokenizer.batch_decode(*__A , **__A )
def UpperCamelCase__ ( self , *__A , **__A ) -> Optional[int]:
return self.tokenizer.decode(*__A , **__A )
@property
def UpperCamelCase__ ( self ) -> Tuple:
_lowerCAmelCase =self.tokenizer.model_input_names
_lowerCAmelCase =self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def UpperCamelCase__ ( self ) -> Optional[int]:
warnings.warn(
'`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' , __A , )
return self.image_processor_class
@property
def UpperCamelCase__ ( self ) -> Optional[Any]:
warnings.warn(
'`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , __A , )
return self.image_processor
| 58
|
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : Any = ['image_processor', 'tokenizer']
lowercase : Any = 'CLIPImageProcessor'
lowercase : int = ('CLIPTokenizer', 'CLIPTokenizerFast')
def __init__( self , __A=None , __A=None , **__A ) -> str:
_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.' , __A , )
_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__(__A , __A )
def __call__( self , __A=None , __A=None , __A=None , **__A ) -> Optional[int]:
if text is None and images is None:
raise ValueError('You have to specify either text or images. Both cannot be none.' )
if text is not None:
_lowerCAmelCase =self.tokenizer(__A , return_tensors=__A , **__A )
if images is not None:
_lowerCAmelCase =self.image_processor(__A , return_tensors=__A , **__A )
if text is not None and images is not None:
_lowerCAmelCase =image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**__A ) , tensor_type=__A )
def UpperCamelCase__ ( self , *__A , **__A ) -> Any:
return self.tokenizer.batch_decode(*__A , **__A )
def UpperCamelCase__ ( self , *__A , **__A ) -> Optional[int]:
return self.tokenizer.decode(*__A , **__A )
@property
def UpperCamelCase__ ( self ) -> Tuple:
_lowerCAmelCase =self.tokenizer.model_input_names
_lowerCAmelCase =self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def UpperCamelCase__ ( self ) -> Optional[int]:
warnings.warn(
'`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' , __A , )
return self.image_processor_class
@property
def UpperCamelCase__ ( self ) -> Optional[Any]:
warnings.warn(
'`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , __A , )
return self.image_processor
| 58
| 1
|
'''simple docstring'''
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging
sys.path.append(os.path.join(os.getcwd())) # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score # noqa: E402 # isort:skip
lowercase_ = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)
transformers_logging.set_verbosity_info()
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
if "token" in model_name_or_path:
return "rag_token"
if "sequence" in model_name_or_path:
return "rag_sequence"
if "bart" in model_name_or_path:
return "bart"
return None
def UpperCamelCase__ ( a__ , a__ , a__ ):
'''simple docstring'''
return max(metric_fn(a__ , a__ ) for gt in ground_truths )
def UpperCamelCase__ ( a__ , a__ , a__ ):
'''simple docstring'''
_lowerCAmelCase =[line.strip() for line in open(a__ , 'r' ).readlines()]
_lowerCAmelCase =[]
if args.gold_data_mode == "qa":
_lowerCAmelCase =pd.read_csv(a__ , sep='\t' , header=a__ )
for answer_list in data[1]:
_lowerCAmelCase =ast.literal_eval(a__ )
answers.append(a__ )
else:
_lowerCAmelCase =[line.strip() for line in open(a__ , 'r' ).readlines()]
_lowerCAmelCase =[[reference] for reference in references]
_lowerCAmelCase =_lowerCAmelCase =_lowerCAmelCase =0
for prediction, ground_truths in zip(a__ , a__ ):
total += 1
em += metric_max_over_ground_truths(a__ , a__ , a__ )
fa += metric_max_over_ground_truths(a__ , a__ , a__ )
_lowerCAmelCase =100.0 * em / total
_lowerCAmelCase =100.0 * fa / total
logger.info(F'''F1: {fa:.2f}''' )
logger.info(F'''EM: {em:.2f}''' )
def UpperCamelCase__ ( a__ , a__ , a__ ):
'''simple docstring'''
_lowerCAmelCase =args.k
_lowerCAmelCase =[line.strip() for line in open(a__ , 'r' ).readlines()]
_lowerCAmelCase =[line.strip() for line in open(a__ , 'r' ).readlines()]
_lowerCAmelCase =_lowerCAmelCase =0
for hypo, reference in zip(a__ , a__ ):
_lowerCAmelCase =set(hypo.split('\t' )[:k] )
_lowerCAmelCase =set(reference.split('\t' ) )
total += 1
em += len(hypo_provenance & ref_provenance ) / k
_lowerCAmelCase =100.0 * em / total
logger.info(F'''Precision@{k}: {em: .2f}''' )
def UpperCamelCase__ ( a__ , a__ , a__ ):
'''simple docstring'''
def strip_title(a__ ):
if title.startswith('"' ):
_lowerCAmelCase =title[1:]
if title.endswith('"' ):
_lowerCAmelCase =title[:-1]
return title
_lowerCAmelCase =rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
a__ , return_tensors='pt' , padding=a__ , truncation=a__ , )['input_ids'].to(args.device )
_lowerCAmelCase =rag_model.rag.question_encoder(a__ )
_lowerCAmelCase =question_enc_outputs[0]
_lowerCAmelCase =rag_model.retriever(
a__ , question_enc_pool_output.cpu().detach().to(torch.floataa ).numpy() , prefix=rag_model.rag.generator.config.prefix , n_docs=rag_model.config.n_docs , return_tensors='pt' , )
_lowerCAmelCase =rag_model.retriever.index.get_doc_dicts(result.doc_ids )
_lowerCAmelCase =[]
for docs in all_docs:
_lowerCAmelCase =[strip_title(a__ ) for title in docs['title']]
provenance_strings.append('\t'.join(a__ ) )
return provenance_strings
def UpperCamelCase__ ( a__ , a__ , a__ ):
'''simple docstring'''
with torch.no_grad():
_lowerCAmelCase =rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
a__ , return_tensors='pt' , padding=a__ , truncation=a__ )
_lowerCAmelCase =inputs_dict.input_ids.to(args.device )
_lowerCAmelCase =inputs_dict.attention_mask.to(args.device )
_lowerCAmelCase =rag_model.generate( # rag_model overwrites generate
a__ , attention_mask=a__ , num_beams=args.num_beams , min_length=args.min_length , max_length=args.max_length , early_stopping=a__ , num_return_sequences=1 , bad_words_ids=[[0, 0]] , )
_lowerCAmelCase =rag_model.retriever.generator_tokenizer.batch_decode(a__ , skip_special_tokens=a__ )
if args.print_predictions:
for q, a in zip(a__ , a__ ):
logger.info('Q: {} - A: {}'.format(a__ , a__ ) )
return answers
def UpperCamelCase__ ( ):
'''simple docstring'''
_lowerCAmelCase =argparse.ArgumentParser()
parser.add_argument(
'--model_type' , choices=['rag_sequence', 'rag_token', 'bart'] , type=a__ , help=(
'RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the'
' model_name_or_path'
) , )
parser.add_argument(
'--index_name' , default=a__ , choices=['exact', 'compressed', 'legacy'] , type=a__ , help='RAG model retriever type' , )
parser.add_argument(
'--index_path' , default=a__ , type=a__ , help='Path to the retrieval index' , )
parser.add_argument('--n_docs' , default=5 , type=a__ , help='Number of retrieved docs' )
parser.add_argument(
'--model_name_or_path' , default=a__ , type=a__ , required=a__ , help='Path to pretrained checkpoints or model identifier from huggingface.co/models' , )
parser.add_argument(
'--eval_mode' , choices=['e2e', 'retrieval'] , default='e2e' , type=a__ , help=(
'Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'
' precision@k.'
) , )
parser.add_argument('--k' , default=1 , type=a__ , help='k for the precision@k calculation' )
parser.add_argument(
'--evaluation_set' , default=a__ , type=a__ , required=a__ , help='Path to a file containing evaluation samples' , )
parser.add_argument(
'--gold_data_path' , default=a__ , type=a__ , required=a__ , help='Path to a tab-separated file with gold samples' , )
parser.add_argument(
'--gold_data_mode' , default='qa' , type=a__ , choices=['qa', 'ans'] , help=(
'Format of the gold data file'
'qa - a single line in the following format: question [tab] answer_list'
'ans - a single line of the gold file contains the expected answer string'
) , )
parser.add_argument(
'--predictions_path' , type=a__ , default='predictions.txt' , help='Name of the predictions file, to be stored in the checkpoints directory' , )
parser.add_argument(
'--eval_all_checkpoints' , action='store_true' , help='Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number' , )
parser.add_argument(
'--eval_batch_size' , default=8 , type=a__ , help='Batch size per GPU/CPU for evaluation.' , )
parser.add_argument(
'--recalculate' , help='Recalculate predictions even if the prediction file exists' , action='store_true' , )
parser.add_argument(
'--num_beams' , default=4 , type=a__ , help='Number of beams to be used when generating answers' , )
parser.add_argument('--min_length' , default=1 , type=a__ , help='Min length of the generated answers' )
parser.add_argument('--max_length' , default=5_0 , type=a__ , help='Max length of the generated answers' )
parser.add_argument(
'--print_predictions' , action='store_true' , help='If True, prints predictions while evaluating.' , )
parser.add_argument(
'--print_docs' , action='store_true' , help='If True, prints docs retried while generating.' , )
_lowerCAmelCase =parser.parse_args()
_lowerCAmelCase =torch.device('cuda' if torch.cuda.is_available() else 'cpu' )
return args
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
_lowerCAmelCase ={}
if args.model_type is None:
_lowerCAmelCase =infer_model_type(args.model_name_or_path )
assert args.model_type is not None
if args.model_type.startswith('rag' ):
_lowerCAmelCase =RagTokenForGeneration if args.model_type == 'rag_token' else RagSequenceForGeneration
_lowerCAmelCase =args.n_docs
if args.index_name is not None:
_lowerCAmelCase =args.index_name
if args.index_path is not None:
_lowerCAmelCase =args.index_path
else:
_lowerCAmelCase =BartForConditionalGeneration
_lowerCAmelCase =(
[f.path for f in os.scandir(args.model_name_or_path ) if f.is_dir()]
if args.eval_all_checkpoints
else [args.model_name_or_path]
)
logger.info('Evaluate the following checkpoints: %s' , a__ )
_lowerCAmelCase =get_scores if args.eval_mode == 'e2e' else get_precision_at_k
_lowerCAmelCase =evaluate_batch_eae if args.eval_mode == 'e2e' else evaluate_batch_retrieval
for checkpoint in checkpoints:
if os.path.exists(args.predictions_path ) and (not args.recalculate):
logger.info('Calculating metrics based on an existing predictions file: {}'.format(args.predictions_path ) )
score_fn(a__ , args.predictions_path , args.gold_data_path )
continue
logger.info('***** Running evaluation for {} *****'.format(a__ ) )
logger.info(' Batch size = %d' , args.eval_batch_size )
logger.info(' Predictions will be stored under {}'.format(args.predictions_path ) )
if args.model_type.startswith('rag' ):
_lowerCAmelCase =RagRetriever.from_pretrained(a__ , **a__ )
_lowerCAmelCase =model_class.from_pretrained(a__ , retriever=a__ , **a__ )
model.retriever.init_retrieval()
else:
_lowerCAmelCase =model_class.from_pretrained(a__ , **a__ )
model.to(args.device )
with open(args.evaluation_set , 'r' ) as eval_file, open(args.predictions_path , 'w' ) as preds_file:
_lowerCAmelCase =[]
for line in tqdm(a__ ):
questions.append(line.strip() )
if len(a__ ) == args.eval_batch_size:
_lowerCAmelCase =evaluate_batch_fn(a__ , a__ , a__ )
preds_file.write('\n'.join(a__ ) + '\n' )
preds_file.flush()
_lowerCAmelCase =[]
if len(a__ ) > 0:
_lowerCAmelCase =evaluate_batch_fn(a__ , a__ , a__ )
preds_file.write('\n'.join(a__ ) )
preds_file.flush()
score_fn(a__ , args.predictions_path , args.gold_data_path )
if __name__ == "__main__":
lowercase_ = get_args()
main(args)
| 58
|
'''simple docstring'''
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class SCREAMING_SNAKE_CASE ( __lowercase , __lowercase):
"""simple docstring"""
@register_to_config
def __init__( self , __A = 128 , __A = 256 , __A = 2_000.0 , __A = 768 , __A = 12 , __A = 12 , __A = 64 , __A = 2048 , __A = 0.1 , ) -> str:
super().__init__()
_lowerCAmelCase =nn.Sequential(
nn.Linear(__A , d_model * 4 , bias=__A ) , nn.SiLU() , nn.Linear(d_model * 4 , d_model * 4 , bias=__A ) , nn.SiLU() , )
_lowerCAmelCase =nn.Embedding(__A , __A )
_lowerCAmelCase =False
_lowerCAmelCase =nn.Linear(__A , __A , bias=__A )
_lowerCAmelCase =nn.Dropout(p=__A )
_lowerCAmelCase =nn.ModuleList()
for lyr_num in range(__A ):
# FiLM conditional T5 decoder
_lowerCAmelCase =DecoderLayer(d_model=__A , d_kv=__A , num_heads=__A , d_ff=__A , dropout_rate=__A )
self.decoders.append(__A )
_lowerCAmelCase =TaLayerNorm(__A )
_lowerCAmelCase =nn.Dropout(p=__A )
_lowerCAmelCase =nn.Linear(__A , __A , bias=__A )
def UpperCamelCase__ ( self , __A , __A ) -> Any:
_lowerCAmelCase =torch.mul(query_input.unsqueeze(-1 ) , key_input.unsqueeze(-2 ) )
return mask.unsqueeze(-3 )
def UpperCamelCase__ ( self , __A , __A , __A ) -> Optional[Any]:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase =decoder_input_tokens.shape
assert decoder_noise_time.shape == (batch,)
# decoder_noise_time is in [0, 1), so rescale to expected timing range.
_lowerCAmelCase =get_timestep_embedding(
decoder_noise_time * self.config.max_decoder_noise_time , embedding_dim=self.config.d_model , max_period=self.config.max_decoder_noise_time , ).to(dtype=self.dtype )
_lowerCAmelCase =self.conditioning_emb(__A ).unsqueeze(1 )
assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)
_lowerCAmelCase =decoder_input_tokens.shape[1]
# If we want to use relative positions for audio context, we can just offset
# this sequence by the length of encodings_and_masks.
_lowerCAmelCase =torch.broadcast_to(
torch.arange(__A , device=decoder_input_tokens.device ) , (batch, seq_length) , )
_lowerCAmelCase =self.position_encoding(__A )
_lowerCAmelCase =self.continuous_inputs_projection(__A )
inputs += position_encodings
_lowerCAmelCase =self.dropout(__A )
# decoder: No padding present.
_lowerCAmelCase =torch.ones(
decoder_input_tokens.shape[:2] , device=decoder_input_tokens.device , dtype=inputs.dtype )
# Translate encoding masks to encoder-decoder masks.
_lowerCAmelCase =[(x, self.encoder_decoder_mask(__A , __A )) for x, y in encodings_and_masks]
# cross attend style: concat encodings
_lowerCAmelCase =torch.cat([x[0] for x in encodings_and_encdec_masks] , dim=1 )
_lowerCAmelCase =torch.cat([x[1] for x in encodings_and_encdec_masks] , dim=-1 )
for lyr in self.decoders:
_lowerCAmelCase =lyr(
__A , conditioning_emb=__A , encoder_hidden_states=__A , encoder_attention_mask=__A , )[0]
_lowerCAmelCase =self.decoder_norm(__A )
_lowerCAmelCase =self.post_dropout(__A )
_lowerCAmelCase =self.spec_out(__A )
return spec_out
class SCREAMING_SNAKE_CASE ( nn.Module):
"""simple docstring"""
def __init__( self , __A , __A , __A , __A , __A , __A=1E-6 ) -> Union[str, Any]:
super().__init__()
_lowerCAmelCase =nn.ModuleList()
# cond self attention: layer 0
self.layer.append(
TaLayerSelfAttentionCond(d_model=__A , d_kv=__A , num_heads=__A , dropout_rate=__A ) )
# cross attention: layer 1
self.layer.append(
TaLayerCrossAttention(
d_model=__A , d_kv=__A , num_heads=__A , dropout_rate=__A , layer_norm_epsilon=__A , ) )
# Film Cond MLP + dropout: last layer
self.layer.append(
TaLayerFFCond(d_model=__A , d_ff=__A , dropout_rate=__A , layer_norm_epsilon=__A ) )
def UpperCamelCase__ ( self , __A , __A=None , __A=None , __A=None , __A=None , __A=None , ) -> Any:
_lowerCAmelCase =self.layer[0](
__A , conditioning_emb=__A , attention_mask=__A , )
if encoder_hidden_states is not None:
_lowerCAmelCase =torch.where(encoder_attention_mask > 0 , 0 , -1E10 ).to(
encoder_hidden_states.dtype )
_lowerCAmelCase =self.layer[1](
__A , key_value_states=__A , attention_mask=__A , )
# Apply Film Conditional Feed Forward layer
_lowerCAmelCase =self.layer[-1](__A , __A )
return (hidden_states,)
class SCREAMING_SNAKE_CASE ( nn.Module):
"""simple docstring"""
def __init__( self , __A , __A , __A , __A ) -> Optional[Any]:
super().__init__()
_lowerCAmelCase =TaLayerNorm(__A )
_lowerCAmelCase =TaFiLMLayer(in_features=d_model * 4 , out_features=__A )
_lowerCAmelCase =Attention(query_dim=__A , heads=__A , dim_head=__A , out_bias=__A , scale_qk=__A )
_lowerCAmelCase =nn.Dropout(__A )
def UpperCamelCase__ ( self , __A , __A=None , __A=None , ) -> List[Any]:
# pre_self_attention_layer_norm
_lowerCAmelCase =self.layer_norm(__A )
if conditioning_emb is not None:
_lowerCAmelCase =self.FiLMLayer(__A , __A )
# Self-attention block
_lowerCAmelCase =self.attention(__A )
_lowerCAmelCase =hidden_states + self.dropout(__A )
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module):
"""simple docstring"""
def __init__( self , __A , __A , __A , __A , __A ) -> Optional[int]:
super().__init__()
_lowerCAmelCase =Attention(query_dim=__A , heads=__A , dim_head=__A , out_bias=__A , scale_qk=__A )
_lowerCAmelCase =TaLayerNorm(__A , eps=__A )
_lowerCAmelCase =nn.Dropout(__A )
def UpperCamelCase__ ( self , __A , __A=None , __A=None , ) -> Tuple:
_lowerCAmelCase =self.layer_norm(__A )
_lowerCAmelCase =self.attention(
__A , encoder_hidden_states=__A , attention_mask=attention_mask.squeeze(1 ) , )
_lowerCAmelCase =hidden_states + self.dropout(__A )
return layer_output
class SCREAMING_SNAKE_CASE ( nn.Module):
"""simple docstring"""
def __init__( self , __A , __A , __A , __A ) -> Optional[Any]:
super().__init__()
_lowerCAmelCase =TaDenseGatedActDense(d_model=__A , d_ff=__A , dropout_rate=__A )
_lowerCAmelCase =TaFiLMLayer(in_features=d_model * 4 , out_features=__A )
_lowerCAmelCase =TaLayerNorm(__A , eps=__A )
_lowerCAmelCase =nn.Dropout(__A )
def UpperCamelCase__ ( self , __A , __A=None ) -> List[Any]:
_lowerCAmelCase =self.layer_norm(__A )
if conditioning_emb is not None:
_lowerCAmelCase =self.film(__A , __A )
_lowerCAmelCase =self.DenseReluDense(__A )
_lowerCAmelCase =hidden_states + self.dropout(__A )
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module):
"""simple docstring"""
def __init__( self , __A , __A , __A ) -> Union[str, Any]:
super().__init__()
_lowerCAmelCase =nn.Linear(__A , __A , bias=__A )
_lowerCAmelCase =nn.Linear(__A , __A , bias=__A )
_lowerCAmelCase =nn.Linear(__A , __A , bias=__A )
_lowerCAmelCase =nn.Dropout(__A )
_lowerCAmelCase =NewGELUActivation()
def UpperCamelCase__ ( self , __A ) -> List[Any]:
_lowerCAmelCase =self.act(self.wi_a(__A ) )
_lowerCAmelCase =self.wi_a(__A )
_lowerCAmelCase =hidden_gelu * hidden_linear
_lowerCAmelCase =self.dropout(__A )
_lowerCAmelCase =self.wo(__A )
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module):
"""simple docstring"""
def __init__( self , __A , __A=1E-6 ) -> int:
super().__init__()
_lowerCAmelCase =nn.Parameter(torch.ones(__A ) )
_lowerCAmelCase =eps
def UpperCamelCase__ ( self , __A ) -> Dict:
# T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
# Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
# w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
# half-precision inputs is done in fp32
_lowerCAmelCase =hidden_states.to(torch.floataa ).pow(2 ).mean(-1 , keepdim=__A )
_lowerCAmelCase =hidden_states * torch.rsqrt(variance + self.variance_epsilon )
# convert into half-precision if necessary
if self.weight.dtype in [torch.floataa, torch.bfloataa]:
_lowerCAmelCase =hidden_states.to(self.weight.dtype )
return self.weight * hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module):
"""simple docstring"""
def UpperCamelCase__ ( self , __A ) -> torch.Tensor:
return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi ) * (input + 0.044_715 * torch.pow(__A , 3.0 )) ))
class SCREAMING_SNAKE_CASE ( nn.Module):
"""simple docstring"""
def __init__( self , __A , __A ) -> Optional[Any]:
super().__init__()
_lowerCAmelCase =nn.Linear(__A , out_features * 2 , bias=__A )
def UpperCamelCase__ ( self , __A , __A ) -> Optional[Any]:
_lowerCAmelCase =self.scale_bias(__A )
_lowerCAmelCase , _lowerCAmelCase =torch.chunk(__A , 2 , -1 )
_lowerCAmelCase =x * (1 + scale) + shift
return x
| 58
| 1
|
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase__ ( a__ , a__ ):
'''simple docstring'''
_lowerCAmelCase =[]
create_all_state(1 , a__ , a__ , [] , a__ )
return result
def UpperCamelCase__ ( a__ , a__ , a__ , a__ , a__ , ):
'''simple docstring'''
if level == 0:
total_list.append(current_list[:] )
return
for i in range(a__ , total_number - level + 2 ):
current_list.append(a__ )
create_all_state(i + 1 , a__ , level - 1 , a__ , a__ )
current_list.pop()
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
for i in total_list:
print(*a__ )
if __name__ == "__main__":
lowercase_ = 4
lowercase_ = 2
lowercase_ = generate_all_combinations(n, k)
print_all_state(total_list)
| 58
|
'''simple docstring'''
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_available() and not is_torch_available():
raise RuntimeError('''At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training''')
# TF training parameters
lowercase_ = False
lowercase_ = False
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
return TrainCommand(a__ )
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
@staticmethod
def UpperCamelCase__ ( __A ) -> Tuple:
_lowerCAmelCase =parser.add_parser('train' , help='CLI tool to train a model on a task.' )
train_parser.add_argument(
'--train_data' , type=__A , required=__A , help='path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.' , )
train_parser.add_argument(
'--column_label' , type=__A , default=0 , help='Column of the dataset csv file with example labels.' )
train_parser.add_argument(
'--column_text' , type=__A , default=1 , help='Column of the dataset csv file with example texts.' )
train_parser.add_argument(
'--column_id' , type=__A , default=2 , help='Column of the dataset csv file with example ids.' )
train_parser.add_argument(
'--skip_first_row' , action='store_true' , help='Skip the first row of the csv file (headers).' )
train_parser.add_argument('--validation_data' , type=__A , default='' , help='path to validation dataset.' )
train_parser.add_argument(
'--validation_split' , type=__A , default=0.1 , help='if validation dataset is not provided, fraction of train dataset to use as validation dataset.' , )
train_parser.add_argument('--output' , type=__A , default='./' , help='path to saved the trained model.' )
train_parser.add_argument(
'--task' , type=__A , default='text_classification' , help='Task to train the model on.' )
train_parser.add_argument(
'--model' , type=__A , default='bert-base-uncased' , help='Model\'s name or path to stored model.' )
train_parser.add_argument('--train_batch_size' , type=__A , default=32 , help='Batch size for training.' )
train_parser.add_argument('--valid_batch_size' , type=__A , default=64 , help='Batch size for validation.' )
train_parser.add_argument('--learning_rate' , type=__A , default=3E-5 , help='Learning rate.' )
train_parser.add_argument('--adam_epsilon' , type=__A , default=1E-08 , help='Epsilon for Adam optimizer.' )
train_parser.set_defaults(func=__A )
def __init__( self , __A ) -> List[str]:
_lowerCAmelCase =logging.get_logger('transformers-cli/training' )
_lowerCAmelCase ='tf' if is_tf_available() else 'torch'
os.makedirs(args.output , exist_ok=__A )
_lowerCAmelCase =args.output
_lowerCAmelCase =args.column_label
_lowerCAmelCase =args.column_text
_lowerCAmelCase =args.column_id
self.logger.info(F'''Loading {args.task} pipeline for {args.model}''' )
if args.task == "text_classification":
_lowerCAmelCase =TextClassificationPipeline.from_pretrained(args.model )
elif args.task == "token_classification":
raise NotImplementedError
elif args.task == "question_answering":
raise NotImplementedError
self.logger.info(F'''Loading dataset from {args.train_data}''' )
_lowerCAmelCase =Processor.create_from_csv(
args.train_data , column_label=args.column_label , column_text=args.column_text , column_id=args.column_id , skip_first_row=args.skip_first_row , )
_lowerCAmelCase =None
if args.validation_data:
self.logger.info(F'''Loading validation dataset from {args.validation_data}''' )
_lowerCAmelCase =Processor.create_from_csv(
args.validation_data , column_label=args.column_label , column_text=args.column_text , column_id=args.column_id , skip_first_row=args.skip_first_row , )
_lowerCAmelCase =args.validation_split
_lowerCAmelCase =args.train_batch_size
_lowerCAmelCase =args.valid_batch_size
_lowerCAmelCase =args.learning_rate
_lowerCAmelCase =args.adam_epsilon
def UpperCamelCase__ ( self ) -> List[str]:
if self.framework == "tf":
return self.run_tf()
return self.run_torch()
def UpperCamelCase__ ( self ) -> Union[str, Any]:
raise NotImplementedError
def UpperCamelCase__ ( self ) -> List[Any]:
self.pipeline.fit(
self.train_dataset , validation_data=self.valid_dataset , validation_split=self.validation_split , learning_rate=self.learning_rate , adam_epsilon=self.adam_epsilon , train_batch_size=self.train_batch_size , valid_batch_size=self.valid_batch_size , )
# Save trained pipeline
self.pipeline.save_pretrained(self.output )
| 58
| 1
|
'''simple docstring'''
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCamelCase__ ( a__ , a__ , a__ , a__ ):
'''simple docstring'''
_lowerCAmelCase =FunnelConfig.from_json_file(a__ )
print(F'''Building PyTorch model from configuration: {config}''' )
_lowerCAmelCase =FunnelBaseModel(a__ ) if base_model else FunnelModel(a__ )
# Load weights from tf checkpoint
load_tf_weights_in_funnel(a__ , a__ , a__ )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
torch.save(model.state_dict() , a__ )
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help='''The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.''',
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--base_model''', action='''store_true''', help='''Whether you want just the base model (no decoder) or not.'''
)
lowercase_ = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
)
| 58
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowercase_ = {'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
'''VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ViTMAEForPreTraining''',
'''ViTMAELayer''',
'''ViTMAEModel''',
'''ViTMAEPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
'''TFViTMAEForPreTraining''',
'''TFViTMAEModel''',
'''TFViTMAEPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit_mae import (
VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTMAEForPreTraining,
ViTMAELayer,
ViTMAEModel,
ViTMAEPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel
else:
import sys
lowercase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 58
| 1
|
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE ( unittest.TestCase):
"""simple docstring"""
def UpperCamelCase__ ( self ) -> Union[str, Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase__ ( self ) -> Tuple:
_lowerCAmelCase =StableDiffusionKDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4' )
_lowerCAmelCase =sd_pipe.to(__A )
sd_pipe.set_progress_bar_config(disable=__A )
sd_pipe.set_scheduler('sample_euler' )
_lowerCAmelCase ='A painting of a squirrel eating a burger'
_lowerCAmelCase =torch.manual_seed(0 )
_lowerCAmelCase =sd_pipe([prompt] , generator=__A , guidance_scale=9.0 , num_inference_steps=20 , output_type='np' )
_lowerCAmelCase =output.images
_lowerCAmelCase =image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
_lowerCAmelCase =np.array([0.0_447, 0.0_492, 0.0_468, 0.0_408, 0.0_383, 0.0_408, 0.0_354, 0.0_380, 0.0_339] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def UpperCamelCase__ ( self ) -> Any:
_lowerCAmelCase =StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base' )
_lowerCAmelCase =sd_pipe.to(__A )
sd_pipe.set_progress_bar_config(disable=__A )
sd_pipe.set_scheduler('sample_euler' )
_lowerCAmelCase ='A painting of a squirrel eating a burger'
_lowerCAmelCase =torch.manual_seed(0 )
_lowerCAmelCase =sd_pipe([prompt] , generator=__A , guidance_scale=9.0 , num_inference_steps=20 , output_type='np' )
_lowerCAmelCase =output.images
_lowerCAmelCase =image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
_lowerCAmelCase =np.array([0.1_237, 0.1_320, 0.1_438, 0.1_359, 0.1_390, 0.1_132, 0.1_277, 0.1_175, 0.1_112] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-1
def UpperCamelCase__ ( self ) -> int:
_lowerCAmelCase =StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base' )
_lowerCAmelCase =sd_pipe.to(__A )
sd_pipe.set_progress_bar_config(disable=__A )
sd_pipe.set_scheduler('sample_dpmpp_2m' )
_lowerCAmelCase ='A painting of a squirrel eating a burger'
_lowerCAmelCase =torch.manual_seed(0 )
_lowerCAmelCase =sd_pipe(
[prompt] , generator=__A , guidance_scale=7.5 , num_inference_steps=15 , output_type='np' , use_karras_sigmas=__A , )
_lowerCAmelCase =output.images
_lowerCAmelCase =image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
_lowerCAmelCase =np.array(
[0.11_381_689, 0.12_112_921, 0.1_389_457, 0.12_549_606, 0.1_244_964, 0.10_831_517, 0.11_562_866, 0.10_867_816, 0.10_499_048] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 58
|
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
_lowerCAmelCase =os.path.join(args.tf_model_dir , 'parameters.json' )
_lowerCAmelCase =json.loads(open(a__ ).read() )
if not params:
raise ValueError(
F'''It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file.''' )
if not args.output.endswith('.pt' ):
_lowerCAmelCase =args.output + '.pt'
_lowerCAmelCase =OrderedDict()
with tf.device('/CPU:0' ):
_lowerCAmelCase =tf.train.load_checkpoint(args.tf_model_dir )
_lowerCAmelCase =reader.get_variable_to_shape_map()
for key_name in shapes.keys():
_lowerCAmelCase =reader.get_tensor(a__ ).astype(np.floataa )
if key_name.endswith('/adam_m' ) or key_name.endswith('/adam_v' ):
continue
if key_name.startswith('pasts/' ):
if key_name.startswith('pasts/mlp' ):
_lowerCAmelCase =int(key_name[9] )
elif key_name.startswith('pasts/out' ):
_lowerCAmelCase =8
_lowerCAmelCase ='model.sqout.%d.weight' % (player * 2) # enter to nn.Sequencial with Tanh, so 2 at a time
_lowerCAmelCase =vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.startswith('model/moe' ):
_lowerCAmelCase =int(key_name[9:].split('/' )[0] )
if key_name.endswith('/switch_gating/kernel' ):
_lowerCAmelCase ='model.blocks.%d.feed_forward.mlp.router.classifier.weight' % player
_lowerCAmelCase =vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.endswith('/softmlp/kernel' ):
_lowerCAmelCase ='model.blocks.%d.feed_forward.soft_bypass_mlp.weight' % player
_lowerCAmelCase =vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.endswith('/wo/kernel' ) or key_name.endswith('/wi/kernel' ):
_lowerCAmelCase =key_name[-9:-7]
for i in range(1_6 ):
_lowerCAmelCase ='model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight' % (player, i, nlayer)
_lowerCAmelCase =(
vnp[i].transpose([1, 0] ).copy()
) # In Mesh-Tensorflow, it is one array, so it is divided
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.startswith('model/mlp' ):
_lowerCAmelCase =int(key_name[9:].split('/' )[0] )
if key_name.endswith('/p1/kernel' ):
_lowerCAmelCase ='model.blocks.%d.feed_forward.mlp.wi.weight' % player
_lowerCAmelCase =vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.endswith('/p1/bias' ):
_lowerCAmelCase ='model.blocks.%d.feed_forward.mlp.wi.bias' % player
_lowerCAmelCase =vnp.copy() # same because it is one dimensional
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.endswith('/p2/kernel' ):
_lowerCAmelCase ='model.blocks.%d.feed_forward.mlp.wo.weight' % player
_lowerCAmelCase =vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.endswith('/p2/bias' ):
_lowerCAmelCase ='model.blocks.%d.feed_forward.mlp.wo.bias' % player
_lowerCAmelCase =vnp.copy() # same because it is one dimensional
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.startswith('model/ln' ):
_lowerCAmelCase =int(key_name[8:].split('/' )[0] )
if key_name.endswith('/b' ):
_lowerCAmelCase ='model.blocks.%d.feed_forward.norm.bias' % player
_lowerCAmelCase =vnp.copy() # same because it is one dimensional
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.endswith('/g' ):
_lowerCAmelCase ='model.blocks.%d.feed_forward.norm.weight' % player
_lowerCAmelCase =vnp.copy() # same because it is one dimensional
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.startswith('model/att' ):
_lowerCAmelCase =int(key_name[9:].split('/' )[0] )
if key_name.endswith('/qkv/kernel' ):
_lowerCAmelCase =vnp.copy() # Compute same dimension as Mesh-tensorflow using einsum
_lowerCAmelCase =state[:, 0, :, :]
_lowerCAmelCase =state[:, 1, :, :]
_lowerCAmelCase =state[:, 2, :, :]
_lowerCAmelCase =(
state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
_lowerCAmelCase =(
state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
_lowerCAmelCase =(
state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
_lowerCAmelCase ='model.blocks.%d.self_attn.self_attn.q_proj.weight' % player
_lowerCAmelCase =torch.tensor(a__ )
_lowerCAmelCase ='model.blocks.%d.self_attn.self_attn.k_proj.weight' % player
_lowerCAmelCase =torch.tensor(a__ )
_lowerCAmelCase ='model.blocks.%d.self_attn.self_attn.v_proj.weight' % player
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.endswith('/o/kernel' ):
_lowerCAmelCase ='model.blocks.%d.self_attn.self_attn.out_proj.weight' % player
_lowerCAmelCase =(
vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]] ).transpose([1, 0] ).copy()
) # Mesh-Tensorflow is a diagonal matrix
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.startswith('model/an' ):
_lowerCAmelCase =int(key_name[8:].split('/' )[0] )
if key_name.endswith('/b' ):
_lowerCAmelCase ='model.blocks.%d.self_attn.norm.bias' % player
_lowerCAmelCase =vnp.copy() # same because it is one dimensional
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.endswith('/g' ):
_lowerCAmelCase ='model.blocks.%d.self_attn.norm.weight' % player
_lowerCAmelCase =vnp.copy() # same because it is one dimensional
_lowerCAmelCase =torch.tensor(a__ )
elif (
key_name.startswith('model/wte' )
or key_name.startswith('model/wpe' )
or key_name.startswith('model/ete' )
):
_lowerCAmelCase ={'wte': 'embed_tokens', 'wpe': 'position_embeddings', 'ete': 'extra_position_embeddings'}[
key_name[-3:]
]
_lowerCAmelCase ='model.%s.weight' % nlayer
_lowerCAmelCase =vnp.copy() # same in embedded
_lowerCAmelCase =torch.tensor(a__ )
if key_name.startswith('model/wte' ):
_lowerCAmelCase ='lm_head.weight'
_lowerCAmelCase =vnp.copy() # same in embedded
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.startswith('model/wob' ):
_lowerCAmelCase ='final_logits_bias'
_lowerCAmelCase =vnp.copy() # same in embedded
_lowerCAmelCase =state.reshape((1, -1) )
_lowerCAmelCase =torch.tensor(a__ )
elif key_name == "model/dense/kernel":
_lowerCAmelCase ='model.last_project.weight'
_lowerCAmelCase =vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_lowerCAmelCase =torch.tensor(a__ )
elif key_name == "model/dense_1/bias":
_lowerCAmelCase ='model.last_project.bias'
_lowerCAmelCase =vnp.copy() # same because it is one dimensional
_lowerCAmelCase =torch.tensor(a__ )
torch.save(a__ , args.output )
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser(
description='''model converter.''', formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument('''--tf_model_dir''', metavar='''PATH''', type=str, required=True, help='''import model''')
parser.add_argument('''--output''', metavar='''PATH''', type=str, required=True, help='''output model''')
lowercase_ = parser.parse_args()
convert_tf_gptsan_to_pt(args)
| 58
| 1
|
'''simple docstring'''
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
_lowerCAmelCase =set()
# To detect a back edge, keep track of vertices currently in the recursion stack
_lowerCAmelCase =set()
return any(
node not in visited and depth_first_search(a__ , a__ , a__ , a__ )
for node in graph )
def UpperCamelCase__ ( a__ , a__ , a__ , a__ ):
'''simple docstring'''
visited.add(a__ )
rec_stk.add(a__ )
for node in graph[vertex]:
if node not in visited:
if depth_first_search(a__ , a__ , a__ , a__ ):
return True
elif node in rec_stk:
return True
# The node needs to be removed from recursion stack before function ends
rec_stk.remove(a__ )
return False
if __name__ == "__main__":
from doctest import testmod
testmod()
| 58
|
'''simple docstring'''
def UpperCamelCase__ ( a__ = 1_0_0_0 ):
'''simple docstring'''
_lowerCAmelCase =2**power
_lowerCAmelCase =0
while n:
_lowerCAmelCase , _lowerCAmelCase =r + n % 1_0, n // 1_0
return r
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 58
| 1
|
'''simple docstring'''
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __A , __A , __A ) -> Tuple:
_lowerCAmelCase =None
_lowerCAmelCase =None
_lowerCAmelCase =graph
self._normalize_graph(__A , __A )
_lowerCAmelCase =len(__A )
_lowerCAmelCase =None
def UpperCamelCase__ ( self , __A , __A ) -> Optional[int]:
if sources is int:
_lowerCAmelCase =[sources]
if sinks is int:
_lowerCAmelCase =[sinks]
if len(__A ) == 0 or len(__A ) == 0:
return
_lowerCAmelCase =sources[0]
_lowerCAmelCase =sinks[0]
# make fake vertex if there are more
# than one source or sink
if len(__A ) > 1 or len(__A ) > 1:
_lowerCAmelCase =0
for i in sources:
max_input_flow += sum(self.graph[i] )
_lowerCAmelCase =len(self.graph ) + 1
for room in self.graph:
room.insert(0 , 0 )
self.graph.insert(0 , [0] * size )
for i in sources:
_lowerCAmelCase =max_input_flow
_lowerCAmelCase =0
_lowerCAmelCase =len(self.graph ) + 1
for room in self.graph:
room.append(0 )
self.graph.append([0] * size )
for i in sinks:
_lowerCAmelCase =max_input_flow
_lowerCAmelCase =size - 1
def UpperCamelCase__ ( self ) -> Optional[Any]:
if self.maximum_flow_algorithm is None:
raise Exception('You need to set maximum flow algorithm before.' )
if self.source_index is None or self.sink_index is None:
return 0
self.maximum_flow_algorithm.execute()
return self.maximum_flow_algorithm.getMaximumFlow()
def UpperCamelCase__ ( self , __A ) -> Dict:
_lowerCAmelCase =algorithm(self )
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __A ) -> Dict:
_lowerCAmelCase =flow_network
_lowerCAmelCase =flow_network.verticesCount
_lowerCAmelCase =flow_network.sourceIndex
_lowerCAmelCase =flow_network.sinkIndex
# it's just a reference, so you shouldn't change
# it in your algorithms, use deep copy before doing that
_lowerCAmelCase =flow_network.graph
_lowerCAmelCase =False
def UpperCamelCase__ ( self ) -> List[str]:
if not self.executed:
self._algorithm()
_lowerCAmelCase =True
def UpperCamelCase__ ( self ) -> Tuple:
pass
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
def __init__( self , __A ) -> List[Any]:
super().__init__(__A )
# use this to save your result
_lowerCAmelCase =-1
def UpperCamelCase__ ( self ) -> Dict:
if not self.executed:
raise Exception('You should execute algorithm before using its result!' )
return self.maximum_flow
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
def __init__( self , __A ) -> List[str]:
super().__init__(__A )
_lowerCAmelCase =[[0] * self.verticies_count for i in range(self.verticies_count )]
_lowerCAmelCase =[0] * self.verticies_count
_lowerCAmelCase =[0] * self.verticies_count
def UpperCamelCase__ ( self ) -> List[str]:
_lowerCAmelCase =self.verticies_count
# push some substance to graph
for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index] ):
self.preflow[self.source_index][nextvertex_index] += bandwidth
self.preflow[nextvertex_index][self.source_index] -= bandwidth
self.excesses[nextvertex_index] += bandwidth
# Relabel-to-front selection rule
_lowerCAmelCase =[
i
for i in range(self.verticies_count )
if i != self.source_index and i != self.sink_index
]
# move through list
_lowerCAmelCase =0
while i < len(__A ):
_lowerCAmelCase =vertices_list[i]
_lowerCAmelCase =self.heights[vertex_index]
self.process_vertex(__A )
if self.heights[vertex_index] > previous_height:
# if it was relabeled, swap elements
# and start from 0 index
vertices_list.insert(0 , vertices_list.pop(__A ) )
_lowerCAmelCase =0
else:
i += 1
_lowerCAmelCase =sum(self.preflow[self.source_index] )
def UpperCamelCase__ ( self , __A ) -> Union[str, Any]:
while self.excesses[vertex_index] > 0:
for neighbour_index in range(self.verticies_count ):
# if it's neighbour and current vertex is higher
if (
self.graph[vertex_index][neighbour_index]
- self.preflow[vertex_index][neighbour_index]
> 0
and self.heights[vertex_index] > self.heights[neighbour_index]
):
self.push(__A , __A )
self.relabel(__A )
def UpperCamelCase__ ( self , __A , __A ) -> List[Any]:
_lowerCAmelCase =min(
self.excesses[from_index] , self.graph[from_index][to_index] - self.preflow[from_index][to_index] , )
self.preflow[from_index][to_index] += preflow_delta
self.preflow[to_index][from_index] -= preflow_delta
self.excesses[from_index] -= preflow_delta
self.excesses[to_index] += preflow_delta
def UpperCamelCase__ ( self , __A ) -> Optional[int]:
_lowerCAmelCase =None
for to_index in range(self.verticies_count ):
if (
self.graph[vertex_index][to_index]
- self.preflow[vertex_index][to_index]
> 0
) and (min_height is None or self.heights[to_index] < min_height):
_lowerCAmelCase =self.heights[to_index]
if min_height is not None:
_lowerCAmelCase =min_height + 1
if __name__ == "__main__":
lowercase_ = [0]
lowercase_ = [3]
# graph = [
# [0, 0, 4, 6, 0, 0],
# [0, 0, 5, 2, 0, 0],
# [0, 0, 0, 0, 4, 4],
# [0, 0, 0, 0, 6, 6],
# [0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0],
# ]
lowercase_ = [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]
# prepare our network
lowercase_ = FlowNetwork(graph, entrances, exits)
# set algorithm
flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
# and calculate
lowercase_ = flow_network.find_maximum_flow()
print(F'maximum flow is {maximum_flow}')
| 58
|
'''simple docstring'''
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
_lowerCAmelCase =set()
# To detect a back edge, keep track of vertices currently in the recursion stack
_lowerCAmelCase =set()
return any(
node not in visited and depth_first_search(a__ , a__ , a__ , a__ )
for node in graph )
def UpperCamelCase__ ( a__ , a__ , a__ , a__ ):
'''simple docstring'''
visited.add(a__ )
rec_stk.add(a__ )
for node in graph[vertex]:
if node not in visited:
if depth_first_search(a__ , a__ , a__ , a__ ):
return True
elif node in rec_stk:
return True
# The node needs to be removed from recursion stack before function ends
rec_stk.remove(a__ )
return False
if __name__ == "__main__":
from doctest import testmod
testmod()
| 58
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowercase_ = {'''configuration_swin''': ['''SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SwinConfig''', '''SwinOnnxConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
'''SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''SwinForImageClassification''',
'''SwinForMaskedImageModeling''',
'''SwinModel''',
'''SwinPreTrainedModel''',
'''SwinBackbone''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
'''TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFSwinForImageClassification''',
'''TFSwinForMaskedImageModeling''',
'''TFSwinModel''',
'''TFSwinPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swin import (
SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
SwinBackbone,
SwinForImageClassification,
SwinForMaskedImageModeling,
SwinModel,
SwinPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_swin import (
TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
TFSwinForImageClassification,
TFSwinForMaskedImageModeling,
TFSwinModel,
TFSwinPreTrainedModel,
)
else:
import sys
lowercase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 58
|
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
'''salesforce/blip2-opt-2.7b''': '''https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json''',
}
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : Tuple = 'blip_2_vision_model'
def __init__( self , __A=1408 , __A=6144 , __A=39 , __A=16 , __A=224 , __A=14 , __A="gelu" , __A=0.00_001 , __A=0.0 , __A=1E-10 , __A=True , **__A , ) -> int:
super().__init__(**__A )
_lowerCAmelCase =hidden_size
_lowerCAmelCase =intermediate_size
_lowerCAmelCase =num_hidden_layers
_lowerCAmelCase =num_attention_heads
_lowerCAmelCase =patch_size
_lowerCAmelCase =image_size
_lowerCAmelCase =initializer_range
_lowerCAmelCase =attention_dropout
_lowerCAmelCase =layer_norm_eps
_lowerCAmelCase =hidden_act
_lowerCAmelCase =qkv_bias
@classmethod
def UpperCamelCase__ ( cls , __A , **__A ) -> "PretrainedConfig":
cls._set_token_in_kwargs(__A )
_lowerCAmelCase , _lowerCAmelCase =cls.get_config_dict(__A , **__A )
# get the vision config dict if we are loading from Blip2Config
if config_dict.get('model_type' ) == "blip-2":
_lowerCAmelCase =config_dict['vision_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(__A , **__A )
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : int = 'blip_2_qformer'
def __init__( self , __A=3_0522 , __A=768 , __A=12 , __A=12 , __A=3072 , __A="gelu" , __A=0.1 , __A=0.1 , __A=512 , __A=0.02 , __A=1E-12 , __A=0 , __A="absolute" , __A=2 , __A=1408 , **__A , ) -> List[str]:
super().__init__(pad_token_id=__A , **__A )
_lowerCAmelCase =vocab_size
_lowerCAmelCase =hidden_size
_lowerCAmelCase =num_hidden_layers
_lowerCAmelCase =num_attention_heads
_lowerCAmelCase =hidden_act
_lowerCAmelCase =intermediate_size
_lowerCAmelCase =hidden_dropout_prob
_lowerCAmelCase =attention_probs_dropout_prob
_lowerCAmelCase =max_position_embeddings
_lowerCAmelCase =initializer_range
_lowerCAmelCase =layer_norm_eps
_lowerCAmelCase =position_embedding_type
_lowerCAmelCase =cross_attention_frequency
_lowerCAmelCase =encoder_hidden_size
@classmethod
def UpperCamelCase__ ( cls , __A , **__A ) -> "PretrainedConfig":
cls._set_token_in_kwargs(__A )
_lowerCAmelCase , _lowerCAmelCase =cls.get_config_dict(__A , **__A )
# get the qformer config dict if we are loading from Blip2Config
if config_dict.get('model_type' ) == "blip-2":
_lowerCAmelCase =config_dict['qformer_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(__A , **__A )
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : Optional[int] = 'blip-2'
lowercase : Any = True
def __init__( self , __A=None , __A=None , __A=None , __A=32 , **__A ) -> int:
super().__init__(**__A )
if vision_config is None:
_lowerCAmelCase ={}
logger.info('vision_config is None. initializing the Blip2VisionConfig with default values.' )
if qformer_config is None:
_lowerCAmelCase ={}
logger.info('qformer_config is None. Initializing the Blip2QFormerConfig with default values.' )
if text_config is None:
_lowerCAmelCase ={}
logger.info('text_config is None. Initializing the text config with default values (`OPTConfig`).' )
_lowerCAmelCase =BlipaVisionConfig(**__A )
_lowerCAmelCase =BlipaQFormerConfig(**__A )
_lowerCAmelCase =text_config['model_type'] if 'model_type' in text_config else 'opt'
_lowerCAmelCase =CONFIG_MAPPING[text_model_type](**__A )
_lowerCAmelCase =self.text_config.tie_word_embeddings
_lowerCAmelCase =self.text_config.is_encoder_decoder
_lowerCAmelCase =num_query_tokens
_lowerCAmelCase =self.vision_config.hidden_size
_lowerCAmelCase =self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
_lowerCAmelCase =1.0
_lowerCAmelCase =0.02
@classmethod
def UpperCamelCase__ ( cls , __A , __A , __A , **__A , ) -> Any:
return cls(
vision_config=vision_config.to_dict() , qformer_config=qformer_config.to_dict() , text_config=text_config.to_dict() , **__A , )
def UpperCamelCase__ ( self ) -> Tuple:
_lowerCAmelCase =copy.deepcopy(self.__dict__ )
_lowerCAmelCase =self.vision_config.to_dict()
_lowerCAmelCase =self.qformer_config.to_dict()
_lowerCAmelCase =self.text_config.to_dict()
_lowerCAmelCase =self.__class__.model_type
return output
| 58
| 1
|
'''simple docstring'''
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def UpperCamelCase__ ( a__ = "" ):
'''simple docstring'''
_lowerCAmelCase =url or 'https://www.imdb.com/chart/top/?ref_=nv_mv_250'
_lowerCAmelCase =BeautifulSoup(requests.get(a__ ).text , 'html.parser' )
_lowerCAmelCase =soup.find_all('td' , attrs='titleColumn' )
_lowerCAmelCase =soup.find_all('td' , class_='ratingColumn imdbRating' )
return {
title.a.text: float(rating.strong.text )
for title, rating in zip(a__ , a__ )
}
def UpperCamelCase__ ( a__ = "IMDb_Top_250_Movies.csv" ):
'''simple docstring'''
_lowerCAmelCase =get_imdb_top_aaa_movies()
with open(a__ , 'w' , newline='' ) as out_file:
_lowerCAmelCase =csv.writer(a__ )
writer.writerow(['Movie title', 'IMDb rating'] )
for title, rating in movies.items():
writer.writerow([title, rating] )
if __name__ == "__main__":
write_movies()
| 58
|
'''simple docstring'''
lowercase_ = {
'''A''': '''.-''', '''B''': '''-...''', '''C''': '''-.-.''', '''D''': '''-..''', '''E''': '''.''', '''F''': '''..-.''', '''G''': '''--.''',
'''H''': '''....''', '''I''': '''..''', '''J''': '''.---''', '''K''': '''-.-''', '''L''': '''.-..''', '''M''': '''--''', '''N''': '''-.''',
'''O''': '''---''', '''P''': '''.--.''', '''Q''': '''--.-''', '''R''': '''.-.''', '''S''': '''...''', '''T''': '''-''', '''U''': '''..-''',
'''V''': '''...-''', '''W''': '''.--''', '''X''': '''-..-''', '''Y''': '''-.--''', '''Z''': '''--..''', '''1''': '''.----''',
'''2''': '''..---''', '''3''': '''...--''', '''4''': '''....-''', '''5''': '''.....''', '''6''': '''-....''', '''7''': '''--...''',
'''8''': '''---..''', '''9''': '''----.''', '''0''': '''-----''', '''&''': '''.-...''', '''@''': '''.--.-.''',
''':''': '''---...''', ''',''': '''--..--''', '''.''': '''.-.-.-''', '''\'''': '''.----.''', '''"''': '''.-..-.''',
'''?''': '''..--..''', '''/''': '''-..-.''', '''=''': '''-...-''', '''+''': '''.-.-.''', '''-''': '''-....-''',
'''(''': '''-.--.''', ''')''': '''-.--.-''', '''!''': '''-.-.--''', ''' ''': '''/'''
} # Exclamation mark is not in ITU-R recommendation
# fmt: on
lowercase_ = {value: key for key, value in MORSE_CODE_DICT.items()}
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
return " ".join(MORSE_CODE_DICT[char] for char in message.upper() )
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
return "".join(REVERSE_DICT[char] for char in message.split() )
def UpperCamelCase__ ( ):
'''simple docstring'''
_lowerCAmelCase ='Morse code here!'
print(a__ )
_lowerCAmelCase =encrypt(a__ )
print(a__ )
_lowerCAmelCase =decrypt(a__ )
print(a__ )
if __name__ == "__main__":
main()
| 58
| 1
|
'''simple docstring'''
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_xpu,
require_torch_min_version,
require_tpu,
require_xpu,
skip,
slow,
)
from .training import RegressionDataset, RegressionModel, RegressionModelaXPU
from .scripts import test_script, test_sync, test_ops # isort: skip
| 58
|
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
'''facebook/data2vec-text-base''': '''https://huggingface.co/data2vec/resolve/main/config.json''',
}
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : List[str] = 'data2vec-text'
def __init__( self , __A=3_0522 , __A=768 , __A=12 , __A=12 , __A=3072 , __A="gelu" , __A=0.1 , __A=0.1 , __A=512 , __A=2 , __A=0.02 , __A=1E-12 , __A=1 , __A=0 , __A=2 , __A="absolute" , __A=True , __A=None , **__A , ) -> List[Any]:
super().__init__(pad_token_id=__A , bos_token_id=__A , eos_token_id=__A , **__A )
_lowerCAmelCase =vocab_size
_lowerCAmelCase =hidden_size
_lowerCAmelCase =num_hidden_layers
_lowerCAmelCase =num_attention_heads
_lowerCAmelCase =hidden_act
_lowerCAmelCase =intermediate_size
_lowerCAmelCase =hidden_dropout_prob
_lowerCAmelCase =attention_probs_dropout_prob
_lowerCAmelCase =max_position_embeddings
_lowerCAmelCase =type_vocab_size
_lowerCAmelCase =initializer_range
_lowerCAmelCase =layer_norm_eps
_lowerCAmelCase =position_embedding_type
_lowerCAmelCase =use_cache
_lowerCAmelCase =classifier_dropout
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
@property
def UpperCamelCase__ ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_lowerCAmelCase ={0: 'batch', 1: 'choice', 2: 'sequence'}
else:
_lowerCAmelCase ={0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
] )
| 58
| 1
|
'''simple docstring'''
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
lowercase_ = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
lowercase_ = [file for file in filepaths if file != file.lower()]
if upper_files:
print(F'{len(upper_files)} files contain uppercase characters:')
print('''\n'''.join(upper_files) + '''\n''')
lowercase_ = [file for file in filepaths if ''' ''' in file]
if space_files:
print(F'{len(space_files)} files contain space characters:')
print('''\n'''.join(space_files) + '''\n''')
lowercase_ = [file for file in filepaths if '''-''' in file]
if hyphen_files:
print(F'{len(hyphen_files)} files contain hyphen characters:')
print('''\n'''.join(hyphen_files) + '''\n''')
lowercase_ = [file for file in filepaths if os.sep not in file]
if nodir_files:
print(F'{len(nodir_files)} files are not in a directory:')
print('''\n'''.join(nodir_files) + '''\n''')
lowercase_ = len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
import sys
sys.exit(bad_files)
| 58
|
'''simple docstring'''
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin
@skip_mps
class SCREAMING_SNAKE_CASE ( __lowercase , __lowercase , unittest.TestCase):
"""simple docstring"""
lowercase : List[Any] = IFPipeline
lowercase : Tuple = TEXT_TO_IMAGE_PARAMS - {'width', 'height', 'latents'}
lowercase : Union[str, Any] = TEXT_TO_IMAGE_BATCH_PARAMS
lowercase : int = PipelineTesterMixin.required_optional_params - {'latents'}
def UpperCamelCase__ ( self ) -> str:
return self._get_dummy_components()
def UpperCamelCase__ ( self , __A , __A=0 ) -> int:
if str(__A ).startswith('mps' ):
_lowerCAmelCase =torch.manual_seed(__A )
else:
_lowerCAmelCase =torch.Generator(device=__A ).manual_seed(__A )
_lowerCAmelCase ={
'prompt': 'A painting of a squirrel eating a burger',
'generator': generator,
'num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
def UpperCamelCase__ ( self ) -> Optional[Any]:
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != 'cuda' , reason='float16 requires CUDA' )
def UpperCamelCase__ ( self ) -> Tuple:
# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
super().test_save_load_floataa(expected_max_diff=1E-1 )
def UpperCamelCase__ ( self ) -> List[Any]:
self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )
def UpperCamelCase__ ( self ) -> str:
self._test_save_load_local()
def UpperCamelCase__ ( self ) -> Union[str, Any]:
self._test_inference_batch_single_identical(
expected_max_diff=1E-2 , )
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def UpperCamelCase__ ( self ) -> List[str]:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE ( unittest.TestCase):
"""simple docstring"""
def UpperCamelCase__ ( self ) -> Optional[int]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase__ ( self ) -> Optional[Any]:
# if
_lowerCAmelCase =IFPipeline.from_pretrained('DeepFloyd/IF-I-XL-v1.0' , variant='fp16' , torch_dtype=torch.floataa )
_lowerCAmelCase =IFSuperResolutionPipeline.from_pretrained(
'DeepFloyd/IF-II-L-v1.0' , variant='fp16' , torch_dtype=torch.floataa , text_encoder=__A , tokenizer=__A )
# pre compute text embeddings and remove T5 to save memory
pipe_a.text_encoder.to('cuda' )
_lowerCAmelCase , _lowerCAmelCase =pipe_a.encode_prompt('anime turtle' , device='cuda' )
del pipe_a.tokenizer
del pipe_a.text_encoder
gc.collect()
_lowerCAmelCase =None
_lowerCAmelCase =None
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if(__A , __A , __A , __A )
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# img2img
_lowerCAmelCase =IFImgaImgPipeline(**pipe_a.components )
_lowerCAmelCase =IFImgaImgSuperResolutionPipeline(**pipe_a.components )
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if_imgaimg(__A , __A , __A , __A )
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# inpainting
_lowerCAmelCase =IFInpaintingPipeline(**pipe_a.components )
_lowerCAmelCase =IFInpaintingSuperResolutionPipeline(**pipe_a.components )
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if_inpainting(__A , __A , __A , __A )
def UpperCamelCase__ ( self , __A , __A , __A , __A ) -> str:
# pipeline 1
_start_torch_memory_measurement()
_lowerCAmelCase =torch.Generator(device='cpu' ).manual_seed(0 )
_lowerCAmelCase =pipe_a(
prompt_embeds=__A , negative_prompt_embeds=__A , num_inference_steps=2 , generator=__A , output_type='np' , )
_lowerCAmelCase =output.images[0]
assert image.shape == (64, 64, 3)
_lowerCAmelCase =torch.cuda.max_memory_allocated()
assert mem_bytes < 13 * 10**9
_lowerCAmelCase =load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy' )
assert_mean_pixel_difference(__A , __A )
# pipeline 2
_start_torch_memory_measurement()
_lowerCAmelCase =torch.Generator(device='cpu' ).manual_seed(0 )
_lowerCAmelCase =floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(__A )
_lowerCAmelCase =pipe_a(
prompt_embeds=__A , negative_prompt_embeds=__A , image=__A , generator=__A , num_inference_steps=2 , output_type='np' , )
_lowerCAmelCase =output.images[0]
assert image.shape == (256, 256, 3)
_lowerCAmelCase =torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
_lowerCAmelCase =load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy' )
assert_mean_pixel_difference(__A , __A )
def UpperCamelCase__ ( self , __A , __A , __A , __A ) -> Optional[int]:
# pipeline 1
_start_torch_memory_measurement()
_lowerCAmelCase =floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(__A )
_lowerCAmelCase =torch.Generator(device='cpu' ).manual_seed(0 )
_lowerCAmelCase =pipe_a(
prompt_embeds=__A , negative_prompt_embeds=__A , image=__A , num_inference_steps=2 , generator=__A , output_type='np' , )
_lowerCAmelCase =output.images[0]
assert image.shape == (64, 64, 3)
_lowerCAmelCase =torch.cuda.max_memory_allocated()
assert mem_bytes < 10 * 10**9
_lowerCAmelCase =load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy' )
assert_mean_pixel_difference(__A , __A )
# pipeline 2
_start_torch_memory_measurement()
_lowerCAmelCase =torch.Generator(device='cpu' ).manual_seed(0 )
_lowerCAmelCase =floats_tensor((1, 3, 256, 256) , rng=random.Random(0 ) ).to(__A )
_lowerCAmelCase =floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(__A )
_lowerCAmelCase =pipe_a(
prompt_embeds=__A , negative_prompt_embeds=__A , image=__A , original_image=__A , generator=__A , num_inference_steps=2 , output_type='np' , )
_lowerCAmelCase =output.images[0]
assert image.shape == (256, 256, 3)
_lowerCAmelCase =torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
_lowerCAmelCase =load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy' )
assert_mean_pixel_difference(__A , __A )
def UpperCamelCase__ ( self , __A , __A , __A , __A ) -> Dict:
# pipeline 1
_start_torch_memory_measurement()
_lowerCAmelCase =floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(__A )
_lowerCAmelCase =floats_tensor((1, 3, 64, 64) , rng=random.Random(1 ) ).to(__A )
_lowerCAmelCase =torch.Generator(device='cpu' ).manual_seed(0 )
_lowerCAmelCase =pipe_a(
prompt_embeds=__A , negative_prompt_embeds=__A , image=__A , mask_image=__A , num_inference_steps=2 , generator=__A , output_type='np' , )
_lowerCAmelCase =output.images[0]
assert image.shape == (64, 64, 3)
_lowerCAmelCase =torch.cuda.max_memory_allocated()
assert mem_bytes < 10 * 10**9
_lowerCAmelCase =load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy' )
assert_mean_pixel_difference(__A , __A )
# pipeline 2
_start_torch_memory_measurement()
_lowerCAmelCase =torch.Generator(device='cpu' ).manual_seed(0 )
_lowerCAmelCase =floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(__A )
_lowerCAmelCase =floats_tensor((1, 3, 256, 256) , rng=random.Random(0 ) ).to(__A )
_lowerCAmelCase =floats_tensor((1, 3, 256, 256) , rng=random.Random(1 ) ).to(__A )
_lowerCAmelCase =pipe_a(
prompt_embeds=__A , negative_prompt_embeds=__A , image=__A , mask_image=__A , original_image=__A , generator=__A , num_inference_steps=2 , output_type='np' , )
_lowerCAmelCase =output.images[0]
assert image.shape == (256, 256, 3)
_lowerCAmelCase =torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
_lowerCAmelCase =load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy' )
assert_mean_pixel_difference(__A , __A )
def UpperCamelCase__ ( ):
'''simple docstring'''
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
| 58
| 1
|
'''simple docstring'''
lowercase_ = [
'''DownloadConfig''',
'''DownloadManager''',
'''DownloadMode''',
'''StreamingDownloadManager''',
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager
| 58
|
'''simple docstring'''
import unittest
from knapsack import knapsack as k
class SCREAMING_SNAKE_CASE ( unittest.TestCase):
"""simple docstring"""
def UpperCamelCase__ ( self ) -> Optional[Any]:
_lowerCAmelCase =0
_lowerCAmelCase =[0]
_lowerCAmelCase =[0]
_lowerCAmelCase =len(__A )
self.assertEqual(k.knapsack(__A , __A , __A , __A ) , 0 )
_lowerCAmelCase =[60]
_lowerCAmelCase =[10]
_lowerCAmelCase =len(__A )
self.assertEqual(k.knapsack(__A , __A , __A , __A ) , 0 )
def UpperCamelCase__ ( self ) -> Tuple:
_lowerCAmelCase =3
_lowerCAmelCase =[1, 2, 3]
_lowerCAmelCase =[3, 2, 1]
_lowerCAmelCase =len(__A )
self.assertEqual(k.knapsack(__A , __A , __A , __A ) , 5 )
def UpperCamelCase__ ( self ) -> Union[str, Any]:
_lowerCAmelCase =50
_lowerCAmelCase =[60, 100, 120]
_lowerCAmelCase =[10, 20, 30]
_lowerCAmelCase =len(__A )
self.assertEqual(k.knapsack(__A , __A , __A , __A ) , 220 )
if __name__ == "__main__":
unittest.main()
| 58
| 1
|
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
lowercase_ = get_tests_dir('''fixtures/test_sentencepiece.model''')
if is_torch_available():
from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right
lowercase_ = 25_6047
lowercase_ = 25_6145
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE ( __lowercase , unittest.TestCase):
"""simple docstring"""
lowercase : int = NllbTokenizer
lowercase : Union[str, Any] = NllbTokenizerFast
lowercase : Tuple = True
lowercase : Dict = True
lowercase : Tuple = {}
def UpperCamelCase__ ( self ) -> List[str]:
super().setUp()
# We have a SentencePiece fixture for testing
_lowerCAmelCase =NllbTokenizer(__A , keep_accents=__A )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCamelCase__ ( self ) -> Optional[int]:
_lowerCAmelCase =NllbTokenizer(__A , keep_accents=__A )
_lowerCAmelCase =tokenizer.tokenize('This is a test' )
self.assertListEqual(__A , ['▁This', '▁is', '▁a', '▁t', 'est'] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__A ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
_lowerCAmelCase =tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
__A , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'9',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'é',
'.',
] , )
_lowerCAmelCase =tokenizer.convert_tokens_to_ids(__A )
self.assertListEqual(
__A , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] , )
_lowerCAmelCase =tokenizer.convert_ids_to_tokens(__A )
self.assertListEqual(
__A , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'<unk>',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'<unk>',
'.',
] , )
def UpperCamelCase__ ( self ) -> str:
_lowerCAmelCase =(self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-nllb', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
_lowerCAmelCase =self.rust_tokenizer_class.from_pretrained(__A , **__A )
_lowerCAmelCase =self.tokenizer_class.from_pretrained(__A , **__A )
_lowerCAmelCase =tempfile.mkdtemp()
_lowerCAmelCase =tokenizer_r.save_pretrained(__A )
_lowerCAmelCase =tokenizer_p.save_pretrained(__A )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )
_lowerCAmelCase =tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f )
self.assertSequenceEqual(__A , __A )
# Checks everything loads correctly in the same way
_lowerCAmelCase =tokenizer_r.from_pretrained(__A )
_lowerCAmelCase =tokenizer_p.from_pretrained(__A )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__A , __A ) )
shutil.rmtree(__A )
# Save tokenizer rust, legacy_format=True
_lowerCAmelCase =tempfile.mkdtemp()
_lowerCAmelCase =tokenizer_r.save_pretrained(__A , legacy_format=__A )
_lowerCAmelCase =tokenizer_p.save_pretrained(__A )
# Checks it save with the same files
self.assertSequenceEqual(__A , __A )
# Checks everything loads correctly in the same way
_lowerCAmelCase =tokenizer_r.from_pretrained(__A )
_lowerCAmelCase =tokenizer_p.from_pretrained(__A )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__A , __A ) )
shutil.rmtree(__A )
# Save tokenizer rust, legacy_format=False
_lowerCAmelCase =tempfile.mkdtemp()
_lowerCAmelCase =tokenizer_r.save_pretrained(__A , legacy_format=__A )
_lowerCAmelCase =tokenizer_p.save_pretrained(__A )
# Checks it saved the tokenizer.json file
self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
_lowerCAmelCase =tokenizer_r.from_pretrained(__A )
_lowerCAmelCase =tokenizer_p.from_pretrained(__A )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__A , __A ) )
shutil.rmtree(__A )
@require_torch
def UpperCamelCase__ ( self ) -> str:
if not self.test_seqaseq:
return
_lowerCAmelCase =self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F'''{tokenizer.__class__.__name__}''' ):
# Longer text that will definitely require truncation.
_lowerCAmelCase =[
' UN Chief Says There Is No Military Solution in Syria',
' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for'
' Syria is that \'there is no military solution\' to the nearly five-year conflict and more weapons'
' will only worsen the violence and misery for millions of people.',
]
_lowerCAmelCase =[
'Şeful ONU declară că nu există o soluţie militară în Siria',
'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al'
' Rusiei pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi'
' că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
]
try:
_lowerCAmelCase =tokenizer.prepare_seqaseq_batch(
src_texts=__A , tgt_texts=__A , max_length=3 , max_target_length=10 , return_tensors='pt' , src_lang='eng_Latn' , tgt_lang='ron_Latn' , )
except NotImplementedError:
return
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.labels.shape[1] , 10 )
# max_target_length will default to max_length if not specified
_lowerCAmelCase =tokenizer.prepare_seqaseq_batch(
__A , tgt_texts=__A , max_length=3 , return_tensors='pt' )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.labels.shape[1] , 3 )
_lowerCAmelCase =tokenizer.prepare_seqaseq_batch(
src_texts=__A , max_length=3 , max_target_length=10 , return_tensors='pt' )
self.assertEqual(batch_encoder_only.input_ids.shape[1] , 3 )
self.assertEqual(batch_encoder_only.attention_mask.shape[1] , 3 )
self.assertNotIn('decoder_input_ids' , __A )
@unittest.skip('Unfortunately way too slow to build a BPE with SentencePiece.' )
def UpperCamelCase__ ( self ) -> Any:
pass
def UpperCamelCase__ ( self ) -> Optional[Any]:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
_lowerCAmelCase =[AddedToken('<special>' , lstrip=__A )]
_lowerCAmelCase =self.rust_tokenizer_class.from_pretrained(
__A , additional_special_tokens=__A , **__A )
_lowerCAmelCase =tokenizer_r.encode('Hey this is a <special> token' )
_lowerCAmelCase =tokenizer_r.encode('<special>' , add_special_tokens=__A )[0]
self.assertTrue(special_token_id in r_output )
if self.test_slow_tokenizer:
_lowerCAmelCase =self.rust_tokenizer_class.from_pretrained(
__A , additional_special_tokens=__A , **__A , )
_lowerCAmelCase =self.tokenizer_class.from_pretrained(
__A , additional_special_tokens=__A , **__A )
_lowerCAmelCase =tokenizer_p.encode('Hey this is a <special> token' )
_lowerCAmelCase =tokenizer_cr.encode('Hey this is a <special> token' )
self.assertEqual(__A , __A )
self.assertEqual(__A , __A )
self.assertTrue(special_token_id in p_output )
self.assertTrue(special_token_id in cr_output )
@require_torch
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE ( unittest.TestCase):
"""simple docstring"""
lowercase : Optional[Any] = 'facebook/nllb-200-distilled-600M'
lowercase : List[Any] = [
' UN Chief Says There Is No Military Solution in Syria',
' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
]
lowercase : Tuple = [
'Şeful ONU declară că nu există o soluţie militară în Siria',
'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
]
lowercase : int = [
25_60_47,
1_62_97,
13_44_08,
81_65,
24_80_66,
1_47_34,
9_50,
11_35,
10_57_21,
35_73,
83,
2_73_52,
1_08,
4_94_86,
2,
]
@classmethod
def UpperCamelCase__ ( cls ) -> Optional[Any]:
_lowerCAmelCase =NllbTokenizer.from_pretrained(
cls.checkpoint_name , src_lang='eng_Latn' , tgt_lang='ron_Latn' )
_lowerCAmelCase =1
return cls
def UpperCamelCase__ ( self ) -> List[Any]:
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Arab'] , 25_6001 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Latn'] , 25_6002 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['fra_Latn'] , 25_6057 )
def UpperCamelCase__ ( self ) -> Any:
_lowerCAmelCase =self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , __A )
def UpperCamelCase__ ( self ) -> List[str]:
self.assertIn(__A , self.tokenizer.all_special_ids )
# fmt: off
_lowerCAmelCase =[RO_CODE, 4254, 9_8068, 11_2923, 3_9072, 3909, 713, 10_2767, 26, 1_7314, 3_5642, 1_4683, 3_3118, 2022, 6_6987, 2, 25_6047]
# fmt: on
_lowerCAmelCase =self.tokenizer.decode(__A , skip_special_tokens=__A )
_lowerCAmelCase =self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=__A )
self.assertEqual(__A , __A )
self.assertNotIn(self.tokenizer.eos_token , __A )
def UpperCamelCase__ ( self ) -> Optional[Any]:
_lowerCAmelCase =['this is gunna be a long sentence ' * 20]
assert isinstance(src_text[0] , __A )
_lowerCAmelCase =10
_lowerCAmelCase =self.tokenizer(__A , max_length=__A , truncation=__A ).input_ids[0]
self.assertEqual(ids[-1] , 2 )
self.assertEqual(ids[0] , __A )
self.assertEqual(len(__A ) , __A )
def UpperCamelCase__ ( self ) -> Optional[Any]:
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR'] ) , [25_6203, 3] )
def UpperCamelCase__ ( self ) -> Optional[int]:
_lowerCAmelCase =tempfile.mkdtemp()
_lowerCAmelCase =self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(__A )
_lowerCAmelCase =NllbTokenizer.from_pretrained(__A )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , __A )
@require_torch
def UpperCamelCase__ ( self ) -> List[str]:
_lowerCAmelCase =self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=__A , truncation=__A , max_length=len(self.expected_src_tokens ) , return_tensors='pt' , )
_lowerCAmelCase =shift_tokens_right(
batch['labels'] , self.tokenizer.pad_token_id , self.tokenizer.lang_code_to_id['ron_Latn'] )
self.assertIsInstance(__A , __A )
self.assertEqual((2, 15) , batch.input_ids.shape )
self.assertEqual((2, 15) , batch.attention_mask.shape )
_lowerCAmelCase =batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , __A )
self.assertEqual(__A , batch.decoder_input_ids[0, 0] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [EN_CODE] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
def UpperCamelCase__ ( self ) -> Union[str, Any]:
_lowerCAmelCase =self.tokenizer(self.src_text , padding=__A , truncation=__A , max_length=3 , return_tensors='pt' )
_lowerCAmelCase =self.tokenizer(
text_target=self.tgt_text , padding=__A , truncation=__A , max_length=10 , return_tensors='pt' )
_lowerCAmelCase =targets['input_ids']
_lowerCAmelCase =shift_tokens_right(
__A , self.tokenizer.pad_token_id , decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang] , )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def UpperCamelCase__ ( self ) -> List[str]:
_lowerCAmelCase =self.tokenizer._build_translation_inputs(
'A test' , return_tensors='pt' , src_lang='eng_Latn' , tgt_lang='fra_Latn' )
self.assertEqual(
nested_simplify(__A ) , {
# A, test, EOS, en_XX
'input_ids': [[25_6047, 70, 7356, 2]],
'attention_mask': [[1, 1, 1, 1]],
# ar_AR
'forced_bos_token_id': 25_6057,
} , )
@require_torch
def UpperCamelCase__ ( self ) -> List[Any]:
_lowerCAmelCase =True
_lowerCAmelCase =self.tokenizer(
'UN Chief says there is no military solution in Syria' , src_lang='eng_Latn' , tgt_lang='fra_Latn' )
self.assertEqual(
inputs.input_ids , [1_6297, 13_4408, 2_5653, 6370, 248, 254, 10_3929, 9_4995, 108, 4_9486, 2, 25_6047] )
_lowerCAmelCase =False
_lowerCAmelCase =self.tokenizer(
'UN Chief says there is no military solution in Syria' , src_lang='eng_Latn' , tgt_lang='fra_Latn' )
self.assertEqual(
inputs.input_ids , [25_6047, 1_6297, 13_4408, 2_5653, 6370, 248, 254, 10_3929, 9_4995, 108, 4_9486, 2] )
| 58
|
'''simple docstring'''
lowercase_ = '''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
lowercase_ = [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
lowercase_ = {
'''{processor_class}''': '''FakeProcessorClass''',
'''{model_class}''': '''FakeModelClass''',
'''{object_class}''': '''FakeObjectClass''',
}
| 58
| 1
|
'''simple docstring'''
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse('''3.8'''):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
lowercase_ = ''''''
if version.parse(importlib_metadata.version('''jiwer''')) < version.parse('''2.3.0'''):
class SCREAMING_SNAKE_CASE ( tr.AbstractTransform):
"""simple docstring"""
def __init__( self , __A = " " ) -> str:
_lowerCAmelCase =sentence_delimiter
def UpperCamelCase__ ( self , __A ) -> Optional[Any]:
return list(__A )
def UpperCamelCase__ ( self , __A ) -> Any:
_lowerCAmelCase =[]
for sent_idx, sentence in enumerate(__A ):
chars.extend(self.process_string(__A ) )
if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(__A ) - 1:
chars.append(self.sentence_delimiter )
return chars
lowercase_ = tr.Compose(
[tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
)
else:
lowercase_ = tr.Compose(
[
tr.RemoveMultipleSpaces(),
tr.Strip(),
tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
tr.ReduceToListOfListOfChars(),
]
)
lowercase_ = '''\
@inproceedings{inproceedings,
author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
year = {2004},
month = {01},
pages = {},
title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}
}
'''
lowercase_ = '''\
Character error rate (CER) is a common metric of the performance of an automatic speech recognition system.
CER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.
Character error rate can be computed as:
CER = (S + D + I) / N = (S + D + I) / (S + D + C)
where
S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct characters,
N is the number of characters in the reference (N=S+D+C).
CER\'s output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the
performance of the ASR system with a CER of 0 being a perfect score.
'''
lowercase_ = '''
Computes CER score of transcribed segments against references.
Args:
references: list of references for each speech input.
predictions: list of transcribtions to score.
concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.
Returns:
(float): the character error rate
Examples:
>>> predictions = ["this is the prediction", "there is an other sample"]
>>> references = ["this is the reference", "there is another one"]
>>> cer = datasets.load_metric("cer")
>>> cer_score = cer.compute(predictions=predictions, references=references)
>>> print(cer_score)
0.34146341463414637
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class SCREAMING_SNAKE_CASE ( datasets.Metric):
"""simple docstring"""
def UpperCamelCase__ ( self ) -> Optional[int]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('string' , id='sequence' ),
'references': datasets.Value('string' , id='sequence' ),
} ) , codebase_urls=['https://github.com/jitsi/jiwer/'] , reference_urls=[
'https://en.wikipedia.org/wiki/Word_error_rate',
'https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates',
] , )
def UpperCamelCase__ ( self , __A , __A , __A=False ) -> str:
if concatenate_texts:
return jiwer.compute_measures(
__A , __A , truth_transform=__A , hypothesis_transform=__A , )["wer"]
_lowerCAmelCase =0
_lowerCAmelCase =0
for prediction, reference in zip(__A , __A ):
_lowerCAmelCase =jiwer.compute_measures(
__A , __A , truth_transform=__A , hypothesis_transform=__A , )
incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
total += measures["substitutions"] + measures["deletions"] + measures["hits"]
return incorrect / total
| 58
|
'''simple docstring'''
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json
lowercase_ = '''sshleifer/mar_enro_6_3_student'''
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
def UpperCamelCase__ ( self ) -> Optional[Any]:
super().setUp()
_lowerCAmelCase =cached_path(
'https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz' , extract_compressed_file=__A , )
_lowerCAmelCase =F'''{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'''
@slow
@require_torch_gpu
def UpperCamelCase__ ( self ) -> Union[str, Any]:
MarianMTModel.from_pretrained(__A )
@slow
@require_torch_gpu
def UpperCamelCase__ ( self ) -> Union[str, Any]:
_lowerCAmelCase ={
'$MAX_LEN': 64,
'$BS': 64,
'$GAS': 1,
'$ENRO_DIR': self.data_dir,
'facebook/mbart-large-cc25': MARIAN_MODEL,
# "val_check_interval=0.25": "val_check_interval=1.0",
'--learning_rate=3e-5': '--learning_rate 3e-4',
'--num_train_epochs 6': '--num_train_epochs 1',
}
# Clean up bash script
_lowerCAmelCase =(self.test_file_dir / 'train_mbart_cc25_enro.sh').open().read().split('finetune.py' )[1].strip()
_lowerCAmelCase =bash_script.replace('\\\n' , '' ).strip().replace('"$@"' , '' )
for k, v in env_vars_to_replace.items():
_lowerCAmelCase =bash_script.replace(__A , str(__A ) )
_lowerCAmelCase =self.get_auto_remove_tmp_dir()
# bash_script = bash_script.replace("--fp16 ", "")
_lowerCAmelCase =F'''
--output_dir {output_dir}
--tokenizer_name Helsinki-NLP/opus-mt-en-ro
--sortish_sampler
--do_predict
--gpus 1
--freeze_encoder
--n_train 40000
--n_val 500
--n_test 500
--fp16_opt_level O1
--num_sanity_val_steps 0
--eval_beams 2
'''.split()
# XXX: args.gpus > 1 : handle multi_gpu in the future
_lowerCAmelCase =['finetune.py'] + bash_script.split() + args
with patch.object(__A , 'argv' , __A ):
_lowerCAmelCase =argparse.ArgumentParser()
_lowerCAmelCase =pl.Trainer.add_argparse_args(__A )
_lowerCAmelCase =SummarizationModule.add_model_specific_args(__A , os.getcwd() )
_lowerCAmelCase =parser.parse_args()
_lowerCAmelCase =main(__A )
# Check metrics
_lowerCAmelCase =load_json(model.metrics_save_path )
_lowerCAmelCase =metrics['val'][0]
_lowerCAmelCase =metrics['val'][-1]
self.assertEqual(len(metrics['val'] ) , (args.max_epochs / args.val_check_interval) )
assert isinstance(last_step_stats[F'''val_avg_{model.val_metric}'''] , __A )
self.assertGreater(last_step_stats['val_avg_gen_time'] , 0.01 )
# model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
self.assertLessEqual(last_step_stats['val_avg_gen_time'] , 1.0 )
# test learning requirements:
# 1. BLEU improves over the course of training by more than 2 pts
self.assertGreater(last_step_stats['val_avg_bleu'] - first_step_stats['val_avg_bleu'] , 2 )
# 2. BLEU finishes above 17
self.assertGreater(last_step_stats['val_avg_bleu'] , 17 )
# 3. test BLEU and val BLEU within ~1.1 pt.
self.assertLess(abs(metrics['val'][-1]['val_avg_bleu'] - metrics['test'][-1]['test_avg_bleu'] ) , 1.1 )
# check lightning ckpt can be loaded and has a reasonable statedict
_lowerCAmelCase =os.listdir(__A )
_lowerCAmelCase =[x for x in contents if x.endswith('.ckpt' )][0]
_lowerCAmelCase =os.path.join(args.output_dir , __A )
_lowerCAmelCase =torch.load(__A , map_location='cpu' )
_lowerCAmelCase ='model.model.decoder.layers.0.encoder_attn_layer_norm.weight'
assert expected_key in ckpt["state_dict"]
assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa
# TODO: turn on args.do_predict when PL bug fixed.
if args.do_predict:
_lowerCAmelCase ={os.path.basename(__A ) for p in contents}
assert "test_generations.txt" in contents
assert "test_results.txt" in contents
# assert len(metrics["val"]) == desired_n_evals
assert len(metrics['test'] ) == 1
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
@timeout_decorator.timeout(600 )
@slow
@require_torch_gpu
def UpperCamelCase__ ( self ) -> Tuple:
_lowerCAmelCase =F'''{self.test_file_dir_str}/test_data/wmt_en_ro'''
_lowerCAmelCase ={
'--fp16_opt_level=O1': '',
'$MAX_LEN': 128,
'$BS': 16,
'$GAS': 1,
'$ENRO_DIR': data_dir,
'$m': 'sshleifer/student_marian_en_ro_6_1',
'val_check_interval=0.25': 'val_check_interval=1.0',
}
# Clean up bash script
_lowerCAmelCase =(
(self.test_file_dir / 'distil_marian_no_teacher.sh').open().read().split('distillation.py' )[1].strip()
)
_lowerCAmelCase =bash_script.replace('\\\n' , '' ).strip().replace('"$@"' , '' )
_lowerCAmelCase =bash_script.replace('--fp16 ' , ' ' )
for k, v in env_vars_to_replace.items():
_lowerCAmelCase =bash_script.replace(__A , str(__A ) )
_lowerCAmelCase =self.get_auto_remove_tmp_dir()
_lowerCAmelCase =bash_script.replace('--fp16' , '' )
_lowerCAmelCase =6
_lowerCAmelCase =(
['distillation.py']
+ bash_script.split()
+ [
F'''--output_dir={output_dir}''',
'--gpus=1',
'--learning_rate=1e-3',
F'''--num_train_epochs={epochs}''',
'--warmup_steps=10',
'--val_check_interval=1.0',
'--do_predict',
]
)
with patch.object(__A , 'argv' , __A ):
_lowerCAmelCase =argparse.ArgumentParser()
_lowerCAmelCase =pl.Trainer.add_argparse_args(__A )
_lowerCAmelCase =SummarizationDistiller.add_model_specific_args(__A , os.getcwd() )
_lowerCAmelCase =parser.parse_args()
# assert args.gpus == gpus THIS BREAKS for multi_gpu
_lowerCAmelCase =distill_main(__A )
# Check metrics
_lowerCAmelCase =load_json(model.metrics_save_path )
_lowerCAmelCase =metrics['val'][0]
_lowerCAmelCase =metrics['val'][-1]
assert len(metrics['val'] ) >= (args.max_epochs / args.val_check_interval) # +1 accounts for val_sanity_check
assert last_step_stats["val_avg_gen_time"] >= 0.01
assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"] # model learned nothing
assert 1.0 >= last_step_stats["val_avg_gen_time"] # model hanging on generate. Maybe bad config was saved.
assert isinstance(last_step_stats[F'''val_avg_{model.val_metric}'''] , __A )
# check lightning ckpt can be loaded and has a reasonable statedict
_lowerCAmelCase =os.listdir(__A )
_lowerCAmelCase =[x for x in contents if x.endswith('.ckpt' )][0]
_lowerCAmelCase =os.path.join(args.output_dir , __A )
_lowerCAmelCase =torch.load(__A , map_location='cpu' )
_lowerCAmelCase ='model.model.decoder.layers.0.encoder_attn_layer_norm.weight'
assert expected_key in ckpt["state_dict"]
assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa
# TODO: turn on args.do_predict when PL bug fixed.
if args.do_predict:
_lowerCAmelCase ={os.path.basename(__A ) for p in contents}
assert "test_generations.txt" in contents
assert "test_results.txt" in contents
# assert len(metrics["val"]) == desired_n_evals
assert len(metrics['test'] ) == 1
| 58
| 1
|
'''simple docstring'''
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
lowercase_ = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : List[str] = ['pixel_values']
def __init__( self , __A = True , __A = None , __A = PILImageResampling.BICUBIC , __A = True , __A = None , __A = True , __A = 1 / 255 , __A = True , __A = IMAGENET_DEFAULT_MEAN , __A = IMAGENET_DEFAULT_STD , **__A , ) -> None:
super().__init__(**__A )
_lowerCAmelCase =size if size is not None else {'shortest_edge': 224}
_lowerCAmelCase =get_size_dict(__A , default_to_square=__A )
_lowerCAmelCase =crop_size if crop_size is not None else {'height': 224, 'width': 224}
_lowerCAmelCase =get_size_dict(__A , param_name='crop_size' )
_lowerCAmelCase =do_resize
_lowerCAmelCase =size
_lowerCAmelCase =resample
_lowerCAmelCase =do_center_crop
_lowerCAmelCase =crop_size
_lowerCAmelCase =do_rescale
_lowerCAmelCase =rescale_factor
_lowerCAmelCase =do_normalize
_lowerCAmelCase =image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
_lowerCAmelCase =image_std if image_std is not None else IMAGENET_DEFAULT_STD
def UpperCamelCase__ ( self , __A , __A , __A = PILImageResampling.BICUBIC , __A = None , **__A , ) -> np.ndarray:
_lowerCAmelCase =get_size_dict(__A , default_to_square=__A )
# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
if "shortest_edge" in size:
_lowerCAmelCase =int((256 / 224) * size['shortest_edge'] )
_lowerCAmelCase =get_resize_output_image_size(__A , size=__A , default_to_square=__A )
_lowerCAmelCase ={'height': output_size[0], 'width': output_size[1]}
if "height" not in size_dict or "width" not in size_dict:
raise ValueError(
F'''Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}''' )
return resize(
__A , size=(size_dict['height'], size_dict['width']) , resample=__A , data_format=__A , **__A )
def UpperCamelCase__ ( self , __A , __A , __A = None , **__A , ) -> np.ndarray:
_lowerCAmelCase =get_size_dict(__A )
if "height" not in size or "width" not in size:
raise ValueError(F'''Size dict must have keys \'height\' and \'width\'. Got {size.keys()}''' )
return center_crop(__A , size=(size['height'], size['width']) , data_format=__A , **__A )
def UpperCamelCase__ ( self , __A , __A , __A = None , **__A , ) -> np.ndarray:
return rescale(__A , scale=__A , data_format=__A , **__A )
def UpperCamelCase__ ( self , __A , __A , __A , __A = None , **__A , ) -> np.ndarray:
return normalize(__A , mean=__A , std=__A , data_format=__A , **__A )
def UpperCamelCase__ ( self , __A , __A = None , __A = None , __A = None , __A = None , __A = None , __A = None , __A = None , __A = None , __A = None , __A = None , __A = None , __A = ChannelDimension.FIRST , **__A , ) -> BatchFeature:
_lowerCAmelCase =do_resize if do_resize is not None else self.do_resize
_lowerCAmelCase =resample if resample is not None else self.resample
_lowerCAmelCase =do_center_crop if do_center_crop is not None else self.do_center_crop
_lowerCAmelCase =do_rescale if do_rescale is not None else self.do_rescale
_lowerCAmelCase =rescale_factor if rescale_factor is not None else self.rescale_factor
_lowerCAmelCase =do_normalize if do_normalize is not None else self.do_normalize
_lowerCAmelCase =image_mean if image_mean is not None else self.image_mean
_lowerCAmelCase =image_std if image_std is not None else self.image_std
_lowerCAmelCase =size if size is not None else self.size
_lowerCAmelCase =get_size_dict(__A , default_to_square=__A )
_lowerCAmelCase =crop_size if crop_size is not None else self.crop_size
_lowerCAmelCase =get_size_dict(__A , param_name='crop_size' )
_lowerCAmelCase =make_list_of_images(__A )
if not valid_images(__A ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None:
raise ValueError('Size must be specified if do_resize is True.' )
if do_center_crop and crop_size is None:
raise ValueError('Crop size must be specified if do_center_crop is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.' )
# All transformations expect numpy arrays.
_lowerCAmelCase =[to_numpy_array(__A ) for image in images]
if do_resize:
_lowerCAmelCase =[self.resize(__A , __A , __A ) for image in images]
if do_center_crop:
_lowerCAmelCase =[self.center_crop(__A , __A ) for image in images]
if do_rescale:
_lowerCAmelCase =[self.rescale(__A , __A ) for image in images]
if do_normalize:
_lowerCAmelCase =[self.normalize(__A , __A , __A ) for image in images]
_lowerCAmelCase =[to_channel_dimension_format(__A , __A ) for image in images]
_lowerCAmelCase ={'pixel_values': images}
return BatchFeature(data=__A , tensor_type=__A )
| 58
|
'''simple docstring'''
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors
lowercase_ = logging.getLogger(__name__)
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : int = 'sequence-classification'
def __init__( self , __A ) -> List[Any]:
if type(__A ) == dict:
_lowerCAmelCase =Namespace(**__A )
_lowerCAmelCase =glue_output_modes[hparams.task]
_lowerCAmelCase =glue_tasks_num_labels[hparams.task]
super().__init__(__A , __A , self.mode )
def UpperCamelCase__ ( self , **__A ) -> Any:
return self.model(**__A )
def UpperCamelCase__ ( self , __A , __A ) -> Union[str, Any]:
_lowerCAmelCase ={'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
_lowerCAmelCase =batch[2] if self.config.model_type in ['bert', 'xlnet', 'albert'] else None
_lowerCAmelCase =self(**__A )
_lowerCAmelCase =outputs[0]
_lowerCAmelCase =self.trainer.lr_schedulers[0]['scheduler']
_lowerCAmelCase ={'loss': loss, 'rate': lr_scheduler.get_last_lr()[-1]}
return {"loss": loss, "log": tensorboard_logs}
def UpperCamelCase__ ( self ) -> Any:
_lowerCAmelCase =self.hparams
_lowerCAmelCase =processors[args.task]()
_lowerCAmelCase =processor.get_labels()
for mode in ["train", "dev"]:
_lowerCAmelCase =self._feature_file(__A )
if os.path.exists(__A ) and not args.overwrite_cache:
logger.info('Loading features from cached file %s' , __A )
else:
logger.info('Creating features from dataset file at %s' , args.data_dir )
_lowerCAmelCase =(
processor.get_dev_examples(args.data_dir )
if mode == 'dev'
else processor.get_train_examples(args.data_dir )
)
_lowerCAmelCase =convert_examples_to_features(
__A , self.tokenizer , max_length=args.max_seq_length , label_list=self.labels , output_mode=args.glue_output_mode , )
logger.info('Saving features into cached file %s' , __A )
torch.save(__A , __A )
def UpperCamelCase__ ( self , __A , __A , __A = False ) -> DataLoader:
_lowerCAmelCase ='dev' if mode == 'test' else mode
_lowerCAmelCase =self._feature_file(__A )
logger.info('Loading features from cached file %s' , __A )
_lowerCAmelCase =torch.load(__A )
_lowerCAmelCase =torch.tensor([f.input_ids for f in features] , dtype=torch.long )
_lowerCAmelCase =torch.tensor([f.attention_mask for f in features] , dtype=torch.long )
_lowerCAmelCase =torch.tensor([f.token_type_ids for f in features] , dtype=torch.long )
if self.hparams.glue_output_mode == "classification":
_lowerCAmelCase =torch.tensor([f.label for f in features] , dtype=torch.long )
elif self.hparams.glue_output_mode == "regression":
_lowerCAmelCase =torch.tensor([f.label for f in features] , dtype=torch.float )
return DataLoader(
TensorDataset(__A , __A , __A , __A ) , batch_size=__A , shuffle=__A , )
def UpperCamelCase__ ( self , __A , __A ) -> List[str]:
_lowerCAmelCase ={'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
_lowerCAmelCase =batch[2] if self.config.model_type in ['bert', 'xlnet', 'albert'] else None
_lowerCAmelCase =self(**__A )
_lowerCAmelCase , _lowerCAmelCase =outputs[:2]
_lowerCAmelCase =logits.detach().cpu().numpy()
_lowerCAmelCase =inputs['labels'].detach().cpu().numpy()
return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
def UpperCamelCase__ ( self , __A ) -> tuple:
_lowerCAmelCase =torch.stack([x['val_loss'] for x in outputs] ).mean().detach().cpu().item()
_lowerCAmelCase =np.concatenate([x['pred'] for x in outputs] , axis=0 )
if self.hparams.glue_output_mode == "classification":
_lowerCAmelCase =np.argmax(__A , axis=1 )
elif self.hparams.glue_output_mode == "regression":
_lowerCAmelCase =np.squeeze(__A )
_lowerCAmelCase =np.concatenate([x['target'] for x in outputs] , axis=0 )
_lowerCAmelCase =[[] for _ in range(out_label_ids.shape[0] )]
_lowerCAmelCase =[[] for _ in range(out_label_ids.shape[0] )]
_lowerCAmelCase ={**{'val_loss': val_loss_mean}, **compute_metrics(self.hparams.task , __A , __A )}
_lowerCAmelCase =dict(results.items() )
_lowerCAmelCase =results
return ret, preds_list, out_label_list
def UpperCamelCase__ ( self , __A ) -> dict:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase =self._eval_end(__A )
_lowerCAmelCase =ret['log']
return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
def UpperCamelCase__ ( self , __A ) -> dict:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase =self._eval_end(__A )
_lowerCAmelCase =ret['log']
# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
@staticmethod
def UpperCamelCase__ ( __A , __A ) -> Any:
BaseTransformer.add_model_specific_args(__A , __A )
parser.add_argument(
'--max_seq_length' , default=128 , type=__A , help=(
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
) , )
parser.add_argument(
'--task' , default='' , type=__A , required=__A , help='The GLUE task to run' , )
parser.add_argument(
'--gpus' , default=0 , type=__A , help='The number of GPUs allocated for this, it is by default 0 meaning none' , )
parser.add_argument(
'--overwrite_cache' , action='store_true' , help='Overwrite the cached training and evaluation sets' )
return parser
def UpperCamelCase__ ( ):
'''simple docstring'''
_lowerCAmelCase =argparse.ArgumentParser()
add_generic_args(a__ , os.getcwd() )
_lowerCAmelCase =GLUETransformer.add_model_specific_args(a__ , os.getcwd() )
_lowerCAmelCase =parser.parse_args()
# If output_dir not provided, a folder will be generated in pwd
if args.output_dir is None:
_lowerCAmelCase =os.path.join(
'./results' , F'''{args.task}_{time.strftime('%Y%m%d_%H%M%S' )}''' , )
os.makedirs(args.output_dir )
_lowerCAmelCase =GLUETransformer(a__ )
_lowerCAmelCase =generic_train(a__ , a__ )
# Optionally, predict on dev set and write to output_dir
if args.do_predict:
_lowerCAmelCase =sorted(glob.glob(os.path.join(args.output_dir , 'checkpoint-epoch=*.ckpt' ) , recursive=a__ ) )
_lowerCAmelCase =model.load_from_checkpoint(checkpoints[-1] )
return trainer.test(a__ )
if __name__ == "__main__":
main()
| 58
| 1
|
'''simple docstring'''
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
PixaStructTextConfig,
PixaStructVisionConfig,
)
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
_lowerCAmelCase =checkpoints.load_tax_checkpoint(a__ )
_lowerCAmelCase =flatten_dict(a__ )
return flax_params
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
_lowerCAmelCase ={}
_lowerCAmelCase ={
'token_embedder': 'embeddings',
'encoder_norm': 'layernorm',
'kernel': 'weight',
'.out': '.output',
'scale': 'weight',
'embedders_0.pos_embedding': 'row_embedder.weight',
'embedders_1.pos_embedding': 'column_embedder.weight',
}
_lowerCAmelCase ={
'query': 'attention.query',
'key': 'attention.key',
'value': 'attention.value',
'output.dense': 'output',
'encoder_decoder_attention.o': 'encoder_decoder_attention.attention.o',
'pre_self_attention_layer_norm': 'self_attention.layer_norm',
'pre_cross_attention_layer_norm': 'encoder_decoder_attention.layer_norm',
'mlp.': 'mlp.DenseReluDense.',
'pre_mlp_layer_norm': 'mlp.layer_norm',
'self_attention.o': 'self_attention.attention.o',
'decoder.embeddings.embedding': 'decoder.embed_tokens.weight',
'decoder.relpos_bias.rel_embedding': 'decoder.layer.0.self_attention.attention.relative_attention_bias.weight',
'decoder.decoder_norm.weight': 'decoder.final_layer_norm.weight',
'decoder.logits_dense.weight': 'decoder.lm_head.weight',
}
for key in flax_dict.keys():
if "target" in key:
# remove the first prefix from the key
_lowerCAmelCase ='.'.join(key[1:] )
# rename the key
for old, new in CONVERSION_MAPPING.items():
_lowerCAmelCase =new_key.replace(a__ , a__ )
if "decoder" in new_key:
for old, new in DECODER_CONVERSION_MAPPING.items():
_lowerCAmelCase =new_key.replace(a__ , a__ )
if "layers" in new_key and "decoder" not in new_key:
# use regex to replace the layer number
_lowerCAmelCase =re.sub(r'layers_(\d+)' , r'layer.\1' , a__ )
_lowerCAmelCase =new_key.replace('encoder' , 'encoder.encoder' )
elif "layers" in new_key and "decoder" in new_key:
# use regex to replace the layer number
_lowerCAmelCase =re.sub(r'layers_(\d+)' , r'layer.\1' , a__ )
_lowerCAmelCase =flax_dict[key]
_lowerCAmelCase ={}
# convert converted_dict into torch format
for key in converted_dict.keys():
if ("embed_tokens" not in key) and ("embedder" not in key):
_lowerCAmelCase =torch.from_numpy(converted_dict[key].T )
else:
_lowerCAmelCase =torch.from_numpy(converted_dict[key] )
return converted_torch_dict
def UpperCamelCase__ ( a__ , a__ , a__=False , a__=False ):
'''simple docstring'''
_lowerCAmelCase =get_flax_param(a__ )
if not use_large:
_lowerCAmelCase =PixaStructVisionConfig()
_lowerCAmelCase =PixaStructTextConfig()
else:
_lowerCAmelCase =PixaStructVisionConfig(
hidden_size=1_5_3_6 , d_ff=3_9_6_8 , num_attention_heads=2_4 , num_hidden_layers=1_8 )
_lowerCAmelCase =PixaStructTextConfig(hidden_size=1_5_3_6 , d_ff=3_9_6_8 , num_heads=2_4 , num_layers=1_8 )
_lowerCAmelCase =PixaStructConfig(
vision_config=encoder_config.to_dict() , text_config=decoder_config.to_dict() , is_vqa=a__ )
_lowerCAmelCase =PixaStructForConditionalGeneration(a__ )
_lowerCAmelCase =rename_and_convert_flax_params(a__ )
model.load_state_dict(a__ )
_lowerCAmelCase =AutoTokenizer.from_pretrained('ybelkada/test-pix2struct-tokenizer' )
_lowerCAmelCase =PixaStructImageProcessor()
_lowerCAmelCase =PixaStructProcessor(image_processor=a__ , tokenizer=a__ )
if use_large:
_lowerCAmelCase =4_0_9_6
_lowerCAmelCase =True
# mkdir if needed
os.makedirs(a__ , exist_ok=a__ )
model.save_pretrained(a__ )
processor.save_pretrained(a__ )
print('Model saved in {}'.format(a__ ) )
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
parser.add_argument('''--t5x_checkpoint_path''', default=None, type=str, help='''Path to the original T5x checkpoint.''')
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--use_large''', action='''store_true''', help='''Use large model.''')
parser.add_argument('''--is_vqa''', action='''store_true''', help='''Use large model.''')
lowercase_ = parser.parse_args()
convert_pixastruct_original_pytorch_checkpoint_to_hf(
args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
)
| 58
|
'''simple docstring'''
from __future__ import annotations
from typing import Any
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __A ) -> None:
_lowerCAmelCase =num_of_nodes
_lowerCAmelCase =[]
_lowerCAmelCase ={}
def UpperCamelCase__ ( self , __A , __A , __A ) -> None:
self.m_edges.append([u_node, v_node, weight] )
def UpperCamelCase__ ( self , __A ) -> int:
if self.m_component[u_node] == u_node:
return u_node
return self.find_component(self.m_component[u_node] )
def UpperCamelCase__ ( self , __A ) -> None:
if self.m_component[u_node] != u_node:
for k in self.m_component:
_lowerCAmelCase =self.find_component(__A )
def UpperCamelCase__ ( self , __A , __A , __A ) -> None:
if component_size[u_node] <= component_size[v_node]:
_lowerCAmelCase =v_node
component_size[v_node] += component_size[u_node]
self.set_component(__A )
elif component_size[u_node] >= component_size[v_node]:
_lowerCAmelCase =self.find_component(__A )
component_size[u_node] += component_size[v_node]
self.set_component(__A )
def UpperCamelCase__ ( self ) -> None:
_lowerCAmelCase =[]
_lowerCAmelCase =0
_lowerCAmelCase =[-1] * self.m_num_of_nodes
# A list of components (initialized to all of the nodes)
for node in range(self.m_num_of_nodes ):
self.m_component.update({node: node} )
component_size.append(1 )
_lowerCAmelCase =self.m_num_of_nodes
while num_of_components > 1:
for edge in self.m_edges:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase =edge
_lowerCAmelCase =self.m_component[u]
_lowerCAmelCase =self.m_component[v]
if u_component != v_component:
for component in (u_component, v_component):
if (
minimum_weight_edge[component] == -1
or minimum_weight_edge[component][2] > w
):
_lowerCAmelCase =[u, v, w]
for edge in minimum_weight_edge:
if isinstance(__A , __A ):
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase =edge
_lowerCAmelCase =self.m_component[u]
_lowerCAmelCase =self.m_component[v]
if u_component != v_component:
mst_weight += w
self.union(__A , __A , __A )
print(F'''Added edge [{u} - {v}]\nAdded weight: {w}\n''' )
num_of_components -= 1
_lowerCAmelCase =[-1] * self.m_num_of_nodes
print(F'''The total weight of the minimal spanning tree is: {mst_weight}''' )
def UpperCamelCase__ ( ):
'''simple docstring'''
if __name__ == "__main__":
import doctest
doctest.testmod()
| 58
| 1
|
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
lowercase_ = '''docs/source/en/_toctree.yml'''
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
_lowerCAmelCase =defaultdict(a__ )
_lowerCAmelCase =[]
_lowerCAmelCase =[]
for doc in doc_list:
if "local" in doc:
counts[doc["local"]] += 1
if doc["title"].lower() == "overview":
overview_doc.append({'local': doc['local'], 'title': doc['title']} )
else:
new_doc_list.append(a__ )
_lowerCAmelCase =new_doc_list
_lowerCAmelCase =[key for key, value in counts.items() if value > 1]
_lowerCAmelCase =[]
for duplicate_key in duplicates:
_lowerCAmelCase =list({doc['title'] for doc in doc_list if doc['local'] == duplicate_key} )
if len(a__ ) > 1:
raise ValueError(
F'''{duplicate_key} is present several times in the documentation table of content at '''
'`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
'others.' )
# Only add this once
new_doc.append({'local': duplicate_key, 'title': titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in doc_list if 'local' not in counts or counts[doc['local']] == 1] )
_lowerCAmelCase =sorted(a__ , key=lambda a__ : s["title"].lower() )
# "overview" gets special treatment and is always first
if len(a__ ) > 1:
raise ValueError('{doc_list} has two \'overview\' docs which is not allowed.' )
overview_doc.extend(a__ )
# Sort
return overview_doc
def UpperCamelCase__ ( a__=False ):
'''simple docstring'''
with open(a__ , encoding='utf-8' ) as f:
_lowerCAmelCase =yaml.safe_load(f.read() )
# Get to the API doc
_lowerCAmelCase =0
while content[api_idx]["title"] != "API":
api_idx += 1
_lowerCAmelCase =content[api_idx]['sections']
# Then to the model doc
_lowerCAmelCase =0
while api_doc[scheduler_idx]["title"] != "Schedulers":
scheduler_idx += 1
_lowerCAmelCase =api_doc[scheduler_idx]['sections']
_lowerCAmelCase =clean_doc_toc(a__ )
_lowerCAmelCase =False
if new_scheduler_doc != scheduler_doc:
_lowerCAmelCase =True
if overwrite:
_lowerCAmelCase =new_scheduler_doc
if diff:
if overwrite:
_lowerCAmelCase =api_doc
with open(a__ , 'w' , encoding='utf-8' ) as f:
f.write(yaml.dump(a__ , allow_unicode=a__ ) )
else:
raise ValueError(
'The model doc part of the table of content is not properly sorted, run `make style` to fix this.' )
def UpperCamelCase__ ( a__=False ):
'''simple docstring'''
with open(a__ , encoding='utf-8' ) as f:
_lowerCAmelCase =yaml.safe_load(f.read() )
# Get to the API doc
_lowerCAmelCase =0
while content[api_idx]["title"] != "API":
api_idx += 1
_lowerCAmelCase =content[api_idx]['sections']
# Then to the model doc
_lowerCAmelCase =0
while api_doc[pipeline_idx]["title"] != "Pipelines":
pipeline_idx += 1
_lowerCAmelCase =False
_lowerCAmelCase =api_doc[pipeline_idx]['sections']
_lowerCAmelCase =[]
# sort sub pipeline docs
for pipeline_doc in pipeline_docs:
if "section" in pipeline_doc:
_lowerCAmelCase =pipeline_doc['section']
_lowerCAmelCase =clean_doc_toc(a__ )
if overwrite:
_lowerCAmelCase =new_sub_pipeline_doc
new_pipeline_docs.append(a__ )
# sort overall pipeline doc
_lowerCAmelCase =clean_doc_toc(a__ )
if new_pipeline_docs != pipeline_docs:
_lowerCAmelCase =True
if overwrite:
_lowerCAmelCase =new_pipeline_docs
if diff:
if overwrite:
_lowerCAmelCase =api_doc
with open(a__ , 'w' , encoding='utf-8' ) as f:
f.write(yaml.dump(a__ , allow_unicode=a__ ) )
else:
raise ValueError(
'The model doc part of the table of content is not properly sorted, run `make style` to fix this.' )
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
lowercase_ = parser.parse_args()
check_scheduler_doc(args.fix_and_overwrite)
check_pipeline_doc(args.fix_and_overwrite)
| 58
|
'''simple docstring'''
from PIL import Image
def UpperCamelCase__ ( a__ , a__ ):
'''simple docstring'''
def brightness(a__ ) -> float:
return 1_2_8 + level + (c - 1_2_8)
if not -255.0 <= level <= 255.0:
raise ValueError('level must be between -255.0 (black) and 255.0 (white)' )
return img.point(a__ )
if __name__ == "__main__":
# Load image
with Image.open('''image_data/lena.jpg''') as img:
# Change brightness to 100
lowercase_ = change_brightness(img, 100)
brigt_img.save('''image_data/lena_brightness.png''', format='''png''')
| 58
| 1
|
'''simple docstring'''
from typing import Union
import fire
import torch
from tqdm import tqdm
def UpperCamelCase__ ( a__ , a__ = "cpu" , a__ = None ):
'''simple docstring'''
_lowerCAmelCase =torch.load(a__ , map_location=a__ )
for k, v in tqdm(state_dict.items() ):
if not isinstance(a__ , torch.Tensor ):
raise TypeError('FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin' )
_lowerCAmelCase =v.half()
if save_path is None: # overwrite src_path
_lowerCAmelCase =src_path
torch.save(a__ , a__ )
if __name__ == "__main__":
fire.Fire(convert)
| 58
|
'''simple docstring'''
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / '''utils'''))
from test_module.custom_configuration import CustomConfig # noqa E402
lowercase_ = {
'''return_dict''': False,
'''output_hidden_states''': True,
'''output_attentions''': True,
'''torchscript''': True,
'''torch_dtype''': '''float16''',
'''use_bfloat16''': True,
'''tf_legacy_loss''': True,
'''pruned_heads''': {'''a''': 1},
'''tie_word_embeddings''': False,
'''is_decoder''': True,
'''cross_attention_hidden_size''': 128,
'''add_cross_attention''': True,
'''tie_encoder_decoder''': True,
'''max_length''': 50,
'''min_length''': 3,
'''do_sample''': True,
'''early_stopping''': True,
'''num_beams''': 3,
'''num_beam_groups''': 3,
'''diversity_penalty''': 0.5,
'''temperature''': 2.0,
'''top_k''': 10,
'''top_p''': 0.7,
'''typical_p''': 0.2,
'''repetition_penalty''': 0.8,
'''length_penalty''': 0.8,
'''no_repeat_ngram_size''': 5,
'''encoder_no_repeat_ngram_size''': 5,
'''bad_words_ids''': [1, 2, 3],
'''num_return_sequences''': 3,
'''chunk_size_feed_forward''': 5,
'''output_scores''': True,
'''return_dict_in_generate''': True,
'''forced_bos_token_id''': 2,
'''forced_eos_token_id''': 3,
'''remove_invalid_values''': True,
'''architectures''': ['''BertModel'''],
'''finetuning_task''': '''translation''',
'''id2label''': {0: '''label'''},
'''label2id''': {'''label''': '''0'''},
'''tokenizer_class''': '''BertTokenizerFast''',
'''prefix''': '''prefix''',
'''bos_token_id''': 6,
'''pad_token_id''': 7,
'''eos_token_id''': 8,
'''sep_token_id''': 9,
'''decoder_start_token_id''': 10,
'''exponential_decay_length_penalty''': (5, 1.01),
'''suppress_tokens''': [0, 1],
'''begin_suppress_tokens''': 2,
'''task_specific_params''': {'''translation''': '''some_params'''},
'''problem_type''': '''regression''',
}
@is_staging_test
class SCREAMING_SNAKE_CASE ( unittest.TestCase):
"""simple docstring"""
@classmethod
def UpperCamelCase__ ( cls ) -> Optional[Any]:
_lowerCAmelCase =TOKEN
HfFolder.save_token(__A )
@classmethod
def UpperCamelCase__ ( cls ) -> List[str]:
try:
delete_repo(token=cls._token , repo_id='test-config' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='valid_org/test-config-org' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='test-dynamic-config' )
except HTTPError:
pass
def UpperCamelCase__ ( self ) -> str:
_lowerCAmelCase =BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
config.push_to_hub('test-config' , use_auth_token=self._token )
_lowerCAmelCase =BertConfig.from_pretrained(F'''{USER}/test-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__A , getattr(__A , __A ) )
# Reset repo
delete_repo(token=self._token , repo_id='test-config' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(__A , repo_id='test-config' , push_to_hub=__A , use_auth_token=self._token )
_lowerCAmelCase =BertConfig.from_pretrained(F'''{USER}/test-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__A , getattr(__A , __A ) )
def UpperCamelCase__ ( self ) -> Dict:
_lowerCAmelCase =BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
config.push_to_hub('valid_org/test-config-org' , use_auth_token=self._token )
_lowerCAmelCase =BertConfig.from_pretrained('valid_org/test-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__A , getattr(__A , __A ) )
# Reset repo
delete_repo(token=self._token , repo_id='valid_org/test-config-org' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
__A , repo_id='valid_org/test-config-org' , push_to_hub=__A , use_auth_token=self._token )
_lowerCAmelCase =BertConfig.from_pretrained('valid_org/test-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__A , getattr(__A , __A ) )
def UpperCamelCase__ ( self ) -> List[str]:
CustomConfig.register_for_auto_class()
_lowerCAmelCase =CustomConfig(attribute=42 )
config.push_to_hub('test-dynamic-config' , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(config.auto_map , {'AutoConfig': 'custom_configuration.CustomConfig'} )
_lowerCAmelCase =AutoConfig.from_pretrained(F'''{USER}/test-dynamic-config''' , trust_remote_code=__A )
# Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
self.assertEqual(new_config.__class__.__name__ , 'CustomConfig' )
self.assertEqual(new_config.attribute , 42 )
class SCREAMING_SNAKE_CASE ( unittest.TestCase):
"""simple docstring"""
def UpperCamelCase__ ( self ) -> List[Any]:
_lowerCAmelCase =GPTaConfig()
# attempt to modify each of int/float/bool/str config records and verify they were updated
_lowerCAmelCase =c.n_embd + 1 # int
_lowerCAmelCase =c.resid_pdrop + 1.0 # float
_lowerCAmelCase =not c.scale_attn_weights # bool
_lowerCAmelCase =c.summary_type + 'foo' # str
c.update_from_string(
F'''n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}''' )
self.assertEqual(__A , c.n_embd , 'mismatch for key: n_embd' )
self.assertEqual(__A , c.resid_pdrop , 'mismatch for key: resid_pdrop' )
self.assertEqual(__A , c.scale_attn_weights , 'mismatch for key: scale_attn_weights' )
self.assertEqual(__A , c.summary_type , 'mismatch for key: summary_type' )
def UpperCamelCase__ ( self ) -> List[str]:
_lowerCAmelCase =PretrainedConfig()
_lowerCAmelCase =[key for key in base_config.__dict__ if key not in config_common_kwargs]
# If this part of the test fails, you have arguments to addin config_common_kwargs above.
self.assertListEqual(
__A , ['is_encoder_decoder', '_name_or_path', '_commit_hash', 'transformers_version'] )
_lowerCAmelCase =[key for key, value in config_common_kwargs.items() if value == getattr(__A , __A )]
if len(__A ) > 0:
raise ValueError(
'The following keys are set with the default values in'
' `test_configuration_common.config_common_kwargs` pick another value for them:'
F''' {', '.join(__A )}.''' )
def UpperCamelCase__ ( self ) -> Optional[int]:
with self.assertRaises(__A ):
# config is in subfolder, the following should not work without specifying the subfolder
_lowerCAmelCase =BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder' )
_lowerCAmelCase =BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder' , subfolder='bert' )
self.assertIsNotNone(__A )
def UpperCamelCase__ ( self ) -> List[str]:
# A mock response for an HTTP head request to emulate server down
_lowerCAmelCase =mock.Mock()
_lowerCAmelCase =500
_lowerCAmelCase ={}
_lowerCAmelCase =HTTPError
_lowerCAmelCase ={}
# Download this model to make sure it's in the cache.
_lowerCAmelCase =BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert' )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch('requests.Session.request' , return_value=__A ) as mock_head:
_lowerCAmelCase =BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert' )
# This check we did call the fake head request
mock_head.assert_called()
def UpperCamelCase__ ( self ) -> Optional[int]:
# This test is for deprecated behavior and can be removed in v5
_lowerCAmelCase =BertConfig.from_pretrained(
'https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json' )
def UpperCamelCase__ ( self ) -> Any:
_lowerCAmelCase =AutoConfig.from_pretrained('bert-base-cased' )
_lowerCAmelCase =['config.4.0.0.json']
with tempfile.TemporaryDirectory() as tmp_dir:
configuration.save_pretrained(__A )
_lowerCAmelCase =2
json.dump(configuration.to_dict() , open(os.path.join(__A , 'config.4.0.0.json' ) , 'w' ) )
# This should pick the new configuration file as the version of Transformers is > 4.0.0
_lowerCAmelCase =AutoConfig.from_pretrained(__A )
self.assertEqual(new_configuration.hidden_size , 2 )
# Will need to be adjusted if we reach v42 and this test is still here.
# Should pick the old configuration file as the version of Transformers is < 4.42.0
_lowerCAmelCase =['config.42.0.0.json']
_lowerCAmelCase =768
configuration.save_pretrained(__A )
shutil.move(os.path.join(__A , 'config.4.0.0.json' ) , os.path.join(__A , 'config.42.0.0.json' ) )
_lowerCAmelCase =AutoConfig.from_pretrained(__A )
self.assertEqual(new_configuration.hidden_size , 768 )
def UpperCamelCase__ ( self ) -> Any:
# This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
_lowerCAmelCase ='hf-internal-testing/test-two-configs'
import transformers as new_transformers
_lowerCAmelCase ='v4.0.0'
_lowerCAmelCase , _lowerCAmelCase =new_transformers.models.auto.AutoConfig.from_pretrained(
__A , return_unused_kwargs=__A )
self.assertEqual(new_configuration.hidden_size , 2 )
# This checks `_configuration_file` ia not kept in the kwargs by mistake.
self.assertDictEqual(__A , {} )
# Testing an older version by monkey-patching the version in the module it's used.
import transformers as old_transformers
_lowerCAmelCase ='v3.0.0'
_lowerCAmelCase =old_transformers.models.auto.AutoConfig.from_pretrained(__A )
self.assertEqual(old_configuration.hidden_size , 768 )
| 58
| 1
|
'''simple docstring'''
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
return "".join([hex(a__ )[2:].zfill(2 ).upper() for byte in list(a__ )] )
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
if (len(a__ ) % 2) != 0:
raise ValueError(
'Base16 encoded data is invalid:\nData does not have an even number of hex digits.' )
# Check the character set - the standard base16 alphabet
# is uppercase according to RFC3548 section 6
if not set(a__ ) <= set('0123456789ABCDEF' ):
raise ValueError(
'Base16 encoded data is invalid:\nData is not uppercase hex or it contains invalid characters.' )
# For every two hexadecimal digits (= a byte), turn it into an integer.
# Then, string the result together into bytes, and return it.
return bytes(int(data[i] + data[i + 1] , 1_6 ) for i in range(0 , len(a__ ) , 2 ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 58
|
'''simple docstring'''
from __future__ import annotations
lowercase_ = 10
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
_lowerCAmelCase =1
_lowerCAmelCase =max(a__ )
while placement <= max_digit:
# declare and initialize empty buckets
_lowerCAmelCase =[[] for _ in range(a__ )]
# split list_of_ints between the buckets
for i in list_of_ints:
_lowerCAmelCase =int((i / placement) % RADIX )
buckets[tmp].append(a__ )
# put each buckets' contents into list_of_ints
_lowerCAmelCase =0
for b in range(a__ ):
for i in buckets[b]:
_lowerCAmelCase =i
a += 1
# move to next
placement *= RADIX
return list_of_ints
if __name__ == "__main__":
import doctest
doctest.testmod()
| 58
| 1
|
'''simple docstring'''
from PIL import Image
def UpperCamelCase__ ( a__ , a__ ):
'''simple docstring'''
def brightness(a__ ) -> float:
return 1_2_8 + level + (c - 1_2_8)
if not -255.0 <= level <= 255.0:
raise ValueError('level must be between -255.0 (black) and 255.0 (white)' )
return img.point(a__ )
if __name__ == "__main__":
# Load image
with Image.open('''image_data/lena.jpg''') as img:
# Change brightness to 100
lowercase_ = change_brightness(img, 100)
brigt_img.save('''image_data/lena_brightness.png''', format='''png''')
| 58
|
'''simple docstring'''
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
FEATURE_EXTRACTOR_NAME,
FLAX_WEIGHTS_NAME,
HF_MODULES_CACHE,
HUGGINGFACE_CO_PREFIX,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
MODEL_CARD_NAME,
MULTIPLE_CHOICE_DUMMY_INPUTS,
PYTORCH_PRETRAINED_BERT_CACHE,
PYTORCH_TRANSFORMERS_CACHE,
S3_BUCKET_PREFIX,
SENTENCEPIECE_UNDERLINE,
SPIECE_UNDERLINE,
TF2_WEIGHTS_NAME,
TF_WEIGHTS_NAME,
TORCH_FX_REQUIRED_VERSION,
TRANSFORMERS_CACHE,
TRANSFORMERS_DYNAMIC_MODULE_NAME,
USE_JAX,
USE_TF,
USE_TORCH,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
ContextManagers,
DummyObject,
EntryNotFoundError,
ExplicitEnum,
ModelOutput,
PaddingStrategy,
PushToHubMixin,
RepositoryNotFoundError,
RevisionNotFoundError,
TensorType,
_LazyModule,
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
cached_property,
copy_func,
default_cache_path,
define_sagemaker_information,
get_cached_models,
get_file_from_repo,
get_full_repo_name,
get_torch_version,
has_file,
http_user_agent,
is_apex_available,
is_bsa_available,
is_coloredlogs_available,
is_datasets_available,
is_detectrona_available,
is_faiss_available,
is_flax_available,
is_ftfy_available,
is_in_notebook,
is_ipex_available,
is_librosa_available,
is_offline_mode,
is_onnx_available,
is_pandas_available,
is_phonemizer_available,
is_protobuf_available,
is_psutil_available,
is_pyanvml_available,
is_pyctcdecode_available,
is_pytesseract_available,
is_pytorch_quantization_available,
is_rjieba_available,
is_sagemaker_dp_enabled,
is_sagemaker_mp_enabled,
is_scipy_available,
is_sentencepiece_available,
is_seqio_available,
is_sklearn_available,
is_soundfile_availble,
is_spacy_available,
is_speech_available,
is_tensor,
is_tensorflow_probability_available,
is_tfaonnx_available,
is_tf_available,
is_timm_available,
is_tokenizers_available,
is_torch_available,
is_torch_bfaa_available,
is_torch_cuda_available,
is_torch_fx_available,
is_torch_fx_proxy,
is_torch_mps_available,
is_torch_tfaa_available,
is_torch_tpu_available,
is_torchaudio_available,
is_training_run_on_sagemaker,
is_vision_available,
replace_return_docstrings,
requires_backends,
to_numpy,
to_py_obj,
torch_only_method,
)
| 58
| 1
|
'''simple docstring'''
def UpperCamelCase__ ( a__ = 1_0_0_0 ):
'''simple docstring'''
_lowerCAmelCase =2**power
_lowerCAmelCase =0
while n:
_lowerCAmelCase , _lowerCAmelCase =r + n % 1_0, n // 1_0
return r
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 58
|
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
_lowerCAmelCase =len(a__ ) // 2
# choose the middle 3 elements
_lowerCAmelCase =lst[m - 1 : m + 2]
# if middle element is peak
if three[1] > three[0] and three[1] > three[2]:
return three[1]
# if increasing, recurse on right
elif three[0] < three[2]:
if len(lst[:m] ) == 2:
m -= 1
return peak(lst[m:] )
# decreasing
else:
if len(lst[:m] ) == 2:
m += 1
return peak(lst[:m] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 58
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase_ = {
'''configuration_whisper''': ['''WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WhisperConfig''', '''WhisperOnnxConfig'''],
'''feature_extraction_whisper''': ['''WhisperFeatureExtractor'''],
'''processing_whisper''': ['''WhisperProcessor'''],
'''tokenization_whisper''': ['''WhisperTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = ['''WhisperTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
'''WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''WhisperForConditionalGeneration''',
'''WhisperModel''',
'''WhisperPreTrainedModel''',
'''WhisperForAudioClassification''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
'''TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFWhisperForConditionalGeneration''',
'''TFWhisperModel''',
'''TFWhisperPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
'''FlaxWhisperForConditionalGeneration''',
'''FlaxWhisperModel''',
'''FlaxWhisperPreTrainedModel''',
'''FlaxWhisperForAudioClassification''',
]
if TYPE_CHECKING:
from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
from .feature_extraction_whisper import WhisperFeatureExtractor
from .processing_whisper import WhisperProcessor
from .tokenization_whisper import WhisperTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_whisper_fast import WhisperTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_whisper import (
WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
WhisperForAudioClassification,
WhisperForConditionalGeneration,
WhisperModel,
WhisperPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_whisper import (
TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFWhisperForConditionalGeneration,
TFWhisperModel,
TFWhisperPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_whisper import (
FlaxWhisperForAudioClassification,
FlaxWhisperForConditionalGeneration,
FlaxWhisperModel,
FlaxWhisperPreTrainedModel,
)
else:
import sys
lowercase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 58
|
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
lowercase_ = logging.get_logger(__name__)
lowercase_ = {'''vocab_file''': '''vocab.txt'''}
lowercase_ = {
'''vocab_file''': {
'''YituTech/conv-bert-base''': '''https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt''',
'''YituTech/conv-bert-medium-small''': (
'''https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt'''
),
'''YituTech/conv-bert-small''': '''https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt''',
}
}
lowercase_ = {
'''YituTech/conv-bert-base''': 512,
'''YituTech/conv-bert-medium-small''': 512,
'''YituTech/conv-bert-small''': 512,
}
lowercase_ = {
'''YituTech/conv-bert-base''': {'''do_lower_case''': True},
'''YituTech/conv-bert-medium-small''': {'''do_lower_case''': True},
'''YituTech/conv-bert-small''': {'''do_lower_case''': True},
}
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : Union[str, Any] = VOCAB_FILES_NAMES
lowercase : Tuple = PRETRAINED_VOCAB_FILES_MAP
lowercase : Optional[int] = PRETRAINED_INIT_CONFIGURATION
lowercase : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase : List[str] = ConvBertTokenizer
def __init__( self , __A=None , __A=None , __A=True , __A="[UNK]" , __A="[SEP]" , __A="[PAD]" , __A="[CLS]" , __A="[MASK]" , __A=True , __A=None , **__A , ) -> Union[str, Any]:
super().__init__(
__A , tokenizer_file=__A , do_lower_case=__A , unk_token=__A , sep_token=__A , pad_token=__A , cls_token=__A , mask_token=__A , tokenize_chinese_chars=__A , strip_accents=__A , **__A , )
_lowerCAmelCase =json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('lowercase' , __A ) != do_lower_case
or normalizer_state.get('strip_accents' , __A ) != strip_accents
or normalizer_state.get('handle_chinese_chars' , __A ) != tokenize_chinese_chars
):
_lowerCAmelCase =getattr(__A , normalizer_state.pop('type' ) )
_lowerCAmelCase =do_lower_case
_lowerCAmelCase =strip_accents
_lowerCAmelCase =tokenize_chinese_chars
_lowerCAmelCase =normalizer_class(**__A )
_lowerCAmelCase =do_lower_case
def UpperCamelCase__ ( self , __A , __A=None ) -> int:
_lowerCAmelCase =[self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def UpperCamelCase__ ( self , __A , __A = None ) -> List[int]:
_lowerCAmelCase =[self.sep_token_id]
_lowerCAmelCase =[self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def UpperCamelCase__ ( self , __A , __A = None ) -> Tuple[str]:
_lowerCAmelCase =self._tokenizer.model.save(__A , name=__A )
return tuple(__A )
| 58
| 1
|
'''simple docstring'''
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
lowercase_ = logging.get_logger(__name__)
def UpperCamelCase__ ( a__ , a__ , a__ ):
'''simple docstring'''
_lowerCAmelCase =WavaVecaForSequenceClassification.from_pretrained(a__ , config=a__ )
_lowerCAmelCase =downstream_dict['projector.weight']
_lowerCAmelCase =downstream_dict['projector.bias']
_lowerCAmelCase =downstream_dict['model.post_net.linear.weight']
_lowerCAmelCase =downstream_dict['model.post_net.linear.bias']
return model
def UpperCamelCase__ ( a__ , a__ , a__ ):
'''simple docstring'''
_lowerCAmelCase =WavaVecaForAudioFrameClassification.from_pretrained(a__ , config=a__ )
_lowerCAmelCase =downstream_dict['model.linear.weight']
_lowerCAmelCase =downstream_dict['model.linear.bias']
return model
def UpperCamelCase__ ( a__ , a__ , a__ ):
'''simple docstring'''
_lowerCAmelCase =WavaVecaForXVector.from_pretrained(a__ , config=a__ )
_lowerCAmelCase =downstream_dict['connector.weight']
_lowerCAmelCase =downstream_dict['connector.bias']
for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
_lowerCAmelCase =downstream_dict[
F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
]
_lowerCAmelCase =downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']
_lowerCAmelCase =downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
_lowerCAmelCase =downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
_lowerCAmelCase =downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
_lowerCAmelCase =downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
_lowerCAmelCase =downstream_dict['objective.W']
return model
@torch.no_grad()
def UpperCamelCase__ ( a__ , a__ , a__ , a__ ):
'''simple docstring'''
_lowerCAmelCase =torch.load(a__ , map_location='cpu' )
_lowerCAmelCase =checkpoint['Downstream']
_lowerCAmelCase =WavaVecaConfig.from_pretrained(a__ )
_lowerCAmelCase =WavaVecaFeatureExtractor.from_pretrained(
a__ , return_attention_mask=a__ , do_normalize=a__ )
_lowerCAmelCase =hf_config.architectures[0]
if arch.endswith('ForSequenceClassification' ):
_lowerCAmelCase =convert_classification(a__ , a__ , a__ )
elif arch.endswith('ForAudioFrameClassification' ):
_lowerCAmelCase =convert_diarization(a__ , a__ , a__ )
elif arch.endswith('ForXVector' ):
_lowerCAmelCase =convert_xvector(a__ , a__ , a__ )
else:
raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}''' )
if hf_config.use_weighted_layer_sum:
_lowerCAmelCase =checkpoint['Featurizer']['weights']
hf_feature_extractor.save_pretrained(a__ )
hf_model.save_pretrained(a__ )
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
parser.add_argument(
'''--base_model_name''', default=None, type=str, help='''Name of the huggingface pretrained base model.'''
)
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to the huggingface classifier config.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to the s3prl checkpoint.''')
parser.add_argument('''--model_dump_path''', default=None, type=str, help='''Path to the final converted model.''')
lowercase_ = parser.parse_args()
convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
| 58
|
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : Any = ['image_processor', 'tokenizer']
lowercase : Any = 'CLIPImageProcessor'
lowercase : int = ('CLIPTokenizer', 'CLIPTokenizerFast')
def __init__( self , __A=None , __A=None , **__A ) -> str:
_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.' , __A , )
_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__(__A , __A )
def __call__( self , __A=None , __A=None , __A=None , **__A ) -> Optional[int]:
if text is None and images is None:
raise ValueError('You have to specify either text or images. Both cannot be none.' )
if text is not None:
_lowerCAmelCase =self.tokenizer(__A , return_tensors=__A , **__A )
if images is not None:
_lowerCAmelCase =self.image_processor(__A , return_tensors=__A , **__A )
if text is not None and images is not None:
_lowerCAmelCase =image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**__A ) , tensor_type=__A )
def UpperCamelCase__ ( self , *__A , **__A ) -> Any:
return self.tokenizer.batch_decode(*__A , **__A )
def UpperCamelCase__ ( self , *__A , **__A ) -> Optional[int]:
return self.tokenizer.decode(*__A , **__A )
@property
def UpperCamelCase__ ( self ) -> Tuple:
_lowerCAmelCase =self.tokenizer.model_input_names
_lowerCAmelCase =self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def UpperCamelCase__ ( self ) -> Optional[int]:
warnings.warn(
'`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' , __A , )
return self.image_processor_class
@property
def UpperCamelCase__ ( self ) -> Optional[Any]:
warnings.warn(
'`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , __A , )
return self.image_processor
| 58
| 1
|
'''simple docstring'''
def UpperCamelCase__ ( a__ , a__ = False ):
'''simple docstring'''
if not isinstance(a__ , a__ ):
_lowerCAmelCase =F'''Expected string as input, found {type(a__ )}'''
raise ValueError(a__ )
if not isinstance(a__ , a__ ):
_lowerCAmelCase =F'''Expected boolean as use_pascal parameter, found {type(a__ )}'''
raise ValueError(a__ )
_lowerCAmelCase =input_str.split('_' )
_lowerCAmelCase =0 if use_pascal else 1
_lowerCAmelCase =words[start_index:]
_lowerCAmelCase =[word[0].upper() + word[1:] for word in words_to_capitalize]
_lowerCAmelCase ='' if use_pascal else words[0]
return "".join([initial_word, *capitalized_words] )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 58
|
'''simple docstring'''
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class SCREAMING_SNAKE_CASE ( __lowercase , __lowercase):
"""simple docstring"""
@register_to_config
def __init__( self , __A = 128 , __A = 256 , __A = 2_000.0 , __A = 768 , __A = 12 , __A = 12 , __A = 64 , __A = 2048 , __A = 0.1 , ) -> str:
super().__init__()
_lowerCAmelCase =nn.Sequential(
nn.Linear(__A , d_model * 4 , bias=__A ) , nn.SiLU() , nn.Linear(d_model * 4 , d_model * 4 , bias=__A ) , nn.SiLU() , )
_lowerCAmelCase =nn.Embedding(__A , __A )
_lowerCAmelCase =False
_lowerCAmelCase =nn.Linear(__A , __A , bias=__A )
_lowerCAmelCase =nn.Dropout(p=__A )
_lowerCAmelCase =nn.ModuleList()
for lyr_num in range(__A ):
# FiLM conditional T5 decoder
_lowerCAmelCase =DecoderLayer(d_model=__A , d_kv=__A , num_heads=__A , d_ff=__A , dropout_rate=__A )
self.decoders.append(__A )
_lowerCAmelCase =TaLayerNorm(__A )
_lowerCAmelCase =nn.Dropout(p=__A )
_lowerCAmelCase =nn.Linear(__A , __A , bias=__A )
def UpperCamelCase__ ( self , __A , __A ) -> Any:
_lowerCAmelCase =torch.mul(query_input.unsqueeze(-1 ) , key_input.unsqueeze(-2 ) )
return mask.unsqueeze(-3 )
def UpperCamelCase__ ( self , __A , __A , __A ) -> Optional[Any]:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase =decoder_input_tokens.shape
assert decoder_noise_time.shape == (batch,)
# decoder_noise_time is in [0, 1), so rescale to expected timing range.
_lowerCAmelCase =get_timestep_embedding(
decoder_noise_time * self.config.max_decoder_noise_time , embedding_dim=self.config.d_model , max_period=self.config.max_decoder_noise_time , ).to(dtype=self.dtype )
_lowerCAmelCase =self.conditioning_emb(__A ).unsqueeze(1 )
assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)
_lowerCAmelCase =decoder_input_tokens.shape[1]
# If we want to use relative positions for audio context, we can just offset
# this sequence by the length of encodings_and_masks.
_lowerCAmelCase =torch.broadcast_to(
torch.arange(__A , device=decoder_input_tokens.device ) , (batch, seq_length) , )
_lowerCAmelCase =self.position_encoding(__A )
_lowerCAmelCase =self.continuous_inputs_projection(__A )
inputs += position_encodings
_lowerCAmelCase =self.dropout(__A )
# decoder: No padding present.
_lowerCAmelCase =torch.ones(
decoder_input_tokens.shape[:2] , device=decoder_input_tokens.device , dtype=inputs.dtype )
# Translate encoding masks to encoder-decoder masks.
_lowerCAmelCase =[(x, self.encoder_decoder_mask(__A , __A )) for x, y in encodings_and_masks]
# cross attend style: concat encodings
_lowerCAmelCase =torch.cat([x[0] for x in encodings_and_encdec_masks] , dim=1 )
_lowerCAmelCase =torch.cat([x[1] for x in encodings_and_encdec_masks] , dim=-1 )
for lyr in self.decoders:
_lowerCAmelCase =lyr(
__A , conditioning_emb=__A , encoder_hidden_states=__A , encoder_attention_mask=__A , )[0]
_lowerCAmelCase =self.decoder_norm(__A )
_lowerCAmelCase =self.post_dropout(__A )
_lowerCAmelCase =self.spec_out(__A )
return spec_out
class SCREAMING_SNAKE_CASE ( nn.Module):
"""simple docstring"""
def __init__( self , __A , __A , __A , __A , __A , __A=1E-6 ) -> Union[str, Any]:
super().__init__()
_lowerCAmelCase =nn.ModuleList()
# cond self attention: layer 0
self.layer.append(
TaLayerSelfAttentionCond(d_model=__A , d_kv=__A , num_heads=__A , dropout_rate=__A ) )
# cross attention: layer 1
self.layer.append(
TaLayerCrossAttention(
d_model=__A , d_kv=__A , num_heads=__A , dropout_rate=__A , layer_norm_epsilon=__A , ) )
# Film Cond MLP + dropout: last layer
self.layer.append(
TaLayerFFCond(d_model=__A , d_ff=__A , dropout_rate=__A , layer_norm_epsilon=__A ) )
def UpperCamelCase__ ( self , __A , __A=None , __A=None , __A=None , __A=None , __A=None , ) -> Any:
_lowerCAmelCase =self.layer[0](
__A , conditioning_emb=__A , attention_mask=__A , )
if encoder_hidden_states is not None:
_lowerCAmelCase =torch.where(encoder_attention_mask > 0 , 0 , -1E10 ).to(
encoder_hidden_states.dtype )
_lowerCAmelCase =self.layer[1](
__A , key_value_states=__A , attention_mask=__A , )
# Apply Film Conditional Feed Forward layer
_lowerCAmelCase =self.layer[-1](__A , __A )
return (hidden_states,)
class SCREAMING_SNAKE_CASE ( nn.Module):
"""simple docstring"""
def __init__( self , __A , __A , __A , __A ) -> Optional[Any]:
super().__init__()
_lowerCAmelCase =TaLayerNorm(__A )
_lowerCAmelCase =TaFiLMLayer(in_features=d_model * 4 , out_features=__A )
_lowerCAmelCase =Attention(query_dim=__A , heads=__A , dim_head=__A , out_bias=__A , scale_qk=__A )
_lowerCAmelCase =nn.Dropout(__A )
def UpperCamelCase__ ( self , __A , __A=None , __A=None , ) -> List[Any]:
# pre_self_attention_layer_norm
_lowerCAmelCase =self.layer_norm(__A )
if conditioning_emb is not None:
_lowerCAmelCase =self.FiLMLayer(__A , __A )
# Self-attention block
_lowerCAmelCase =self.attention(__A )
_lowerCAmelCase =hidden_states + self.dropout(__A )
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module):
"""simple docstring"""
def __init__( self , __A , __A , __A , __A , __A ) -> Optional[int]:
super().__init__()
_lowerCAmelCase =Attention(query_dim=__A , heads=__A , dim_head=__A , out_bias=__A , scale_qk=__A )
_lowerCAmelCase =TaLayerNorm(__A , eps=__A )
_lowerCAmelCase =nn.Dropout(__A )
def UpperCamelCase__ ( self , __A , __A=None , __A=None , ) -> Tuple:
_lowerCAmelCase =self.layer_norm(__A )
_lowerCAmelCase =self.attention(
__A , encoder_hidden_states=__A , attention_mask=attention_mask.squeeze(1 ) , )
_lowerCAmelCase =hidden_states + self.dropout(__A )
return layer_output
class SCREAMING_SNAKE_CASE ( nn.Module):
"""simple docstring"""
def __init__( self , __A , __A , __A , __A ) -> Optional[Any]:
super().__init__()
_lowerCAmelCase =TaDenseGatedActDense(d_model=__A , d_ff=__A , dropout_rate=__A )
_lowerCAmelCase =TaFiLMLayer(in_features=d_model * 4 , out_features=__A )
_lowerCAmelCase =TaLayerNorm(__A , eps=__A )
_lowerCAmelCase =nn.Dropout(__A )
def UpperCamelCase__ ( self , __A , __A=None ) -> List[Any]:
_lowerCAmelCase =self.layer_norm(__A )
if conditioning_emb is not None:
_lowerCAmelCase =self.film(__A , __A )
_lowerCAmelCase =self.DenseReluDense(__A )
_lowerCAmelCase =hidden_states + self.dropout(__A )
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module):
"""simple docstring"""
def __init__( self , __A , __A , __A ) -> Union[str, Any]:
super().__init__()
_lowerCAmelCase =nn.Linear(__A , __A , bias=__A )
_lowerCAmelCase =nn.Linear(__A , __A , bias=__A )
_lowerCAmelCase =nn.Linear(__A , __A , bias=__A )
_lowerCAmelCase =nn.Dropout(__A )
_lowerCAmelCase =NewGELUActivation()
def UpperCamelCase__ ( self , __A ) -> List[Any]:
_lowerCAmelCase =self.act(self.wi_a(__A ) )
_lowerCAmelCase =self.wi_a(__A )
_lowerCAmelCase =hidden_gelu * hidden_linear
_lowerCAmelCase =self.dropout(__A )
_lowerCAmelCase =self.wo(__A )
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module):
"""simple docstring"""
def __init__( self , __A , __A=1E-6 ) -> int:
super().__init__()
_lowerCAmelCase =nn.Parameter(torch.ones(__A ) )
_lowerCAmelCase =eps
def UpperCamelCase__ ( self , __A ) -> Dict:
# T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
# Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
# w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
# half-precision inputs is done in fp32
_lowerCAmelCase =hidden_states.to(torch.floataa ).pow(2 ).mean(-1 , keepdim=__A )
_lowerCAmelCase =hidden_states * torch.rsqrt(variance + self.variance_epsilon )
# convert into half-precision if necessary
if self.weight.dtype in [torch.floataa, torch.bfloataa]:
_lowerCAmelCase =hidden_states.to(self.weight.dtype )
return self.weight * hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module):
"""simple docstring"""
def UpperCamelCase__ ( self , __A ) -> torch.Tensor:
return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi ) * (input + 0.044_715 * torch.pow(__A , 3.0 )) ))
class SCREAMING_SNAKE_CASE ( nn.Module):
"""simple docstring"""
def __init__( self , __A , __A ) -> Optional[Any]:
super().__init__()
_lowerCAmelCase =nn.Linear(__A , out_features * 2 , bias=__A )
def UpperCamelCase__ ( self , __A , __A ) -> Optional[Any]:
_lowerCAmelCase =self.scale_bias(__A )
_lowerCAmelCase , _lowerCAmelCase =torch.chunk(__A , 2 , -1 )
_lowerCAmelCase =x * (1 + scale) + shift
return x
| 58
| 1
|
'''simple docstring'''
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING
lowercase_ = logging.get_logger(__name__)
@add_end_docstrings(__lowercase)
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
def __init__( self , *__A , **__A ) -> Optional[int]:
super().__init__(*__A , **__A )
requires_backends(self , 'vision' )
self.check_model_type(
TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == 'tf' else MODEL_FOR_VISION_2_SEQ_MAPPING )
def UpperCamelCase__ ( self , __A=None , __A=None , __A=None ) -> Tuple:
_lowerCAmelCase ={}
_lowerCAmelCase ={}
if prompt is not None:
_lowerCAmelCase =prompt
if generate_kwargs is not None:
_lowerCAmelCase =generate_kwargs
if max_new_tokens is not None:
if "generate_kwargs" not in forward_kwargs:
_lowerCAmelCase ={}
if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
raise ValueError(
'\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,'
' please use only one' )
_lowerCAmelCase =max_new_tokens
return preprocess_params, forward_kwargs, {}
def __call__( self , __A , **__A ) -> str:
return super().__call__(__A , **__A )
def UpperCamelCase__ ( self , __A , __A=None ) -> Union[str, Any]:
_lowerCAmelCase =load_image(__A )
if prompt is not None:
if not isinstance(__A , __A ):
raise ValueError(
F'''Received an invalid text input, got - {type(__A )} - but expected a single string. '''
'Note also that one single text can be provided for conditional image to text generation.' )
_lowerCAmelCase =self.model.config.model_type
if model_type == "git":
_lowerCAmelCase =self.image_processor(images=__A , return_tensors=self.framework )
_lowerCAmelCase =self.tokenizer(text=__A , add_special_tokens=__A ).input_ids
_lowerCAmelCase =[self.tokenizer.cls_token_id] + input_ids
_lowerCAmelCase =torch.tensor(__A ).unsqueeze(0 )
model_inputs.update({'input_ids': input_ids} )
elif model_type == "pix2struct":
_lowerCAmelCase =self.image_processor(images=__A , header_text=__A , return_tensors=self.framework )
elif model_type != "vision-encoder-decoder":
# vision-encoder-decoder does not support conditional generation
_lowerCAmelCase =self.image_processor(images=__A , return_tensors=self.framework )
_lowerCAmelCase =self.tokenizer(__A , return_tensors=self.framework )
model_inputs.update(__A )
else:
raise ValueError(F'''Model type {model_type} does not support conditional text generation''' )
else:
_lowerCAmelCase =self.image_processor(images=__A , return_tensors=self.framework )
if self.model.config.model_type == "git" and prompt is None:
_lowerCAmelCase =None
return model_inputs
def UpperCamelCase__ ( self , __A , __A=None ) -> List[Any]:
# Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
# pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
if (
"input_ids" in model_inputs
and isinstance(model_inputs['input_ids'] , __A )
and all(x is None for x in model_inputs['input_ids'] )
):
_lowerCAmelCase =None
if generate_kwargs is None:
_lowerCAmelCase ={}
# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
# parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
# the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
# in the `_prepare_model_inputs` method.
_lowerCAmelCase =model_inputs.pop(self.model.main_input_name )
_lowerCAmelCase =self.model.generate(__A , **__A , **__A )
return model_outputs
def UpperCamelCase__ ( self , __A ) -> List[str]:
_lowerCAmelCase =[]
for output_ids in model_outputs:
_lowerCAmelCase ={
'generated_text': self.tokenizer.decode(
__A , skip_special_tokens=__A , )
}
records.append(__A )
return records
| 58
|
'''simple docstring'''
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_available() and not is_torch_available():
raise RuntimeError('''At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training''')
# TF training parameters
lowercase_ = False
lowercase_ = False
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
return TrainCommand(a__ )
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
@staticmethod
def UpperCamelCase__ ( __A ) -> Tuple:
_lowerCAmelCase =parser.add_parser('train' , help='CLI tool to train a model on a task.' )
train_parser.add_argument(
'--train_data' , type=__A , required=__A , help='path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.' , )
train_parser.add_argument(
'--column_label' , type=__A , default=0 , help='Column of the dataset csv file with example labels.' )
train_parser.add_argument(
'--column_text' , type=__A , default=1 , help='Column of the dataset csv file with example texts.' )
train_parser.add_argument(
'--column_id' , type=__A , default=2 , help='Column of the dataset csv file with example ids.' )
train_parser.add_argument(
'--skip_first_row' , action='store_true' , help='Skip the first row of the csv file (headers).' )
train_parser.add_argument('--validation_data' , type=__A , default='' , help='path to validation dataset.' )
train_parser.add_argument(
'--validation_split' , type=__A , default=0.1 , help='if validation dataset is not provided, fraction of train dataset to use as validation dataset.' , )
train_parser.add_argument('--output' , type=__A , default='./' , help='path to saved the trained model.' )
train_parser.add_argument(
'--task' , type=__A , default='text_classification' , help='Task to train the model on.' )
train_parser.add_argument(
'--model' , type=__A , default='bert-base-uncased' , help='Model\'s name or path to stored model.' )
train_parser.add_argument('--train_batch_size' , type=__A , default=32 , help='Batch size for training.' )
train_parser.add_argument('--valid_batch_size' , type=__A , default=64 , help='Batch size for validation.' )
train_parser.add_argument('--learning_rate' , type=__A , default=3E-5 , help='Learning rate.' )
train_parser.add_argument('--adam_epsilon' , type=__A , default=1E-08 , help='Epsilon for Adam optimizer.' )
train_parser.set_defaults(func=__A )
def __init__( self , __A ) -> List[str]:
_lowerCAmelCase =logging.get_logger('transformers-cli/training' )
_lowerCAmelCase ='tf' if is_tf_available() else 'torch'
os.makedirs(args.output , exist_ok=__A )
_lowerCAmelCase =args.output
_lowerCAmelCase =args.column_label
_lowerCAmelCase =args.column_text
_lowerCAmelCase =args.column_id
self.logger.info(F'''Loading {args.task} pipeline for {args.model}''' )
if args.task == "text_classification":
_lowerCAmelCase =TextClassificationPipeline.from_pretrained(args.model )
elif args.task == "token_classification":
raise NotImplementedError
elif args.task == "question_answering":
raise NotImplementedError
self.logger.info(F'''Loading dataset from {args.train_data}''' )
_lowerCAmelCase =Processor.create_from_csv(
args.train_data , column_label=args.column_label , column_text=args.column_text , column_id=args.column_id , skip_first_row=args.skip_first_row , )
_lowerCAmelCase =None
if args.validation_data:
self.logger.info(F'''Loading validation dataset from {args.validation_data}''' )
_lowerCAmelCase =Processor.create_from_csv(
args.validation_data , column_label=args.column_label , column_text=args.column_text , column_id=args.column_id , skip_first_row=args.skip_first_row , )
_lowerCAmelCase =args.validation_split
_lowerCAmelCase =args.train_batch_size
_lowerCAmelCase =args.valid_batch_size
_lowerCAmelCase =args.learning_rate
_lowerCAmelCase =args.adam_epsilon
def UpperCamelCase__ ( self ) -> List[str]:
if self.framework == "tf":
return self.run_tf()
return self.run_torch()
def UpperCamelCase__ ( self ) -> Union[str, Any]:
raise NotImplementedError
def UpperCamelCase__ ( self ) -> List[Any]:
self.pipeline.fit(
self.train_dataset , validation_data=self.valid_dataset , validation_split=self.validation_split , learning_rate=self.learning_rate , adam_epsilon=self.adam_epsilon , train_batch_size=self.train_batch_size , valid_batch_size=self.valid_batch_size , )
# Save trained pipeline
self.pipeline.save_pretrained(self.output )
| 58
| 1
|
'''simple docstring'''
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands
def UpperCamelCase__ ( ):
'''simple docstring'''
_lowerCAmelCase =ArgumentParser('Transformers CLI tool' , usage='transformers-cli <command> [<args>]' )
_lowerCAmelCase =parser.add_subparsers(help='transformers-cli command helpers' )
# Register commands
ConvertCommand.register_subcommand(a__ )
DownloadCommand.register_subcommand(a__ )
EnvironmentCommand.register_subcommand(a__ )
RunCommand.register_subcommand(a__ )
ServeCommand.register_subcommand(a__ )
UserCommands.register_subcommand(a__ )
AddNewModelCommand.register_subcommand(a__ )
AddNewModelLikeCommand.register_subcommand(a__ )
LfsCommands.register_subcommand(a__ )
PTtoTFCommand.register_subcommand(a__ )
# Let's go
_lowerCAmelCase =parser.parse_args()
if not hasattr(a__ , 'func' ):
parser.print_help()
exit(1 )
# Run
_lowerCAmelCase =args.func(a__ )
service.run()
if __name__ == "__main__":
main()
| 58
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowercase_ = {'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
'''VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ViTMAEForPreTraining''',
'''ViTMAELayer''',
'''ViTMAEModel''',
'''ViTMAEPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
'''TFViTMAEForPreTraining''',
'''TFViTMAEModel''',
'''TFViTMAEPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit_mae import (
VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTMAEForPreTraining,
ViTMAELayer,
ViTMAEModel,
ViTMAEPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel
else:
import sys
lowercase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 58
| 1
|
'''simple docstring'''
from collections import defaultdict
from math import ceil, sqrt
def UpperCamelCase__ ( a__ = 1_0_0_0_0_0_0 , a__ = 1_0 ):
'''simple docstring'''
_lowerCAmelCase =defaultdict(a__ )
for outer_width in range(3 , (t_limit // 4) + 2 ):
if outer_width * outer_width > t_limit:
_lowerCAmelCase =max(
ceil(sqrt(outer_width * outer_width - t_limit ) ) , 1 )
else:
_lowerCAmelCase =1
hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2
for hole_width in range(a__ , outer_width - 1 , 2 ):
count[outer_width * outer_width - hole_width * hole_width] += 1
return sum(1 for n in count.values() if 1 <= n <= 1_0 )
if __name__ == "__main__":
print(F'{solution() = }')
| 58
|
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
_lowerCAmelCase =os.path.join(args.tf_model_dir , 'parameters.json' )
_lowerCAmelCase =json.loads(open(a__ ).read() )
if not params:
raise ValueError(
F'''It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file.''' )
if not args.output.endswith('.pt' ):
_lowerCAmelCase =args.output + '.pt'
_lowerCAmelCase =OrderedDict()
with tf.device('/CPU:0' ):
_lowerCAmelCase =tf.train.load_checkpoint(args.tf_model_dir )
_lowerCAmelCase =reader.get_variable_to_shape_map()
for key_name in shapes.keys():
_lowerCAmelCase =reader.get_tensor(a__ ).astype(np.floataa )
if key_name.endswith('/adam_m' ) or key_name.endswith('/adam_v' ):
continue
if key_name.startswith('pasts/' ):
if key_name.startswith('pasts/mlp' ):
_lowerCAmelCase =int(key_name[9] )
elif key_name.startswith('pasts/out' ):
_lowerCAmelCase =8
_lowerCAmelCase ='model.sqout.%d.weight' % (player * 2) # enter to nn.Sequencial with Tanh, so 2 at a time
_lowerCAmelCase =vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.startswith('model/moe' ):
_lowerCAmelCase =int(key_name[9:].split('/' )[0] )
if key_name.endswith('/switch_gating/kernel' ):
_lowerCAmelCase ='model.blocks.%d.feed_forward.mlp.router.classifier.weight' % player
_lowerCAmelCase =vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.endswith('/softmlp/kernel' ):
_lowerCAmelCase ='model.blocks.%d.feed_forward.soft_bypass_mlp.weight' % player
_lowerCAmelCase =vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.endswith('/wo/kernel' ) or key_name.endswith('/wi/kernel' ):
_lowerCAmelCase =key_name[-9:-7]
for i in range(1_6 ):
_lowerCAmelCase ='model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight' % (player, i, nlayer)
_lowerCAmelCase =(
vnp[i].transpose([1, 0] ).copy()
) # In Mesh-Tensorflow, it is one array, so it is divided
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.startswith('model/mlp' ):
_lowerCAmelCase =int(key_name[9:].split('/' )[0] )
if key_name.endswith('/p1/kernel' ):
_lowerCAmelCase ='model.blocks.%d.feed_forward.mlp.wi.weight' % player
_lowerCAmelCase =vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.endswith('/p1/bias' ):
_lowerCAmelCase ='model.blocks.%d.feed_forward.mlp.wi.bias' % player
_lowerCAmelCase =vnp.copy() # same because it is one dimensional
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.endswith('/p2/kernel' ):
_lowerCAmelCase ='model.blocks.%d.feed_forward.mlp.wo.weight' % player
_lowerCAmelCase =vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.endswith('/p2/bias' ):
_lowerCAmelCase ='model.blocks.%d.feed_forward.mlp.wo.bias' % player
_lowerCAmelCase =vnp.copy() # same because it is one dimensional
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.startswith('model/ln' ):
_lowerCAmelCase =int(key_name[8:].split('/' )[0] )
if key_name.endswith('/b' ):
_lowerCAmelCase ='model.blocks.%d.feed_forward.norm.bias' % player
_lowerCAmelCase =vnp.copy() # same because it is one dimensional
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.endswith('/g' ):
_lowerCAmelCase ='model.blocks.%d.feed_forward.norm.weight' % player
_lowerCAmelCase =vnp.copy() # same because it is one dimensional
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.startswith('model/att' ):
_lowerCAmelCase =int(key_name[9:].split('/' )[0] )
if key_name.endswith('/qkv/kernel' ):
_lowerCAmelCase =vnp.copy() # Compute same dimension as Mesh-tensorflow using einsum
_lowerCAmelCase =state[:, 0, :, :]
_lowerCAmelCase =state[:, 1, :, :]
_lowerCAmelCase =state[:, 2, :, :]
_lowerCAmelCase =(
state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
_lowerCAmelCase =(
state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
_lowerCAmelCase =(
state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
_lowerCAmelCase ='model.blocks.%d.self_attn.self_attn.q_proj.weight' % player
_lowerCAmelCase =torch.tensor(a__ )
_lowerCAmelCase ='model.blocks.%d.self_attn.self_attn.k_proj.weight' % player
_lowerCAmelCase =torch.tensor(a__ )
_lowerCAmelCase ='model.blocks.%d.self_attn.self_attn.v_proj.weight' % player
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.endswith('/o/kernel' ):
_lowerCAmelCase ='model.blocks.%d.self_attn.self_attn.out_proj.weight' % player
_lowerCAmelCase =(
vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]] ).transpose([1, 0] ).copy()
) # Mesh-Tensorflow is a diagonal matrix
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.startswith('model/an' ):
_lowerCAmelCase =int(key_name[8:].split('/' )[0] )
if key_name.endswith('/b' ):
_lowerCAmelCase ='model.blocks.%d.self_attn.norm.bias' % player
_lowerCAmelCase =vnp.copy() # same because it is one dimensional
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.endswith('/g' ):
_lowerCAmelCase ='model.blocks.%d.self_attn.norm.weight' % player
_lowerCAmelCase =vnp.copy() # same because it is one dimensional
_lowerCAmelCase =torch.tensor(a__ )
elif (
key_name.startswith('model/wte' )
or key_name.startswith('model/wpe' )
or key_name.startswith('model/ete' )
):
_lowerCAmelCase ={'wte': 'embed_tokens', 'wpe': 'position_embeddings', 'ete': 'extra_position_embeddings'}[
key_name[-3:]
]
_lowerCAmelCase ='model.%s.weight' % nlayer
_lowerCAmelCase =vnp.copy() # same in embedded
_lowerCAmelCase =torch.tensor(a__ )
if key_name.startswith('model/wte' ):
_lowerCAmelCase ='lm_head.weight'
_lowerCAmelCase =vnp.copy() # same in embedded
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.startswith('model/wob' ):
_lowerCAmelCase ='final_logits_bias'
_lowerCAmelCase =vnp.copy() # same in embedded
_lowerCAmelCase =state.reshape((1, -1) )
_lowerCAmelCase =torch.tensor(a__ )
elif key_name == "model/dense/kernel":
_lowerCAmelCase ='model.last_project.weight'
_lowerCAmelCase =vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_lowerCAmelCase =torch.tensor(a__ )
elif key_name == "model/dense_1/bias":
_lowerCAmelCase ='model.last_project.bias'
_lowerCAmelCase =vnp.copy() # same because it is one dimensional
_lowerCAmelCase =torch.tensor(a__ )
torch.save(a__ , args.output )
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser(
description='''model converter.''', formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument('''--tf_model_dir''', metavar='''PATH''', type=str, required=True, help='''import model''')
parser.add_argument('''--output''', metavar='''PATH''', type=str, required=True, help='''output model''')
lowercase_ = parser.parse_args()
convert_tf_gptsan_to_pt(args)
| 58
| 1
|
'''simple docstring'''
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEmbeddings,
BertLayer,
BertPooler,
BertPreTrainedModel,
)
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
_lowerCAmelCase =torch.exp(a__ )
_lowerCAmelCase =torch.sum(a__ , dim=1 ) # sum of exp(x_i)
_lowerCAmelCase =torch.sum(x * exp_x , dim=1 ) # sum of x_i * exp(x_i)
return torch.log(a__ ) - B / A
class SCREAMING_SNAKE_CASE ( nn.Module):
"""simple docstring"""
def __init__( self , __A ) -> Optional[int]:
super().__init__()
_lowerCAmelCase =config.output_attentions
_lowerCAmelCase =config.output_hidden_states
_lowerCAmelCase =nn.ModuleList([BertLayer(__A ) for _ in range(config.num_hidden_layers )] )
_lowerCAmelCase =nn.ModuleList([BertHighway(__A ) for _ in range(config.num_hidden_layers )] )
_lowerCAmelCase =[-1 for _ in range(config.num_hidden_layers )]
def UpperCamelCase__ ( self , __A ) -> Union[str, Any]:
if (type(__A ) is float) or (type(__A ) is int):
for i in range(len(self.early_exit_entropy ) ):
_lowerCAmelCase =x
else:
_lowerCAmelCase =x
def UpperCamelCase__ ( self , __A ) -> Union[str, Any]:
_lowerCAmelCase =pooler.state_dict()
for highway in self.highway:
for name, param in highway.pooler.state_dict().items():
param.copy_(loaded_model[name] )
def UpperCamelCase__ ( self , __A , __A=None , __A=None , __A=None , __A=None , ) -> Union[str, Any]:
_lowerCAmelCase =()
_lowerCAmelCase =()
_lowerCAmelCase =()
for i, layer_module in enumerate(self.layer ):
if self.output_hidden_states:
_lowerCAmelCase =all_hidden_states + (hidden_states,)
_lowerCAmelCase =layer_module(
__A , __A , head_mask[i] , __A , __A )
_lowerCAmelCase =layer_outputs[0]
if self.output_attentions:
_lowerCAmelCase =all_attentions + (layer_outputs[1],)
_lowerCAmelCase =(hidden_states,)
if self.output_hidden_states:
_lowerCAmelCase =current_outputs + (all_hidden_states,)
if self.output_attentions:
_lowerCAmelCase =current_outputs + (all_attentions,)
_lowerCAmelCase =self.highway[i](__A )
# logits, pooled_output
if not self.training:
_lowerCAmelCase =highway_exit[0]
_lowerCAmelCase =entropy(__A )
_lowerCAmelCase =highway_exit + (highway_entropy,) # logits, hidden_states(?), entropy
_lowerCAmelCase =all_highway_exits + (highway_exit,)
if highway_entropy < self.early_exit_entropy[i]:
_lowerCAmelCase =(highway_logits,) + current_outputs[1:] + (all_highway_exits,)
raise HighwayException(__A , i + 1 )
else:
_lowerCAmelCase =all_highway_exits + (highway_exit,)
# Add last layer
if self.output_hidden_states:
_lowerCAmelCase =all_hidden_states + (hidden_states,)
_lowerCAmelCase =(hidden_states,)
if self.output_hidden_states:
_lowerCAmelCase =outputs + (all_hidden_states,)
if self.output_attentions:
_lowerCAmelCase =outputs + (all_attentions,)
_lowerCAmelCase =outputs + (all_highway_exits,)
return outputs # last-layer hidden state, (all hidden states), (all attentions), all highway exits
@add_start_docstrings(
'The Bert Model transformer with early exiting (DeeBERT). ' , __lowercase , )
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
def __init__( self , __A ) -> Dict:
super().__init__(__A )
_lowerCAmelCase =config
_lowerCAmelCase =BertEmbeddings(__A )
_lowerCAmelCase =DeeBertEncoder(__A )
_lowerCAmelCase =BertPooler(__A )
self.init_weights()
def UpperCamelCase__ ( self ) -> Dict:
self.encoder.init_highway_pooler(self.pooler )
def UpperCamelCase__ ( self ) -> Any:
return self.embeddings.word_embeddings
def UpperCamelCase__ ( self , __A ) -> Tuple:
_lowerCAmelCase =value
def UpperCamelCase__ ( self , __A ) -> List[str]:
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(__A )
@add_start_docstrings_to_model_forward(__A )
def UpperCamelCase__ ( self , __A=None , __A=None , __A=None , __A=None , __A=None , __A=None , __A=None , __A=None , ) -> int:
if input_ids is not None and inputs_embeds is not None:
raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time' )
elif input_ids is not None:
_lowerCAmelCase =input_ids.size()
elif inputs_embeds is not None:
_lowerCAmelCase =inputs_embeds.size()[:-1]
else:
raise ValueError('You have to specify either input_ids or inputs_embeds' )
_lowerCAmelCase =input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
_lowerCAmelCase =torch.ones(__A , device=__A )
if encoder_attention_mask is None:
_lowerCAmelCase =torch.ones(__A , device=__A )
if token_type_ids is None:
_lowerCAmelCase =torch.zeros(__A , dtype=torch.long , device=__A )
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
_lowerCAmelCase =self.get_extended_attention_mask(__A , __A , __A )
# If a 2D ou 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if encoder_attention_mask.dim() == 3:
_lowerCAmelCase =encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.dim() == 2:
_lowerCAmelCase =encoder_attention_mask[:, None, None, :]
_lowerCAmelCase =encoder_extended_attention_mask.to(
dtype=next(self.parameters() ).dtype ) # fp16 compatibility
_lowerCAmelCase =(1.0 - encoder_extended_attention_mask) * -10_000.0
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
_lowerCAmelCase =self.get_head_mask(__A , self.config.num_hidden_layers )
_lowerCAmelCase =self.embeddings(
input_ids=__A , position_ids=__A , token_type_ids=__A , inputs_embeds=__A )
_lowerCAmelCase =self.encoder(
__A , attention_mask=__A , head_mask=__A , encoder_hidden_states=__A , encoder_attention_mask=__A , )
_lowerCAmelCase =encoder_outputs[0]
_lowerCAmelCase =self.pooler(__A )
_lowerCAmelCase =(
sequence_output,
pooled_output,
) + encoder_outputs[
1:
] # add hidden_states and attentions if they are here
return outputs # sequence_output, pooled_output, (hidden_states), (attentions), highway exits
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
def __init__( self , __A , __A ) -> Dict:
_lowerCAmelCase =message
_lowerCAmelCase =exit_layer # start from 1!
class SCREAMING_SNAKE_CASE ( nn.Module):
"""simple docstring"""
def __init__( self , __A ) -> int:
super().__init__()
_lowerCAmelCase =BertPooler(__A )
_lowerCAmelCase =nn.Dropout(config.hidden_dropout_prob )
_lowerCAmelCase =nn.Linear(config.hidden_size , config.num_labels )
def UpperCamelCase__ ( self , __A ) -> int:
# Pooler
_lowerCAmelCase =encoder_outputs[0]
_lowerCAmelCase =self.pooler(__A )
# "return" pooler_output
# BertModel
_lowerCAmelCase =(pooler_input, pooler_output) + encoder_outputs[1:]
# "return" bmodel_output
# Dropout and classification
_lowerCAmelCase =bmodel_output[1]
_lowerCAmelCase =self.dropout(__A )
_lowerCAmelCase =self.classifier(__A )
return logits, pooled_output
@add_start_docstrings(
'Bert Model (with early exiting - DeeBERT) with a classifier on top,\n also takes care of multi-layer training. ' , __lowercase , )
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
def __init__( self , __A ) -> Union[str, Any]:
super().__init__(__A )
_lowerCAmelCase =config.num_labels
_lowerCAmelCase =config.num_hidden_layers
_lowerCAmelCase =DeeBertModel(__A )
_lowerCAmelCase =nn.Dropout(config.hidden_dropout_prob )
_lowerCAmelCase =nn.Linear(config.hidden_size , self.config.num_labels )
self.init_weights()
@add_start_docstrings_to_model_forward(__A )
def UpperCamelCase__ ( self , __A=None , __A=None , __A=None , __A=None , __A=None , __A=None , __A=None , __A=-1 , __A=False , ) -> Tuple:
_lowerCAmelCase =self.num_layers
try:
_lowerCAmelCase =self.bert(
__A , attention_mask=__A , token_type_ids=__A , position_ids=__A , head_mask=__A , inputs_embeds=__A , )
# sequence_output, pooled_output, (hidden_states), (attentions), highway exits
_lowerCAmelCase =outputs[1]
_lowerCAmelCase =self.dropout(__A )
_lowerCAmelCase =self.classifier(__A )
_lowerCAmelCase =(logits,) + outputs[2:] # add hidden states and attention if they are here
except HighwayException as e:
_lowerCAmelCase =e.message
_lowerCAmelCase =e.exit_layer
_lowerCAmelCase =outputs[0]
if not self.training:
_lowerCAmelCase =entropy(__A )
_lowerCAmelCase =[]
_lowerCAmelCase =[]
if labels is not None:
if self.num_labels == 1:
# We are doing regression
_lowerCAmelCase =MSELoss()
_lowerCAmelCase =loss_fct(logits.view(-1 ) , labels.view(-1 ) )
else:
_lowerCAmelCase =CrossEntropyLoss()
_lowerCAmelCase =loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
# work with highway exits
_lowerCAmelCase =[]
for highway_exit in outputs[-1]:
_lowerCAmelCase =highway_exit[0]
if not self.training:
highway_logits_all.append(__A )
highway_entropy.append(highway_exit[2] )
if self.num_labels == 1:
# We are doing regression
_lowerCAmelCase =MSELoss()
_lowerCAmelCase =loss_fct(highway_logits.view(-1 ) , labels.view(-1 ) )
else:
_lowerCAmelCase =CrossEntropyLoss()
_lowerCAmelCase =loss_fct(highway_logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
highway_losses.append(__A )
if train_highway:
_lowerCAmelCase =(sum(highway_losses[:-1] ),) + outputs
# exclude the final highway, of course
else:
_lowerCAmelCase =(loss,) + outputs
if not self.training:
_lowerCAmelCase =outputs + ((original_entropy, highway_entropy), exit_layer)
if output_layer >= 0:
_lowerCAmelCase =(
(outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
) # use the highway of the last layer
return outputs # (loss), logits, (hidden_states), (attentions), (highway_exits)
| 58
|
'''simple docstring'''
def UpperCamelCase__ ( a__ = 1_0_0_0 ):
'''simple docstring'''
_lowerCAmelCase =2**power
_lowerCAmelCase =0
while n:
_lowerCAmelCase , _lowerCAmelCase =r + n % 1_0, n // 1_0
return r
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 58
| 1
|
'''simple docstring'''
import argparse
import datetime
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
_lowerCAmelCase ={
'0': 'Sunday',
'1': 'Monday',
'2': 'Tuesday',
'3': 'Wednesday',
'4': 'Thursday',
'5': 'Friday',
'6': 'Saturday',
}
_lowerCAmelCase ={0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 0}
# Validate
if not 0 < len(a__ ) < 1_1:
raise ValueError('Must be 10 characters long' )
# Get month
_lowerCAmelCase =int(date_input[0] + date_input[1] )
# Validate
if not 0 < m < 1_3:
raise ValueError('Month must be between 1 - 12' )
_lowerCAmelCase =date_input[2]
# Validate
if sep_a not in ["-", "/"]:
raise ValueError('Date separator must be \'-\' or \'/\'' )
# Get day
_lowerCAmelCase =int(date_input[3] + date_input[4] )
# Validate
if not 0 < d < 3_2:
raise ValueError('Date must be between 1 - 31' )
# Get second separator
_lowerCAmelCase =date_input[5]
# Validate
if sep_a not in ["-", "/"]:
raise ValueError('Date separator must be \'-\' or \'/\'' )
# Get year
_lowerCAmelCase =int(date_input[6] + date_input[7] + date_input[8] + date_input[9] )
# Arbitrary year range
if not 4_5 < y < 8_5_0_0:
raise ValueError(
'Year out of range. There has to be some sort of limit...right?' )
# Get datetime obj for validation
_lowerCAmelCase =datetime.date(int(a__ ) , int(a__ ) , int(a__ ) )
# Start math
if m <= 2:
_lowerCAmelCase =y - 1
_lowerCAmelCase =m + 1_2
# maths var
_lowerCAmelCase =int(str(a__ )[:2] )
_lowerCAmelCase =int(str(a__ )[2:] )
_lowerCAmelCase =int(2.6 * m - 5.39 )
_lowerCAmelCase =int(c / 4 )
_lowerCAmelCase =int(k / 4 )
_lowerCAmelCase =int(d + k )
_lowerCAmelCase =int(t + u + v + x )
_lowerCAmelCase =int(z - (2 * c) )
_lowerCAmelCase =round(w % 7 )
# End math
# Validate math
if f != convert_datetime_days[dt_ck.weekday()]:
raise AssertionError('The date was evaluated incorrectly. Contact developer.' )
# Response
_lowerCAmelCase =F'''Your date {date_input}, is a {days[str(a__ )]}!'''
return response
if __name__ == "__main__":
import doctest
doctest.testmod()
lowercase_ = argparse.ArgumentParser(
description=(
'''Find out what day of the week nearly any date is or was. Enter '''
'''date as a string in the mm-dd-yyyy or mm/dd/yyyy format'''
)
)
parser.add_argument(
'''date_input''', type=str, help='''Date as a string (mm-dd-yyyy or mm/dd/yyyy)'''
)
lowercase_ = parser.parse_args()
zeller(args.date_input)
| 58
|
'''simple docstring'''
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
_lowerCAmelCase =set()
# To detect a back edge, keep track of vertices currently in the recursion stack
_lowerCAmelCase =set()
return any(
node not in visited and depth_first_search(a__ , a__ , a__ , a__ )
for node in graph )
def UpperCamelCase__ ( a__ , a__ , a__ , a__ ):
'''simple docstring'''
visited.add(a__ )
rec_stk.add(a__ )
for node in graph[vertex]:
if node not in visited:
if depth_first_search(a__ , a__ , a__ , a__ ):
return True
elif node in rec_stk:
return True
# The node needs to be removed from recursion stack before function ends
rec_stk.remove(a__ )
return False
if __name__ == "__main__":
from doctest import testmod
testmod()
| 58
| 1
|
'''simple docstring'''
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __A = "cpu" , __A = "openai/clip-vit-large-patch14" ) -> None:
_lowerCAmelCase =device
_lowerCAmelCase =CLIPTokenizerFast.from_pretrained(__A )
_lowerCAmelCase =[0.48_145_466, 0.4_578_275, 0.40_821_073]
_lowerCAmelCase =[0.26_862_954, 0.26_130_258, 0.27_577_711]
_lowerCAmelCase =torchvision.transforms.Normalize(self.image_mean , self.image_std )
_lowerCAmelCase =torchvision.transforms.Resize(224 )
_lowerCAmelCase =torchvision.transforms.CenterCrop(224 )
def UpperCamelCase__ ( self , __A ) -> List[str]:
_lowerCAmelCase =self.resize(__A )
_lowerCAmelCase =self.center_crop(__A )
_lowerCAmelCase =self.normalize(__A )
return images
def __call__( self , __A=None , __A=None , **__A ) -> List[Any]:
_lowerCAmelCase =self.tokenizer(text=__A , **__A )
_lowerCAmelCase =self.preprocess_img(__A )
_lowerCAmelCase ={key: value.to(self.device ) for (key, value) in encoding.items()}
return encoding
class SCREAMING_SNAKE_CASE ( nn.Module):
"""simple docstring"""
def __init__( self , __A=10 , __A=0.01 , __A=None , __A=None , __A=None , __A=None , __A=None , __A=None , __A=False , __A=True , __A="image" , __A=True , __A=False , __A=False , __A=False , ) -> None:
super().__init__()
_lowerCAmelCase =None
_lowerCAmelCase =device if device else get_device()
if vqgan:
_lowerCAmelCase =vqgan
else:
_lowerCAmelCase =load_vqgan(self.device , conf_path=__A , ckpt_path=__A )
self.vqgan.eval()
if clip:
_lowerCAmelCase =clip
else:
_lowerCAmelCase =CLIPModel.from_pretrained('openai/clip-vit-base-patch32' )
self.clip.to(self.device )
_lowerCAmelCase =ProcessorGradientFlow(device=self.device )
_lowerCAmelCase =iterations
_lowerCAmelCase =lr
_lowerCAmelCase =log
_lowerCAmelCase =make_grid
_lowerCAmelCase =return_val
_lowerCAmelCase =quantize
_lowerCAmelCase =self.vqgan.decoder.z_shape
def UpperCamelCase__ ( self , __A=None , __A=None , __A=5 , __A=True ) -> Dict:
_lowerCAmelCase =[]
if output_path is None:
_lowerCAmelCase ='./animation.gif'
if input_path is None:
_lowerCAmelCase =self.save_path
_lowerCAmelCase =sorted(glob(input_path + '/*' ) )
if not len(__A ):
raise ValueError(
'No images found in save path, aborting (did you pass save_intermediate=True to the generate'
' function?)' )
if len(__A ) == 1:
print('Only one image found in save path, (did you pass save_intermediate=True to the generate function?)' )
_lowerCAmelCase =total_duration / len(__A )
_lowerCAmelCase =[frame_duration] * len(__A )
if extend_frames:
_lowerCAmelCase =1.5
_lowerCAmelCase =3
for file_name in paths:
if file_name.endswith('.png' ):
images.append(imageio.imread(__A ) )
imageio.mimsave(__A , __A , duration=__A )
print(F'''gif saved to {output_path}''' )
def UpperCamelCase__ ( self , __A=None , __A=None ) -> List[Any]:
if not (path or img):
raise ValueError('Input either path or tensor' )
if img is not None:
raise NotImplementedError
_lowerCAmelCase =preprocess(Image.open(__A ) , target_image_size=256 ).to(self.device )
_lowerCAmelCase =preprocess_vqgan(__A )
_lowerCAmelCase , *_lowerCAmelCase =self.vqgan.encode(__A )
return z
def UpperCamelCase__ ( self , __A ) -> Any:
_lowerCAmelCase =self.latent.detach().requires_grad_()
_lowerCAmelCase =base_latent + transform_vector
if self.quantize:
_lowerCAmelCase , *_lowerCAmelCase =self.vqgan.quantize(__A )
else:
_lowerCAmelCase =trans_latent
return self.vqgan.decode(__A )
def UpperCamelCase__ ( self , __A , __A , __A=None ) -> Any:
_lowerCAmelCase =self.clip_preprocessor(text=__A , images=__A , return_tensors='pt' , padding=__A )
_lowerCAmelCase =self.clip(**__A )
_lowerCAmelCase =clip_outputs.logits_per_image
if weights is not None:
_lowerCAmelCase =similarity_logits * weights
return similarity_logits.sum()
def UpperCamelCase__ ( self , __A , __A , __A ) -> List[Any]:
_lowerCAmelCase =self._get_clip_similarity(pos_prompts['prompts'] , __A , weights=(1 / pos_prompts['weights']) )
if neg_prompts:
_lowerCAmelCase =self._get_clip_similarity(neg_prompts['prompts'] , __A , weights=neg_prompts['weights'] )
else:
_lowerCAmelCase =torch.tensor([1] , device=self.device )
_lowerCAmelCase =-torch.log(__A ) + torch.log(__A )
return loss
def UpperCamelCase__ ( self , __A , __A , __A ) -> str:
_lowerCAmelCase =torch.randn_like(self.latent , requires_grad=__A , device=self.device )
_lowerCAmelCase =torch.optim.Adam([vector] , lr=self.lr )
for i in range(self.iterations ):
optim.zero_grad()
_lowerCAmelCase =self._add_vector(__A )
_lowerCAmelCase =loop_post_process(__A )
_lowerCAmelCase =self._get_CLIP_loss(__A , __A , __A )
print('CLIP loss' , __A )
if self.log:
wandb.log({'CLIP Loss': clip_loss} )
clip_loss.backward(retain_graph=__A )
optim.step()
if self.return_val == "image":
yield custom_to_pil(transformed_img[0] )
else:
yield vector
def UpperCamelCase__ ( self , __A , __A , __A ) -> Tuple:
wandb.init(reinit=__A , project='face-editor' )
wandb.config.update({'Positive Prompts': positive_prompts} )
wandb.config.update({'Negative Prompts': negative_prompts} )
wandb.config.update({'lr': self.lr, 'iterations': self.iterations} )
if image_path:
_lowerCAmelCase =Image.open(__A )
_lowerCAmelCase =image.resize((256, 256) )
wandb.log('Original Image' , wandb.Image(__A ) )
def UpperCamelCase__ ( self , __A ) -> int:
if not prompts:
return []
_lowerCAmelCase =[]
_lowerCAmelCase =[]
if isinstance(__A , __A ):
_lowerCAmelCase =[prompt.strip() for prompt in prompts.split('|' )]
for prompt in prompts:
if isinstance(__A , (tuple, list) ):
_lowerCAmelCase =prompt[0]
_lowerCAmelCase =float(prompt[1] )
elif ":" in prompt:
_lowerCAmelCase , _lowerCAmelCase =prompt.split(':' )
_lowerCAmelCase =float(__A )
else:
_lowerCAmelCase =prompt
_lowerCAmelCase =1.0
processed_prompts.append(__A )
weights.append(__A )
return {
"prompts": processed_prompts,
"weights": torch.tensor(__A , device=self.device ),
}
def UpperCamelCase__ ( self , __A , __A=None , __A=None , __A=True , __A=False , __A=True , __A=True , __A=None , ) -> Dict:
if image_path:
_lowerCAmelCase =self._get_latent(__A )
else:
_lowerCAmelCase =torch.randn(self.latent_dim , device=self.device )
if self.log:
self._init_logging(__A , __A , __A )
assert pos_prompts, "You must provide at least one positive prompt."
_lowerCAmelCase =self.process_prompts(__A )
_lowerCAmelCase =self.process_prompts(__A )
if save_final and save_path is None:
_lowerCAmelCase =os.path.join('./outputs/' , '_'.join(pos_prompts['prompts'] ) )
if not os.path.exists(__A ):
os.makedirs(__A )
else:
_lowerCAmelCase =save_path + '_' + get_timestamp()
os.makedirs(__A )
_lowerCAmelCase =save_path
_lowerCAmelCase =self.vqgan.decode(self.latent )[0]
if show_intermediate:
print('Original Image' )
show_pil(custom_to_pil(__A ) )
_lowerCAmelCase =loop_post_process(__A )
for iter, transformed_img in enumerate(self._optimize_CLIP(__A , __A , __A ) ):
if show_intermediate:
show_pil(__A )
if save_intermediate:
transformed_img.save(os.path.join(self.save_path , F'''iter_{iter:03d}.png''' ) )
if self.log:
wandb.log({'Image': wandb.Image(__A )} )
if show_final:
show_pil(__A )
if save_final:
transformed_img.save(os.path.join(self.save_path , F'''iter_{iter:03d}_final.png''' ) )
| 58
|
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
'''salesforce/blip2-opt-2.7b''': '''https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json''',
}
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : Tuple = 'blip_2_vision_model'
def __init__( self , __A=1408 , __A=6144 , __A=39 , __A=16 , __A=224 , __A=14 , __A="gelu" , __A=0.00_001 , __A=0.0 , __A=1E-10 , __A=True , **__A , ) -> int:
super().__init__(**__A )
_lowerCAmelCase =hidden_size
_lowerCAmelCase =intermediate_size
_lowerCAmelCase =num_hidden_layers
_lowerCAmelCase =num_attention_heads
_lowerCAmelCase =patch_size
_lowerCAmelCase =image_size
_lowerCAmelCase =initializer_range
_lowerCAmelCase =attention_dropout
_lowerCAmelCase =layer_norm_eps
_lowerCAmelCase =hidden_act
_lowerCAmelCase =qkv_bias
@classmethod
def UpperCamelCase__ ( cls , __A , **__A ) -> "PretrainedConfig":
cls._set_token_in_kwargs(__A )
_lowerCAmelCase , _lowerCAmelCase =cls.get_config_dict(__A , **__A )
# get the vision config dict if we are loading from Blip2Config
if config_dict.get('model_type' ) == "blip-2":
_lowerCAmelCase =config_dict['vision_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(__A , **__A )
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : int = 'blip_2_qformer'
def __init__( self , __A=3_0522 , __A=768 , __A=12 , __A=12 , __A=3072 , __A="gelu" , __A=0.1 , __A=0.1 , __A=512 , __A=0.02 , __A=1E-12 , __A=0 , __A="absolute" , __A=2 , __A=1408 , **__A , ) -> List[str]:
super().__init__(pad_token_id=__A , **__A )
_lowerCAmelCase =vocab_size
_lowerCAmelCase =hidden_size
_lowerCAmelCase =num_hidden_layers
_lowerCAmelCase =num_attention_heads
_lowerCAmelCase =hidden_act
_lowerCAmelCase =intermediate_size
_lowerCAmelCase =hidden_dropout_prob
_lowerCAmelCase =attention_probs_dropout_prob
_lowerCAmelCase =max_position_embeddings
_lowerCAmelCase =initializer_range
_lowerCAmelCase =layer_norm_eps
_lowerCAmelCase =position_embedding_type
_lowerCAmelCase =cross_attention_frequency
_lowerCAmelCase =encoder_hidden_size
@classmethod
def UpperCamelCase__ ( cls , __A , **__A ) -> "PretrainedConfig":
cls._set_token_in_kwargs(__A )
_lowerCAmelCase , _lowerCAmelCase =cls.get_config_dict(__A , **__A )
# get the qformer config dict if we are loading from Blip2Config
if config_dict.get('model_type' ) == "blip-2":
_lowerCAmelCase =config_dict['qformer_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(__A , **__A )
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : Optional[int] = 'blip-2'
lowercase : Any = True
def __init__( self , __A=None , __A=None , __A=None , __A=32 , **__A ) -> int:
super().__init__(**__A )
if vision_config is None:
_lowerCAmelCase ={}
logger.info('vision_config is None. initializing the Blip2VisionConfig with default values.' )
if qformer_config is None:
_lowerCAmelCase ={}
logger.info('qformer_config is None. Initializing the Blip2QFormerConfig with default values.' )
if text_config is None:
_lowerCAmelCase ={}
logger.info('text_config is None. Initializing the text config with default values (`OPTConfig`).' )
_lowerCAmelCase =BlipaVisionConfig(**__A )
_lowerCAmelCase =BlipaQFormerConfig(**__A )
_lowerCAmelCase =text_config['model_type'] if 'model_type' in text_config else 'opt'
_lowerCAmelCase =CONFIG_MAPPING[text_model_type](**__A )
_lowerCAmelCase =self.text_config.tie_word_embeddings
_lowerCAmelCase =self.text_config.is_encoder_decoder
_lowerCAmelCase =num_query_tokens
_lowerCAmelCase =self.vision_config.hidden_size
_lowerCAmelCase =self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
_lowerCAmelCase =1.0
_lowerCAmelCase =0.02
@classmethod
def UpperCamelCase__ ( cls , __A , __A , __A , **__A , ) -> Any:
return cls(
vision_config=vision_config.to_dict() , qformer_config=qformer_config.to_dict() , text_config=text_config.to_dict() , **__A , )
def UpperCamelCase__ ( self ) -> Tuple:
_lowerCAmelCase =copy.deepcopy(self.__dict__ )
_lowerCAmelCase =self.vision_config.to_dict()
_lowerCAmelCase =self.qformer_config.to_dict()
_lowerCAmelCase =self.text_config.to_dict()
_lowerCAmelCase =self.__class__.model_type
return output
| 58
| 1
|
'''simple docstring'''
import argparse
import json
import subprocess
def UpperCamelCase__ ( a__ , a__ ):
'''simple docstring'''
_lowerCAmelCase =[]
_lowerCAmelCase =(
F'''curl -H "Accept: application/vnd.github+json" -H "Authorization: Bearer {token}"'''
' https://api.github.com/repos/huggingface/transformers/actions/runners'
)
_lowerCAmelCase =subprocess.run(a__ , shell=a__ , stdout=subprocess.PIPE )
_lowerCAmelCase =output.stdout.decode('utf-8' )
_lowerCAmelCase =json.loads(a__ )
_lowerCAmelCase =status['runners']
for runner in runners:
if runner["name"] in target_runners:
if runner["status"] == "offline":
offline_runners.append(a__ )
# save the result so we can report them on Slack
with open('offline_runners.txt' , 'w' ) as fp:
fp.write(json.dumps(a__ ) )
if len(a__ ) > 0:
_lowerCAmelCase ='\n'.join([x['name'] for x in offline_runners] )
raise ValueError(F'''The following runners are offline:\n{failed}''' )
if __name__ == "__main__":
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
return values.split(',' )
lowercase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--target_runners''',
default=None,
type=list_str,
required=True,
help='''Comma-separated list of runners to check status.''',
)
parser.add_argument(
'''--token''', default=None, type=str, required=True, help='''A token that has actions:read permission.'''
)
lowercase_ = parser.parse_args()
get_runner_status(args.target_runners, args.token)
| 58
|
'''simple docstring'''
lowercase_ = {
'''A''': '''.-''', '''B''': '''-...''', '''C''': '''-.-.''', '''D''': '''-..''', '''E''': '''.''', '''F''': '''..-.''', '''G''': '''--.''',
'''H''': '''....''', '''I''': '''..''', '''J''': '''.---''', '''K''': '''-.-''', '''L''': '''.-..''', '''M''': '''--''', '''N''': '''-.''',
'''O''': '''---''', '''P''': '''.--.''', '''Q''': '''--.-''', '''R''': '''.-.''', '''S''': '''...''', '''T''': '''-''', '''U''': '''..-''',
'''V''': '''...-''', '''W''': '''.--''', '''X''': '''-..-''', '''Y''': '''-.--''', '''Z''': '''--..''', '''1''': '''.----''',
'''2''': '''..---''', '''3''': '''...--''', '''4''': '''....-''', '''5''': '''.....''', '''6''': '''-....''', '''7''': '''--...''',
'''8''': '''---..''', '''9''': '''----.''', '''0''': '''-----''', '''&''': '''.-...''', '''@''': '''.--.-.''',
''':''': '''---...''', ''',''': '''--..--''', '''.''': '''.-.-.-''', '''\'''': '''.----.''', '''"''': '''.-..-.''',
'''?''': '''..--..''', '''/''': '''-..-.''', '''=''': '''-...-''', '''+''': '''.-.-.''', '''-''': '''-....-''',
'''(''': '''-.--.''', ''')''': '''-.--.-''', '''!''': '''-.-.--''', ''' ''': '''/'''
} # Exclamation mark is not in ITU-R recommendation
# fmt: on
lowercase_ = {value: key for key, value in MORSE_CODE_DICT.items()}
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
return " ".join(MORSE_CODE_DICT[char] for char in message.upper() )
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
return "".join(REVERSE_DICT[char] for char in message.split() )
def UpperCamelCase__ ( ):
'''simple docstring'''
_lowerCAmelCase ='Morse code here!'
print(a__ )
_lowerCAmelCase =encrypt(a__ )
print(a__ )
_lowerCAmelCase =decrypt(a__ )
print(a__ )
if __name__ == "__main__":
main()
| 58
| 1
|
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import LevitImageProcessor
class SCREAMING_SNAKE_CASE ( unittest.TestCase):
"""simple docstring"""
def __init__( self , __A , __A=7 , __A=3 , __A=18 , __A=30 , __A=400 , __A=True , __A=None , __A=True , __A=None , __A=True , __A=[0.5, 0.5, 0.5] , __A=[0.5, 0.5, 0.5] , ) -> Optional[int]:
_lowerCAmelCase =size if size is not None else {'shortest_edge': 18}
_lowerCAmelCase =crop_size if crop_size is not None else {'height': 18, 'width': 18}
_lowerCAmelCase =parent
_lowerCAmelCase =batch_size
_lowerCAmelCase =num_channels
_lowerCAmelCase =image_size
_lowerCAmelCase =min_resolution
_lowerCAmelCase =max_resolution
_lowerCAmelCase =do_resize
_lowerCAmelCase =size
_lowerCAmelCase =do_center_crop
_lowerCAmelCase =crop_size
_lowerCAmelCase =do_normalize
_lowerCAmelCase =image_mean
_lowerCAmelCase =image_std
def UpperCamelCase__ ( self ) -> Dict:
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"do_center_crop": self.do_center_crop,
"size": self.size,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE ( __lowercase , unittest.TestCase):
"""simple docstring"""
lowercase : int = LevitImageProcessor if is_vision_available() else None
def UpperCamelCase__ ( self ) -> Dict:
_lowerCAmelCase =LevitImageProcessingTester(self )
@property
def UpperCamelCase__ ( self ) -> int:
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCamelCase__ ( self ) -> List[str]:
_lowerCAmelCase =self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__A , 'image_mean' ) )
self.assertTrue(hasattr(__A , 'image_std' ) )
self.assertTrue(hasattr(__A , 'do_normalize' ) )
self.assertTrue(hasattr(__A , 'do_resize' ) )
self.assertTrue(hasattr(__A , 'do_center_crop' ) )
self.assertTrue(hasattr(__A , 'size' ) )
def UpperCamelCase__ ( self ) -> Optional[int]:
_lowerCAmelCase =self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'shortest_edge': 18} )
self.assertEqual(image_processor.crop_size , {'height': 18, 'width': 18} )
_lowerCAmelCase =self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {'shortest_edge': 42} )
self.assertEqual(image_processor.crop_size , {'height': 84, 'width': 84} )
def UpperCamelCase__ ( self ) -> List[str]:
pass
def UpperCamelCase__ ( self ) -> Optional[Any]:
# Initialize image_processing
_lowerCAmelCase =self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_lowerCAmelCase =prepare_image_inputs(self.image_processor_tester , equal_resolution=__A )
for image in image_inputs:
self.assertIsInstance(__A , Image.Image )
# Test not batched input
_lowerCAmelCase =image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
_lowerCAmelCase =image_processing(__A , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
def UpperCamelCase__ ( self ) -> int:
# Initialize image_processing
_lowerCAmelCase =self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_lowerCAmelCase =prepare_image_inputs(self.image_processor_tester , equal_resolution=__A , numpify=__A )
for image in image_inputs:
self.assertIsInstance(__A , np.ndarray )
# Test not batched input
_lowerCAmelCase =image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
_lowerCAmelCase =image_processing(__A , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
def UpperCamelCase__ ( self ) -> int:
# Initialize image_processing
_lowerCAmelCase =self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_lowerCAmelCase =prepare_image_inputs(self.image_processor_tester , equal_resolution=__A , torchify=__A )
for image in image_inputs:
self.assertIsInstance(__A , torch.Tensor )
# Test not batched input
_lowerCAmelCase =image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
_lowerCAmelCase =image_processing(__A , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
| 58
|
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
'''facebook/data2vec-text-base''': '''https://huggingface.co/data2vec/resolve/main/config.json''',
}
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : List[str] = 'data2vec-text'
def __init__( self , __A=3_0522 , __A=768 , __A=12 , __A=12 , __A=3072 , __A="gelu" , __A=0.1 , __A=0.1 , __A=512 , __A=2 , __A=0.02 , __A=1E-12 , __A=1 , __A=0 , __A=2 , __A="absolute" , __A=True , __A=None , **__A , ) -> List[Any]:
super().__init__(pad_token_id=__A , bos_token_id=__A , eos_token_id=__A , **__A )
_lowerCAmelCase =vocab_size
_lowerCAmelCase =hidden_size
_lowerCAmelCase =num_hidden_layers
_lowerCAmelCase =num_attention_heads
_lowerCAmelCase =hidden_act
_lowerCAmelCase =intermediate_size
_lowerCAmelCase =hidden_dropout_prob
_lowerCAmelCase =attention_probs_dropout_prob
_lowerCAmelCase =max_position_embeddings
_lowerCAmelCase =type_vocab_size
_lowerCAmelCase =initializer_range
_lowerCAmelCase =layer_norm_eps
_lowerCAmelCase =position_embedding_type
_lowerCAmelCase =use_cache
_lowerCAmelCase =classifier_dropout
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
@property
def UpperCamelCase__ ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_lowerCAmelCase ={0: 'batch', 1: 'choice', 2: 'sequence'}
else:
_lowerCAmelCase ={0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
] )
| 58
| 1
|
'''simple docstring'''
import os
lowercase_ = {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 100, '''D''': 500, '''M''': 1000}
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
_lowerCAmelCase =0
_lowerCAmelCase =0
while index < len(a__ ) - 1:
_lowerCAmelCase =SYMBOLS[numerals[index]]
_lowerCAmelCase =SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
_lowerCAmelCase =''
_lowerCAmelCase =num // 1_0_0_0
numerals += m_count * "M"
num %= 1_0_0_0
_lowerCAmelCase =num // 1_0_0
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 1_0_0
_lowerCAmelCase =num // 1_0
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 1_0
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def UpperCamelCase__ ( a__ = "/p089_roman.txt" ):
'''simple docstring'''
_lowerCAmelCase =0
with open(os.path.dirname(a__ ) + roman_numerals_filename ) as filea:
_lowerCAmelCase =filea.readlines()
for line in lines:
_lowerCAmelCase =line.strip()
_lowerCAmelCase =parse_roman_numerals(a__ )
_lowerCAmelCase =generate_roman_numerals(a__ )
savings += len(a__ ) - len(a__ )
return savings
if __name__ == "__main__":
print(F'{solution() = }')
| 58
|
'''simple docstring'''
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin
@skip_mps
class SCREAMING_SNAKE_CASE ( __lowercase , __lowercase , unittest.TestCase):
"""simple docstring"""
lowercase : List[Any] = IFPipeline
lowercase : Tuple = TEXT_TO_IMAGE_PARAMS - {'width', 'height', 'latents'}
lowercase : Union[str, Any] = TEXT_TO_IMAGE_BATCH_PARAMS
lowercase : int = PipelineTesterMixin.required_optional_params - {'latents'}
def UpperCamelCase__ ( self ) -> str:
return self._get_dummy_components()
def UpperCamelCase__ ( self , __A , __A=0 ) -> int:
if str(__A ).startswith('mps' ):
_lowerCAmelCase =torch.manual_seed(__A )
else:
_lowerCAmelCase =torch.Generator(device=__A ).manual_seed(__A )
_lowerCAmelCase ={
'prompt': 'A painting of a squirrel eating a burger',
'generator': generator,
'num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
def UpperCamelCase__ ( self ) -> Optional[Any]:
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != 'cuda' , reason='float16 requires CUDA' )
def UpperCamelCase__ ( self ) -> Tuple:
# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
super().test_save_load_floataa(expected_max_diff=1E-1 )
def UpperCamelCase__ ( self ) -> List[Any]:
self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )
def UpperCamelCase__ ( self ) -> str:
self._test_save_load_local()
def UpperCamelCase__ ( self ) -> Union[str, Any]:
self._test_inference_batch_single_identical(
expected_max_diff=1E-2 , )
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def UpperCamelCase__ ( self ) -> List[str]:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE ( unittest.TestCase):
"""simple docstring"""
def UpperCamelCase__ ( self ) -> Optional[int]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase__ ( self ) -> Optional[Any]:
# if
_lowerCAmelCase =IFPipeline.from_pretrained('DeepFloyd/IF-I-XL-v1.0' , variant='fp16' , torch_dtype=torch.floataa )
_lowerCAmelCase =IFSuperResolutionPipeline.from_pretrained(
'DeepFloyd/IF-II-L-v1.0' , variant='fp16' , torch_dtype=torch.floataa , text_encoder=__A , tokenizer=__A )
# pre compute text embeddings and remove T5 to save memory
pipe_a.text_encoder.to('cuda' )
_lowerCAmelCase , _lowerCAmelCase =pipe_a.encode_prompt('anime turtle' , device='cuda' )
del pipe_a.tokenizer
del pipe_a.text_encoder
gc.collect()
_lowerCAmelCase =None
_lowerCAmelCase =None
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if(__A , __A , __A , __A )
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# img2img
_lowerCAmelCase =IFImgaImgPipeline(**pipe_a.components )
_lowerCAmelCase =IFImgaImgSuperResolutionPipeline(**pipe_a.components )
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if_imgaimg(__A , __A , __A , __A )
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# inpainting
_lowerCAmelCase =IFInpaintingPipeline(**pipe_a.components )
_lowerCAmelCase =IFInpaintingSuperResolutionPipeline(**pipe_a.components )
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if_inpainting(__A , __A , __A , __A )
def UpperCamelCase__ ( self , __A , __A , __A , __A ) -> str:
# pipeline 1
_start_torch_memory_measurement()
_lowerCAmelCase =torch.Generator(device='cpu' ).manual_seed(0 )
_lowerCAmelCase =pipe_a(
prompt_embeds=__A , negative_prompt_embeds=__A , num_inference_steps=2 , generator=__A , output_type='np' , )
_lowerCAmelCase =output.images[0]
assert image.shape == (64, 64, 3)
_lowerCAmelCase =torch.cuda.max_memory_allocated()
assert mem_bytes < 13 * 10**9
_lowerCAmelCase =load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy' )
assert_mean_pixel_difference(__A , __A )
# pipeline 2
_start_torch_memory_measurement()
_lowerCAmelCase =torch.Generator(device='cpu' ).manual_seed(0 )
_lowerCAmelCase =floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(__A )
_lowerCAmelCase =pipe_a(
prompt_embeds=__A , negative_prompt_embeds=__A , image=__A , generator=__A , num_inference_steps=2 , output_type='np' , )
_lowerCAmelCase =output.images[0]
assert image.shape == (256, 256, 3)
_lowerCAmelCase =torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
_lowerCAmelCase =load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy' )
assert_mean_pixel_difference(__A , __A )
def UpperCamelCase__ ( self , __A , __A , __A , __A ) -> Optional[int]:
# pipeline 1
_start_torch_memory_measurement()
_lowerCAmelCase =floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(__A )
_lowerCAmelCase =torch.Generator(device='cpu' ).manual_seed(0 )
_lowerCAmelCase =pipe_a(
prompt_embeds=__A , negative_prompt_embeds=__A , image=__A , num_inference_steps=2 , generator=__A , output_type='np' , )
_lowerCAmelCase =output.images[0]
assert image.shape == (64, 64, 3)
_lowerCAmelCase =torch.cuda.max_memory_allocated()
assert mem_bytes < 10 * 10**9
_lowerCAmelCase =load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy' )
assert_mean_pixel_difference(__A , __A )
# pipeline 2
_start_torch_memory_measurement()
_lowerCAmelCase =torch.Generator(device='cpu' ).manual_seed(0 )
_lowerCAmelCase =floats_tensor((1, 3, 256, 256) , rng=random.Random(0 ) ).to(__A )
_lowerCAmelCase =floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(__A )
_lowerCAmelCase =pipe_a(
prompt_embeds=__A , negative_prompt_embeds=__A , image=__A , original_image=__A , generator=__A , num_inference_steps=2 , output_type='np' , )
_lowerCAmelCase =output.images[0]
assert image.shape == (256, 256, 3)
_lowerCAmelCase =torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
_lowerCAmelCase =load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy' )
assert_mean_pixel_difference(__A , __A )
def UpperCamelCase__ ( self , __A , __A , __A , __A ) -> Dict:
# pipeline 1
_start_torch_memory_measurement()
_lowerCAmelCase =floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(__A )
_lowerCAmelCase =floats_tensor((1, 3, 64, 64) , rng=random.Random(1 ) ).to(__A )
_lowerCAmelCase =torch.Generator(device='cpu' ).manual_seed(0 )
_lowerCAmelCase =pipe_a(
prompt_embeds=__A , negative_prompt_embeds=__A , image=__A , mask_image=__A , num_inference_steps=2 , generator=__A , output_type='np' , )
_lowerCAmelCase =output.images[0]
assert image.shape == (64, 64, 3)
_lowerCAmelCase =torch.cuda.max_memory_allocated()
assert mem_bytes < 10 * 10**9
_lowerCAmelCase =load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy' )
assert_mean_pixel_difference(__A , __A )
# pipeline 2
_start_torch_memory_measurement()
_lowerCAmelCase =torch.Generator(device='cpu' ).manual_seed(0 )
_lowerCAmelCase =floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(__A )
_lowerCAmelCase =floats_tensor((1, 3, 256, 256) , rng=random.Random(0 ) ).to(__A )
_lowerCAmelCase =floats_tensor((1, 3, 256, 256) , rng=random.Random(1 ) ).to(__A )
_lowerCAmelCase =pipe_a(
prompt_embeds=__A , negative_prompt_embeds=__A , image=__A , mask_image=__A , original_image=__A , generator=__A , num_inference_steps=2 , output_type='np' , )
_lowerCAmelCase =output.images[0]
assert image.shape == (256, 256, 3)
_lowerCAmelCase =torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
_lowerCAmelCase =load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy' )
assert_mean_pixel_difference(__A , __A )
def UpperCamelCase__ ( ):
'''simple docstring'''
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
| 58
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'''simple docstring'''
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def UpperCamelCase__ ( a__=None , a__=None ):
'''simple docstring'''
return field(default_factory=lambda: default , metadata=a__ )
@dataclass
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
lowercase : str = field(
metadata={'help': 'The csv file to plot.'} , )
lowercase : bool = field(
default=__lowercase , metadata={'help': 'Whether to plot along batch size or sequence length. Defaults to sequence length.'} , )
lowercase : bool = field(
default=__lowercase , metadata={'help': 'Whether the csv file has time results or memory results. Defaults to memory results.'} , )
lowercase : bool = field(
default=__lowercase , metadata={'help': 'Disable logarithmic scale when plotting'} , )
lowercase : bool = field(
default=__lowercase , metadata={
'help': 'Whether the csv file has training results or inference results. Defaults to inference results.'
} , )
lowercase : Optional[str] = field(
default=__lowercase , metadata={'help': 'Filename under which the plot will be saved. If unused no plot is saved.'} , )
lowercase : Optional[List[str]] = list_field(
default=__lowercase , metadata={'help': 'List of model names that are used instead of the ones in the csv file.'})
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
try:
int(a__ )
return True
except ValueError:
return False
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
try:
float(a__ )
return True
except ValueError:
return False
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __A ) -> List[Any]:
_lowerCAmelCase =args
_lowerCAmelCase =defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}} )
with open(self.args.csv_file , newline='' ) as csv_file:
_lowerCAmelCase =csv.DictReader(__A )
for row in reader:
_lowerCAmelCase =row['model']
self.result_dict[model_name]["bsz"].append(int(row['batch_size'] ) )
self.result_dict[model_name]["seq_len"].append(int(row['sequence_length'] ) )
if can_convert_to_int(row['result'] ):
# value is not None
_lowerCAmelCase =int(row['result'] )
elif can_convert_to_float(row['result'] ):
# value is not None
_lowerCAmelCase =float(row['result'] )
def UpperCamelCase__ ( self ) -> Dict:
_lowerCAmelCase , _lowerCAmelCase =plt.subplots()
_lowerCAmelCase ='Time usage' if self.args.is_time else 'Memory usage'
_lowerCAmelCase =title_str + ' for training' if self.args.is_train else title_str + ' for inference'
if not self.args.no_log_scale:
# set logarithm scales
ax.set_xscale('log' )
ax.set_yscale('log' )
for axis in [ax.xaxis, ax.yaxis]:
axis.set_major_formatter(ScalarFormatter() )
for model_name_idx, model_name in enumerate(self.result_dict.keys() ):
_lowerCAmelCase =sorted(set(self.result_dict[model_name]['bsz'] ) )
_lowerCAmelCase =sorted(set(self.result_dict[model_name]['seq_len'] ) )
_lowerCAmelCase =self.result_dict[model_name]['result']
((_lowerCAmelCase) , (_lowerCAmelCase)) =(
(batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
)
_lowerCAmelCase =(
model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
)
for inner_loop_value in inner_loop_array:
if self.args.plot_along_batch:
_lowerCAmelCase =np.asarray(
[results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results] , dtype=__A , )
else:
_lowerCAmelCase =np.asarray(
[results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results] , dtype=np.floataa , )
((_lowerCAmelCase) , (_lowerCAmelCase)) =(
('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
)
_lowerCAmelCase =np.asarray(__A , __A )[: len(__A )]
plt.scatter(
__A , __A , label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}''' )
plt.plot(__A , __A , '--' )
title_str += F''' {label_model_name} vs.'''
_lowerCAmelCase =title_str[:-4]
_lowerCAmelCase ='Time in s' if self.args.is_time else 'Memory in MB'
# plot
plt.title(__A )
plt.xlabel(__A )
plt.ylabel(__A )
plt.legend()
if self.args.figure_png_file is not None:
plt.savefig(self.args.figure_png_file )
else:
plt.show()
def UpperCamelCase__ ( ):
'''simple docstring'''
_lowerCAmelCase =HfArgumentParser(a__ )
_lowerCAmelCase =parser.parse_args_into_dataclasses()[0]
_lowerCAmelCase =Plot(args=a__ )
plot.plot()
if __name__ == "__main__":
main()
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'''simple docstring'''
import unittest
from knapsack import knapsack as k
class SCREAMING_SNAKE_CASE ( unittest.TestCase):
"""simple docstring"""
def UpperCamelCase__ ( self ) -> Optional[Any]:
_lowerCAmelCase =0
_lowerCAmelCase =[0]
_lowerCAmelCase =[0]
_lowerCAmelCase =len(__A )
self.assertEqual(k.knapsack(__A , __A , __A , __A ) , 0 )
_lowerCAmelCase =[60]
_lowerCAmelCase =[10]
_lowerCAmelCase =len(__A )
self.assertEqual(k.knapsack(__A , __A , __A , __A ) , 0 )
def UpperCamelCase__ ( self ) -> Tuple:
_lowerCAmelCase =3
_lowerCAmelCase =[1, 2, 3]
_lowerCAmelCase =[3, 2, 1]
_lowerCAmelCase =len(__A )
self.assertEqual(k.knapsack(__A , __A , __A , __A ) , 5 )
def UpperCamelCase__ ( self ) -> Union[str, Any]:
_lowerCAmelCase =50
_lowerCAmelCase =[60, 100, 120]
_lowerCAmelCase =[10, 20, 30]
_lowerCAmelCase =len(__A )
self.assertEqual(k.knapsack(__A , __A , __A , __A ) , 220 )
if __name__ == "__main__":
unittest.main()
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|
'''simple docstring'''
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM
@require_torch
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __A , __A=99 , __A=13 , __A=16 , __A=7 , __A=True , __A=True , __A=True , __A=False , __A=True , __A=2 , __A=32 , __A=4 , __A=4 , __A=30 , __A=0 , __A=1 , __A=2 , __A=None , ) -> Dict:
_lowerCAmelCase =parent
_lowerCAmelCase =batch_size
_lowerCAmelCase =decoder_seq_length
# For common tests
_lowerCAmelCase =self.decoder_seq_length
_lowerCAmelCase =is_training
_lowerCAmelCase =use_attention_mask
_lowerCAmelCase =use_labels
_lowerCAmelCase =vocab_size
_lowerCAmelCase =d_model
_lowerCAmelCase =d_model
_lowerCAmelCase =decoder_layers
_lowerCAmelCase =decoder_layers
_lowerCAmelCase =decoder_ffn_dim
_lowerCAmelCase =decoder_attention_heads
_lowerCAmelCase =decoder_attention_heads
_lowerCAmelCase =eos_token_id
_lowerCAmelCase =bos_token_id
_lowerCAmelCase =pad_token_id
_lowerCAmelCase =decoder_start_token_id
_lowerCAmelCase =use_cache
_lowerCAmelCase =max_position_embeddings
_lowerCAmelCase =None
_lowerCAmelCase =decoder_seq_length
_lowerCAmelCase =2
_lowerCAmelCase =1
def UpperCamelCase__ ( self ) -> List[str]:
_lowerCAmelCase =ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
_lowerCAmelCase =None
if self.use_attention_mask:
_lowerCAmelCase =ids_tensor([self.batch_size, self.decoder_seq_length] , vocab_size=2 )
_lowerCAmelCase =None
if self.use_labels:
_lowerCAmelCase =ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
_lowerCAmelCase =TrOCRConfig(
vocab_size=self.vocab_size , d_model=self.d_model , decoder_layers=self.decoder_layers , decoder_ffn_dim=self.decoder_ffn_dim , decoder_attention_heads=self.decoder_attention_heads , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , use_cache=self.use_cache , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , max_position_embeddings=self.max_position_embeddings , )
return (config, input_ids, attention_mask, lm_labels)
def UpperCamelCase__ ( self , __A , __A , __A , __A , ) -> Union[str, Any]:
_lowerCAmelCase =True
_lowerCAmelCase =TrOCRDecoder(config=__A ).to(__A ).eval()
_lowerCAmelCase =input_ids[:2]
input_ids[input_ids == 0] += 1
# first forward pass
_lowerCAmelCase =model(__A , use_cache=__A )
_lowerCAmelCase =model(__A )
_lowerCAmelCase =model(__A , use_cache=__A )
self.parent.assertTrue(len(__A ) == len(__A ) )
self.parent.assertTrue(len(__A ) == len(__A ) + 1 )
_lowerCAmelCase =outputs['past_key_values']
# create hypothetical next token and extent to next_input_ids
_lowerCAmelCase =ids_tensor((2, 1) , config.vocab_size - 1 ) + 1
# append to next input_ids and
_lowerCAmelCase =torch.cat([input_ids, next_tokens] , dim=-1 )
_lowerCAmelCase =model(__A )['last_hidden_state']
_lowerCAmelCase =model(__A , past_key_values=__A )['last_hidden_state']
# select random slice
_lowerCAmelCase =ids_tensor((1,) , output_from_past.shape[-1] ).item()
_lowerCAmelCase =output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
_lowerCAmelCase =output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
assert torch.allclose(__A , __A , atol=1E-3 )
def UpperCamelCase__ ( self ) -> Tuple:
_lowerCAmelCase =self.prepare_config_and_inputs()
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase =config_and_inputs
_lowerCAmelCase ={'input_ids': input_ids, 'attention_mask': attention_mask}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE ( __lowercase , __lowercase , __lowercase , unittest.TestCase):
"""simple docstring"""
lowercase : Optional[int] = (TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
lowercase : Optional[int] = (TrOCRForCausalLM,) if is_torch_available() else ()
lowercase : Optional[int] = {'text-generation': TrOCRForCausalLM} if is_torch_available() else {}
lowercase : Union[str, Any] = True
lowercase : Union[str, Any] = False
def UpperCamelCase__ ( self ) -> Any:
_lowerCAmelCase =TrOCRStandaloneDecoderModelTester(self , is_training=__A )
_lowerCAmelCase =ConfigTester(self , config_class=__A )
def UpperCamelCase__ ( self ) -> Optional[int]:
pass
def UpperCamelCase__ ( self ) -> Any:
pass
def UpperCamelCase__ ( self ) -> Optional[Any]:
pass
def UpperCamelCase__ ( self ) -> List[str]:
self.config_tester.run_common_tests()
def UpperCamelCase__ ( self ) -> List[str]:
_lowerCAmelCase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past(*__A )
def UpperCamelCase__ ( self ) -> Optional[int]:
return
@unittest.skip('The model doesn\'t support left padding' ) # and it's not used enough to be worth fixing :)
def UpperCamelCase__ ( self ) -> Union[str, Any]:
pass
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|
'''simple docstring'''
lowercase_ = '''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
lowercase_ = [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
lowercase_ = {
'''{processor_class}''': '''FakeProcessorClass''',
'''{model_class}''': '''FakeModelClass''',
'''{object_class}''': '''FakeObjectClass''',
}
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|
'''simple docstring'''
def UpperCamelCase__ ( a__ , a__ , a__ , a__ ):
'''simple docstring'''
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
# 2. Validate that next vertex is not already in path
return not any(vertex == next_ver for vertex in path )
def UpperCamelCase__ ( a__ , a__ , a__ ):
'''simple docstring'''
if curr_ind == len(a__ ):
# return whether path exists between current and starting vertices
return graph[path[curr_ind - 1]][path[0]] == 1
# Recursive Step
for next_ver in range(0 , len(a__ ) ):
if valid_connection(a__ , a__ , a__ , a__ ):
# Insert current vertex into path as next transition
_lowerCAmelCase =next_ver
# Validate created path
if util_hamilton_cycle(a__ , a__ , curr_ind + 1 ):
return True
# Backtrack
_lowerCAmelCase =-1
return False
def UpperCamelCase__ ( a__ , a__ = 0 ):
'''simple docstring'''
_lowerCAmelCase =[-1] * (len(a__ ) + 1)
# initialize start and end of path with starting index
_lowerCAmelCase =_lowerCAmelCase =start_index
# evaluate and if we find answer return path either return empty array
return path if util_hamilton_cycle(a__ , a__ , 1 ) else []
| 58
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'''simple docstring'''
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json
lowercase_ = '''sshleifer/mar_enro_6_3_student'''
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
def UpperCamelCase__ ( self ) -> Optional[Any]:
super().setUp()
_lowerCAmelCase =cached_path(
'https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz' , extract_compressed_file=__A , )
_lowerCAmelCase =F'''{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'''
@slow
@require_torch_gpu
def UpperCamelCase__ ( self ) -> Union[str, Any]:
MarianMTModel.from_pretrained(__A )
@slow
@require_torch_gpu
def UpperCamelCase__ ( self ) -> Union[str, Any]:
_lowerCAmelCase ={
'$MAX_LEN': 64,
'$BS': 64,
'$GAS': 1,
'$ENRO_DIR': self.data_dir,
'facebook/mbart-large-cc25': MARIAN_MODEL,
# "val_check_interval=0.25": "val_check_interval=1.0",
'--learning_rate=3e-5': '--learning_rate 3e-4',
'--num_train_epochs 6': '--num_train_epochs 1',
}
# Clean up bash script
_lowerCAmelCase =(self.test_file_dir / 'train_mbart_cc25_enro.sh').open().read().split('finetune.py' )[1].strip()
_lowerCAmelCase =bash_script.replace('\\\n' , '' ).strip().replace('"$@"' , '' )
for k, v in env_vars_to_replace.items():
_lowerCAmelCase =bash_script.replace(__A , str(__A ) )
_lowerCAmelCase =self.get_auto_remove_tmp_dir()
# bash_script = bash_script.replace("--fp16 ", "")
_lowerCAmelCase =F'''
--output_dir {output_dir}
--tokenizer_name Helsinki-NLP/opus-mt-en-ro
--sortish_sampler
--do_predict
--gpus 1
--freeze_encoder
--n_train 40000
--n_val 500
--n_test 500
--fp16_opt_level O1
--num_sanity_val_steps 0
--eval_beams 2
'''.split()
# XXX: args.gpus > 1 : handle multi_gpu in the future
_lowerCAmelCase =['finetune.py'] + bash_script.split() + args
with patch.object(__A , 'argv' , __A ):
_lowerCAmelCase =argparse.ArgumentParser()
_lowerCAmelCase =pl.Trainer.add_argparse_args(__A )
_lowerCAmelCase =SummarizationModule.add_model_specific_args(__A , os.getcwd() )
_lowerCAmelCase =parser.parse_args()
_lowerCAmelCase =main(__A )
# Check metrics
_lowerCAmelCase =load_json(model.metrics_save_path )
_lowerCAmelCase =metrics['val'][0]
_lowerCAmelCase =metrics['val'][-1]
self.assertEqual(len(metrics['val'] ) , (args.max_epochs / args.val_check_interval) )
assert isinstance(last_step_stats[F'''val_avg_{model.val_metric}'''] , __A )
self.assertGreater(last_step_stats['val_avg_gen_time'] , 0.01 )
# model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
self.assertLessEqual(last_step_stats['val_avg_gen_time'] , 1.0 )
# test learning requirements:
# 1. BLEU improves over the course of training by more than 2 pts
self.assertGreater(last_step_stats['val_avg_bleu'] - first_step_stats['val_avg_bleu'] , 2 )
# 2. BLEU finishes above 17
self.assertGreater(last_step_stats['val_avg_bleu'] , 17 )
# 3. test BLEU and val BLEU within ~1.1 pt.
self.assertLess(abs(metrics['val'][-1]['val_avg_bleu'] - metrics['test'][-1]['test_avg_bleu'] ) , 1.1 )
# check lightning ckpt can be loaded and has a reasonable statedict
_lowerCAmelCase =os.listdir(__A )
_lowerCAmelCase =[x for x in contents if x.endswith('.ckpt' )][0]
_lowerCAmelCase =os.path.join(args.output_dir , __A )
_lowerCAmelCase =torch.load(__A , map_location='cpu' )
_lowerCAmelCase ='model.model.decoder.layers.0.encoder_attn_layer_norm.weight'
assert expected_key in ckpt["state_dict"]
assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa
# TODO: turn on args.do_predict when PL bug fixed.
if args.do_predict:
_lowerCAmelCase ={os.path.basename(__A ) for p in contents}
assert "test_generations.txt" in contents
assert "test_results.txt" in contents
# assert len(metrics["val"]) == desired_n_evals
assert len(metrics['test'] ) == 1
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
@timeout_decorator.timeout(600 )
@slow
@require_torch_gpu
def UpperCamelCase__ ( self ) -> Tuple:
_lowerCAmelCase =F'''{self.test_file_dir_str}/test_data/wmt_en_ro'''
_lowerCAmelCase ={
'--fp16_opt_level=O1': '',
'$MAX_LEN': 128,
'$BS': 16,
'$GAS': 1,
'$ENRO_DIR': data_dir,
'$m': 'sshleifer/student_marian_en_ro_6_1',
'val_check_interval=0.25': 'val_check_interval=1.0',
}
# Clean up bash script
_lowerCAmelCase =(
(self.test_file_dir / 'distil_marian_no_teacher.sh').open().read().split('distillation.py' )[1].strip()
)
_lowerCAmelCase =bash_script.replace('\\\n' , '' ).strip().replace('"$@"' , '' )
_lowerCAmelCase =bash_script.replace('--fp16 ' , ' ' )
for k, v in env_vars_to_replace.items():
_lowerCAmelCase =bash_script.replace(__A , str(__A ) )
_lowerCAmelCase =self.get_auto_remove_tmp_dir()
_lowerCAmelCase =bash_script.replace('--fp16' , '' )
_lowerCAmelCase =6
_lowerCAmelCase =(
['distillation.py']
+ bash_script.split()
+ [
F'''--output_dir={output_dir}''',
'--gpus=1',
'--learning_rate=1e-3',
F'''--num_train_epochs={epochs}''',
'--warmup_steps=10',
'--val_check_interval=1.0',
'--do_predict',
]
)
with patch.object(__A , 'argv' , __A ):
_lowerCAmelCase =argparse.ArgumentParser()
_lowerCAmelCase =pl.Trainer.add_argparse_args(__A )
_lowerCAmelCase =SummarizationDistiller.add_model_specific_args(__A , os.getcwd() )
_lowerCAmelCase =parser.parse_args()
# assert args.gpus == gpus THIS BREAKS for multi_gpu
_lowerCAmelCase =distill_main(__A )
# Check metrics
_lowerCAmelCase =load_json(model.metrics_save_path )
_lowerCAmelCase =metrics['val'][0]
_lowerCAmelCase =metrics['val'][-1]
assert len(metrics['val'] ) >= (args.max_epochs / args.val_check_interval) # +1 accounts for val_sanity_check
assert last_step_stats["val_avg_gen_time"] >= 0.01
assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"] # model learned nothing
assert 1.0 >= last_step_stats["val_avg_gen_time"] # model hanging on generate. Maybe bad config was saved.
assert isinstance(last_step_stats[F'''val_avg_{model.val_metric}'''] , __A )
# check lightning ckpt can be loaded and has a reasonable statedict
_lowerCAmelCase =os.listdir(__A )
_lowerCAmelCase =[x for x in contents if x.endswith('.ckpt' )][0]
_lowerCAmelCase =os.path.join(args.output_dir , __A )
_lowerCAmelCase =torch.load(__A , map_location='cpu' )
_lowerCAmelCase ='model.model.decoder.layers.0.encoder_attn_layer_norm.weight'
assert expected_key in ckpt["state_dict"]
assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa
# TODO: turn on args.do_predict when PL bug fixed.
if args.do_predict:
_lowerCAmelCase ={os.path.basename(__A ) for p in contents}
assert "test_generations.txt" in contents
assert "test_results.txt" in contents
# assert len(metrics["val"]) == desired_n_evals
assert len(metrics['test'] ) == 1
| 58
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ = {
'''configuration_bigbird_pegasus''': [
'''BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''BigBirdPegasusConfig''',
'''BigBirdPegasusOnnxConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
'''BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''BigBirdPegasusForCausalLM''',
'''BigBirdPegasusForConditionalGeneration''',
'''BigBirdPegasusForQuestionAnswering''',
'''BigBirdPegasusForSequenceClassification''',
'''BigBirdPegasusModel''',
'''BigBirdPegasusPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_bigbird_pegasus import (
BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
BigBirdPegasusConfig,
BigBirdPegasusOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bigbird_pegasus import (
BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
BigBirdPegasusForCausalLM,
BigBirdPegasusForConditionalGeneration,
BigBirdPegasusForQuestionAnswering,
BigBirdPegasusForSequenceClassification,
BigBirdPegasusModel,
BigBirdPegasusPreTrainedModel,
)
else:
import sys
lowercase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 58
|
'''simple docstring'''
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors
lowercase_ = logging.getLogger(__name__)
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : int = 'sequence-classification'
def __init__( self , __A ) -> List[Any]:
if type(__A ) == dict:
_lowerCAmelCase =Namespace(**__A )
_lowerCAmelCase =glue_output_modes[hparams.task]
_lowerCAmelCase =glue_tasks_num_labels[hparams.task]
super().__init__(__A , __A , self.mode )
def UpperCamelCase__ ( self , **__A ) -> Any:
return self.model(**__A )
def UpperCamelCase__ ( self , __A , __A ) -> Union[str, Any]:
_lowerCAmelCase ={'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
_lowerCAmelCase =batch[2] if self.config.model_type in ['bert', 'xlnet', 'albert'] else None
_lowerCAmelCase =self(**__A )
_lowerCAmelCase =outputs[0]
_lowerCAmelCase =self.trainer.lr_schedulers[0]['scheduler']
_lowerCAmelCase ={'loss': loss, 'rate': lr_scheduler.get_last_lr()[-1]}
return {"loss": loss, "log": tensorboard_logs}
def UpperCamelCase__ ( self ) -> Any:
_lowerCAmelCase =self.hparams
_lowerCAmelCase =processors[args.task]()
_lowerCAmelCase =processor.get_labels()
for mode in ["train", "dev"]:
_lowerCAmelCase =self._feature_file(__A )
if os.path.exists(__A ) and not args.overwrite_cache:
logger.info('Loading features from cached file %s' , __A )
else:
logger.info('Creating features from dataset file at %s' , args.data_dir )
_lowerCAmelCase =(
processor.get_dev_examples(args.data_dir )
if mode == 'dev'
else processor.get_train_examples(args.data_dir )
)
_lowerCAmelCase =convert_examples_to_features(
__A , self.tokenizer , max_length=args.max_seq_length , label_list=self.labels , output_mode=args.glue_output_mode , )
logger.info('Saving features into cached file %s' , __A )
torch.save(__A , __A )
def UpperCamelCase__ ( self , __A , __A , __A = False ) -> DataLoader:
_lowerCAmelCase ='dev' if mode == 'test' else mode
_lowerCAmelCase =self._feature_file(__A )
logger.info('Loading features from cached file %s' , __A )
_lowerCAmelCase =torch.load(__A )
_lowerCAmelCase =torch.tensor([f.input_ids for f in features] , dtype=torch.long )
_lowerCAmelCase =torch.tensor([f.attention_mask for f in features] , dtype=torch.long )
_lowerCAmelCase =torch.tensor([f.token_type_ids for f in features] , dtype=torch.long )
if self.hparams.glue_output_mode == "classification":
_lowerCAmelCase =torch.tensor([f.label for f in features] , dtype=torch.long )
elif self.hparams.glue_output_mode == "regression":
_lowerCAmelCase =torch.tensor([f.label for f in features] , dtype=torch.float )
return DataLoader(
TensorDataset(__A , __A , __A , __A ) , batch_size=__A , shuffle=__A , )
def UpperCamelCase__ ( self , __A , __A ) -> List[str]:
_lowerCAmelCase ={'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
_lowerCAmelCase =batch[2] if self.config.model_type in ['bert', 'xlnet', 'albert'] else None
_lowerCAmelCase =self(**__A )
_lowerCAmelCase , _lowerCAmelCase =outputs[:2]
_lowerCAmelCase =logits.detach().cpu().numpy()
_lowerCAmelCase =inputs['labels'].detach().cpu().numpy()
return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
def UpperCamelCase__ ( self , __A ) -> tuple:
_lowerCAmelCase =torch.stack([x['val_loss'] for x in outputs] ).mean().detach().cpu().item()
_lowerCAmelCase =np.concatenate([x['pred'] for x in outputs] , axis=0 )
if self.hparams.glue_output_mode == "classification":
_lowerCAmelCase =np.argmax(__A , axis=1 )
elif self.hparams.glue_output_mode == "regression":
_lowerCAmelCase =np.squeeze(__A )
_lowerCAmelCase =np.concatenate([x['target'] for x in outputs] , axis=0 )
_lowerCAmelCase =[[] for _ in range(out_label_ids.shape[0] )]
_lowerCAmelCase =[[] for _ in range(out_label_ids.shape[0] )]
_lowerCAmelCase ={**{'val_loss': val_loss_mean}, **compute_metrics(self.hparams.task , __A , __A )}
_lowerCAmelCase =dict(results.items() )
_lowerCAmelCase =results
return ret, preds_list, out_label_list
def UpperCamelCase__ ( self , __A ) -> dict:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase =self._eval_end(__A )
_lowerCAmelCase =ret['log']
return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
def UpperCamelCase__ ( self , __A ) -> dict:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase =self._eval_end(__A )
_lowerCAmelCase =ret['log']
# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
@staticmethod
def UpperCamelCase__ ( __A , __A ) -> Any:
BaseTransformer.add_model_specific_args(__A , __A )
parser.add_argument(
'--max_seq_length' , default=128 , type=__A , help=(
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
) , )
parser.add_argument(
'--task' , default='' , type=__A , required=__A , help='The GLUE task to run' , )
parser.add_argument(
'--gpus' , default=0 , type=__A , help='The number of GPUs allocated for this, it is by default 0 meaning none' , )
parser.add_argument(
'--overwrite_cache' , action='store_true' , help='Overwrite the cached training and evaluation sets' )
return parser
def UpperCamelCase__ ( ):
'''simple docstring'''
_lowerCAmelCase =argparse.ArgumentParser()
add_generic_args(a__ , os.getcwd() )
_lowerCAmelCase =GLUETransformer.add_model_specific_args(a__ , os.getcwd() )
_lowerCAmelCase =parser.parse_args()
# If output_dir not provided, a folder will be generated in pwd
if args.output_dir is None:
_lowerCAmelCase =os.path.join(
'./results' , F'''{args.task}_{time.strftime('%Y%m%d_%H%M%S' )}''' , )
os.makedirs(args.output_dir )
_lowerCAmelCase =GLUETransformer(a__ )
_lowerCAmelCase =generic_train(a__ , a__ )
# Optionally, predict on dev set and write to output_dir
if args.do_predict:
_lowerCAmelCase =sorted(glob.glob(os.path.join(args.output_dir , 'checkpoint-epoch=*.ckpt' ) , recursive=a__ ) )
_lowerCAmelCase =model.load_from_checkpoint(checkpoints[-1] )
return trainer.test(a__ )
if __name__ == "__main__":
main()
| 58
| 1
|
'''simple docstring'''
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def UpperCamelCase__ ( a__ , a__ , a__ ):
'''simple docstring'''
_lowerCAmelCase ={
'en': 'Machine learning is great, isn\'t it?',
'ru': 'Машинное обучение - это здорово, не так ли?',
'de': 'Maschinelles Lernen ist großartig, oder?',
}
# BLUE scores as follows:
# "pair": [fairseq, transformers]
_lowerCAmelCase ={
'ru-en': ['[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)', '39.20'],
'en-ru': ['[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)', '33.47'],
'en-de': ['[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)', '42.83'],
'de-en': ['[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)', '41.35'],
}
_lowerCAmelCase =F'''{src_lang}-{tgt_lang}'''
_lowerCAmelCase =F'''
---
language:
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt19
- facebook
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
---
# FSMT
## Model description
This is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.
For more details, please see, [Facebook FAIR\'s WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).
The abbreviation FSMT stands for FairSeqMachineTranslation
All four models are available:
* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)
* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)
* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)
* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)
## Intended uses & limitations
#### How to use
```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = "facebook/wmt19-{src_lang}-{tgt_lang}"
tokenizer = FSMTTokenizer.from_pretrained(mname)
model = FSMTForConditionalGeneration.from_pretrained(mname)
input = "{texts[src_lang]}"
input_ids = tokenizer.encode(input, return_tensors="pt")
outputs = model.generate(input_ids)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(decoded) # {texts[tgt_lang]}
```
#### Limitations and bias
- The original (and this ported model) doesn\'t seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)
## Training data
Pretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).
## Eval results
pair | fairseq | transformers
-------|---------|----------
{pair} | {scores[pair][0]} | {scores[pair][1]}
The score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn\'t support:
- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).
- re-ranking
The score was calculated using this code:
```bash
git clone https://github.com/huggingface/transformers
cd transformers
export PAIR={pair}
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=8
export NUM_BEAMS=15
mkdir -p $DATA_DIR
sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS
```
note: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.
## Data Sources
- [training, etc.](http://www.statmt.org/wmt19/)
- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)
### BibTeX entry and citation info
```bibtex
@inproceedings{{...,
year={{2020}},
title={{Facebook FAIR\'s WMT19 News Translation Task Submission}},
author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},
booktitle={{Proc. of WMT}},
}}
```
## TODO
- port model ensemble (fairseq uses 4 model checkpoints)
'''
os.makedirs(a__ , exist_ok=a__ )
_lowerCAmelCase =os.path.join(a__ , 'README.md' )
print(F'''Generating {path}''' )
with open(a__ , 'w' , encoding='utf-8' ) as f:
f.write(a__ )
# make sure we are under the root of the project
lowercase_ = Path(__file__).resolve().parent.parent.parent
lowercase_ = repo_dir / '''model_cards'''
for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
lowercase_ , lowercase_ , lowercase_ = model_name.split('''-''')
lowercase_ = model_cards_dir / '''facebook''' / model_name
write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)
| 58
|
'''simple docstring'''
from __future__ import annotations
from typing import Any
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __A ) -> None:
_lowerCAmelCase =num_of_nodes
_lowerCAmelCase =[]
_lowerCAmelCase ={}
def UpperCamelCase__ ( self , __A , __A , __A ) -> None:
self.m_edges.append([u_node, v_node, weight] )
def UpperCamelCase__ ( self , __A ) -> int:
if self.m_component[u_node] == u_node:
return u_node
return self.find_component(self.m_component[u_node] )
def UpperCamelCase__ ( self , __A ) -> None:
if self.m_component[u_node] != u_node:
for k in self.m_component:
_lowerCAmelCase =self.find_component(__A )
def UpperCamelCase__ ( self , __A , __A , __A ) -> None:
if component_size[u_node] <= component_size[v_node]:
_lowerCAmelCase =v_node
component_size[v_node] += component_size[u_node]
self.set_component(__A )
elif component_size[u_node] >= component_size[v_node]:
_lowerCAmelCase =self.find_component(__A )
component_size[u_node] += component_size[v_node]
self.set_component(__A )
def UpperCamelCase__ ( self ) -> None:
_lowerCAmelCase =[]
_lowerCAmelCase =0
_lowerCAmelCase =[-1] * self.m_num_of_nodes
# A list of components (initialized to all of the nodes)
for node in range(self.m_num_of_nodes ):
self.m_component.update({node: node} )
component_size.append(1 )
_lowerCAmelCase =self.m_num_of_nodes
while num_of_components > 1:
for edge in self.m_edges:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase =edge
_lowerCAmelCase =self.m_component[u]
_lowerCAmelCase =self.m_component[v]
if u_component != v_component:
for component in (u_component, v_component):
if (
minimum_weight_edge[component] == -1
or minimum_weight_edge[component][2] > w
):
_lowerCAmelCase =[u, v, w]
for edge in minimum_weight_edge:
if isinstance(__A , __A ):
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase =edge
_lowerCAmelCase =self.m_component[u]
_lowerCAmelCase =self.m_component[v]
if u_component != v_component:
mst_weight += w
self.union(__A , __A , __A )
print(F'''Added edge [{u} - {v}]\nAdded weight: {w}\n''' )
num_of_components -= 1
_lowerCAmelCase =[-1] * self.m_num_of_nodes
print(F'''The total weight of the minimal spanning tree is: {mst_weight}''' )
def UpperCamelCase__ ( ):
'''simple docstring'''
if __name__ == "__main__":
import doctest
doctest.testmod()
| 58
| 1
|
'''simple docstring'''
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
lowercase : float
lowercase : TreeNode | None = None
lowercase : TreeNode | None = None
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
def is_valid_tree(a__ ) -> bool:
if node is None:
return True
if not isinstance(a__ , a__ ):
return False
try:
float(node.data )
except (TypeError, ValueError):
return False
return is_valid_tree(node.left ) and is_valid_tree(node.right )
if not is_valid_tree(a__ ):
raise ValueError(
'Each node should be type of TreeNode and data should be float.' )
def is_binary_search_tree_recursive_check(
a__ , a__ , a__ ) -> bool:
if node is None:
return True
return (
left_bound < node.data < right_bound
and is_binary_search_tree_recursive_check(node.left , a__ , node.data )
and is_binary_search_tree_recursive_check(
node.right , node.data , a__ )
)
return is_binary_search_tree_recursive_check(a__ , -float('inf' ) , float('inf' ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 58
|
'''simple docstring'''
from PIL import Image
def UpperCamelCase__ ( a__ , a__ ):
'''simple docstring'''
def brightness(a__ ) -> float:
return 1_2_8 + level + (c - 1_2_8)
if not -255.0 <= level <= 255.0:
raise ValueError('level must be between -255.0 (black) and 255.0 (white)' )
return img.point(a__ )
if __name__ == "__main__":
# Load image
with Image.open('''image_data/lena.jpg''') as img:
# Change brightness to 100
lowercase_ = change_brightness(img, 100)
brigt_img.save('''image_data/lena_brightness.png''', format='''png''')
| 58
| 1
|
'''simple docstring'''
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : List[str] = (EulerDiscreteScheduler,)
lowercase : Optional[Any] = 10
def UpperCamelCase__ ( self , **__A ) -> Union[str, Any]:
_lowerCAmelCase ={
'num_train_timesteps': 1100,
'beta_start': 0.0_001,
'beta_end': 0.02,
'beta_schedule': 'linear',
}
config.update(**__A )
return config
def UpperCamelCase__ ( self ) -> Any:
for timesteps in [10, 50, 100, 1000]:
self.check_over_configs(num_train_timesteps=__A )
def UpperCamelCase__ ( self ) -> List[Any]:
for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001] , [0.0_002, 0.002, 0.02] ):
self.check_over_configs(beta_start=__A , beta_end=__A )
def UpperCamelCase__ ( self ) -> List[Any]:
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=__A )
def UpperCamelCase__ ( self ) -> str:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__A )
def UpperCamelCase__ ( self ) -> List[str]:
_lowerCAmelCase =self.scheduler_classes[0]
_lowerCAmelCase =self.get_scheduler_config()
_lowerCAmelCase =scheduler_class(**__A )
scheduler.set_timesteps(self.num_inference_steps )
_lowerCAmelCase =torch.manual_seed(0 )
_lowerCAmelCase =self.dummy_model()
_lowerCAmelCase =self.dummy_sample_deter * scheduler.init_noise_sigma
_lowerCAmelCase =sample.to(__A )
for i, t in enumerate(scheduler.timesteps ):
_lowerCAmelCase =scheduler.scale_model_input(__A , __A )
_lowerCAmelCase =model(__A , __A )
_lowerCAmelCase =scheduler.step(__A , __A , __A , generator=__A )
_lowerCAmelCase =output.prev_sample
_lowerCAmelCase =torch.sum(torch.abs(__A ) )
_lowerCAmelCase =torch.mean(torch.abs(__A ) )
assert abs(result_sum.item() - 10.0_807 ) < 1E-2
assert abs(result_mean.item() - 0.0_131 ) < 1E-3
def UpperCamelCase__ ( self ) -> int:
_lowerCAmelCase =self.scheduler_classes[0]
_lowerCAmelCase =self.get_scheduler_config(prediction_type='v_prediction' )
_lowerCAmelCase =scheduler_class(**__A )
scheduler.set_timesteps(self.num_inference_steps )
_lowerCAmelCase =torch.manual_seed(0 )
_lowerCAmelCase =self.dummy_model()
_lowerCAmelCase =self.dummy_sample_deter * scheduler.init_noise_sigma
_lowerCAmelCase =sample.to(__A )
for i, t in enumerate(scheduler.timesteps ):
_lowerCAmelCase =scheduler.scale_model_input(__A , __A )
_lowerCAmelCase =model(__A , __A )
_lowerCAmelCase =scheduler.step(__A , __A , __A , generator=__A )
_lowerCAmelCase =output.prev_sample
_lowerCAmelCase =torch.sum(torch.abs(__A ) )
_lowerCAmelCase =torch.mean(torch.abs(__A ) )
assert abs(result_sum.item() - 0.0_002 ) < 1E-2
assert abs(result_mean.item() - 2.26_76E-06 ) < 1E-3
def UpperCamelCase__ ( self ) -> List[str]:
_lowerCAmelCase =self.scheduler_classes[0]
_lowerCAmelCase =self.get_scheduler_config()
_lowerCAmelCase =scheduler_class(**__A )
scheduler.set_timesteps(self.num_inference_steps , device=__A )
_lowerCAmelCase =torch.manual_seed(0 )
_lowerCAmelCase =self.dummy_model()
_lowerCAmelCase =self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
_lowerCAmelCase =sample.to(__A )
for t in scheduler.timesteps:
_lowerCAmelCase =scheduler.scale_model_input(__A , __A )
_lowerCAmelCase =model(__A , __A )
_lowerCAmelCase =scheduler.step(__A , __A , __A , generator=__A )
_lowerCAmelCase =output.prev_sample
_lowerCAmelCase =torch.sum(torch.abs(__A ) )
_lowerCAmelCase =torch.mean(torch.abs(__A ) )
assert abs(result_sum.item() - 10.0_807 ) < 1E-2
assert abs(result_mean.item() - 0.0_131 ) < 1E-3
def UpperCamelCase__ ( self ) -> str:
_lowerCAmelCase =self.scheduler_classes[0]
_lowerCAmelCase =self.get_scheduler_config()
_lowerCAmelCase =scheduler_class(**__A , use_karras_sigmas=__A )
scheduler.set_timesteps(self.num_inference_steps , device=__A )
_lowerCAmelCase =torch.manual_seed(0 )
_lowerCAmelCase =self.dummy_model()
_lowerCAmelCase =self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
_lowerCAmelCase =sample.to(__A )
for t in scheduler.timesteps:
_lowerCAmelCase =scheduler.scale_model_input(__A , __A )
_lowerCAmelCase =model(__A , __A )
_lowerCAmelCase =scheduler.step(__A , __A , __A , generator=__A )
_lowerCAmelCase =output.prev_sample
_lowerCAmelCase =torch.sum(torch.abs(__A ) )
_lowerCAmelCase =torch.mean(torch.abs(__A ) )
assert abs(result_sum.item() - 124.52_299_499_511_719 ) < 1E-2
assert abs(result_mean.item() - 0.16_213_932_633_399_963 ) < 1E-3
| 58
|
'''simple docstring'''
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / '''utils'''))
from test_module.custom_configuration import CustomConfig # noqa E402
lowercase_ = {
'''return_dict''': False,
'''output_hidden_states''': True,
'''output_attentions''': True,
'''torchscript''': True,
'''torch_dtype''': '''float16''',
'''use_bfloat16''': True,
'''tf_legacy_loss''': True,
'''pruned_heads''': {'''a''': 1},
'''tie_word_embeddings''': False,
'''is_decoder''': True,
'''cross_attention_hidden_size''': 128,
'''add_cross_attention''': True,
'''tie_encoder_decoder''': True,
'''max_length''': 50,
'''min_length''': 3,
'''do_sample''': True,
'''early_stopping''': True,
'''num_beams''': 3,
'''num_beam_groups''': 3,
'''diversity_penalty''': 0.5,
'''temperature''': 2.0,
'''top_k''': 10,
'''top_p''': 0.7,
'''typical_p''': 0.2,
'''repetition_penalty''': 0.8,
'''length_penalty''': 0.8,
'''no_repeat_ngram_size''': 5,
'''encoder_no_repeat_ngram_size''': 5,
'''bad_words_ids''': [1, 2, 3],
'''num_return_sequences''': 3,
'''chunk_size_feed_forward''': 5,
'''output_scores''': True,
'''return_dict_in_generate''': True,
'''forced_bos_token_id''': 2,
'''forced_eos_token_id''': 3,
'''remove_invalid_values''': True,
'''architectures''': ['''BertModel'''],
'''finetuning_task''': '''translation''',
'''id2label''': {0: '''label'''},
'''label2id''': {'''label''': '''0'''},
'''tokenizer_class''': '''BertTokenizerFast''',
'''prefix''': '''prefix''',
'''bos_token_id''': 6,
'''pad_token_id''': 7,
'''eos_token_id''': 8,
'''sep_token_id''': 9,
'''decoder_start_token_id''': 10,
'''exponential_decay_length_penalty''': (5, 1.01),
'''suppress_tokens''': [0, 1],
'''begin_suppress_tokens''': 2,
'''task_specific_params''': {'''translation''': '''some_params'''},
'''problem_type''': '''regression''',
}
@is_staging_test
class SCREAMING_SNAKE_CASE ( unittest.TestCase):
"""simple docstring"""
@classmethod
def UpperCamelCase__ ( cls ) -> Optional[Any]:
_lowerCAmelCase =TOKEN
HfFolder.save_token(__A )
@classmethod
def UpperCamelCase__ ( cls ) -> List[str]:
try:
delete_repo(token=cls._token , repo_id='test-config' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='valid_org/test-config-org' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='test-dynamic-config' )
except HTTPError:
pass
def UpperCamelCase__ ( self ) -> str:
_lowerCAmelCase =BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
config.push_to_hub('test-config' , use_auth_token=self._token )
_lowerCAmelCase =BertConfig.from_pretrained(F'''{USER}/test-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__A , getattr(__A , __A ) )
# Reset repo
delete_repo(token=self._token , repo_id='test-config' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(__A , repo_id='test-config' , push_to_hub=__A , use_auth_token=self._token )
_lowerCAmelCase =BertConfig.from_pretrained(F'''{USER}/test-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__A , getattr(__A , __A ) )
def UpperCamelCase__ ( self ) -> Dict:
_lowerCAmelCase =BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
config.push_to_hub('valid_org/test-config-org' , use_auth_token=self._token )
_lowerCAmelCase =BertConfig.from_pretrained('valid_org/test-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__A , getattr(__A , __A ) )
# Reset repo
delete_repo(token=self._token , repo_id='valid_org/test-config-org' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
__A , repo_id='valid_org/test-config-org' , push_to_hub=__A , use_auth_token=self._token )
_lowerCAmelCase =BertConfig.from_pretrained('valid_org/test-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__A , getattr(__A , __A ) )
def UpperCamelCase__ ( self ) -> List[str]:
CustomConfig.register_for_auto_class()
_lowerCAmelCase =CustomConfig(attribute=42 )
config.push_to_hub('test-dynamic-config' , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(config.auto_map , {'AutoConfig': 'custom_configuration.CustomConfig'} )
_lowerCAmelCase =AutoConfig.from_pretrained(F'''{USER}/test-dynamic-config''' , trust_remote_code=__A )
# Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
self.assertEqual(new_config.__class__.__name__ , 'CustomConfig' )
self.assertEqual(new_config.attribute , 42 )
class SCREAMING_SNAKE_CASE ( unittest.TestCase):
"""simple docstring"""
def UpperCamelCase__ ( self ) -> List[Any]:
_lowerCAmelCase =GPTaConfig()
# attempt to modify each of int/float/bool/str config records and verify they were updated
_lowerCAmelCase =c.n_embd + 1 # int
_lowerCAmelCase =c.resid_pdrop + 1.0 # float
_lowerCAmelCase =not c.scale_attn_weights # bool
_lowerCAmelCase =c.summary_type + 'foo' # str
c.update_from_string(
F'''n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}''' )
self.assertEqual(__A , c.n_embd , 'mismatch for key: n_embd' )
self.assertEqual(__A , c.resid_pdrop , 'mismatch for key: resid_pdrop' )
self.assertEqual(__A , c.scale_attn_weights , 'mismatch for key: scale_attn_weights' )
self.assertEqual(__A , c.summary_type , 'mismatch for key: summary_type' )
def UpperCamelCase__ ( self ) -> List[str]:
_lowerCAmelCase =PretrainedConfig()
_lowerCAmelCase =[key for key in base_config.__dict__ if key not in config_common_kwargs]
# If this part of the test fails, you have arguments to addin config_common_kwargs above.
self.assertListEqual(
__A , ['is_encoder_decoder', '_name_or_path', '_commit_hash', 'transformers_version'] )
_lowerCAmelCase =[key for key, value in config_common_kwargs.items() if value == getattr(__A , __A )]
if len(__A ) > 0:
raise ValueError(
'The following keys are set with the default values in'
' `test_configuration_common.config_common_kwargs` pick another value for them:'
F''' {', '.join(__A )}.''' )
def UpperCamelCase__ ( self ) -> Optional[int]:
with self.assertRaises(__A ):
# config is in subfolder, the following should not work without specifying the subfolder
_lowerCAmelCase =BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder' )
_lowerCAmelCase =BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder' , subfolder='bert' )
self.assertIsNotNone(__A )
def UpperCamelCase__ ( self ) -> List[str]:
# A mock response for an HTTP head request to emulate server down
_lowerCAmelCase =mock.Mock()
_lowerCAmelCase =500
_lowerCAmelCase ={}
_lowerCAmelCase =HTTPError
_lowerCAmelCase ={}
# Download this model to make sure it's in the cache.
_lowerCAmelCase =BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert' )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch('requests.Session.request' , return_value=__A ) as mock_head:
_lowerCAmelCase =BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert' )
# This check we did call the fake head request
mock_head.assert_called()
def UpperCamelCase__ ( self ) -> Optional[int]:
# This test is for deprecated behavior and can be removed in v5
_lowerCAmelCase =BertConfig.from_pretrained(
'https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json' )
def UpperCamelCase__ ( self ) -> Any:
_lowerCAmelCase =AutoConfig.from_pretrained('bert-base-cased' )
_lowerCAmelCase =['config.4.0.0.json']
with tempfile.TemporaryDirectory() as tmp_dir:
configuration.save_pretrained(__A )
_lowerCAmelCase =2
json.dump(configuration.to_dict() , open(os.path.join(__A , 'config.4.0.0.json' ) , 'w' ) )
# This should pick the new configuration file as the version of Transformers is > 4.0.0
_lowerCAmelCase =AutoConfig.from_pretrained(__A )
self.assertEqual(new_configuration.hidden_size , 2 )
# Will need to be adjusted if we reach v42 and this test is still here.
# Should pick the old configuration file as the version of Transformers is < 4.42.0
_lowerCAmelCase =['config.42.0.0.json']
_lowerCAmelCase =768
configuration.save_pretrained(__A )
shutil.move(os.path.join(__A , 'config.4.0.0.json' ) , os.path.join(__A , 'config.42.0.0.json' ) )
_lowerCAmelCase =AutoConfig.from_pretrained(__A )
self.assertEqual(new_configuration.hidden_size , 768 )
def UpperCamelCase__ ( self ) -> Any:
# This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
_lowerCAmelCase ='hf-internal-testing/test-two-configs'
import transformers as new_transformers
_lowerCAmelCase ='v4.0.0'
_lowerCAmelCase , _lowerCAmelCase =new_transformers.models.auto.AutoConfig.from_pretrained(
__A , return_unused_kwargs=__A )
self.assertEqual(new_configuration.hidden_size , 2 )
# This checks `_configuration_file` ia not kept in the kwargs by mistake.
self.assertDictEqual(__A , {} )
# Testing an older version by monkey-patching the version in the module it's used.
import transformers as old_transformers
_lowerCAmelCase ='v3.0.0'
_lowerCAmelCase =old_transformers.models.auto.AutoConfig.from_pretrained(__A )
self.assertEqual(old_configuration.hidden_size , 768 )
| 58
| 1
|
'''simple docstring'''
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def UpperCamelCase__ ( a__ , a__ , a__ = 1 / sqrt(2 ) ):
'''simple docstring'''
_lowerCAmelCase =tau * frequency / samplerate
_lowerCAmelCase =sin(a__ )
_lowerCAmelCase =cos(a__ )
_lowerCAmelCase =_sin / (2 * q_factor)
_lowerCAmelCase =(1 - _cos) / 2
_lowerCAmelCase =1 - _cos
_lowerCAmelCase =1 + alpha
_lowerCAmelCase =-2 * _cos
_lowerCAmelCase =1 - alpha
_lowerCAmelCase =IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def UpperCamelCase__ ( a__ , a__ , a__ = 1 / sqrt(2 ) ):
'''simple docstring'''
_lowerCAmelCase =tau * frequency / samplerate
_lowerCAmelCase =sin(a__ )
_lowerCAmelCase =cos(a__ )
_lowerCAmelCase =_sin / (2 * q_factor)
_lowerCAmelCase =(1 + _cos) / 2
_lowerCAmelCase =-1 - _cos
_lowerCAmelCase =1 + alpha
_lowerCAmelCase =-2 * _cos
_lowerCAmelCase =1 - alpha
_lowerCAmelCase =IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def UpperCamelCase__ ( a__ , a__ , a__ = 1 / sqrt(2 ) ):
'''simple docstring'''
_lowerCAmelCase =tau * frequency / samplerate
_lowerCAmelCase =sin(a__ )
_lowerCAmelCase =cos(a__ )
_lowerCAmelCase =_sin / (2 * q_factor)
_lowerCAmelCase =_sin / 2
_lowerCAmelCase =0
_lowerCAmelCase =-ba
_lowerCAmelCase =1 + alpha
_lowerCAmelCase =-2 * _cos
_lowerCAmelCase =1 - alpha
_lowerCAmelCase =IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def UpperCamelCase__ ( a__ , a__ , a__ = 1 / sqrt(2 ) ):
'''simple docstring'''
_lowerCAmelCase =tau * frequency / samplerate
_lowerCAmelCase =sin(a__ )
_lowerCAmelCase =cos(a__ )
_lowerCAmelCase =_sin / (2 * q_factor)
_lowerCAmelCase =1 - alpha
_lowerCAmelCase =-2 * _cos
_lowerCAmelCase =1 + alpha
_lowerCAmelCase =IIRFilter(2 )
filt.set_coefficients([ba, ba, ba] , [ba, ba, ba] )
return filt
def UpperCamelCase__ ( a__ , a__ , a__ , a__ = 1 / sqrt(2 ) , ):
'''simple docstring'''
_lowerCAmelCase =tau * frequency / samplerate
_lowerCAmelCase =sin(a__ )
_lowerCAmelCase =cos(a__ )
_lowerCAmelCase =_sin / (2 * q_factor)
_lowerCAmelCase =1_0 ** (gain_db / 4_0)
_lowerCAmelCase =1 + alpha * big_a
_lowerCAmelCase =-2 * _cos
_lowerCAmelCase =1 - alpha * big_a
_lowerCAmelCase =1 + alpha / big_a
_lowerCAmelCase =-2 * _cos
_lowerCAmelCase =1 - alpha / big_a
_lowerCAmelCase =IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def UpperCamelCase__ ( a__ , a__ , a__ , a__ = 1 / sqrt(2 ) , ):
'''simple docstring'''
_lowerCAmelCase =tau * frequency / samplerate
_lowerCAmelCase =sin(a__ )
_lowerCAmelCase =cos(a__ )
_lowerCAmelCase =_sin / (2 * q_factor)
_lowerCAmelCase =1_0 ** (gain_db / 4_0)
_lowerCAmelCase =(big_a + 1) - (big_a - 1) * _cos
_lowerCAmelCase =(big_a + 1) + (big_a - 1) * _cos
_lowerCAmelCase =(big_a - 1) - (big_a + 1) * _cos
_lowerCAmelCase =(big_a - 1) + (big_a + 1) * _cos
_lowerCAmelCase =2 * sqrt(a__ ) * alpha
_lowerCAmelCase =big_a * (pmc + aaa)
_lowerCAmelCase =2 * big_a * mpc
_lowerCAmelCase =big_a * (pmc - aaa)
_lowerCAmelCase =ppmc + aaa
_lowerCAmelCase =-2 * pmpc
_lowerCAmelCase =ppmc - aaa
_lowerCAmelCase =IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def UpperCamelCase__ ( a__ , a__ , a__ , a__ = 1 / sqrt(2 ) , ):
'''simple docstring'''
_lowerCAmelCase =tau * frequency / samplerate
_lowerCAmelCase =sin(a__ )
_lowerCAmelCase =cos(a__ )
_lowerCAmelCase =_sin / (2 * q_factor)
_lowerCAmelCase =1_0 ** (gain_db / 4_0)
_lowerCAmelCase =(big_a + 1) - (big_a - 1) * _cos
_lowerCAmelCase =(big_a + 1) + (big_a - 1) * _cos
_lowerCAmelCase =(big_a - 1) - (big_a + 1) * _cos
_lowerCAmelCase =(big_a - 1) + (big_a + 1) * _cos
_lowerCAmelCase =2 * sqrt(a__ ) * alpha
_lowerCAmelCase =big_a * (ppmc + aaa)
_lowerCAmelCase =-2 * big_a * pmpc
_lowerCAmelCase =big_a * (ppmc - aaa)
_lowerCAmelCase =pmc + aaa
_lowerCAmelCase =2 * mpc
_lowerCAmelCase =pmc - aaa
_lowerCAmelCase =IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
| 58
|
'''simple docstring'''
from __future__ import annotations
lowercase_ = 10
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
_lowerCAmelCase =1
_lowerCAmelCase =max(a__ )
while placement <= max_digit:
# declare and initialize empty buckets
_lowerCAmelCase =[[] for _ in range(a__ )]
# split list_of_ints between the buckets
for i in list_of_ints:
_lowerCAmelCase =int((i / placement) % RADIX )
buckets[tmp].append(a__ )
# put each buckets' contents into list_of_ints
_lowerCAmelCase =0
for b in range(a__ ):
for i in buckets[b]:
_lowerCAmelCase =i
a += 1
# move to next
placement *= RADIX
return list_of_ints
if __name__ == "__main__":
import doctest
doctest.testmod()
| 58
| 1
|
'''simple docstring'''
from collections.abc import Generator
def UpperCamelCase__ ( ):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase =0, 1
while True:
_lowerCAmelCase , _lowerCAmelCase =b, a + b
yield b
def UpperCamelCase__ ( a__ = 1_0_0_0 ):
'''simple docstring'''
_lowerCAmelCase =1
_lowerCAmelCase =fibonacci_generator()
while len(str(next(a__ ) ) ) < n:
answer += 1
return answer + 1
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 58
|
'''simple docstring'''
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
FEATURE_EXTRACTOR_NAME,
FLAX_WEIGHTS_NAME,
HF_MODULES_CACHE,
HUGGINGFACE_CO_PREFIX,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
MODEL_CARD_NAME,
MULTIPLE_CHOICE_DUMMY_INPUTS,
PYTORCH_PRETRAINED_BERT_CACHE,
PYTORCH_TRANSFORMERS_CACHE,
S3_BUCKET_PREFIX,
SENTENCEPIECE_UNDERLINE,
SPIECE_UNDERLINE,
TF2_WEIGHTS_NAME,
TF_WEIGHTS_NAME,
TORCH_FX_REQUIRED_VERSION,
TRANSFORMERS_CACHE,
TRANSFORMERS_DYNAMIC_MODULE_NAME,
USE_JAX,
USE_TF,
USE_TORCH,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
ContextManagers,
DummyObject,
EntryNotFoundError,
ExplicitEnum,
ModelOutput,
PaddingStrategy,
PushToHubMixin,
RepositoryNotFoundError,
RevisionNotFoundError,
TensorType,
_LazyModule,
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
cached_property,
copy_func,
default_cache_path,
define_sagemaker_information,
get_cached_models,
get_file_from_repo,
get_full_repo_name,
get_torch_version,
has_file,
http_user_agent,
is_apex_available,
is_bsa_available,
is_coloredlogs_available,
is_datasets_available,
is_detectrona_available,
is_faiss_available,
is_flax_available,
is_ftfy_available,
is_in_notebook,
is_ipex_available,
is_librosa_available,
is_offline_mode,
is_onnx_available,
is_pandas_available,
is_phonemizer_available,
is_protobuf_available,
is_psutil_available,
is_pyanvml_available,
is_pyctcdecode_available,
is_pytesseract_available,
is_pytorch_quantization_available,
is_rjieba_available,
is_sagemaker_dp_enabled,
is_sagemaker_mp_enabled,
is_scipy_available,
is_sentencepiece_available,
is_seqio_available,
is_sklearn_available,
is_soundfile_availble,
is_spacy_available,
is_speech_available,
is_tensor,
is_tensorflow_probability_available,
is_tfaonnx_available,
is_tf_available,
is_timm_available,
is_tokenizers_available,
is_torch_available,
is_torch_bfaa_available,
is_torch_cuda_available,
is_torch_fx_available,
is_torch_fx_proxy,
is_torch_mps_available,
is_torch_tfaa_available,
is_torch_tpu_available,
is_torchaudio_available,
is_training_run_on_sagemaker,
is_vision_available,
replace_return_docstrings,
requires_backends,
to_numpy,
to_py_obj,
torch_only_method,
)
| 58
| 1
|
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageResampling,
get_image_size,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
lowercase_ = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : Tuple = ['pixel_values']
def __init__( self , __A = True , __A = 32 , __A=PILImageResampling.BILINEAR , __A = True , **__A , ) -> None:
_lowerCAmelCase =do_resize
_lowerCAmelCase =do_rescale
_lowerCAmelCase =size_divisor
_lowerCAmelCase =resample
super().__init__(**__A )
def UpperCamelCase__ ( self , __A , __A , __A , __A = None , **__A ) -> np.ndarray:
_lowerCAmelCase , _lowerCAmelCase =get_image_size(__A )
# Rounds the height and width down to the closest multiple of size_divisor
_lowerCAmelCase =height // size_divisor * size_divisor
_lowerCAmelCase =width // size_divisor * size_divisor
_lowerCAmelCase =resize(__A , (new_h, new_w) , resample=__A , data_format=__A , **__A )
return image
def UpperCamelCase__ ( self , __A , __A , __A = None , **__A ) -> np.ndarray:
return rescale(image=__A , scale=__A , data_format=__A , **__A )
def UpperCamelCase__ ( self , __A , __A = None , __A = None , __A=None , __A = None , __A = None , __A = ChannelDimension.FIRST , **__A , ) -> BatchFeature:
_lowerCAmelCase =do_resize if do_resize is not None else self.do_resize
_lowerCAmelCase =do_rescale if do_rescale is not None else self.do_rescale
_lowerCAmelCase =size_divisor if size_divisor is not None else self.size_divisor
_lowerCAmelCase =resample if resample is not None else self.resample
if do_resize and size_divisor is None:
raise ValueError('size_divisor is required for resizing' )
_lowerCAmelCase =make_list_of_images(__A )
if not valid_images(__A ):
raise ValueError('Invalid image(s)' )
# All transformations expect numpy arrays.
_lowerCAmelCase =[to_numpy_array(__A ) for img in images]
if do_resize:
_lowerCAmelCase =[self.resize(__A , size_divisor=__A , resample=__A ) for image in images]
if do_rescale:
_lowerCAmelCase =[self.rescale(__A , scale=1 / 255 ) for image in images]
_lowerCAmelCase =[to_channel_dimension_format(__A , __A ) for image in images]
_lowerCAmelCase ={'pixel_values': images}
return BatchFeature(data=__A , tensor_type=__A )
| 58
|
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
_lowerCAmelCase =len(a__ ) // 2
# choose the middle 3 elements
_lowerCAmelCase =lst[m - 1 : m + 2]
# if middle element is peak
if three[1] > three[0] and three[1] > three[2]:
return three[1]
# if increasing, recurse on right
elif three[0] < three[2]:
if len(lst[:m] ) == 2:
m -= 1
return peak(lst[m:] )
# decreasing
else:
if len(lst[:m] ) == 2:
m += 1
return peak(lst[:m] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 58
| 1
|
'''simple docstring'''
import argparse
import os
import re
import packaging.version
lowercase_ = '''examples/'''
lowercase_ = {
'''examples''': (re.compile(r'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
'''init''': (re.compile(r'''^__version__\s+=\s+"([^"]+)"\s*$''', re.MULTILINE), '''__version__ = "VERSION"\n'''),
'''setup''': (re.compile(r'''^(\s*)version\s*=\s*"[^"]+",''', re.MULTILINE), r'''\1version="VERSION",'''),
'''doc''': (re.compile(r'''^(\s*)release\s*=\s*"[^"]+"$''', re.MULTILINE), '''release = "VERSION"\n'''),
}
lowercase_ = {
'''init''': '''src/diffusers/__init__.py''',
'''setup''': '''setup.py''',
}
lowercase_ = '''README.md'''
def UpperCamelCase__ ( a__ , a__ , a__ ):
'''simple docstring'''
with open(a__ , 'r' , encoding='utf-8' , newline='\n' ) as f:
_lowerCAmelCase =f.read()
_lowerCAmelCase , _lowerCAmelCase =REPLACE_PATTERNS[pattern]
_lowerCAmelCase =replace.replace('VERSION' , a__ )
_lowerCAmelCase =re_pattern.sub(a__ , a__ )
with open(a__ , 'w' , encoding='utf-8' , newline='\n' ) as f:
f.write(a__ )
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
for folder, directories, fnames in os.walk(a__ ):
# Removing some of the folders with non-actively maintained examples from the walk
if "research_projects" in directories:
directories.remove('research_projects' )
if "legacy" in directories:
directories.remove('legacy' )
for fname in fnames:
if fname.endswith('.py' ):
update_version_in_file(os.path.join(a__ , a__ ) , a__ , pattern='examples' )
def UpperCamelCase__ ( a__ , a__=False ):
'''simple docstring'''
for pattern, fname in REPLACE_FILES.items():
update_version_in_file(a__ , a__ , a__ )
if not patch:
update_version_in_examples(a__ )
def UpperCamelCase__ ( ):
'''simple docstring'''
_lowerCAmelCase ='🤗 Transformers currently provides the following architectures'
_lowerCAmelCase ='1. Want to contribute a new model?'
with open(a__ , 'r' , encoding='utf-8' , newline='\n' ) as f:
_lowerCAmelCase =f.readlines()
# Find the start of the list.
_lowerCAmelCase =0
while not lines[start_index].startswith(_start_prompt ):
start_index += 1
start_index += 1
_lowerCAmelCase =start_index
# Update the lines in the model list.
while not lines[index].startswith(_end_prompt ):
if lines[index].startswith('1.' ):
_lowerCAmelCase =lines[index].replace(
'https://huggingface.co/docs/diffusers/main/model_doc' , 'https://huggingface.co/docs/diffusers/model_doc' , )
index += 1
with open(a__ , 'w' , encoding='utf-8' , newline='\n' ) as f:
f.writelines(a__ )
def UpperCamelCase__ ( ):
'''simple docstring'''
with open(REPLACE_FILES['init'] , 'r' ) as f:
_lowerCAmelCase =f.read()
_lowerCAmelCase =REPLACE_PATTERNS['init'][0].search(a__ ).groups()[0]
return packaging.version.parse(a__ )
def UpperCamelCase__ ( a__=False ):
'''simple docstring'''
_lowerCAmelCase =get_version()
if patch and default_version.is_devrelease:
raise ValueError('Can\'t create a patch version from the dev branch, checkout a released version!' )
if default_version.is_devrelease:
_lowerCAmelCase =default_version.base_version
elif patch:
_lowerCAmelCase =F'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''
else:
_lowerCAmelCase =F'''{default_version.major}.{default_version.minor + 1}.0'''
# Now let's ask nicely if that's the right one.
_lowerCAmelCase =input(F'''Which version are you releasing? [{default_version}]''' )
if len(a__ ) == 0:
_lowerCAmelCase =default_version
print(F'''Updating version to {version}.''' )
global_version_update(a__ , patch=a__ )
def UpperCamelCase__ ( ):
'''simple docstring'''
_lowerCAmelCase =get_version()
_lowerCAmelCase =F'''{current_version.major}.{current_version.minor + 1}.0.dev0'''
_lowerCAmelCase =current_version.base_version
# Check with the user we got that right.
_lowerCAmelCase =input(F'''Which version are we developing now? [{dev_version}]''' )
if len(a__ ) == 0:
_lowerCAmelCase =dev_version
print(F'''Updating version to {version}.''' )
global_version_update(a__ )
# print("Cleaning main README, don't forget to run `make fix-copies`.")
# clean_main_ref_in_model_list()
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
parser.add_argument('''--post_release''', action='''store_true''', help='''Whether this is pre or post release.''')
parser.add_argument('''--patch''', action='''store_true''', help='''Whether or not this is a patch release.''')
lowercase_ = parser.parse_args()
if not args.post_release:
pre_release_work(patch=args.patch)
elif args.patch:
print('''Nothing to do after a patch :-)''')
else:
post_release_work()
| 58
|
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
lowercase_ = logging.get_logger(__name__)
lowercase_ = {'''vocab_file''': '''vocab.txt'''}
lowercase_ = {
'''vocab_file''': {
'''YituTech/conv-bert-base''': '''https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt''',
'''YituTech/conv-bert-medium-small''': (
'''https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt'''
),
'''YituTech/conv-bert-small''': '''https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt''',
}
}
lowercase_ = {
'''YituTech/conv-bert-base''': 512,
'''YituTech/conv-bert-medium-small''': 512,
'''YituTech/conv-bert-small''': 512,
}
lowercase_ = {
'''YituTech/conv-bert-base''': {'''do_lower_case''': True},
'''YituTech/conv-bert-medium-small''': {'''do_lower_case''': True},
'''YituTech/conv-bert-small''': {'''do_lower_case''': True},
}
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : Union[str, Any] = VOCAB_FILES_NAMES
lowercase : Tuple = PRETRAINED_VOCAB_FILES_MAP
lowercase : Optional[int] = PRETRAINED_INIT_CONFIGURATION
lowercase : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase : List[str] = ConvBertTokenizer
def __init__( self , __A=None , __A=None , __A=True , __A="[UNK]" , __A="[SEP]" , __A="[PAD]" , __A="[CLS]" , __A="[MASK]" , __A=True , __A=None , **__A , ) -> Union[str, Any]:
super().__init__(
__A , tokenizer_file=__A , do_lower_case=__A , unk_token=__A , sep_token=__A , pad_token=__A , cls_token=__A , mask_token=__A , tokenize_chinese_chars=__A , strip_accents=__A , **__A , )
_lowerCAmelCase =json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('lowercase' , __A ) != do_lower_case
or normalizer_state.get('strip_accents' , __A ) != strip_accents
or normalizer_state.get('handle_chinese_chars' , __A ) != tokenize_chinese_chars
):
_lowerCAmelCase =getattr(__A , normalizer_state.pop('type' ) )
_lowerCAmelCase =do_lower_case
_lowerCAmelCase =strip_accents
_lowerCAmelCase =tokenize_chinese_chars
_lowerCAmelCase =normalizer_class(**__A )
_lowerCAmelCase =do_lower_case
def UpperCamelCase__ ( self , __A , __A=None ) -> int:
_lowerCAmelCase =[self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def UpperCamelCase__ ( self , __A , __A = None ) -> List[int]:
_lowerCAmelCase =[self.sep_token_id]
_lowerCAmelCase =[self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def UpperCamelCase__ ( self , __A , __A = None ) -> Tuple[str]:
_lowerCAmelCase =self._tokenizer.model.save(__A , name=__A )
return tuple(__A )
| 58
| 1
|
'''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=__lowercase)
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : str = field(default='automatic-speech-recognition' , metadata={'include_in_asdict_even_if_is_default': True})
lowercase : ClassVar[Features] = Features({'audio': Audio()})
lowercase : ClassVar[Features] = Features({'transcription': Value('string')})
lowercase : str = "audio"
lowercase : str = "transcription"
def UpperCamelCase__ ( self , __A ) -> Tuple:
if self.audio_column not in features:
raise ValueError(F'''Column {self.audio_column} is not present in features.''' )
if not isinstance(features[self.audio_column] , __A ):
raise ValueError(F'''Column {self.audio_column} is not an Audio type.''' )
_lowerCAmelCase =copy.deepcopy(self )
_lowerCAmelCase =self.input_schema.copy()
_lowerCAmelCase =features[self.audio_column]
_lowerCAmelCase =input_schema
return task_template
@property
def UpperCamelCase__ ( self ) -> Dict[str, str]:
return {self.audio_column: "audio", self.transcription_column: "transcription"}
| 58
|
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : Any = ['image_processor', 'tokenizer']
lowercase : Any = 'CLIPImageProcessor'
lowercase : int = ('CLIPTokenizer', 'CLIPTokenizerFast')
def __init__( self , __A=None , __A=None , **__A ) -> str:
_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.' , __A , )
_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__(__A , __A )
def __call__( self , __A=None , __A=None , __A=None , **__A ) -> Optional[int]:
if text is None and images is None:
raise ValueError('You have to specify either text or images. Both cannot be none.' )
if text is not None:
_lowerCAmelCase =self.tokenizer(__A , return_tensors=__A , **__A )
if images is not None:
_lowerCAmelCase =self.image_processor(__A , return_tensors=__A , **__A )
if text is not None and images is not None:
_lowerCAmelCase =image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**__A ) , tensor_type=__A )
def UpperCamelCase__ ( self , *__A , **__A ) -> Any:
return self.tokenizer.batch_decode(*__A , **__A )
def UpperCamelCase__ ( self , *__A , **__A ) -> Optional[int]:
return self.tokenizer.decode(*__A , **__A )
@property
def UpperCamelCase__ ( self ) -> Tuple:
_lowerCAmelCase =self.tokenizer.model_input_names
_lowerCAmelCase =self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def UpperCamelCase__ ( self ) -> Optional[int]:
warnings.warn(
'`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' , __A , )
return self.image_processor_class
@property
def UpperCamelCase__ ( self ) -> Optional[Any]:
warnings.warn(
'`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , __A , )
return self.image_processor
| 58
| 1
|
'''simple docstring'''
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
lowercase_ = logging.get_logger(__name__) # pylint: disable=invalid-name
class SCREAMING_SNAKE_CASE ( __lowercase , __lowercase):
"""simple docstring"""
@register_to_config
def __init__( self , __A , __A = None , __A = None ) -> Tuple:
super().__init__()
_lowerCAmelCase =learnable
if self.learnable:
assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
assert length is not None, "learnable=True requires `length` to be set"
_lowerCAmelCase =torch.zeros(__A , __A )
else:
_lowerCAmelCase =None
_lowerCAmelCase =torch.nn.Parameter(__A )
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : VQModel
lowercase : CLIPTextModel
lowercase : CLIPTokenizer
lowercase : TransformeraDModel
lowercase : LearnedClassifierFreeSamplingEmbeddings
lowercase : VQDiffusionScheduler
def __init__( self , __A , __A , __A , __A , __A , __A , ) -> int:
super().__init__()
self.register_modules(
vqvae=__A , transformer=__A , text_encoder=__A , tokenizer=__A , scheduler=__A , learned_classifier_free_sampling_embeddings=__A , )
def UpperCamelCase__ ( self , __A , __A , __A ) -> Tuple:
_lowerCAmelCase =len(__A ) if isinstance(__A , __A ) else 1
# get prompt text embeddings
_lowerCAmelCase =self.tokenizer(
__A , padding='max_length' , max_length=self.tokenizer.model_max_length , return_tensors='pt' , )
_lowerCAmelCase =text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
_lowerCAmelCase =self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
logger.warning(
'The following part of your input was truncated because CLIP can only handle sequences up to'
F''' {self.tokenizer.model_max_length} tokens: {removed_text}''' )
_lowerCAmelCase =text_input_ids[:, : self.tokenizer.model_max_length]
_lowerCAmelCase =self.text_encoder(text_input_ids.to(self.device ) )[0]
# NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
# While CLIP does normalize the pooled output of the text transformer when combining
# the image and text embeddings, CLIP does not directly normalize the last hidden state.
#
# CLIP normalizing the pooled output.
# https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
_lowerCAmelCase =prompt_embeds / prompt_embeds.norm(dim=-1 , keepdim=__A )
# duplicate text embeddings for each generation per prompt
_lowerCAmelCase =prompt_embeds.repeat_interleave(__A , dim=0 )
if do_classifier_free_guidance:
if self.learned_classifier_free_sampling_embeddings.learnable:
_lowerCAmelCase =self.learned_classifier_free_sampling_embeddings.embeddings
_lowerCAmelCase =negative_prompt_embeds.unsqueeze(0 ).repeat(__A , 1 , 1 )
else:
_lowerCAmelCase =[''] * batch_size
_lowerCAmelCase =text_input_ids.shape[-1]
_lowerCAmelCase =self.tokenizer(
__A , padding='max_length' , max_length=__A , truncation=__A , return_tensors='pt' , )
_lowerCAmelCase =self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# See comment for normalizing text embeddings
_lowerCAmelCase =negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1 , keepdim=__A )
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
_lowerCAmelCase =negative_prompt_embeds.shape[1]
_lowerCAmelCase =negative_prompt_embeds.repeat(1 , __A , 1 )
_lowerCAmelCase =negative_prompt_embeds.view(batch_size * num_images_per_prompt , __A , -1 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
_lowerCAmelCase =torch.cat([negative_prompt_embeds, prompt_embeds] )
return prompt_embeds
@torch.no_grad()
def __call__( self , __A , __A = 100 , __A = 5.0 , __A = 1.0 , __A = 1 , __A = None , __A = None , __A = "pil" , __A = True , __A = None , __A = 1 , ) -> Union[ImagePipelineOutput, Tuple]:
if isinstance(__A , __A ):
_lowerCAmelCase =1
elif isinstance(__A , __A ):
_lowerCAmelCase =len(__A )
else:
raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(__A )}''' )
_lowerCAmelCase =batch_size * num_images_per_prompt
_lowerCAmelCase =guidance_scale > 1.0
_lowerCAmelCase =self._encode_prompt(__A , __A , __A )
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(__A , __A ) or callback_steps <= 0)
):
raise ValueError(
F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
F''' {type(__A )}.''' )
# get the initial completely masked latents unless the user supplied it
_lowerCAmelCase =(batch_size, self.transformer.num_latent_pixels)
if latents is None:
_lowerCAmelCase =self.transformer.num_vector_embeds - 1
_lowerCAmelCase =torch.full(__A , __A ).to(self.device )
else:
if latents.shape != latents_shape:
raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''' )
if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
raise ValueError(
'Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,'
F''' {self.transformer.num_vector_embeds - 1} (inclusive).''' )
_lowerCAmelCase =latents.to(self.device )
# set timesteps
self.scheduler.set_timesteps(__A , device=self.device )
_lowerCAmelCase =self.scheduler.timesteps.to(self.device )
_lowerCAmelCase =latents
for i, t in enumerate(self.progress_bar(__A ) ):
# expand the sample if we are doing classifier free guidance
_lowerCAmelCase =torch.cat([sample] * 2 ) if do_classifier_free_guidance else sample
# predict the un-noised image
# model_output == `log_p_x_0`
_lowerCAmelCase =self.transformer(__A , encoder_hidden_states=__A , timestep=__A ).sample
if do_classifier_free_guidance:
_lowerCAmelCase , _lowerCAmelCase =model_output.chunk(2 )
_lowerCAmelCase =model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
model_output -= torch.logsumexp(__A , dim=1 , keepdim=__A )
_lowerCAmelCase =self.truncate(__A , __A )
# remove `log(0)`'s (`-inf`s)
_lowerCAmelCase =model_output.clamp(-70 )
# compute the previous noisy sample x_t -> x_t-1
_lowerCAmelCase =self.scheduler.step(__A , timestep=__A , sample=__A , generator=__A ).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(__A , __A , __A )
_lowerCAmelCase =self.vqvae.config.vq_embed_dim
_lowerCAmelCase =(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
_lowerCAmelCase =self.vqvae.quantize.get_codebook_entry(__A , shape=__A )
_lowerCAmelCase =self.vqvae.decode(__A , force_not_quantize=__A ).sample
_lowerCAmelCase =(image / 2 + 0.5).clamp(0 , 1 )
_lowerCAmelCase =image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
_lowerCAmelCase =self.numpy_to_pil(__A )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=__A )
def UpperCamelCase__ ( self , __A , __A ) -> torch.FloatTensor:
_lowerCAmelCase , _lowerCAmelCase =torch.sort(__A , 1 , descending=__A )
_lowerCAmelCase =torch.exp(__A )
_lowerCAmelCase =sorted_p_x_0.cumsum(dim=1 ) < truncation_rate
# Ensure that at least the largest probability is not zeroed out
_lowerCAmelCase =torch.full_like(keep_mask[:, 0:1, :] , __A )
_lowerCAmelCase =torch.cat((all_true, keep_mask) , dim=1 )
_lowerCAmelCase =keep_mask[:, :-1, :]
_lowerCAmelCase =keep_mask.gather(1 , indices.argsort(1 ) )
_lowerCAmelCase =log_p_x_0.clone()
_lowerCAmelCase =-torch.inf # -inf = log(0)
return rv
| 58
|
'''simple docstring'''
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class SCREAMING_SNAKE_CASE ( __lowercase , __lowercase):
"""simple docstring"""
@register_to_config
def __init__( self , __A = 128 , __A = 256 , __A = 2_000.0 , __A = 768 , __A = 12 , __A = 12 , __A = 64 , __A = 2048 , __A = 0.1 , ) -> str:
super().__init__()
_lowerCAmelCase =nn.Sequential(
nn.Linear(__A , d_model * 4 , bias=__A ) , nn.SiLU() , nn.Linear(d_model * 4 , d_model * 4 , bias=__A ) , nn.SiLU() , )
_lowerCAmelCase =nn.Embedding(__A , __A )
_lowerCAmelCase =False
_lowerCAmelCase =nn.Linear(__A , __A , bias=__A )
_lowerCAmelCase =nn.Dropout(p=__A )
_lowerCAmelCase =nn.ModuleList()
for lyr_num in range(__A ):
# FiLM conditional T5 decoder
_lowerCAmelCase =DecoderLayer(d_model=__A , d_kv=__A , num_heads=__A , d_ff=__A , dropout_rate=__A )
self.decoders.append(__A )
_lowerCAmelCase =TaLayerNorm(__A )
_lowerCAmelCase =nn.Dropout(p=__A )
_lowerCAmelCase =nn.Linear(__A , __A , bias=__A )
def UpperCamelCase__ ( self , __A , __A ) -> Any:
_lowerCAmelCase =torch.mul(query_input.unsqueeze(-1 ) , key_input.unsqueeze(-2 ) )
return mask.unsqueeze(-3 )
def UpperCamelCase__ ( self , __A , __A , __A ) -> Optional[Any]:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase =decoder_input_tokens.shape
assert decoder_noise_time.shape == (batch,)
# decoder_noise_time is in [0, 1), so rescale to expected timing range.
_lowerCAmelCase =get_timestep_embedding(
decoder_noise_time * self.config.max_decoder_noise_time , embedding_dim=self.config.d_model , max_period=self.config.max_decoder_noise_time , ).to(dtype=self.dtype )
_lowerCAmelCase =self.conditioning_emb(__A ).unsqueeze(1 )
assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)
_lowerCAmelCase =decoder_input_tokens.shape[1]
# If we want to use relative positions for audio context, we can just offset
# this sequence by the length of encodings_and_masks.
_lowerCAmelCase =torch.broadcast_to(
torch.arange(__A , device=decoder_input_tokens.device ) , (batch, seq_length) , )
_lowerCAmelCase =self.position_encoding(__A )
_lowerCAmelCase =self.continuous_inputs_projection(__A )
inputs += position_encodings
_lowerCAmelCase =self.dropout(__A )
# decoder: No padding present.
_lowerCAmelCase =torch.ones(
decoder_input_tokens.shape[:2] , device=decoder_input_tokens.device , dtype=inputs.dtype )
# Translate encoding masks to encoder-decoder masks.
_lowerCAmelCase =[(x, self.encoder_decoder_mask(__A , __A )) for x, y in encodings_and_masks]
# cross attend style: concat encodings
_lowerCAmelCase =torch.cat([x[0] for x in encodings_and_encdec_masks] , dim=1 )
_lowerCAmelCase =torch.cat([x[1] for x in encodings_and_encdec_masks] , dim=-1 )
for lyr in self.decoders:
_lowerCAmelCase =lyr(
__A , conditioning_emb=__A , encoder_hidden_states=__A , encoder_attention_mask=__A , )[0]
_lowerCAmelCase =self.decoder_norm(__A )
_lowerCAmelCase =self.post_dropout(__A )
_lowerCAmelCase =self.spec_out(__A )
return spec_out
class SCREAMING_SNAKE_CASE ( nn.Module):
"""simple docstring"""
def __init__( self , __A , __A , __A , __A , __A , __A=1E-6 ) -> Union[str, Any]:
super().__init__()
_lowerCAmelCase =nn.ModuleList()
# cond self attention: layer 0
self.layer.append(
TaLayerSelfAttentionCond(d_model=__A , d_kv=__A , num_heads=__A , dropout_rate=__A ) )
# cross attention: layer 1
self.layer.append(
TaLayerCrossAttention(
d_model=__A , d_kv=__A , num_heads=__A , dropout_rate=__A , layer_norm_epsilon=__A , ) )
# Film Cond MLP + dropout: last layer
self.layer.append(
TaLayerFFCond(d_model=__A , d_ff=__A , dropout_rate=__A , layer_norm_epsilon=__A ) )
def UpperCamelCase__ ( self , __A , __A=None , __A=None , __A=None , __A=None , __A=None , ) -> Any:
_lowerCAmelCase =self.layer[0](
__A , conditioning_emb=__A , attention_mask=__A , )
if encoder_hidden_states is not None:
_lowerCAmelCase =torch.where(encoder_attention_mask > 0 , 0 , -1E10 ).to(
encoder_hidden_states.dtype )
_lowerCAmelCase =self.layer[1](
__A , key_value_states=__A , attention_mask=__A , )
# Apply Film Conditional Feed Forward layer
_lowerCAmelCase =self.layer[-1](__A , __A )
return (hidden_states,)
class SCREAMING_SNAKE_CASE ( nn.Module):
"""simple docstring"""
def __init__( self , __A , __A , __A , __A ) -> Optional[Any]:
super().__init__()
_lowerCAmelCase =TaLayerNorm(__A )
_lowerCAmelCase =TaFiLMLayer(in_features=d_model * 4 , out_features=__A )
_lowerCAmelCase =Attention(query_dim=__A , heads=__A , dim_head=__A , out_bias=__A , scale_qk=__A )
_lowerCAmelCase =nn.Dropout(__A )
def UpperCamelCase__ ( self , __A , __A=None , __A=None , ) -> List[Any]:
# pre_self_attention_layer_norm
_lowerCAmelCase =self.layer_norm(__A )
if conditioning_emb is not None:
_lowerCAmelCase =self.FiLMLayer(__A , __A )
# Self-attention block
_lowerCAmelCase =self.attention(__A )
_lowerCAmelCase =hidden_states + self.dropout(__A )
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module):
"""simple docstring"""
def __init__( self , __A , __A , __A , __A , __A ) -> Optional[int]:
super().__init__()
_lowerCAmelCase =Attention(query_dim=__A , heads=__A , dim_head=__A , out_bias=__A , scale_qk=__A )
_lowerCAmelCase =TaLayerNorm(__A , eps=__A )
_lowerCAmelCase =nn.Dropout(__A )
def UpperCamelCase__ ( self , __A , __A=None , __A=None , ) -> Tuple:
_lowerCAmelCase =self.layer_norm(__A )
_lowerCAmelCase =self.attention(
__A , encoder_hidden_states=__A , attention_mask=attention_mask.squeeze(1 ) , )
_lowerCAmelCase =hidden_states + self.dropout(__A )
return layer_output
class SCREAMING_SNAKE_CASE ( nn.Module):
"""simple docstring"""
def __init__( self , __A , __A , __A , __A ) -> Optional[Any]:
super().__init__()
_lowerCAmelCase =TaDenseGatedActDense(d_model=__A , d_ff=__A , dropout_rate=__A )
_lowerCAmelCase =TaFiLMLayer(in_features=d_model * 4 , out_features=__A )
_lowerCAmelCase =TaLayerNorm(__A , eps=__A )
_lowerCAmelCase =nn.Dropout(__A )
def UpperCamelCase__ ( self , __A , __A=None ) -> List[Any]:
_lowerCAmelCase =self.layer_norm(__A )
if conditioning_emb is not None:
_lowerCAmelCase =self.film(__A , __A )
_lowerCAmelCase =self.DenseReluDense(__A )
_lowerCAmelCase =hidden_states + self.dropout(__A )
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module):
"""simple docstring"""
def __init__( self , __A , __A , __A ) -> Union[str, Any]:
super().__init__()
_lowerCAmelCase =nn.Linear(__A , __A , bias=__A )
_lowerCAmelCase =nn.Linear(__A , __A , bias=__A )
_lowerCAmelCase =nn.Linear(__A , __A , bias=__A )
_lowerCAmelCase =nn.Dropout(__A )
_lowerCAmelCase =NewGELUActivation()
def UpperCamelCase__ ( self , __A ) -> List[Any]:
_lowerCAmelCase =self.act(self.wi_a(__A ) )
_lowerCAmelCase =self.wi_a(__A )
_lowerCAmelCase =hidden_gelu * hidden_linear
_lowerCAmelCase =self.dropout(__A )
_lowerCAmelCase =self.wo(__A )
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module):
"""simple docstring"""
def __init__( self , __A , __A=1E-6 ) -> int:
super().__init__()
_lowerCAmelCase =nn.Parameter(torch.ones(__A ) )
_lowerCAmelCase =eps
def UpperCamelCase__ ( self , __A ) -> Dict:
# T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
# Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
# w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
# half-precision inputs is done in fp32
_lowerCAmelCase =hidden_states.to(torch.floataa ).pow(2 ).mean(-1 , keepdim=__A )
_lowerCAmelCase =hidden_states * torch.rsqrt(variance + self.variance_epsilon )
# convert into half-precision if necessary
if self.weight.dtype in [torch.floataa, torch.bfloataa]:
_lowerCAmelCase =hidden_states.to(self.weight.dtype )
return self.weight * hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module):
"""simple docstring"""
def UpperCamelCase__ ( self , __A ) -> torch.Tensor:
return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi ) * (input + 0.044_715 * torch.pow(__A , 3.0 )) ))
class SCREAMING_SNAKE_CASE ( nn.Module):
"""simple docstring"""
def __init__( self , __A , __A ) -> Optional[Any]:
super().__init__()
_lowerCAmelCase =nn.Linear(__A , out_features * 2 , bias=__A )
def UpperCamelCase__ ( self , __A , __A ) -> Optional[Any]:
_lowerCAmelCase =self.scale_bias(__A )
_lowerCAmelCase , _lowerCAmelCase =torch.chunk(__A , 2 , -1 )
_lowerCAmelCase =x * (1 + scale) + shift
return x
| 58
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase_ = {
'''configuration_poolformer''': [
'''POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''PoolFormerConfig''',
'''PoolFormerOnnxConfig''',
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = ['''PoolFormerFeatureExtractor''']
lowercase_ = ['''PoolFormerImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
'''POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''PoolFormerForImageClassification''',
'''PoolFormerModel''',
'''PoolFormerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_poolformer import (
POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
PoolFormerConfig,
PoolFormerOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_poolformer import PoolFormerFeatureExtractor
from .image_processing_poolformer import PoolFormerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_poolformer import (
POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
PoolFormerForImageClassification,
PoolFormerModel,
PoolFormerPreTrainedModel,
)
else:
import sys
lowercase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 58
|
'''simple docstring'''
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_available() and not is_torch_available():
raise RuntimeError('''At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training''')
# TF training parameters
lowercase_ = False
lowercase_ = False
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
return TrainCommand(a__ )
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
@staticmethod
def UpperCamelCase__ ( __A ) -> Tuple:
_lowerCAmelCase =parser.add_parser('train' , help='CLI tool to train a model on a task.' )
train_parser.add_argument(
'--train_data' , type=__A , required=__A , help='path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.' , )
train_parser.add_argument(
'--column_label' , type=__A , default=0 , help='Column of the dataset csv file with example labels.' )
train_parser.add_argument(
'--column_text' , type=__A , default=1 , help='Column of the dataset csv file with example texts.' )
train_parser.add_argument(
'--column_id' , type=__A , default=2 , help='Column of the dataset csv file with example ids.' )
train_parser.add_argument(
'--skip_first_row' , action='store_true' , help='Skip the first row of the csv file (headers).' )
train_parser.add_argument('--validation_data' , type=__A , default='' , help='path to validation dataset.' )
train_parser.add_argument(
'--validation_split' , type=__A , default=0.1 , help='if validation dataset is not provided, fraction of train dataset to use as validation dataset.' , )
train_parser.add_argument('--output' , type=__A , default='./' , help='path to saved the trained model.' )
train_parser.add_argument(
'--task' , type=__A , default='text_classification' , help='Task to train the model on.' )
train_parser.add_argument(
'--model' , type=__A , default='bert-base-uncased' , help='Model\'s name or path to stored model.' )
train_parser.add_argument('--train_batch_size' , type=__A , default=32 , help='Batch size for training.' )
train_parser.add_argument('--valid_batch_size' , type=__A , default=64 , help='Batch size for validation.' )
train_parser.add_argument('--learning_rate' , type=__A , default=3E-5 , help='Learning rate.' )
train_parser.add_argument('--adam_epsilon' , type=__A , default=1E-08 , help='Epsilon for Adam optimizer.' )
train_parser.set_defaults(func=__A )
def __init__( self , __A ) -> List[str]:
_lowerCAmelCase =logging.get_logger('transformers-cli/training' )
_lowerCAmelCase ='tf' if is_tf_available() else 'torch'
os.makedirs(args.output , exist_ok=__A )
_lowerCAmelCase =args.output
_lowerCAmelCase =args.column_label
_lowerCAmelCase =args.column_text
_lowerCAmelCase =args.column_id
self.logger.info(F'''Loading {args.task} pipeline for {args.model}''' )
if args.task == "text_classification":
_lowerCAmelCase =TextClassificationPipeline.from_pretrained(args.model )
elif args.task == "token_classification":
raise NotImplementedError
elif args.task == "question_answering":
raise NotImplementedError
self.logger.info(F'''Loading dataset from {args.train_data}''' )
_lowerCAmelCase =Processor.create_from_csv(
args.train_data , column_label=args.column_label , column_text=args.column_text , column_id=args.column_id , skip_first_row=args.skip_first_row , )
_lowerCAmelCase =None
if args.validation_data:
self.logger.info(F'''Loading validation dataset from {args.validation_data}''' )
_lowerCAmelCase =Processor.create_from_csv(
args.validation_data , column_label=args.column_label , column_text=args.column_text , column_id=args.column_id , skip_first_row=args.skip_first_row , )
_lowerCAmelCase =args.validation_split
_lowerCAmelCase =args.train_batch_size
_lowerCAmelCase =args.valid_batch_size
_lowerCAmelCase =args.learning_rate
_lowerCAmelCase =args.adam_epsilon
def UpperCamelCase__ ( self ) -> List[str]:
if self.framework == "tf":
return self.run_tf()
return self.run_torch()
def UpperCamelCase__ ( self ) -> Union[str, Any]:
raise NotImplementedError
def UpperCamelCase__ ( self ) -> List[Any]:
self.pipeline.fit(
self.train_dataset , validation_data=self.valid_dataset , validation_split=self.validation_split , learning_rate=self.learning_rate , adam_epsilon=self.adam_epsilon , train_batch_size=self.train_batch_size , valid_batch_size=self.valid_batch_size , )
# Save trained pipeline
self.pipeline.save_pretrained(self.output )
| 58
| 1
|
'''simple docstring'''
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
lowercase_ = object()
# For specifying empty leaf dict `{}`
lowercase_ = object()
def UpperCamelCase__ ( a__ , a__ ):
'''simple docstring'''
_lowerCAmelCase =tuple((re.compile(x + '$' ) for x in qs) )
for i in range(len(a__ ) - len(a__ ) + 1 ):
_lowerCAmelCase =[x.match(a__ ) for x, y in zip(a__ , ks[i:] )]
if matches and all(a__ ):
return True
return False
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
def replace(a__ , a__ ):
for rule, replacement in rules:
if _match(a__ , a__ ):
return replacement
return val
return replace
def UpperCamelCase__ ( ):
'''simple docstring'''
return [
# embeddings
(("transformer", "wpe", "embedding"), P('mp' , a__ )),
(("transformer", "wte", "embedding"), P('mp' , a__ )),
# atention
(("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(a__ , 'mp' )),
(("attention", "out_proj", "kernel"), P('mp' , a__ )),
(("attention", "out_proj", "bias"), None),
# mlp
(("mlp", "c_fc", "kernel"), P(a__ , 'mp' )),
(("mlp", "c_fc", "bias"), P('mp' )),
(("mlp", "c_proj", "kernel"), P('mp' , a__ )),
(("mlp", "c_proj", "bias"), None),
# layer norms
((r"ln_\d+", "bias"), None),
((r"\d+", r"ln_\d+", "scale"), None),
(("ln_f", "bias"), None),
(("ln_f", "scale"), None),
]
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
_lowerCAmelCase =_get_partition_rules()
_lowerCAmelCase =_replacement_rules(a__ )
_lowerCAmelCase ={k: _unmatched for k in flatten_dict(a__ )}
_lowerCAmelCase ={k: replace(a__ , a__ ) for k, v in initd.items()}
assert _unmatched not in result.values(), "Incomplete partition spec."
return freeze(unflatten_dict(a__ ) )
| 58
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowercase_ = {'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
'''VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ViTMAEForPreTraining''',
'''ViTMAELayer''',
'''ViTMAEModel''',
'''ViTMAEPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
'''TFViTMAEForPreTraining''',
'''TFViTMAEModel''',
'''TFViTMAEPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit_mae import (
VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTMAEForPreTraining,
ViTMAELayer,
ViTMAEModel,
ViTMAEPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel
else:
import sys
lowercase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 58
| 1
|
'''simple docstring'''
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
assert isinstance(a__ , a__ ), F'''The input value of [n={number}] is not an integer'''
if number == 1:
return 2
elif number < 1:
_lowerCAmelCase =F'''The input value of [n={number}] has to be > 0'''
raise ValueError(a__ )
else:
_lowerCAmelCase =sylvester(number - 1 )
_lowerCAmelCase =num - 1
_lowerCAmelCase =num
return lower * upper + 1
if __name__ == "__main__":
print(F'The 8th number in Sylvester\'s sequence: {sylvester(8)}')
| 58
|
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
_lowerCAmelCase =os.path.join(args.tf_model_dir , 'parameters.json' )
_lowerCAmelCase =json.loads(open(a__ ).read() )
if not params:
raise ValueError(
F'''It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file.''' )
if not args.output.endswith('.pt' ):
_lowerCAmelCase =args.output + '.pt'
_lowerCAmelCase =OrderedDict()
with tf.device('/CPU:0' ):
_lowerCAmelCase =tf.train.load_checkpoint(args.tf_model_dir )
_lowerCAmelCase =reader.get_variable_to_shape_map()
for key_name in shapes.keys():
_lowerCAmelCase =reader.get_tensor(a__ ).astype(np.floataa )
if key_name.endswith('/adam_m' ) or key_name.endswith('/adam_v' ):
continue
if key_name.startswith('pasts/' ):
if key_name.startswith('pasts/mlp' ):
_lowerCAmelCase =int(key_name[9] )
elif key_name.startswith('pasts/out' ):
_lowerCAmelCase =8
_lowerCAmelCase ='model.sqout.%d.weight' % (player * 2) # enter to nn.Sequencial with Tanh, so 2 at a time
_lowerCAmelCase =vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.startswith('model/moe' ):
_lowerCAmelCase =int(key_name[9:].split('/' )[0] )
if key_name.endswith('/switch_gating/kernel' ):
_lowerCAmelCase ='model.blocks.%d.feed_forward.mlp.router.classifier.weight' % player
_lowerCAmelCase =vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.endswith('/softmlp/kernel' ):
_lowerCAmelCase ='model.blocks.%d.feed_forward.soft_bypass_mlp.weight' % player
_lowerCAmelCase =vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.endswith('/wo/kernel' ) or key_name.endswith('/wi/kernel' ):
_lowerCAmelCase =key_name[-9:-7]
for i in range(1_6 ):
_lowerCAmelCase ='model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight' % (player, i, nlayer)
_lowerCAmelCase =(
vnp[i].transpose([1, 0] ).copy()
) # In Mesh-Tensorflow, it is one array, so it is divided
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.startswith('model/mlp' ):
_lowerCAmelCase =int(key_name[9:].split('/' )[0] )
if key_name.endswith('/p1/kernel' ):
_lowerCAmelCase ='model.blocks.%d.feed_forward.mlp.wi.weight' % player
_lowerCAmelCase =vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.endswith('/p1/bias' ):
_lowerCAmelCase ='model.blocks.%d.feed_forward.mlp.wi.bias' % player
_lowerCAmelCase =vnp.copy() # same because it is one dimensional
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.endswith('/p2/kernel' ):
_lowerCAmelCase ='model.blocks.%d.feed_forward.mlp.wo.weight' % player
_lowerCAmelCase =vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.endswith('/p2/bias' ):
_lowerCAmelCase ='model.blocks.%d.feed_forward.mlp.wo.bias' % player
_lowerCAmelCase =vnp.copy() # same because it is one dimensional
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.startswith('model/ln' ):
_lowerCAmelCase =int(key_name[8:].split('/' )[0] )
if key_name.endswith('/b' ):
_lowerCAmelCase ='model.blocks.%d.feed_forward.norm.bias' % player
_lowerCAmelCase =vnp.copy() # same because it is one dimensional
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.endswith('/g' ):
_lowerCAmelCase ='model.blocks.%d.feed_forward.norm.weight' % player
_lowerCAmelCase =vnp.copy() # same because it is one dimensional
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.startswith('model/att' ):
_lowerCAmelCase =int(key_name[9:].split('/' )[0] )
if key_name.endswith('/qkv/kernel' ):
_lowerCAmelCase =vnp.copy() # Compute same dimension as Mesh-tensorflow using einsum
_lowerCAmelCase =state[:, 0, :, :]
_lowerCAmelCase =state[:, 1, :, :]
_lowerCAmelCase =state[:, 2, :, :]
_lowerCAmelCase =(
state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
_lowerCAmelCase =(
state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
_lowerCAmelCase =(
state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
_lowerCAmelCase ='model.blocks.%d.self_attn.self_attn.q_proj.weight' % player
_lowerCAmelCase =torch.tensor(a__ )
_lowerCAmelCase ='model.blocks.%d.self_attn.self_attn.k_proj.weight' % player
_lowerCAmelCase =torch.tensor(a__ )
_lowerCAmelCase ='model.blocks.%d.self_attn.self_attn.v_proj.weight' % player
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.endswith('/o/kernel' ):
_lowerCAmelCase ='model.blocks.%d.self_attn.self_attn.out_proj.weight' % player
_lowerCAmelCase =(
vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]] ).transpose([1, 0] ).copy()
) # Mesh-Tensorflow is a diagonal matrix
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.startswith('model/an' ):
_lowerCAmelCase =int(key_name[8:].split('/' )[0] )
if key_name.endswith('/b' ):
_lowerCAmelCase ='model.blocks.%d.self_attn.norm.bias' % player
_lowerCAmelCase =vnp.copy() # same because it is one dimensional
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.endswith('/g' ):
_lowerCAmelCase ='model.blocks.%d.self_attn.norm.weight' % player
_lowerCAmelCase =vnp.copy() # same because it is one dimensional
_lowerCAmelCase =torch.tensor(a__ )
elif (
key_name.startswith('model/wte' )
or key_name.startswith('model/wpe' )
or key_name.startswith('model/ete' )
):
_lowerCAmelCase ={'wte': 'embed_tokens', 'wpe': 'position_embeddings', 'ete': 'extra_position_embeddings'}[
key_name[-3:]
]
_lowerCAmelCase ='model.%s.weight' % nlayer
_lowerCAmelCase =vnp.copy() # same in embedded
_lowerCAmelCase =torch.tensor(a__ )
if key_name.startswith('model/wte' ):
_lowerCAmelCase ='lm_head.weight'
_lowerCAmelCase =vnp.copy() # same in embedded
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.startswith('model/wob' ):
_lowerCAmelCase ='final_logits_bias'
_lowerCAmelCase =vnp.copy() # same in embedded
_lowerCAmelCase =state.reshape((1, -1) )
_lowerCAmelCase =torch.tensor(a__ )
elif key_name == "model/dense/kernel":
_lowerCAmelCase ='model.last_project.weight'
_lowerCAmelCase =vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_lowerCAmelCase =torch.tensor(a__ )
elif key_name == "model/dense_1/bias":
_lowerCAmelCase ='model.last_project.bias'
_lowerCAmelCase =vnp.copy() # same because it is one dimensional
_lowerCAmelCase =torch.tensor(a__ )
torch.save(a__ , args.output )
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser(
description='''model converter.''', formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument('''--tf_model_dir''', metavar='''PATH''', type=str, required=True, help='''import model''')
parser.add_argument('''--output''', metavar='''PATH''', type=str, required=True, help='''output model''')
lowercase_ = parser.parse_args()
convert_tf_gptsan_to_pt(args)
| 58
| 1
|
'''simple docstring'''
def UpperCamelCase__ ( a__ , a__ ):
'''simple docstring'''
if not isinstance(a__ , a__ ):
raise ValueError('iterations must be defined as integers' )
if not isinstance(a__ , a__ ) or not number >= 1:
raise ValueError(
'starting number must be\n and integer and be more than 0' )
if not iterations >= 1:
raise ValueError('Iterations must be done more than 0 times to play FizzBuzz' )
_lowerCAmelCase =''
while number <= iterations:
if number % 3 == 0:
out += "Fizz"
if number % 5 == 0:
out += "Buzz"
if 0 not in (number % 3, number % 5):
out += str(a__ )
# print(out)
number += 1
out += " "
return out
if __name__ == "__main__":
import doctest
doctest.testmod()
| 58
|
'''simple docstring'''
def UpperCamelCase__ ( a__ = 1_0_0_0 ):
'''simple docstring'''
_lowerCAmelCase =2**power
_lowerCAmelCase =0
while n:
_lowerCAmelCase , _lowerCAmelCase =r + n % 1_0, n // 1_0
return r
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 58
| 1
|
'''simple docstring'''
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def UpperCamelCase__ ( a__ , a__ , **a__ ):
'''simple docstring'''
_lowerCAmelCase =AutoConfig.from_pretrained(a__ , **a__ )
_lowerCAmelCase =AutoModelForSeqaSeqLM.from_config(a__ )
model.save_pretrained(a__ )
AutoTokenizer.from_pretrained(a__ ).save_pretrained(a__ )
return model
if __name__ == "__main__":
fire.Fire(save_randomly_initialized_version)
| 58
|
'''simple docstring'''
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
_lowerCAmelCase =set()
# To detect a back edge, keep track of vertices currently in the recursion stack
_lowerCAmelCase =set()
return any(
node not in visited and depth_first_search(a__ , a__ , a__ , a__ )
for node in graph )
def UpperCamelCase__ ( a__ , a__ , a__ , a__ ):
'''simple docstring'''
visited.add(a__ )
rec_stk.add(a__ )
for node in graph[vertex]:
if node not in visited:
if depth_first_search(a__ , a__ , a__ , a__ ):
return True
elif node in rec_stk:
return True
# The node needs to be removed from recursion stack before function ends
rec_stk.remove(a__ )
return False
if __name__ == "__main__":
from doctest import testmod
testmod()
| 58
| 1
|
'''simple docstring'''
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def UpperCamelCase__ ( a__ , a__ , a__ , a__=5 ):
'''simple docstring'''
assert masked_input.count('<mask>' ) == 1
_lowerCAmelCase =torch.tensor(tokenizer.encode(a__ , add_special_tokens=a__ ) ).unsqueeze(0 ) # Batch size 1
_lowerCAmelCase =model(a__ )[0] # The last hidden-state is the first element of the output tuple
_lowerCAmelCase =(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
_lowerCAmelCase =logits[0, masked_index, :]
_lowerCAmelCase =logits.softmax(dim=0 )
_lowerCAmelCase , _lowerCAmelCase =prob.topk(k=a__ , dim=0 )
_lowerCAmelCase =' '.join(
[tokenizer.convert_ids_to_tokens(indices[i].item() ) for i in range(len(a__ ) )] )
_lowerCAmelCase =tokenizer.mask_token
_lowerCAmelCase =[]
for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(' ' ) ):
_lowerCAmelCase =predicted_token_bpe.replace('\u2581' , ' ' )
if " {0}".format(a__ ) in masked_input:
topk_filled_outputs.append(
(
masked_input.replace(' {0}'.format(a__ ) , a__ ),
values[index].item(),
predicted_token,
) )
else:
topk_filled_outputs.append(
(
masked_input.replace(a__ , a__ ),
values[index].item(),
predicted_token,
) )
return topk_filled_outputs
lowercase_ = CamembertTokenizer.from_pretrained('''camembert-base''')
lowercase_ = CamembertForMaskedLM.from_pretrained('''camembert-base''')
model.eval()
lowercase_ = '''Le camembert est <mask> :)'''
print(fill_mask(masked_input, model, tokenizer, topk=3))
| 58
|
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
'''salesforce/blip2-opt-2.7b''': '''https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json''',
}
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : Tuple = 'blip_2_vision_model'
def __init__( self , __A=1408 , __A=6144 , __A=39 , __A=16 , __A=224 , __A=14 , __A="gelu" , __A=0.00_001 , __A=0.0 , __A=1E-10 , __A=True , **__A , ) -> int:
super().__init__(**__A )
_lowerCAmelCase =hidden_size
_lowerCAmelCase =intermediate_size
_lowerCAmelCase =num_hidden_layers
_lowerCAmelCase =num_attention_heads
_lowerCAmelCase =patch_size
_lowerCAmelCase =image_size
_lowerCAmelCase =initializer_range
_lowerCAmelCase =attention_dropout
_lowerCAmelCase =layer_norm_eps
_lowerCAmelCase =hidden_act
_lowerCAmelCase =qkv_bias
@classmethod
def UpperCamelCase__ ( cls , __A , **__A ) -> "PretrainedConfig":
cls._set_token_in_kwargs(__A )
_lowerCAmelCase , _lowerCAmelCase =cls.get_config_dict(__A , **__A )
# get the vision config dict if we are loading from Blip2Config
if config_dict.get('model_type' ) == "blip-2":
_lowerCAmelCase =config_dict['vision_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(__A , **__A )
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : int = 'blip_2_qformer'
def __init__( self , __A=3_0522 , __A=768 , __A=12 , __A=12 , __A=3072 , __A="gelu" , __A=0.1 , __A=0.1 , __A=512 , __A=0.02 , __A=1E-12 , __A=0 , __A="absolute" , __A=2 , __A=1408 , **__A , ) -> List[str]:
super().__init__(pad_token_id=__A , **__A )
_lowerCAmelCase =vocab_size
_lowerCAmelCase =hidden_size
_lowerCAmelCase =num_hidden_layers
_lowerCAmelCase =num_attention_heads
_lowerCAmelCase =hidden_act
_lowerCAmelCase =intermediate_size
_lowerCAmelCase =hidden_dropout_prob
_lowerCAmelCase =attention_probs_dropout_prob
_lowerCAmelCase =max_position_embeddings
_lowerCAmelCase =initializer_range
_lowerCAmelCase =layer_norm_eps
_lowerCAmelCase =position_embedding_type
_lowerCAmelCase =cross_attention_frequency
_lowerCAmelCase =encoder_hidden_size
@classmethod
def UpperCamelCase__ ( cls , __A , **__A ) -> "PretrainedConfig":
cls._set_token_in_kwargs(__A )
_lowerCAmelCase , _lowerCAmelCase =cls.get_config_dict(__A , **__A )
# get the qformer config dict if we are loading from Blip2Config
if config_dict.get('model_type' ) == "blip-2":
_lowerCAmelCase =config_dict['qformer_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(__A , **__A )
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : Optional[int] = 'blip-2'
lowercase : Any = True
def __init__( self , __A=None , __A=None , __A=None , __A=32 , **__A ) -> int:
super().__init__(**__A )
if vision_config is None:
_lowerCAmelCase ={}
logger.info('vision_config is None. initializing the Blip2VisionConfig with default values.' )
if qformer_config is None:
_lowerCAmelCase ={}
logger.info('qformer_config is None. Initializing the Blip2QFormerConfig with default values.' )
if text_config is None:
_lowerCAmelCase ={}
logger.info('text_config is None. Initializing the text config with default values (`OPTConfig`).' )
_lowerCAmelCase =BlipaVisionConfig(**__A )
_lowerCAmelCase =BlipaQFormerConfig(**__A )
_lowerCAmelCase =text_config['model_type'] if 'model_type' in text_config else 'opt'
_lowerCAmelCase =CONFIG_MAPPING[text_model_type](**__A )
_lowerCAmelCase =self.text_config.tie_word_embeddings
_lowerCAmelCase =self.text_config.is_encoder_decoder
_lowerCAmelCase =num_query_tokens
_lowerCAmelCase =self.vision_config.hidden_size
_lowerCAmelCase =self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
_lowerCAmelCase =1.0
_lowerCAmelCase =0.02
@classmethod
def UpperCamelCase__ ( cls , __A , __A , __A , **__A , ) -> Any:
return cls(
vision_config=vision_config.to_dict() , qformer_config=qformer_config.to_dict() , text_config=text_config.to_dict() , **__A , )
def UpperCamelCase__ ( self ) -> Tuple:
_lowerCAmelCase =copy.deepcopy(self.__dict__ )
_lowerCAmelCase =self.vision_config.to_dict()
_lowerCAmelCase =self.qformer_config.to_dict()
_lowerCAmelCase =self.text_config.to_dict()
_lowerCAmelCase =self.__class__.model_type
return output
| 58
| 1
|
'''simple docstring'''
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
lowercase : Tuple = None
def UpperCamelCase__ ( self ) -> int:
_lowerCAmelCase =self.feature_extraction_class(**self.feat_extract_dict )
_lowerCAmelCase =json.loads(feat_extract.to_json_string() )
for key, value in self.feat_extract_dict.items():
self.assertEqual(obj[key] , __A )
def UpperCamelCase__ ( self ) -> List[Any]:
_lowerCAmelCase =self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
_lowerCAmelCase =os.path.join(__A , 'feat_extract.json' )
feat_extract_first.to_json_file(__A )
_lowerCAmelCase =self.feature_extraction_class.from_json_file(__A )
self.assertEqual(feat_extract_second.to_dict() , feat_extract_first.to_dict() )
def UpperCamelCase__ ( self ) -> Tuple:
_lowerCAmelCase =self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
_lowerCAmelCase =feat_extract_first.save_pretrained(__A )[0]
check_json_file_has_correct_format(__A )
_lowerCAmelCase =self.feature_extraction_class.from_pretrained(__A )
self.assertEqual(feat_extract_second.to_dict() , feat_extract_first.to_dict() )
def UpperCamelCase__ ( self ) -> int:
_lowerCAmelCase =self.feature_extraction_class()
self.assertIsNotNone(__A )
| 58
|
'''simple docstring'''
lowercase_ = {
'''A''': '''.-''', '''B''': '''-...''', '''C''': '''-.-.''', '''D''': '''-..''', '''E''': '''.''', '''F''': '''..-.''', '''G''': '''--.''',
'''H''': '''....''', '''I''': '''..''', '''J''': '''.---''', '''K''': '''-.-''', '''L''': '''.-..''', '''M''': '''--''', '''N''': '''-.''',
'''O''': '''---''', '''P''': '''.--.''', '''Q''': '''--.-''', '''R''': '''.-.''', '''S''': '''...''', '''T''': '''-''', '''U''': '''..-''',
'''V''': '''...-''', '''W''': '''.--''', '''X''': '''-..-''', '''Y''': '''-.--''', '''Z''': '''--..''', '''1''': '''.----''',
'''2''': '''..---''', '''3''': '''...--''', '''4''': '''....-''', '''5''': '''.....''', '''6''': '''-....''', '''7''': '''--...''',
'''8''': '''---..''', '''9''': '''----.''', '''0''': '''-----''', '''&''': '''.-...''', '''@''': '''.--.-.''',
''':''': '''---...''', ''',''': '''--..--''', '''.''': '''.-.-.-''', '''\'''': '''.----.''', '''"''': '''.-..-.''',
'''?''': '''..--..''', '''/''': '''-..-.''', '''=''': '''-...-''', '''+''': '''.-.-.''', '''-''': '''-....-''',
'''(''': '''-.--.''', ''')''': '''-.--.-''', '''!''': '''-.-.--''', ''' ''': '''/'''
} # Exclamation mark is not in ITU-R recommendation
# fmt: on
lowercase_ = {value: key for key, value in MORSE_CODE_DICT.items()}
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
return " ".join(MORSE_CODE_DICT[char] for char in message.upper() )
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
return "".join(REVERSE_DICT[char] for char in message.split() )
def UpperCamelCase__ ( ):
'''simple docstring'''
_lowerCAmelCase ='Morse code here!'
print(a__ )
_lowerCAmelCase =encrypt(a__ )
print(a__ )
_lowerCAmelCase =decrypt(a__ )
print(a__ )
if __name__ == "__main__":
main()
| 58
| 1
|
'''simple docstring'''
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
_lowerCAmelCase =1
_lowerCAmelCase =1
while repunit:
_lowerCAmelCase =(1_0 * repunit + 1) % divisor
repunit_index += 1
return repunit_index
def UpperCamelCase__ ( a__ = 1_0_0_0_0_0_0 ):
'''simple docstring'''
_lowerCAmelCase =limit - 1
if divisor % 2 == 0:
divisor += 1
while least_divisible_repunit(a__ ) <= limit:
divisor += 2
return divisor
if __name__ == "__main__":
print(F'{solution() = }')
| 58
|
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
'''facebook/data2vec-text-base''': '''https://huggingface.co/data2vec/resolve/main/config.json''',
}
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : List[str] = 'data2vec-text'
def __init__( self , __A=3_0522 , __A=768 , __A=12 , __A=12 , __A=3072 , __A="gelu" , __A=0.1 , __A=0.1 , __A=512 , __A=2 , __A=0.02 , __A=1E-12 , __A=1 , __A=0 , __A=2 , __A="absolute" , __A=True , __A=None , **__A , ) -> List[Any]:
super().__init__(pad_token_id=__A , bos_token_id=__A , eos_token_id=__A , **__A )
_lowerCAmelCase =vocab_size
_lowerCAmelCase =hidden_size
_lowerCAmelCase =num_hidden_layers
_lowerCAmelCase =num_attention_heads
_lowerCAmelCase =hidden_act
_lowerCAmelCase =intermediate_size
_lowerCAmelCase =hidden_dropout_prob
_lowerCAmelCase =attention_probs_dropout_prob
_lowerCAmelCase =max_position_embeddings
_lowerCAmelCase =type_vocab_size
_lowerCAmelCase =initializer_range
_lowerCAmelCase =layer_norm_eps
_lowerCAmelCase =position_embedding_type
_lowerCAmelCase =use_cache
_lowerCAmelCase =classifier_dropout
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
@property
def UpperCamelCase__ ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_lowerCAmelCase ={0: 'batch', 1: 'choice', 2: 'sequence'}
else:
_lowerCAmelCase ={0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
] )
| 58
| 1
|
'''simple docstring'''
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
lowercase_ = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __A , __A ) -> Union[str, Any]:
_lowerCAmelCase =question_encoder
_lowerCAmelCase =generator
_lowerCAmelCase =self.question_encoder
def UpperCamelCase__ ( self , __A ) -> Optional[int]:
if os.path.isfile(__A ):
raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file''' )
os.makedirs(__A , exist_ok=__A )
_lowerCAmelCase =os.path.join(__A , 'question_encoder_tokenizer' )
_lowerCAmelCase =os.path.join(__A , 'generator_tokenizer' )
self.question_encoder.save_pretrained(__A )
self.generator.save_pretrained(__A )
@classmethod
def UpperCamelCase__ ( cls , __A , **__A ) -> Optional[int]:
# dynamically import AutoTokenizer
from ..auto.tokenization_auto import AutoTokenizer
_lowerCAmelCase =kwargs.pop('config' , __A )
if config is None:
_lowerCAmelCase =RagConfig.from_pretrained(__A )
_lowerCAmelCase =AutoTokenizer.from_pretrained(
__A , config=config.question_encoder , subfolder='question_encoder_tokenizer' )
_lowerCAmelCase =AutoTokenizer.from_pretrained(
__A , config=config.generator , subfolder='generator_tokenizer' )
return cls(question_encoder=__A , generator=__A )
def __call__( self , *__A , **__A ) -> Union[str, Any]:
return self.current_tokenizer(*__A , **__A )
def UpperCamelCase__ ( self , *__A , **__A ) -> Optional[int]:
return self.generator.batch_decode(*__A , **__A )
def UpperCamelCase__ ( self , *__A , **__A ) -> List[Any]:
return self.generator.decode(*__A , **__A )
def UpperCamelCase__ ( self ) -> Any:
_lowerCAmelCase =self.question_encoder
def UpperCamelCase__ ( self ) -> Optional[int]:
_lowerCAmelCase =self.generator
def UpperCamelCase__ ( self , __A , __A = None , __A = None , __A = None , __A = "longest" , __A = None , __A = True , **__A , ) -> BatchEncoding:
warnings.warn(
'`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '
'regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '
'context manager to prepare your targets. See the documentation of your specific tokenizer for more '
'details' , __A , )
if max_length is None:
_lowerCAmelCase =self.current_tokenizer.model_max_length
_lowerCAmelCase =self(
__A , add_special_tokens=__A , return_tensors=__A , max_length=__A , padding=__A , truncation=__A , **__A , )
if tgt_texts is None:
return model_inputs
# Process tgt_texts
if max_target_length is None:
_lowerCAmelCase =self.current_tokenizer.model_max_length
_lowerCAmelCase =self(
text_target=__A , add_special_tokens=__A , return_tensors=__A , padding=__A , max_length=__A , truncation=__A , **__A , )
_lowerCAmelCase =labels['input_ids']
return model_inputs
| 58
|
'''simple docstring'''
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin
@skip_mps
class SCREAMING_SNAKE_CASE ( __lowercase , __lowercase , unittest.TestCase):
"""simple docstring"""
lowercase : List[Any] = IFPipeline
lowercase : Tuple = TEXT_TO_IMAGE_PARAMS - {'width', 'height', 'latents'}
lowercase : Union[str, Any] = TEXT_TO_IMAGE_BATCH_PARAMS
lowercase : int = PipelineTesterMixin.required_optional_params - {'latents'}
def UpperCamelCase__ ( self ) -> str:
return self._get_dummy_components()
def UpperCamelCase__ ( self , __A , __A=0 ) -> int:
if str(__A ).startswith('mps' ):
_lowerCAmelCase =torch.manual_seed(__A )
else:
_lowerCAmelCase =torch.Generator(device=__A ).manual_seed(__A )
_lowerCAmelCase ={
'prompt': 'A painting of a squirrel eating a burger',
'generator': generator,
'num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
def UpperCamelCase__ ( self ) -> Optional[Any]:
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != 'cuda' , reason='float16 requires CUDA' )
def UpperCamelCase__ ( self ) -> Tuple:
# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
super().test_save_load_floataa(expected_max_diff=1E-1 )
def UpperCamelCase__ ( self ) -> List[Any]:
self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )
def UpperCamelCase__ ( self ) -> str:
self._test_save_load_local()
def UpperCamelCase__ ( self ) -> Union[str, Any]:
self._test_inference_batch_single_identical(
expected_max_diff=1E-2 , )
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def UpperCamelCase__ ( self ) -> List[str]:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE ( unittest.TestCase):
"""simple docstring"""
def UpperCamelCase__ ( self ) -> Optional[int]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase__ ( self ) -> Optional[Any]:
# if
_lowerCAmelCase =IFPipeline.from_pretrained('DeepFloyd/IF-I-XL-v1.0' , variant='fp16' , torch_dtype=torch.floataa )
_lowerCAmelCase =IFSuperResolutionPipeline.from_pretrained(
'DeepFloyd/IF-II-L-v1.0' , variant='fp16' , torch_dtype=torch.floataa , text_encoder=__A , tokenizer=__A )
# pre compute text embeddings and remove T5 to save memory
pipe_a.text_encoder.to('cuda' )
_lowerCAmelCase , _lowerCAmelCase =pipe_a.encode_prompt('anime turtle' , device='cuda' )
del pipe_a.tokenizer
del pipe_a.text_encoder
gc.collect()
_lowerCAmelCase =None
_lowerCAmelCase =None
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if(__A , __A , __A , __A )
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# img2img
_lowerCAmelCase =IFImgaImgPipeline(**pipe_a.components )
_lowerCAmelCase =IFImgaImgSuperResolutionPipeline(**pipe_a.components )
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if_imgaimg(__A , __A , __A , __A )
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# inpainting
_lowerCAmelCase =IFInpaintingPipeline(**pipe_a.components )
_lowerCAmelCase =IFInpaintingSuperResolutionPipeline(**pipe_a.components )
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if_inpainting(__A , __A , __A , __A )
def UpperCamelCase__ ( self , __A , __A , __A , __A ) -> str:
# pipeline 1
_start_torch_memory_measurement()
_lowerCAmelCase =torch.Generator(device='cpu' ).manual_seed(0 )
_lowerCAmelCase =pipe_a(
prompt_embeds=__A , negative_prompt_embeds=__A , num_inference_steps=2 , generator=__A , output_type='np' , )
_lowerCAmelCase =output.images[0]
assert image.shape == (64, 64, 3)
_lowerCAmelCase =torch.cuda.max_memory_allocated()
assert mem_bytes < 13 * 10**9
_lowerCAmelCase =load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy' )
assert_mean_pixel_difference(__A , __A )
# pipeline 2
_start_torch_memory_measurement()
_lowerCAmelCase =torch.Generator(device='cpu' ).manual_seed(0 )
_lowerCAmelCase =floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(__A )
_lowerCAmelCase =pipe_a(
prompt_embeds=__A , negative_prompt_embeds=__A , image=__A , generator=__A , num_inference_steps=2 , output_type='np' , )
_lowerCAmelCase =output.images[0]
assert image.shape == (256, 256, 3)
_lowerCAmelCase =torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
_lowerCAmelCase =load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy' )
assert_mean_pixel_difference(__A , __A )
def UpperCamelCase__ ( self , __A , __A , __A , __A ) -> Optional[int]:
# pipeline 1
_start_torch_memory_measurement()
_lowerCAmelCase =floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(__A )
_lowerCAmelCase =torch.Generator(device='cpu' ).manual_seed(0 )
_lowerCAmelCase =pipe_a(
prompt_embeds=__A , negative_prompt_embeds=__A , image=__A , num_inference_steps=2 , generator=__A , output_type='np' , )
_lowerCAmelCase =output.images[0]
assert image.shape == (64, 64, 3)
_lowerCAmelCase =torch.cuda.max_memory_allocated()
assert mem_bytes < 10 * 10**9
_lowerCAmelCase =load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy' )
assert_mean_pixel_difference(__A , __A )
# pipeline 2
_start_torch_memory_measurement()
_lowerCAmelCase =torch.Generator(device='cpu' ).manual_seed(0 )
_lowerCAmelCase =floats_tensor((1, 3, 256, 256) , rng=random.Random(0 ) ).to(__A )
_lowerCAmelCase =floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(__A )
_lowerCAmelCase =pipe_a(
prompt_embeds=__A , negative_prompt_embeds=__A , image=__A , original_image=__A , generator=__A , num_inference_steps=2 , output_type='np' , )
_lowerCAmelCase =output.images[0]
assert image.shape == (256, 256, 3)
_lowerCAmelCase =torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
_lowerCAmelCase =load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy' )
assert_mean_pixel_difference(__A , __A )
def UpperCamelCase__ ( self , __A , __A , __A , __A ) -> Dict:
# pipeline 1
_start_torch_memory_measurement()
_lowerCAmelCase =floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(__A )
_lowerCAmelCase =floats_tensor((1, 3, 64, 64) , rng=random.Random(1 ) ).to(__A )
_lowerCAmelCase =torch.Generator(device='cpu' ).manual_seed(0 )
_lowerCAmelCase =pipe_a(
prompt_embeds=__A , negative_prompt_embeds=__A , image=__A , mask_image=__A , num_inference_steps=2 , generator=__A , output_type='np' , )
_lowerCAmelCase =output.images[0]
assert image.shape == (64, 64, 3)
_lowerCAmelCase =torch.cuda.max_memory_allocated()
assert mem_bytes < 10 * 10**9
_lowerCAmelCase =load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy' )
assert_mean_pixel_difference(__A , __A )
# pipeline 2
_start_torch_memory_measurement()
_lowerCAmelCase =torch.Generator(device='cpu' ).manual_seed(0 )
_lowerCAmelCase =floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(__A )
_lowerCAmelCase =floats_tensor((1, 3, 256, 256) , rng=random.Random(0 ) ).to(__A )
_lowerCAmelCase =floats_tensor((1, 3, 256, 256) , rng=random.Random(1 ) ).to(__A )
_lowerCAmelCase =pipe_a(
prompt_embeds=__A , negative_prompt_embeds=__A , image=__A , mask_image=__A , original_image=__A , generator=__A , num_inference_steps=2 , output_type='np' , )
_lowerCAmelCase =output.images[0]
assert image.shape == (256, 256, 3)
_lowerCAmelCase =torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
_lowerCAmelCase =load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy' )
assert_mean_pixel_difference(__A , __A )
def UpperCamelCase__ ( ):
'''simple docstring'''
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
| 58
| 1
|
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow
from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class SCREAMING_SNAKE_CASE ( __lowercase , __lowercase , __lowercase , unittest.TestCase):
"""simple docstring"""
lowercase : str = StableDiffusionInpaintPipeline
lowercase : Dict = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
lowercase : str = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
lowercase : Optional[int] = frozenset(
[]) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
lowercase : Any = frozenset([])
def UpperCamelCase__ ( self ) -> List[str]:
torch.manual_seed(0 )
_lowerCAmelCase =UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=9 , out_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') , cross_attention_dim=32 , attention_head_dim=(2, 4) , use_linear_projection=__A , )
_lowerCAmelCase =PNDMScheduler(skip_prk_steps=__A )
torch.manual_seed(0 )
_lowerCAmelCase =AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=4 , sample_size=128 , )
torch.manual_seed(0 )
_lowerCAmelCase =CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act='gelu' , projection_dim=512 , )
_lowerCAmelCase =CLIPTextModel(__A )
_lowerCAmelCase =CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
_lowerCAmelCase ={
'unet': unet,
'scheduler': scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'safety_checker': None,
'feature_extractor': None,
}
return components
def UpperCamelCase__ ( self , __A , __A=0 ) -> Tuple:
# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
_lowerCAmelCase =floats_tensor((1, 3, 32, 32) , rng=random.Random(__A ) ).to(__A )
_lowerCAmelCase =image.cpu().permute(0 , 2 , 3 , 1 )[0]
_lowerCAmelCase =Image.fromarray(np.uinta(__A ) ).convert('RGB' ).resize((64, 64) )
_lowerCAmelCase =Image.fromarray(np.uinta(image + 4 ) ).convert('RGB' ).resize((64, 64) )
if str(__A ).startswith('mps' ):
_lowerCAmelCase =torch.manual_seed(__A )
else:
_lowerCAmelCase =torch.Generator(device=__A ).manual_seed(__A )
_lowerCAmelCase ={
'prompt': 'A painting of a squirrel eating a burger',
'image': init_image,
'mask_image': mask_image,
'generator': generator,
'num_inference_steps': 2,
'guidance_scale': 6.0,
'output_type': 'numpy',
}
return inputs
def UpperCamelCase__ ( self ) -> Optional[Any]:
_lowerCAmelCase ='cpu' # ensure determinism for the device-dependent torch.Generator
_lowerCAmelCase =self.get_dummy_components()
_lowerCAmelCase =StableDiffusionInpaintPipeline(**__A )
_lowerCAmelCase =sd_pipe.to(__A )
sd_pipe.set_progress_bar_config(disable=__A )
_lowerCAmelCase =self.get_dummy_inputs(__A )
_lowerCAmelCase =sd_pipe(**__A ).images
_lowerCAmelCase =image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_lowerCAmelCase =np.array([0.4_727, 0.5_735, 0.3_941, 0.5_446, 0.5_926, 0.4_394, 0.5_062, 0.4_654, 0.4_476] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def UpperCamelCase__ ( self ) -> Optional[Any]:
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE ( unittest.TestCase):
"""simple docstring"""
def UpperCamelCase__ ( self ) -> Tuple:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase__ ( self ) -> Optional[int]:
_lowerCAmelCase =load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/sd2-inpaint/init_image.png' )
_lowerCAmelCase =load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png' )
_lowerCAmelCase =load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
'/yellow_cat_sitting_on_a_park_bench.npy' )
_lowerCAmelCase ='stabilityai/stable-diffusion-2-inpainting'
_lowerCAmelCase =StableDiffusionInpaintPipeline.from_pretrained(__A , safety_checker=__A )
pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
pipe.enable_attention_slicing()
_lowerCAmelCase ='Face of a yellow cat, high resolution, sitting on a park bench'
_lowerCAmelCase =torch.manual_seed(0 )
_lowerCAmelCase =pipe(
prompt=__A , image=__A , mask_image=__A , generator=__A , output_type='np' , )
_lowerCAmelCase =output.images[0]
assert image.shape == (512, 512, 3)
assert np.abs(expected_image - image ).max() < 9E-3
def UpperCamelCase__ ( self ) -> Optional[Any]:
_lowerCAmelCase =load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/sd2-inpaint/init_image.png' )
_lowerCAmelCase =load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png' )
_lowerCAmelCase =load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
'/yellow_cat_sitting_on_a_park_bench_fp16.npy' )
_lowerCAmelCase ='stabilityai/stable-diffusion-2-inpainting'
_lowerCAmelCase =StableDiffusionInpaintPipeline.from_pretrained(
__A , torch_dtype=torch.floataa , safety_checker=__A , )
pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
pipe.enable_attention_slicing()
_lowerCAmelCase ='Face of a yellow cat, high resolution, sitting on a park bench'
_lowerCAmelCase =torch.manual_seed(0 )
_lowerCAmelCase =pipe(
prompt=__A , image=__A , mask_image=__A , generator=__A , output_type='np' , )
_lowerCAmelCase =output.images[0]
assert image.shape == (512, 512, 3)
assert np.abs(expected_image - image ).max() < 5E-1
def UpperCamelCase__ ( self ) -> List[Any]:
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
_lowerCAmelCase =load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/sd2-inpaint/init_image.png' )
_lowerCAmelCase =load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png' )
_lowerCAmelCase ='stabilityai/stable-diffusion-2-inpainting'
_lowerCAmelCase =PNDMScheduler.from_pretrained(__A , subfolder='scheduler' )
_lowerCAmelCase =StableDiffusionInpaintPipeline.from_pretrained(
__A , safety_checker=__A , scheduler=__A , torch_dtype=torch.floataa , )
pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
_lowerCAmelCase ='Face of a yellow cat, high resolution, sitting on a park bench'
_lowerCAmelCase =torch.manual_seed(0 )
_lowerCAmelCase =pipe(
prompt=__A , image=__A , mask_image=__A , generator=__A , num_inference_steps=2 , output_type='np' , )
_lowerCAmelCase =torch.cuda.max_memory_allocated()
# make sure that less than 2.65 GB is allocated
assert mem_bytes < 2.65 * 10**9
| 58
|
'''simple docstring'''
import unittest
from knapsack import knapsack as k
class SCREAMING_SNAKE_CASE ( unittest.TestCase):
"""simple docstring"""
def UpperCamelCase__ ( self ) -> Optional[Any]:
_lowerCAmelCase =0
_lowerCAmelCase =[0]
_lowerCAmelCase =[0]
_lowerCAmelCase =len(__A )
self.assertEqual(k.knapsack(__A , __A , __A , __A ) , 0 )
_lowerCAmelCase =[60]
_lowerCAmelCase =[10]
_lowerCAmelCase =len(__A )
self.assertEqual(k.knapsack(__A , __A , __A , __A ) , 0 )
def UpperCamelCase__ ( self ) -> Tuple:
_lowerCAmelCase =3
_lowerCAmelCase =[1, 2, 3]
_lowerCAmelCase =[3, 2, 1]
_lowerCAmelCase =len(__A )
self.assertEqual(k.knapsack(__A , __A , __A , __A ) , 5 )
def UpperCamelCase__ ( self ) -> Union[str, Any]:
_lowerCAmelCase =50
_lowerCAmelCase =[60, 100, 120]
_lowerCAmelCase =[10, 20, 30]
_lowerCAmelCase =len(__A )
self.assertEqual(k.knapsack(__A , __A , __A , __A ) , 220 )
if __name__ == "__main__":
unittest.main()
| 58
| 1
|
'''simple docstring'''
import numpy
# List of input, output pairs
lowercase_ = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
lowercase_ = (((515, 22, 13), 555), ((61, 35, 49), 150))
lowercase_ = [2, 4, 1, 5]
lowercase_ = len(train_data)
lowercase_ = 0.009
def UpperCamelCase__ ( a__ , a__="train" ):
'''simple docstring'''
return calculate_hypothesis_value(a__ , a__ ) - output(
a__ , a__ )
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
_lowerCAmelCase =0
for i in range(len(a__ ) - 1 ):
hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
hyp_val += parameter_vector[0]
return hyp_val
def UpperCamelCase__ ( a__ , a__ ):
'''simple docstring'''
if data_set == "train":
return train_data[example_no][1]
elif data_set == "test":
return test_data[example_no][1]
return None
def UpperCamelCase__ ( a__ , a__ ):
'''simple docstring'''
if data_set == "train":
return _hypothesis_value(train_data[example_no][0] )
elif data_set == "test":
return _hypothesis_value(test_data[example_no][0] )
return None
def UpperCamelCase__ ( a__ , a__=m ):
'''simple docstring'''
_lowerCAmelCase =0
for i in range(a__ ):
if index == -1:
summation_value += _error(a__ )
else:
summation_value += _error(a__ ) * train_data[i][0][index]
return summation_value
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
_lowerCAmelCase =summation_of_cost_derivative(a__ , a__ ) / m
return cost_derivative_value
def UpperCamelCase__ ( ):
'''simple docstring'''
global parameter_vector
# Tune these values to set a tolerance value for predicted output
_lowerCAmelCase =0.000_002
_lowerCAmelCase =0
_lowerCAmelCase =0
while True:
j += 1
_lowerCAmelCase =[0, 0, 0, 0]
for i in range(0 , len(a__ ) ):
_lowerCAmelCase =get_cost_derivative(i - 1 )
_lowerCAmelCase =(
parameter_vector[i] - LEARNING_RATE * cost_derivative
)
if numpy.allclose(
a__ , a__ , atol=a__ , rtol=a__ , ):
break
_lowerCAmelCase =temp_parameter_vector
print(('Number of iterations:', j) )
def UpperCamelCase__ ( ):
'''simple docstring'''
for i in range(len(a__ ) ):
print(('Actual output value:', output(a__ , 'test' )) )
print(('Hypothesis output:', calculate_hypothesis_value(a__ , 'test' )) )
if __name__ == "__main__":
run_gradient_descent()
print('''\nTesting gradient descent for a linear hypothesis function.\n''')
test_gradient_descent()
| 58
|
'''simple docstring'''
lowercase_ = '''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
lowercase_ = [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
lowercase_ = {
'''{processor_class}''': '''FakeProcessorClass''',
'''{model_class}''': '''FakeModelClass''',
'''{object_class}''': '''FakeObjectClass''',
}
| 58
| 1
|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
'''MIT/ast-finetuned-audioset-10-10-0.4593''': (
'''https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json'''
),
}
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : str = 'audio-spectrogram-transformer'
def __init__( self , __A=768 , __A=12 , __A=12 , __A=3072 , __A="gelu" , __A=0.0 , __A=0.0 , __A=0.02 , __A=1E-12 , __A=16 , __A=True , __A=10 , __A=10 , __A=1024 , __A=128 , **__A , ) -> Tuple:
super().__init__(**__A )
_lowerCAmelCase =hidden_size
_lowerCAmelCase =num_hidden_layers
_lowerCAmelCase =num_attention_heads
_lowerCAmelCase =intermediate_size
_lowerCAmelCase =hidden_act
_lowerCAmelCase =hidden_dropout_prob
_lowerCAmelCase =attention_probs_dropout_prob
_lowerCAmelCase =initializer_range
_lowerCAmelCase =layer_norm_eps
_lowerCAmelCase =patch_size
_lowerCAmelCase =qkv_bias
_lowerCAmelCase =frequency_stride
_lowerCAmelCase =time_stride
_lowerCAmelCase =max_length
_lowerCAmelCase =num_mel_bins
| 58
|
'''simple docstring'''
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json
lowercase_ = '''sshleifer/mar_enro_6_3_student'''
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
def UpperCamelCase__ ( self ) -> Optional[Any]:
super().setUp()
_lowerCAmelCase =cached_path(
'https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz' , extract_compressed_file=__A , )
_lowerCAmelCase =F'''{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'''
@slow
@require_torch_gpu
def UpperCamelCase__ ( self ) -> Union[str, Any]:
MarianMTModel.from_pretrained(__A )
@slow
@require_torch_gpu
def UpperCamelCase__ ( self ) -> Union[str, Any]:
_lowerCAmelCase ={
'$MAX_LEN': 64,
'$BS': 64,
'$GAS': 1,
'$ENRO_DIR': self.data_dir,
'facebook/mbart-large-cc25': MARIAN_MODEL,
# "val_check_interval=0.25": "val_check_interval=1.0",
'--learning_rate=3e-5': '--learning_rate 3e-4',
'--num_train_epochs 6': '--num_train_epochs 1',
}
# Clean up bash script
_lowerCAmelCase =(self.test_file_dir / 'train_mbart_cc25_enro.sh').open().read().split('finetune.py' )[1].strip()
_lowerCAmelCase =bash_script.replace('\\\n' , '' ).strip().replace('"$@"' , '' )
for k, v in env_vars_to_replace.items():
_lowerCAmelCase =bash_script.replace(__A , str(__A ) )
_lowerCAmelCase =self.get_auto_remove_tmp_dir()
# bash_script = bash_script.replace("--fp16 ", "")
_lowerCAmelCase =F'''
--output_dir {output_dir}
--tokenizer_name Helsinki-NLP/opus-mt-en-ro
--sortish_sampler
--do_predict
--gpus 1
--freeze_encoder
--n_train 40000
--n_val 500
--n_test 500
--fp16_opt_level O1
--num_sanity_val_steps 0
--eval_beams 2
'''.split()
# XXX: args.gpus > 1 : handle multi_gpu in the future
_lowerCAmelCase =['finetune.py'] + bash_script.split() + args
with patch.object(__A , 'argv' , __A ):
_lowerCAmelCase =argparse.ArgumentParser()
_lowerCAmelCase =pl.Trainer.add_argparse_args(__A )
_lowerCAmelCase =SummarizationModule.add_model_specific_args(__A , os.getcwd() )
_lowerCAmelCase =parser.parse_args()
_lowerCAmelCase =main(__A )
# Check metrics
_lowerCAmelCase =load_json(model.metrics_save_path )
_lowerCAmelCase =metrics['val'][0]
_lowerCAmelCase =metrics['val'][-1]
self.assertEqual(len(metrics['val'] ) , (args.max_epochs / args.val_check_interval) )
assert isinstance(last_step_stats[F'''val_avg_{model.val_metric}'''] , __A )
self.assertGreater(last_step_stats['val_avg_gen_time'] , 0.01 )
# model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
self.assertLessEqual(last_step_stats['val_avg_gen_time'] , 1.0 )
# test learning requirements:
# 1. BLEU improves over the course of training by more than 2 pts
self.assertGreater(last_step_stats['val_avg_bleu'] - first_step_stats['val_avg_bleu'] , 2 )
# 2. BLEU finishes above 17
self.assertGreater(last_step_stats['val_avg_bleu'] , 17 )
# 3. test BLEU and val BLEU within ~1.1 pt.
self.assertLess(abs(metrics['val'][-1]['val_avg_bleu'] - metrics['test'][-1]['test_avg_bleu'] ) , 1.1 )
# check lightning ckpt can be loaded and has a reasonable statedict
_lowerCAmelCase =os.listdir(__A )
_lowerCAmelCase =[x for x in contents if x.endswith('.ckpt' )][0]
_lowerCAmelCase =os.path.join(args.output_dir , __A )
_lowerCAmelCase =torch.load(__A , map_location='cpu' )
_lowerCAmelCase ='model.model.decoder.layers.0.encoder_attn_layer_norm.weight'
assert expected_key in ckpt["state_dict"]
assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa
# TODO: turn on args.do_predict when PL bug fixed.
if args.do_predict:
_lowerCAmelCase ={os.path.basename(__A ) for p in contents}
assert "test_generations.txt" in contents
assert "test_results.txt" in contents
# assert len(metrics["val"]) == desired_n_evals
assert len(metrics['test'] ) == 1
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
@timeout_decorator.timeout(600 )
@slow
@require_torch_gpu
def UpperCamelCase__ ( self ) -> Tuple:
_lowerCAmelCase =F'''{self.test_file_dir_str}/test_data/wmt_en_ro'''
_lowerCAmelCase ={
'--fp16_opt_level=O1': '',
'$MAX_LEN': 128,
'$BS': 16,
'$GAS': 1,
'$ENRO_DIR': data_dir,
'$m': 'sshleifer/student_marian_en_ro_6_1',
'val_check_interval=0.25': 'val_check_interval=1.0',
}
# Clean up bash script
_lowerCAmelCase =(
(self.test_file_dir / 'distil_marian_no_teacher.sh').open().read().split('distillation.py' )[1].strip()
)
_lowerCAmelCase =bash_script.replace('\\\n' , '' ).strip().replace('"$@"' , '' )
_lowerCAmelCase =bash_script.replace('--fp16 ' , ' ' )
for k, v in env_vars_to_replace.items():
_lowerCAmelCase =bash_script.replace(__A , str(__A ) )
_lowerCAmelCase =self.get_auto_remove_tmp_dir()
_lowerCAmelCase =bash_script.replace('--fp16' , '' )
_lowerCAmelCase =6
_lowerCAmelCase =(
['distillation.py']
+ bash_script.split()
+ [
F'''--output_dir={output_dir}''',
'--gpus=1',
'--learning_rate=1e-3',
F'''--num_train_epochs={epochs}''',
'--warmup_steps=10',
'--val_check_interval=1.0',
'--do_predict',
]
)
with patch.object(__A , 'argv' , __A ):
_lowerCAmelCase =argparse.ArgumentParser()
_lowerCAmelCase =pl.Trainer.add_argparse_args(__A )
_lowerCAmelCase =SummarizationDistiller.add_model_specific_args(__A , os.getcwd() )
_lowerCAmelCase =parser.parse_args()
# assert args.gpus == gpus THIS BREAKS for multi_gpu
_lowerCAmelCase =distill_main(__A )
# Check metrics
_lowerCAmelCase =load_json(model.metrics_save_path )
_lowerCAmelCase =metrics['val'][0]
_lowerCAmelCase =metrics['val'][-1]
assert len(metrics['val'] ) >= (args.max_epochs / args.val_check_interval) # +1 accounts for val_sanity_check
assert last_step_stats["val_avg_gen_time"] >= 0.01
assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"] # model learned nothing
assert 1.0 >= last_step_stats["val_avg_gen_time"] # model hanging on generate. Maybe bad config was saved.
assert isinstance(last_step_stats[F'''val_avg_{model.val_metric}'''] , __A )
# check lightning ckpt can be loaded and has a reasonable statedict
_lowerCAmelCase =os.listdir(__A )
_lowerCAmelCase =[x for x in contents if x.endswith('.ckpt' )][0]
_lowerCAmelCase =os.path.join(args.output_dir , __A )
_lowerCAmelCase =torch.load(__A , map_location='cpu' )
_lowerCAmelCase ='model.model.decoder.layers.0.encoder_attn_layer_norm.weight'
assert expected_key in ckpt["state_dict"]
assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa
# TODO: turn on args.do_predict when PL bug fixed.
if args.do_predict:
_lowerCAmelCase ={os.path.basename(__A ) for p in contents}
assert "test_generations.txt" in contents
assert "test_results.txt" in contents
# assert len(metrics["val"]) == desired_n_evals
assert len(metrics['test'] ) == 1
| 58
| 1
|
'''simple docstring'''
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def UpperCamelCase__ ( a__ = "isbn/0140328726" ):
'''simple docstring'''
_lowerCAmelCase =olid.strip().strip('/' ) # Remove leading/trailing whitespace & slashes
if new_olid.count('/' ) != 1:
_lowerCAmelCase =F'''{olid} is not a valid Open Library olid'''
raise ValueError(a__ )
return requests.get(F'''https://openlibrary.org/{new_olid}.json''' ).json()
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
_lowerCAmelCase ={
'title': 'Title',
'publish_date': 'Publish date',
'authors': 'Authors',
'number_of_pages': 'Number of pages:',
'first_sentence': 'First sentence',
'isbn_10': 'ISBN (10)',
'isbn_13': 'ISBN (13)',
}
_lowerCAmelCase ={better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
_lowerCAmelCase =[
get_openlibrary_data(author['key'] )['name'] for author in data['Authors']
]
_lowerCAmelCase =data['First sentence']['value']
for key, value in data.items():
if isinstance(a__ , a__ ):
_lowerCAmelCase =', '.join(a__ )
return data
if __name__ == "__main__":
import doctest
doctest.testmod()
while True:
lowercase_ = input('''\nEnter the ISBN code to search (or \'quit\' to stop): ''').strip()
if isbn.lower() in ("", "q", "quit", "exit", "stop"):
break
if len(isbn) not in (10, 13) or not isbn.isdigit():
print(F'Sorry, {isbn} is not a valid ISBN. Please, input a valid ISBN.')
continue
print(F'\nSearching Open Library for ISBN: {isbn}...\n')
try:
lowercase_ = summarize_book(get_openlibrary_data(F'isbn/{isbn}'))
print('''\n'''.join(F'{key}: {value}' for key, value in book_summary.items()))
except JSONDecodeError: # Workaround for requests.exceptions.RequestException:
print(F'Sorry, there are no results for ISBN: {isbn}.')
| 58
|
'''simple docstring'''
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors
lowercase_ = logging.getLogger(__name__)
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : int = 'sequence-classification'
def __init__( self , __A ) -> List[Any]:
if type(__A ) == dict:
_lowerCAmelCase =Namespace(**__A )
_lowerCAmelCase =glue_output_modes[hparams.task]
_lowerCAmelCase =glue_tasks_num_labels[hparams.task]
super().__init__(__A , __A , self.mode )
def UpperCamelCase__ ( self , **__A ) -> Any:
return self.model(**__A )
def UpperCamelCase__ ( self , __A , __A ) -> Union[str, Any]:
_lowerCAmelCase ={'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
_lowerCAmelCase =batch[2] if self.config.model_type in ['bert', 'xlnet', 'albert'] else None
_lowerCAmelCase =self(**__A )
_lowerCAmelCase =outputs[0]
_lowerCAmelCase =self.trainer.lr_schedulers[0]['scheduler']
_lowerCAmelCase ={'loss': loss, 'rate': lr_scheduler.get_last_lr()[-1]}
return {"loss": loss, "log": tensorboard_logs}
def UpperCamelCase__ ( self ) -> Any:
_lowerCAmelCase =self.hparams
_lowerCAmelCase =processors[args.task]()
_lowerCAmelCase =processor.get_labels()
for mode in ["train", "dev"]:
_lowerCAmelCase =self._feature_file(__A )
if os.path.exists(__A ) and not args.overwrite_cache:
logger.info('Loading features from cached file %s' , __A )
else:
logger.info('Creating features from dataset file at %s' , args.data_dir )
_lowerCAmelCase =(
processor.get_dev_examples(args.data_dir )
if mode == 'dev'
else processor.get_train_examples(args.data_dir )
)
_lowerCAmelCase =convert_examples_to_features(
__A , self.tokenizer , max_length=args.max_seq_length , label_list=self.labels , output_mode=args.glue_output_mode , )
logger.info('Saving features into cached file %s' , __A )
torch.save(__A , __A )
def UpperCamelCase__ ( self , __A , __A , __A = False ) -> DataLoader:
_lowerCAmelCase ='dev' if mode == 'test' else mode
_lowerCAmelCase =self._feature_file(__A )
logger.info('Loading features from cached file %s' , __A )
_lowerCAmelCase =torch.load(__A )
_lowerCAmelCase =torch.tensor([f.input_ids for f in features] , dtype=torch.long )
_lowerCAmelCase =torch.tensor([f.attention_mask for f in features] , dtype=torch.long )
_lowerCAmelCase =torch.tensor([f.token_type_ids for f in features] , dtype=torch.long )
if self.hparams.glue_output_mode == "classification":
_lowerCAmelCase =torch.tensor([f.label for f in features] , dtype=torch.long )
elif self.hparams.glue_output_mode == "regression":
_lowerCAmelCase =torch.tensor([f.label for f in features] , dtype=torch.float )
return DataLoader(
TensorDataset(__A , __A , __A , __A ) , batch_size=__A , shuffle=__A , )
def UpperCamelCase__ ( self , __A , __A ) -> List[str]:
_lowerCAmelCase ={'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
_lowerCAmelCase =batch[2] if self.config.model_type in ['bert', 'xlnet', 'albert'] else None
_lowerCAmelCase =self(**__A )
_lowerCAmelCase , _lowerCAmelCase =outputs[:2]
_lowerCAmelCase =logits.detach().cpu().numpy()
_lowerCAmelCase =inputs['labels'].detach().cpu().numpy()
return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
def UpperCamelCase__ ( self , __A ) -> tuple:
_lowerCAmelCase =torch.stack([x['val_loss'] for x in outputs] ).mean().detach().cpu().item()
_lowerCAmelCase =np.concatenate([x['pred'] for x in outputs] , axis=0 )
if self.hparams.glue_output_mode == "classification":
_lowerCAmelCase =np.argmax(__A , axis=1 )
elif self.hparams.glue_output_mode == "regression":
_lowerCAmelCase =np.squeeze(__A )
_lowerCAmelCase =np.concatenate([x['target'] for x in outputs] , axis=0 )
_lowerCAmelCase =[[] for _ in range(out_label_ids.shape[0] )]
_lowerCAmelCase =[[] for _ in range(out_label_ids.shape[0] )]
_lowerCAmelCase ={**{'val_loss': val_loss_mean}, **compute_metrics(self.hparams.task , __A , __A )}
_lowerCAmelCase =dict(results.items() )
_lowerCAmelCase =results
return ret, preds_list, out_label_list
def UpperCamelCase__ ( self , __A ) -> dict:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase =self._eval_end(__A )
_lowerCAmelCase =ret['log']
return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
def UpperCamelCase__ ( self , __A ) -> dict:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase =self._eval_end(__A )
_lowerCAmelCase =ret['log']
# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
@staticmethod
def UpperCamelCase__ ( __A , __A ) -> Any:
BaseTransformer.add_model_specific_args(__A , __A )
parser.add_argument(
'--max_seq_length' , default=128 , type=__A , help=(
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
) , )
parser.add_argument(
'--task' , default='' , type=__A , required=__A , help='The GLUE task to run' , )
parser.add_argument(
'--gpus' , default=0 , type=__A , help='The number of GPUs allocated for this, it is by default 0 meaning none' , )
parser.add_argument(
'--overwrite_cache' , action='store_true' , help='Overwrite the cached training and evaluation sets' )
return parser
def UpperCamelCase__ ( ):
'''simple docstring'''
_lowerCAmelCase =argparse.ArgumentParser()
add_generic_args(a__ , os.getcwd() )
_lowerCAmelCase =GLUETransformer.add_model_specific_args(a__ , os.getcwd() )
_lowerCAmelCase =parser.parse_args()
# If output_dir not provided, a folder will be generated in pwd
if args.output_dir is None:
_lowerCAmelCase =os.path.join(
'./results' , F'''{args.task}_{time.strftime('%Y%m%d_%H%M%S' )}''' , )
os.makedirs(args.output_dir )
_lowerCAmelCase =GLUETransformer(a__ )
_lowerCAmelCase =generic_train(a__ , a__ )
# Optionally, predict on dev set and write to output_dir
if args.do_predict:
_lowerCAmelCase =sorted(glob.glob(os.path.join(args.output_dir , 'checkpoint-epoch=*.ckpt' ) , recursive=a__ ) )
_lowerCAmelCase =model.load_from_checkpoint(checkpoints[-1] )
return trainer.test(a__ )
if __name__ == "__main__":
main()
| 58
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowercase_ = {
'''configuration_rag''': ['''RagConfig'''],
'''retrieval_rag''': ['''RagRetriever'''],
'''tokenization_rag''': ['''RagTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
'''RagModel''',
'''RagPreTrainedModel''',
'''RagSequenceForGeneration''',
'''RagTokenForGeneration''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
'''TFRagModel''',
'''TFRagPreTrainedModel''',
'''TFRagSequenceForGeneration''',
'''TFRagTokenForGeneration''',
]
if TYPE_CHECKING:
from .configuration_rag import RagConfig
from .retrieval_rag import RagRetriever
from .tokenization_rag import RagTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_rag import (
TFRagModel,
TFRagPreTrainedModel,
TFRagSequenceForGeneration,
TFRagTokenForGeneration,
)
else:
import sys
lowercase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 58
|
'''simple docstring'''
from __future__ import annotations
from typing import Any
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __A ) -> None:
_lowerCAmelCase =num_of_nodes
_lowerCAmelCase =[]
_lowerCAmelCase ={}
def UpperCamelCase__ ( self , __A , __A , __A ) -> None:
self.m_edges.append([u_node, v_node, weight] )
def UpperCamelCase__ ( self , __A ) -> int:
if self.m_component[u_node] == u_node:
return u_node
return self.find_component(self.m_component[u_node] )
def UpperCamelCase__ ( self , __A ) -> None:
if self.m_component[u_node] != u_node:
for k in self.m_component:
_lowerCAmelCase =self.find_component(__A )
def UpperCamelCase__ ( self , __A , __A , __A ) -> None:
if component_size[u_node] <= component_size[v_node]:
_lowerCAmelCase =v_node
component_size[v_node] += component_size[u_node]
self.set_component(__A )
elif component_size[u_node] >= component_size[v_node]:
_lowerCAmelCase =self.find_component(__A )
component_size[u_node] += component_size[v_node]
self.set_component(__A )
def UpperCamelCase__ ( self ) -> None:
_lowerCAmelCase =[]
_lowerCAmelCase =0
_lowerCAmelCase =[-1] * self.m_num_of_nodes
# A list of components (initialized to all of the nodes)
for node in range(self.m_num_of_nodes ):
self.m_component.update({node: node} )
component_size.append(1 )
_lowerCAmelCase =self.m_num_of_nodes
while num_of_components > 1:
for edge in self.m_edges:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase =edge
_lowerCAmelCase =self.m_component[u]
_lowerCAmelCase =self.m_component[v]
if u_component != v_component:
for component in (u_component, v_component):
if (
minimum_weight_edge[component] == -1
or minimum_weight_edge[component][2] > w
):
_lowerCAmelCase =[u, v, w]
for edge in minimum_weight_edge:
if isinstance(__A , __A ):
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase =edge
_lowerCAmelCase =self.m_component[u]
_lowerCAmelCase =self.m_component[v]
if u_component != v_component:
mst_weight += w
self.union(__A , __A , __A )
print(F'''Added edge [{u} - {v}]\nAdded weight: {w}\n''' )
num_of_components -= 1
_lowerCAmelCase =[-1] * self.m_num_of_nodes
print(F'''The total weight of the minimal spanning tree is: {mst_weight}''' )
def UpperCamelCase__ ( ):
'''simple docstring'''
if __name__ == "__main__":
import doctest
doctest.testmod()
| 58
| 1
|
'''simple docstring'''
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
lowercase_ = logging.getLogger(__name__)
lowercase_ = '''Hello world! cécé herlolip'''
lowercase_ = namedtuple(
'''BertAbsConfig''',
[
'''temp_dir''',
'''large''',
'''use_bert_emb''',
'''finetune_bert''',
'''encoder''',
'''share_emb''',
'''max_pos''',
'''enc_layers''',
'''enc_hidden_size''',
'''enc_heads''',
'''enc_ff_size''',
'''enc_dropout''',
'''dec_layers''',
'''dec_hidden_size''',
'''dec_heads''',
'''dec_ff_size''',
'''dec_dropout''',
],
)
def UpperCamelCase__ ( a__ , a__ ):
'''simple docstring'''
_lowerCAmelCase =BertAbsConfig(
temp_dir='.' , finetune_bert=a__ , large=a__ , share_emb=a__ , use_bert_emb=a__ , encoder='bert' , max_pos=5_1_2 , enc_layers=6 , enc_hidden_size=5_1_2 , enc_heads=8 , enc_ff_size=5_1_2 , enc_dropout=0.2 , dec_layers=6 , dec_hidden_size=7_6_8 , dec_heads=8 , dec_ff_size=2_0_4_8 , dec_dropout=0.2 , )
_lowerCAmelCase =torch.load(a__ , lambda a__ , a__ : storage )
_lowerCAmelCase =AbsSummarizer(a__ , torch.device('cpu' ) , a__ )
original.eval()
_lowerCAmelCase =BertAbsSummarizer(a__ , torch.device('cpu' ) )
new_model.eval()
# -------------------
# Convert the weights
# -------------------
logging.info('convert the model' )
new_model.bert.load_state_dict(original.bert.state_dict() )
new_model.decoder.load_state_dict(original.decoder.state_dict() )
new_model.generator.load_state_dict(original.generator.state_dict() )
# ----------------------------------
# Make sure the outpus are identical
# ----------------------------------
logging.info('Make sure that the models\' outputs are identical' )
_lowerCAmelCase =BertTokenizer.from_pretrained('bert-base-uncased' )
# prepare the model inputs
_lowerCAmelCase =tokenizer.encode('This is sample éàalj\'-.' )
encoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(a__ )) )
_lowerCAmelCase =torch.tensor(a__ ).unsqueeze(0 )
_lowerCAmelCase =tokenizer.encode('This is sample 3 éàalj\'-.' )
decoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(a__ )) )
_lowerCAmelCase =torch.tensor(a__ ).unsqueeze(0 )
# failsafe to make sure the weights reset does not affect the
# loaded weights.
assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight ) ) == 0
# forward pass
_lowerCAmelCase =encoder_input_ids
_lowerCAmelCase =decoder_input_ids
_lowerCAmelCase =_lowerCAmelCase =None
_lowerCAmelCase =None
_lowerCAmelCase =_lowerCAmelCase =None
_lowerCAmelCase =_lowerCAmelCase =None
_lowerCAmelCase =None
# The original model does not apply the geneator layer immediatly but rather in
# the beam search (where it combines softmax + linear layer). Since we already
# apply the softmax in our generation process we only apply the linear layer here.
# We make sure that the outputs of the full stack are identical
_lowerCAmelCase =original(a__ , a__ , a__ , a__ , a__ , a__ , a__ )[0]
_lowerCAmelCase =original.generator(a__ )
_lowerCAmelCase =new_model(
a__ , a__ , a__ , a__ , a__ )[0]
_lowerCAmelCase =new_model.generator(a__ )
_lowerCAmelCase =torch.max(torch.abs(output_converted_model - output_original_model ) ).item()
print('Maximum absolute difference beween weights: {:.2f}'.format(a__ ) )
_lowerCAmelCase =torch.max(torch.abs(output_converted_generator - output_original_generator ) ).item()
print('Maximum absolute difference beween weights: {:.2f}'.format(a__ ) )
_lowerCAmelCase =torch.allclose(a__ , a__ , atol=1E-3 )
if are_identical:
logging.info('all weights are equal up to 1e-3' )
else:
raise ValueError('the weights are different. The new model is likely different from the original one.' )
# The model has been saved with torch.save(model) and this is bound to the exact
# directory structure. We save the state_dict instead.
logging.info('saving the model\'s state dictionary' )
torch.save(
new_model.state_dict() , './bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin' )
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
parser.add_argument(
'''--bertabs_checkpoint_path''',
default=None,
type=str,
required=True,
help='''Path the official PyTorch dump.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default=None,
type=str,
required=True,
help='''Path to the output PyTorch model.''',
)
lowercase_ = parser.parse_args()
convert_bertabs_checkpoints(
args.bertabs_checkpoint_path,
args.pytorch_dump_folder_path,
)
| 58
|
'''simple docstring'''
from PIL import Image
def UpperCamelCase__ ( a__ , a__ ):
'''simple docstring'''
def brightness(a__ ) -> float:
return 1_2_8 + level + (c - 1_2_8)
if not -255.0 <= level <= 255.0:
raise ValueError('level must be between -255.0 (black) and 255.0 (white)' )
return img.point(a__ )
if __name__ == "__main__":
# Load image
with Image.open('''image_data/lena.jpg''') as img:
# Change brightness to 100
lowercase_ = change_brightness(img, 100)
brigt_img.save('''image_data/lena_brightness.png''', format='''png''')
| 58
| 1
|
'''simple docstring'''
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging
lowercase_ = logging.get_logger(__name__) # pylint: disable=invalid-name
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
def __init__( self , __A , __A , __A , __A , __A , __A , __A , ) -> int:
super().__init__()
self.register_modules(
vae=__A , text_encoder=__A , tokenizer=__A , unet=__A , scheduler=__A , safety_checker=__A , feature_extractor=__A , )
def UpperCamelCase__ ( self , __A = "auto" ) -> List[str]:
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
_lowerCAmelCase =self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(__A )
def UpperCamelCase__ ( self ) -> int:
self.enable_attention_slicing(__A )
@torch.no_grad()
def __call__( self , __A , __A = 512 , __A = 512 , __A = 50 , __A = 7.5 , __A = None , __A = 1 , __A = 0.0 , __A = None , __A = None , __A = "pil" , __A = True , __A = None , __A = 1 , __A = None , **__A , ) -> Tuple:
if isinstance(__A , __A ):
_lowerCAmelCase =1
elif isinstance(__A , __A ):
_lowerCAmelCase =len(__A )
else:
raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(__A )}''' )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(F'''`height` and `width` have to be divisible by 8 but are {height} and {width}.''' )
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(__A , __A ) or callback_steps <= 0)
):
raise ValueError(
F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
F''' {type(__A )}.''' )
# get prompt text embeddings
_lowerCAmelCase =self.tokenizer(
__A , padding='max_length' , max_length=self.tokenizer.model_max_length , return_tensors='pt' , )
_lowerCAmelCase =text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
_lowerCAmelCase =self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
logger.warning(
'The following part of your input was truncated because CLIP can only handle sequences up to'
F''' {self.tokenizer.model_max_length} tokens: {removed_text}''' )
_lowerCAmelCase =text_input_ids[:, : self.tokenizer.model_max_length]
if text_embeddings is None:
_lowerCAmelCase =self.text_encoder(text_input_ids.to(self.device ) )[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase =text_embeddings.shape
_lowerCAmelCase =text_embeddings.repeat(1 , __A , 1 )
_lowerCAmelCase =text_embeddings.view(bs_embed * num_images_per_prompt , __A , -1 )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
_lowerCAmelCase =guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
_lowerCAmelCase =42
if negative_prompt is None:
_lowerCAmelCase =['']
elif type(__A ) is not type(__A ):
raise TypeError(
F'''`negative_prompt` should be the same type to `prompt`, but got {type(__A )} !='''
F''' {type(__A )}.''' )
elif isinstance(__A , __A ):
_lowerCAmelCase =[negative_prompt]
elif batch_size != len(__A ):
raise ValueError(
F'''`negative_prompt`: {negative_prompt} has batch size {len(__A )}, but `prompt`:'''
F''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
' the batch size of `prompt`.' )
else:
_lowerCAmelCase =negative_prompt
_lowerCAmelCase =text_input_ids.shape[-1]
_lowerCAmelCase =self.tokenizer(
__A , padding='max_length' , max_length=__A , truncation=__A , return_tensors='pt' , )
_lowerCAmelCase =self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
_lowerCAmelCase =uncond_embeddings.shape[1]
_lowerCAmelCase =uncond_embeddings.repeat(__A , __A , 1 )
_lowerCAmelCase =uncond_embeddings.view(batch_size * num_images_per_prompt , __A , -1 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
_lowerCAmelCase =torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
_lowerCAmelCase =(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
_lowerCAmelCase =(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
_lowerCAmelCase =text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not exist on mps
_lowerCAmelCase =torch.randn(
__A , generator=__A , device='cpu' , dtype=__A ).to(self.device )
_lowerCAmelCase =torch.randn(__A , generator=__A , device='cpu' , dtype=__A ).to(
self.device )
else:
_lowerCAmelCase =torch.randn(
__A , generator=__A , device=self.device , dtype=__A )
_lowerCAmelCase =torch.randn(__A , generator=__A , device=self.device , dtype=__A )
else:
if latents_reference.shape != latents_shape:
raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''' )
_lowerCAmelCase =latents_reference.to(self.device )
_lowerCAmelCase =latents.to(self.device )
# This is the key part of the pipeline where we
# try to ensure that the generated images w/ the same seed
# but different sizes actually result in similar images
_lowerCAmelCase =(latents_shape[3] - latents_shape_reference[3]) // 2
_lowerCAmelCase =(latents_shape[2] - latents_shape_reference[2]) // 2
_lowerCAmelCase =latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
_lowerCAmelCase =latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
_lowerCAmelCase =0 if dx < 0 else dx
_lowerCAmelCase =0 if dy < 0 else dy
_lowerCAmelCase =max(-dx , 0 )
_lowerCAmelCase =max(-dy , 0 )
# import pdb
# pdb.set_trace()
_lowerCAmelCase =latents_reference[:, :, dy : dy + h, dx : dx + w]
# set timesteps
self.scheduler.set_timesteps(__A )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
_lowerCAmelCase =self.scheduler.timesteps.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
_lowerCAmelCase =latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
_lowerCAmelCase ='eta' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
_lowerCAmelCase ={}
if accepts_eta:
_lowerCAmelCase =eta
for i, t in enumerate(self.progress_bar(__A ) ):
# expand the latents if we are doing classifier free guidance
_lowerCAmelCase =torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
_lowerCAmelCase =self.scheduler.scale_model_input(__A , __A )
# predict the noise residual
_lowerCAmelCase =self.unet(__A , __A , encoder_hidden_states=__A ).sample
# perform guidance
if do_classifier_free_guidance:
_lowerCAmelCase , _lowerCAmelCase =noise_pred.chunk(2 )
_lowerCAmelCase =noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
_lowerCAmelCase =self.scheduler.step(__A , __A , __A , **__A ).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(__A , __A , __A )
_lowerCAmelCase =1 / 0.18_215 * latents
_lowerCAmelCase =self.vae.decode(__A ).sample
_lowerCAmelCase =(image / 2 + 0.5).clamp(0 , 1 )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
_lowerCAmelCase =image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if self.safety_checker is not None:
_lowerCAmelCase =self.feature_extractor(self.numpy_to_pil(__A ) , return_tensors='pt' ).to(
self.device )
_lowerCAmelCase , _lowerCAmelCase =self.safety_checker(
images=__A , clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype ) )
else:
_lowerCAmelCase =None
if output_type == "pil":
_lowerCAmelCase =self.numpy_to_pil(__A )
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=__A , nsfw_content_detected=__A )
| 58
|
'''simple docstring'''
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / '''utils'''))
from test_module.custom_configuration import CustomConfig # noqa E402
lowercase_ = {
'''return_dict''': False,
'''output_hidden_states''': True,
'''output_attentions''': True,
'''torchscript''': True,
'''torch_dtype''': '''float16''',
'''use_bfloat16''': True,
'''tf_legacy_loss''': True,
'''pruned_heads''': {'''a''': 1},
'''tie_word_embeddings''': False,
'''is_decoder''': True,
'''cross_attention_hidden_size''': 128,
'''add_cross_attention''': True,
'''tie_encoder_decoder''': True,
'''max_length''': 50,
'''min_length''': 3,
'''do_sample''': True,
'''early_stopping''': True,
'''num_beams''': 3,
'''num_beam_groups''': 3,
'''diversity_penalty''': 0.5,
'''temperature''': 2.0,
'''top_k''': 10,
'''top_p''': 0.7,
'''typical_p''': 0.2,
'''repetition_penalty''': 0.8,
'''length_penalty''': 0.8,
'''no_repeat_ngram_size''': 5,
'''encoder_no_repeat_ngram_size''': 5,
'''bad_words_ids''': [1, 2, 3],
'''num_return_sequences''': 3,
'''chunk_size_feed_forward''': 5,
'''output_scores''': True,
'''return_dict_in_generate''': True,
'''forced_bos_token_id''': 2,
'''forced_eos_token_id''': 3,
'''remove_invalid_values''': True,
'''architectures''': ['''BertModel'''],
'''finetuning_task''': '''translation''',
'''id2label''': {0: '''label'''},
'''label2id''': {'''label''': '''0'''},
'''tokenizer_class''': '''BertTokenizerFast''',
'''prefix''': '''prefix''',
'''bos_token_id''': 6,
'''pad_token_id''': 7,
'''eos_token_id''': 8,
'''sep_token_id''': 9,
'''decoder_start_token_id''': 10,
'''exponential_decay_length_penalty''': (5, 1.01),
'''suppress_tokens''': [0, 1],
'''begin_suppress_tokens''': 2,
'''task_specific_params''': {'''translation''': '''some_params'''},
'''problem_type''': '''regression''',
}
@is_staging_test
class SCREAMING_SNAKE_CASE ( unittest.TestCase):
"""simple docstring"""
@classmethod
def UpperCamelCase__ ( cls ) -> Optional[Any]:
_lowerCAmelCase =TOKEN
HfFolder.save_token(__A )
@classmethod
def UpperCamelCase__ ( cls ) -> List[str]:
try:
delete_repo(token=cls._token , repo_id='test-config' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='valid_org/test-config-org' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='test-dynamic-config' )
except HTTPError:
pass
def UpperCamelCase__ ( self ) -> str:
_lowerCAmelCase =BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
config.push_to_hub('test-config' , use_auth_token=self._token )
_lowerCAmelCase =BertConfig.from_pretrained(F'''{USER}/test-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__A , getattr(__A , __A ) )
# Reset repo
delete_repo(token=self._token , repo_id='test-config' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(__A , repo_id='test-config' , push_to_hub=__A , use_auth_token=self._token )
_lowerCAmelCase =BertConfig.from_pretrained(F'''{USER}/test-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__A , getattr(__A , __A ) )
def UpperCamelCase__ ( self ) -> Dict:
_lowerCAmelCase =BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
config.push_to_hub('valid_org/test-config-org' , use_auth_token=self._token )
_lowerCAmelCase =BertConfig.from_pretrained('valid_org/test-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__A , getattr(__A , __A ) )
# Reset repo
delete_repo(token=self._token , repo_id='valid_org/test-config-org' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
__A , repo_id='valid_org/test-config-org' , push_to_hub=__A , use_auth_token=self._token )
_lowerCAmelCase =BertConfig.from_pretrained('valid_org/test-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__A , getattr(__A , __A ) )
def UpperCamelCase__ ( self ) -> List[str]:
CustomConfig.register_for_auto_class()
_lowerCAmelCase =CustomConfig(attribute=42 )
config.push_to_hub('test-dynamic-config' , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(config.auto_map , {'AutoConfig': 'custom_configuration.CustomConfig'} )
_lowerCAmelCase =AutoConfig.from_pretrained(F'''{USER}/test-dynamic-config''' , trust_remote_code=__A )
# Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
self.assertEqual(new_config.__class__.__name__ , 'CustomConfig' )
self.assertEqual(new_config.attribute , 42 )
class SCREAMING_SNAKE_CASE ( unittest.TestCase):
"""simple docstring"""
def UpperCamelCase__ ( self ) -> List[Any]:
_lowerCAmelCase =GPTaConfig()
# attempt to modify each of int/float/bool/str config records and verify they were updated
_lowerCAmelCase =c.n_embd + 1 # int
_lowerCAmelCase =c.resid_pdrop + 1.0 # float
_lowerCAmelCase =not c.scale_attn_weights # bool
_lowerCAmelCase =c.summary_type + 'foo' # str
c.update_from_string(
F'''n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}''' )
self.assertEqual(__A , c.n_embd , 'mismatch for key: n_embd' )
self.assertEqual(__A , c.resid_pdrop , 'mismatch for key: resid_pdrop' )
self.assertEqual(__A , c.scale_attn_weights , 'mismatch for key: scale_attn_weights' )
self.assertEqual(__A , c.summary_type , 'mismatch for key: summary_type' )
def UpperCamelCase__ ( self ) -> List[str]:
_lowerCAmelCase =PretrainedConfig()
_lowerCAmelCase =[key for key in base_config.__dict__ if key not in config_common_kwargs]
# If this part of the test fails, you have arguments to addin config_common_kwargs above.
self.assertListEqual(
__A , ['is_encoder_decoder', '_name_or_path', '_commit_hash', 'transformers_version'] )
_lowerCAmelCase =[key for key, value in config_common_kwargs.items() if value == getattr(__A , __A )]
if len(__A ) > 0:
raise ValueError(
'The following keys are set with the default values in'
' `test_configuration_common.config_common_kwargs` pick another value for them:'
F''' {', '.join(__A )}.''' )
def UpperCamelCase__ ( self ) -> Optional[int]:
with self.assertRaises(__A ):
# config is in subfolder, the following should not work without specifying the subfolder
_lowerCAmelCase =BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder' )
_lowerCAmelCase =BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder' , subfolder='bert' )
self.assertIsNotNone(__A )
def UpperCamelCase__ ( self ) -> List[str]:
# A mock response for an HTTP head request to emulate server down
_lowerCAmelCase =mock.Mock()
_lowerCAmelCase =500
_lowerCAmelCase ={}
_lowerCAmelCase =HTTPError
_lowerCAmelCase ={}
# Download this model to make sure it's in the cache.
_lowerCAmelCase =BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert' )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch('requests.Session.request' , return_value=__A ) as mock_head:
_lowerCAmelCase =BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert' )
# This check we did call the fake head request
mock_head.assert_called()
def UpperCamelCase__ ( self ) -> Optional[int]:
# This test is for deprecated behavior and can be removed in v5
_lowerCAmelCase =BertConfig.from_pretrained(
'https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json' )
def UpperCamelCase__ ( self ) -> Any:
_lowerCAmelCase =AutoConfig.from_pretrained('bert-base-cased' )
_lowerCAmelCase =['config.4.0.0.json']
with tempfile.TemporaryDirectory() as tmp_dir:
configuration.save_pretrained(__A )
_lowerCAmelCase =2
json.dump(configuration.to_dict() , open(os.path.join(__A , 'config.4.0.0.json' ) , 'w' ) )
# This should pick the new configuration file as the version of Transformers is > 4.0.0
_lowerCAmelCase =AutoConfig.from_pretrained(__A )
self.assertEqual(new_configuration.hidden_size , 2 )
# Will need to be adjusted if we reach v42 and this test is still here.
# Should pick the old configuration file as the version of Transformers is < 4.42.0
_lowerCAmelCase =['config.42.0.0.json']
_lowerCAmelCase =768
configuration.save_pretrained(__A )
shutil.move(os.path.join(__A , 'config.4.0.0.json' ) , os.path.join(__A , 'config.42.0.0.json' ) )
_lowerCAmelCase =AutoConfig.from_pretrained(__A )
self.assertEqual(new_configuration.hidden_size , 768 )
def UpperCamelCase__ ( self ) -> Any:
# This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
_lowerCAmelCase ='hf-internal-testing/test-two-configs'
import transformers as new_transformers
_lowerCAmelCase ='v4.0.0'
_lowerCAmelCase , _lowerCAmelCase =new_transformers.models.auto.AutoConfig.from_pretrained(
__A , return_unused_kwargs=__A )
self.assertEqual(new_configuration.hidden_size , 2 )
# This checks `_configuration_file` ia not kept in the kwargs by mistake.
self.assertDictEqual(__A , {} )
# Testing an older version by monkey-patching the version in the module it's used.
import transformers as old_transformers
_lowerCAmelCase ='v3.0.0'
_lowerCAmelCase =old_transformers.models.auto.AutoConfig.from_pretrained(__A )
self.assertEqual(old_configuration.hidden_size , 768 )
| 58
| 1
|
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
'''EleutherAI/gpt-j-6B''': '''https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json''',
# See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : Union[str, Any] = 'gptj'
lowercase : Tuple = {
'max_position_embeddings': 'n_positions',
'hidden_size': 'n_embd',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__( self , __A=5_0400 , __A=2048 , __A=4096 , __A=28 , __A=16 , __A=64 , __A=None , __A="gelu_new" , __A=0.0 , __A=0.0 , __A=0.0 , __A=1E-5 , __A=0.02 , __A=True , __A=5_0256 , __A=5_0256 , __A=False , **__A , ) -> Union[str, Any]:
_lowerCAmelCase =vocab_size
_lowerCAmelCase =n_positions
_lowerCAmelCase =n_embd
_lowerCAmelCase =n_layer
_lowerCAmelCase =n_head
_lowerCAmelCase =n_inner
_lowerCAmelCase =rotary_dim
_lowerCAmelCase =activation_function
_lowerCAmelCase =resid_pdrop
_lowerCAmelCase =embd_pdrop
_lowerCAmelCase =attn_pdrop
_lowerCAmelCase =layer_norm_epsilon
_lowerCAmelCase =initializer_range
_lowerCAmelCase =use_cache
_lowerCAmelCase =bos_token_id
_lowerCAmelCase =eos_token_id
super().__init__(
bos_token_id=__A , eos_token_id=__A , tie_word_embeddings=__A , **__A )
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
def __init__( self , __A , __A = "default" , __A = None , __A = False , ) -> Dict:
super().__init__(__A , task=__A , patching_specs=__A , use_past=__A )
if not getattr(self._config , 'pad_token_id' , __A ):
# TODO: how to do that better?
_lowerCAmelCase =0
@property
def UpperCamelCase__ ( self ) -> Mapping[str, Mapping[int, str]]:
_lowerCAmelCase =OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}} )
if self.use_past:
self.fill_with_past_key_values_(__A , direction='inputs' )
_lowerCAmelCase ={0: 'batch', 1: 'past_sequence + sequence'}
else:
_lowerCAmelCase ={0: 'batch', 1: 'sequence'}
return common_inputs
@property
def UpperCamelCase__ ( self ) -> int:
return self._config.n_layer
@property
def UpperCamelCase__ ( self ) -> int:
return self._config.n_head
def UpperCamelCase__ ( self , __A , __A = -1 , __A = -1 , __A = False , __A = None , ) -> Mapping[str, Any]:
_lowerCAmelCase =super(__A , self ).generate_dummy_inputs(
__A , batch_size=__A , seq_length=__A , is_pair=__A , framework=__A )
# We need to order the input in the way they appears in the forward()
_lowerCAmelCase =OrderedDict({'input_ids': common_inputs['input_ids']} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.' )
else:
import torch
_lowerCAmelCase , _lowerCAmelCase =common_inputs['input_ids'].shape
# Not using the same length for past_key_values
_lowerCAmelCase =seqlen + 2
_lowerCAmelCase =(
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
_lowerCAmelCase =[
(torch.zeros(__A ), torch.zeros(__A )) for _ in range(self.num_layers )
]
_lowerCAmelCase =common_inputs['attention_mask']
if self.use_past:
_lowerCAmelCase =ordered_inputs['attention_mask'].dtype
_lowerCAmelCase =torch.cat(
[ordered_inputs['attention_mask'], torch.ones(__A , __A , dtype=__A )] , dim=1 )
return ordered_inputs
@property
def UpperCamelCase__ ( self ) -> int:
return 13
| 58
|
'''simple docstring'''
from __future__ import annotations
lowercase_ = 10
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
_lowerCAmelCase =1
_lowerCAmelCase =max(a__ )
while placement <= max_digit:
# declare and initialize empty buckets
_lowerCAmelCase =[[] for _ in range(a__ )]
# split list_of_ints between the buckets
for i in list_of_ints:
_lowerCAmelCase =int((i / placement) % RADIX )
buckets[tmp].append(a__ )
# put each buckets' contents into list_of_ints
_lowerCAmelCase =0
for b in range(a__ ):
for i in buckets[b]:
_lowerCAmelCase =i
a += 1
# move to next
placement *= RADIX
return list_of_ints
if __name__ == "__main__":
import doctest
doctest.testmod()
| 58
| 1
|
'''simple docstring'''
import math
import sys
import cva
import numpy as np
def UpperCamelCase__ ( a__ , a__ ):
'''simple docstring'''
_lowerCAmelCase =math.sqrt(a__ )
_lowerCAmelCase =1 / (sigma * math.sqrt(2 * math.pi ))
return cons * np.exp(-((img / sigma) ** 2) * 0.5 )
def UpperCamelCase__ ( a__ , a__ , a__ , a__ ):
'''simple docstring'''
_lowerCAmelCase =kernel_size // 2
return img[x - half : x + half + 1, y - half : y + half + 1]
def UpperCamelCase__ ( a__ , a__ ):
'''simple docstring'''
_lowerCAmelCase =np.zeros((kernel_size, kernel_size) )
for i in range(0 , a__ ):
for j in range(0 , a__ ):
_lowerCAmelCase =math.sqrt(
abs(i - kernel_size // 2 ) ** 2 + abs(j - kernel_size // 2 ) ** 2 )
return vec_gaussian(a__ , a__ )
def UpperCamelCase__ ( a__ , a__ , a__ , a__ , ):
'''simple docstring'''
_lowerCAmelCase =np.zeros(img.shape )
_lowerCAmelCase =get_gauss_kernel(a__ , a__ )
_lowerCAmelCase , _lowerCAmelCase =img.shape
for i in range(kernel_size // 2 , size_x - kernel_size // 2 ):
for j in range(kernel_size // 2 , size_y - kernel_size // 2 ):
_lowerCAmelCase =get_slice(a__ , a__ , a__ , a__ )
_lowerCAmelCase =img_s - img_s[kernel_size // 2, kernel_size // 2]
_lowerCAmelCase =vec_gaussian(a__ , a__ )
_lowerCAmelCase =np.multiply(a__ , a__ )
_lowerCAmelCase =np.multiply(a__ , a__ )
_lowerCAmelCase =np.sum(a__ ) / np.sum(a__ )
_lowerCAmelCase =val
return imga
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
_lowerCAmelCase =args[1] if args[1:] else '../image_data/lena.jpg'
_lowerCAmelCase =float(args[2] ) if args[2:] else 1.0
_lowerCAmelCase =float(args[3] ) if args[3:] else 1.0
if args[4:]:
_lowerCAmelCase =int(args[4] )
_lowerCAmelCase =kernel_size + abs(kernel_size % 2 - 1 )
else:
_lowerCAmelCase =5
return filename, spatial_variance, intensity_variance, kernel_size
if __name__ == "__main__":
lowercase_ , lowercase_ , lowercase_ , lowercase_ = parse_args(sys.argv)
lowercase_ = cva.imread(filename, 0)
cva.imshow('''input image''', img)
lowercase_ = img / 255
lowercase_ = out.astype('''float32''')
lowercase_ = bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
lowercase_ = out * 255
lowercase_ = np.uinta(out)
cva.imshow('''output image''', out)
cva.waitKey(0)
cva.destroyAllWindows()
| 58
|
'''simple docstring'''
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
FEATURE_EXTRACTOR_NAME,
FLAX_WEIGHTS_NAME,
HF_MODULES_CACHE,
HUGGINGFACE_CO_PREFIX,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
MODEL_CARD_NAME,
MULTIPLE_CHOICE_DUMMY_INPUTS,
PYTORCH_PRETRAINED_BERT_CACHE,
PYTORCH_TRANSFORMERS_CACHE,
S3_BUCKET_PREFIX,
SENTENCEPIECE_UNDERLINE,
SPIECE_UNDERLINE,
TF2_WEIGHTS_NAME,
TF_WEIGHTS_NAME,
TORCH_FX_REQUIRED_VERSION,
TRANSFORMERS_CACHE,
TRANSFORMERS_DYNAMIC_MODULE_NAME,
USE_JAX,
USE_TF,
USE_TORCH,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
ContextManagers,
DummyObject,
EntryNotFoundError,
ExplicitEnum,
ModelOutput,
PaddingStrategy,
PushToHubMixin,
RepositoryNotFoundError,
RevisionNotFoundError,
TensorType,
_LazyModule,
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
cached_property,
copy_func,
default_cache_path,
define_sagemaker_information,
get_cached_models,
get_file_from_repo,
get_full_repo_name,
get_torch_version,
has_file,
http_user_agent,
is_apex_available,
is_bsa_available,
is_coloredlogs_available,
is_datasets_available,
is_detectrona_available,
is_faiss_available,
is_flax_available,
is_ftfy_available,
is_in_notebook,
is_ipex_available,
is_librosa_available,
is_offline_mode,
is_onnx_available,
is_pandas_available,
is_phonemizer_available,
is_protobuf_available,
is_psutil_available,
is_pyanvml_available,
is_pyctcdecode_available,
is_pytesseract_available,
is_pytorch_quantization_available,
is_rjieba_available,
is_sagemaker_dp_enabled,
is_sagemaker_mp_enabled,
is_scipy_available,
is_sentencepiece_available,
is_seqio_available,
is_sklearn_available,
is_soundfile_availble,
is_spacy_available,
is_speech_available,
is_tensor,
is_tensorflow_probability_available,
is_tfaonnx_available,
is_tf_available,
is_timm_available,
is_tokenizers_available,
is_torch_available,
is_torch_bfaa_available,
is_torch_cuda_available,
is_torch_fx_available,
is_torch_fx_proxy,
is_torch_mps_available,
is_torch_tfaa_available,
is_torch_tpu_available,
is_torchaudio_available,
is_training_run_on_sagemaker,
is_vision_available,
replace_return_docstrings,
requires_backends,
to_numpy,
to_py_obj,
torch_only_method,
)
| 58
| 1
|
'''simple docstring'''
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
lowercase_ = get_tests_dir('''fixtures/spiece.model''')
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE ( __lowercase , unittest.TestCase):
"""simple docstring"""
lowercase : List[Any] = DebertaVaTokenizer
lowercase : List[Any] = DebertaVaTokenizerFast
lowercase : Optional[int] = True
lowercase : Union[str, Any] = True
def UpperCamelCase__ ( self ) -> str:
super().setUp()
# We have a SentencePiece fixture for testing
_lowerCAmelCase =DebertaVaTokenizer(__A , unk_token='<unk>' )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCamelCase__ ( self , __A ) -> Optional[int]:
_lowerCAmelCase ='this is a test'
_lowerCAmelCase ='this is a test'
return input_text, output_text
def UpperCamelCase__ ( self ) -> int:
_lowerCAmelCase ='<pad>'
_lowerCAmelCase =0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__A ) , __A )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__A ) , __A )
def UpperCamelCase__ ( self ) -> int:
_lowerCAmelCase =list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '<pad>' )
self.assertEqual(vocab_keys[1] , '<unk>' )
self.assertEqual(vocab_keys[-1] , '[PAD]' )
self.assertEqual(len(__A ) , 3_0001 )
def UpperCamelCase__ ( self ) -> List[str]:
self.assertEqual(self.get_tokenizer().vocab_size , 3_0000 )
def UpperCamelCase__ ( self ) -> Any:
# fmt: off
_lowerCAmelCase =' \tHeLLo!how \n Are yoU? '
_lowerCAmelCase =['▁hello', '!', 'how', '▁are', '▁you', '?']
# fmt: on
_lowerCAmelCase =DebertaVaTokenizer(__A , do_lower_case=__A )
_lowerCAmelCase =tokenizer.convert_ids_to_tokens(tokenizer.encode(__A , add_special_tokens=__A ) )
self.assertListEqual(__A , __A )
_lowerCAmelCase =DebertaVaTokenizerFast(__A , do_lower_case=__A )
_lowerCAmelCase =rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(__A , add_special_tokens=__A ) )
self.assertListEqual(__A , __A )
@unittest.skip('There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.' )
def UpperCamelCase__ ( self ) -> Tuple:
pass
@unittest.skip('There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.' )
def UpperCamelCase__ ( self ) -> Tuple:
pass
def UpperCamelCase__ ( self ) -> List[Any]:
# fmt: off
_lowerCAmelCase ='I was born in 92000, and this is falsé.'
_lowerCAmelCase =['▁', '<unk>', '▁was', '▁born', '▁in', '▁9', '2000', '▁', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '▁', '.', ]
# fmt: on
_lowerCAmelCase =DebertaVaTokenizer(__A , split_by_punct=__A )
_lowerCAmelCase =tokenizer.convert_ids_to_tokens(tokenizer.encode(__A , add_special_tokens=__A ) )
self.assertListEqual(__A , __A )
_lowerCAmelCase =DebertaVaTokenizerFast(__A , split_by_punct=__A )
_lowerCAmelCase =rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(__A , add_special_tokens=__A ) )
self.assertListEqual(__A , __A )
def UpperCamelCase__ ( self ) -> Tuple:
# fmt: off
_lowerCAmelCase ='I was born in 92000, and this is falsé.'
_lowerCAmelCase =['▁i', '▁was', '▁born', '▁in', '▁9', '2000', '▁', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '▁', '.', ]
# fmt: on
_lowerCAmelCase =DebertaVaTokenizer(__A , do_lower_case=__A , split_by_punct=__A )
_lowerCAmelCase =tokenizer.convert_ids_to_tokens(tokenizer.encode(__A , add_special_tokens=__A ) )
self.assertListEqual(__A , __A )
_lowerCAmelCase =DebertaVaTokenizerFast(__A , do_lower_case=__A , split_by_punct=__A )
_lowerCAmelCase =rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(__A , add_special_tokens=__A ) )
self.assertListEqual(__A , __A )
def UpperCamelCase__ ( self ) -> Any:
# fmt: off
_lowerCAmelCase ='I was born in 92000, and this is falsé.'
_lowerCAmelCase =['▁i', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.', ]
# fmt: on
_lowerCAmelCase =DebertaVaTokenizer(__A , do_lower_case=__A , split_by_punct=__A )
_lowerCAmelCase =tokenizer.convert_ids_to_tokens(tokenizer.encode(__A , add_special_tokens=__A ) )
self.assertListEqual(__A , __A )
_lowerCAmelCase =DebertaVaTokenizerFast(__A , do_lower_case=__A , split_by_punct=__A )
_lowerCAmelCase =rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(__A , add_special_tokens=__A ) )
self.assertListEqual(__A , __A )
def UpperCamelCase__ ( self ) -> Union[str, Any]:
# fmt: off
_lowerCAmelCase ='I was born in 92000, and this is falsé.'
_lowerCAmelCase =['▁', '<unk>', '▁was', '▁born', '▁in', '▁9', '2000', '▁', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '▁', '.', ]
# fmt: on
_lowerCAmelCase =DebertaVaTokenizer(__A , do_lower_case=__A , split_by_punct=__A )
_lowerCAmelCase =tokenizer.convert_ids_to_tokens(tokenizer.encode(__A , add_special_tokens=__A ) )
self.assertListEqual(__A , __A )
_lowerCAmelCase =DebertaVaTokenizerFast(__A , do_lower_case=__A , split_by_punct=__A )
_lowerCAmelCase =rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(__A , add_special_tokens=__A ) )
self.assertListEqual(__A , __A )
def UpperCamelCase__ ( self ) -> int:
# fmt: off
_lowerCAmelCase =' \tHeLLo!how \n Are yoU? '
_lowerCAmelCase =['▁', '<unk>', 'e', '<unk>', 'o', '!', 'how', '▁', '<unk>', 're', '▁yo', '<unk>', '?']
# fmt: on
_lowerCAmelCase =DebertaVaTokenizer(__A , do_lower_case=__A , split_by_punct=__A )
_lowerCAmelCase =tokenizer.convert_ids_to_tokens(tokenizer.encode(__A , add_special_tokens=__A ) )
self.assertListEqual(__A , __A )
_lowerCAmelCase =DebertaVaTokenizerFast(__A , do_lower_case=__A , split_by_punct=__A )
_lowerCAmelCase =rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(__A , add_special_tokens=__A ) )
self.assertListEqual(__A , __A )
def UpperCamelCase__ ( self ) -> List[Any]:
_lowerCAmelCase =self.get_tokenizer()
_lowerCAmelCase =self.get_rust_tokenizer()
_lowerCAmelCase ='I was born in 92000, and this is falsé.'
_lowerCAmelCase =tokenizer.convert_ids_to_tokens(tokenizer.encode(__A , add_special_tokens=__A ) )
_lowerCAmelCase =rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(__A , add_special_tokens=__A ) )
self.assertListEqual(__A , __A )
_lowerCAmelCase =tokenizer.encode(__A , add_special_tokens=__A )
_lowerCAmelCase =rust_tokenizer.encode(__A , add_special_tokens=__A )
self.assertListEqual(__A , __A )
_lowerCAmelCase =self.get_rust_tokenizer()
_lowerCAmelCase =tokenizer.encode(__A )
_lowerCAmelCase =rust_tokenizer.encode(__A )
self.assertListEqual(__A , __A )
def UpperCamelCase__ ( self ) -> Dict:
_lowerCAmelCase ='This is a test'
_lowerCAmelCase =[13, 1, 4398, 25, 21, 1289]
_lowerCAmelCase =['▁', 'T', 'his', '▁is', '▁a', '▁test']
_lowerCAmelCase =['▁', '<unk>', 'his', '▁is', '▁a', '▁test']
_lowerCAmelCase =DebertaVaTokenizer(__A , keep_accents=__A )
_lowerCAmelCase =DebertaVaTokenizerFast(__A , keep_accents=__A )
_lowerCAmelCase =tokenizer.encode(__A , add_special_tokens=__A )
self.assertListEqual(__A , __A )
_lowerCAmelCase =tokenizer.tokenize(__A )
self.assertListEqual(__A , __A )
_lowerCAmelCase =tokenizer.convert_ids_to_tokens(__A )
self.assertListEqual(__A , __A )
_lowerCAmelCase =rust_tokenizer.encode(__A , add_special_tokens=__A )
self.assertListEqual(__A , __A )
_lowerCAmelCase =rust_tokenizer.tokenize(__A )
self.assertListEqual(__A , __A )
_lowerCAmelCase =rust_tokenizer.convert_ids_to_tokens(__A )
self.assertListEqual(__A , __A )
# fmt: off
_lowerCAmelCase ='I was born in 92000, and this is falsé.'
_lowerCAmelCase =[13, 1, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]
_lowerCAmelCase =['▁', 'I', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', 'é', '.', ]
_lowerCAmelCase =['▁', '<unk>', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.', ]
# fmt: on
_lowerCAmelCase =tokenizer.encode(__A , add_special_tokens=__A )
self.assertListEqual(__A , __A )
_lowerCAmelCase =tokenizer.tokenize(__A )
self.assertListEqual(__A , __A )
_lowerCAmelCase =tokenizer.convert_ids_to_tokens(__A )
self.assertListEqual(__A , __A )
_lowerCAmelCase =rust_tokenizer.encode(__A , add_special_tokens=__A )
self.assertListEqual(__A , __A )
_lowerCAmelCase =rust_tokenizer.tokenize(__A )
self.assertListEqual(__A , __A )
_lowerCAmelCase =rust_tokenizer.convert_ids_to_tokens(__A )
self.assertListEqual(__A , __A )
def UpperCamelCase__ ( self ) -> Dict:
_lowerCAmelCase =DebertaVaTokenizer(__A )
_lowerCAmelCase =tokenizer.encode('sequence builders' )
_lowerCAmelCase =tokenizer.encode('multi-sequence build' )
_lowerCAmelCase =tokenizer.build_inputs_with_special_tokens(__A )
_lowerCAmelCase =tokenizer.build_inputs_with_special_tokens(__A , __A )
self.assertEqual([tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] , __A )
self.assertEqual(
[tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [tokenizer.sep_token_id] , __A , )
@slow
def UpperCamelCase__ ( self ) -> List[str]:
# fmt: off
_lowerCAmelCase ={'input_ids': [[1, 3_9867, 36, 1_9390, 486, 27, 3_5052, 8_1436, 18, 6_0685, 1225, 7, 3_5052, 8_1436, 18, 9367, 1_6899, 18, 1_5937, 53, 594, 773, 18, 1_6287, 3_0465, 36, 1_5937, 6, 4_1139, 38, 3_6979, 6_0763, 191, 6, 3_4132, 99, 6, 5_0538, 390, 4_3230, 6, 3_4132, 2779, 2_0850, 14, 699, 1072, 1194, 36, 382, 1_0901, 53, 7, 699, 1072, 2084, 36, 2_0422, 630, 53, 19, 105, 3049, 1896, 1053, 1_6899, 1506, 11, 3_7978, 4243, 7, 1237, 3_1869, 200, 1_6566, 654, 6, 3_5052, 8_1436, 7, 5_5630, 1_3593, 4, 2], [1, 26, 1_5011, 13, 667, 8, 1053, 18, 2_3611, 1237, 7_2356, 1_2820, 34, 10_4134, 1209, 35, 1_3313, 6627, 21, 202, 347, 7, 164, 2399, 11, 46, 4485, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 5, 1232, 2864, 1_5785, 1_4951, 105, 5, 8581, 1250, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=__A , model_name='microsoft/deberta-v2-xlarge' , revision='ad6e42c1532ddf3a15c39246b63f5559d558b670' , )
| 58
|
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
_lowerCAmelCase =len(a__ ) // 2
# choose the middle 3 elements
_lowerCAmelCase =lst[m - 1 : m + 2]
# if middle element is peak
if three[1] > three[0] and three[1] > three[2]:
return three[1]
# if increasing, recurse on right
elif three[0] < three[2]:
if len(lst[:m] ) == 2:
m -= 1
return peak(lst[m:] )
# decreasing
else:
if len(lst[:m] ) == 2:
m += 1
return peak(lst[:m] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 58
| 1
|
'''simple docstring'''
import os
def UpperCamelCase__ ( ):
'''simple docstring'''
with open(os.path.dirname(a__ ) + '/grid.txt' ) as f:
_lowerCAmelCase =[] # noqa: E741
for _ in range(2_0 ):
l.append([int(a__ ) for x in f.readline().split()] )
_lowerCAmelCase =0
# right
for i in range(2_0 ):
for j in range(1_7 ):
_lowerCAmelCase =l[i][j] * l[i][j + 1] * l[i][j + 2] * l[i][j + 3]
if temp > maximum:
_lowerCAmelCase =temp
# down
for i in range(1_7 ):
for j in range(2_0 ):
_lowerCAmelCase =l[i][j] * l[i + 1][j] * l[i + 2][j] * l[i + 3][j]
if temp > maximum:
_lowerCAmelCase =temp
# diagonal 1
for i in range(1_7 ):
for j in range(1_7 ):
_lowerCAmelCase =l[i][j] * l[i + 1][j + 1] * l[i + 2][j + 2] * l[i + 3][j + 3]
if temp > maximum:
_lowerCAmelCase =temp
# diagonal 2
for i in range(1_7 ):
for j in range(3 , 2_0 ):
_lowerCAmelCase =l[i][j] * l[i + 1][j - 1] * l[i + 2][j - 2] * l[i + 3][j - 3]
if temp > maximum:
_lowerCAmelCase =temp
return maximum
if __name__ == "__main__":
print(solution())
| 58
|
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
lowercase_ = logging.get_logger(__name__)
lowercase_ = {'''vocab_file''': '''vocab.txt'''}
lowercase_ = {
'''vocab_file''': {
'''YituTech/conv-bert-base''': '''https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt''',
'''YituTech/conv-bert-medium-small''': (
'''https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt'''
),
'''YituTech/conv-bert-small''': '''https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt''',
}
}
lowercase_ = {
'''YituTech/conv-bert-base''': 512,
'''YituTech/conv-bert-medium-small''': 512,
'''YituTech/conv-bert-small''': 512,
}
lowercase_ = {
'''YituTech/conv-bert-base''': {'''do_lower_case''': True},
'''YituTech/conv-bert-medium-small''': {'''do_lower_case''': True},
'''YituTech/conv-bert-small''': {'''do_lower_case''': True},
}
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : Union[str, Any] = VOCAB_FILES_NAMES
lowercase : Tuple = PRETRAINED_VOCAB_FILES_MAP
lowercase : Optional[int] = PRETRAINED_INIT_CONFIGURATION
lowercase : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase : List[str] = ConvBertTokenizer
def __init__( self , __A=None , __A=None , __A=True , __A="[UNK]" , __A="[SEP]" , __A="[PAD]" , __A="[CLS]" , __A="[MASK]" , __A=True , __A=None , **__A , ) -> Union[str, Any]:
super().__init__(
__A , tokenizer_file=__A , do_lower_case=__A , unk_token=__A , sep_token=__A , pad_token=__A , cls_token=__A , mask_token=__A , tokenize_chinese_chars=__A , strip_accents=__A , **__A , )
_lowerCAmelCase =json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('lowercase' , __A ) != do_lower_case
or normalizer_state.get('strip_accents' , __A ) != strip_accents
or normalizer_state.get('handle_chinese_chars' , __A ) != tokenize_chinese_chars
):
_lowerCAmelCase =getattr(__A , normalizer_state.pop('type' ) )
_lowerCAmelCase =do_lower_case
_lowerCAmelCase =strip_accents
_lowerCAmelCase =tokenize_chinese_chars
_lowerCAmelCase =normalizer_class(**__A )
_lowerCAmelCase =do_lower_case
def UpperCamelCase__ ( self , __A , __A=None ) -> int:
_lowerCAmelCase =[self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def UpperCamelCase__ ( self , __A , __A = None ) -> List[int]:
_lowerCAmelCase =[self.sep_token_id]
_lowerCAmelCase =[self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def UpperCamelCase__ ( self , __A , __A = None ) -> Tuple[str]:
_lowerCAmelCase =self._tokenizer.model.save(__A , name=__A )
return tuple(__A )
| 58
| 1
|
'''simple docstring'''
from __future__ import annotations
from math import pi
def UpperCamelCase__ ( a__ , a__ , a__ ):
'''simple docstring'''
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError('One and only one argument must be 0' )
if inductance < 0:
raise ValueError('Inductance cannot be negative' )
if frequency < 0:
raise ValueError('Frequency cannot be negative' )
if reactance < 0:
raise ValueError('Inductive reactance cannot be negative' )
if inductance == 0:
return {"inductance": reactance / (2 * pi * frequency)}
elif frequency == 0:
return {"frequency": reactance / (2 * pi * inductance)}
elif reactance == 0:
return {"reactance": 2 * pi * frequency * inductance}
else:
raise ValueError('Exactly one argument must be 0' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 58
|
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : Any = ['image_processor', 'tokenizer']
lowercase : Any = 'CLIPImageProcessor'
lowercase : int = ('CLIPTokenizer', 'CLIPTokenizerFast')
def __init__( self , __A=None , __A=None , **__A ) -> str:
_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.' , __A , )
_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__(__A , __A )
def __call__( self , __A=None , __A=None , __A=None , **__A ) -> Optional[int]:
if text is None and images is None:
raise ValueError('You have to specify either text or images. Both cannot be none.' )
if text is not None:
_lowerCAmelCase =self.tokenizer(__A , return_tensors=__A , **__A )
if images is not None:
_lowerCAmelCase =self.image_processor(__A , return_tensors=__A , **__A )
if text is not None and images is not None:
_lowerCAmelCase =image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**__A ) , tensor_type=__A )
def UpperCamelCase__ ( self , *__A , **__A ) -> Any:
return self.tokenizer.batch_decode(*__A , **__A )
def UpperCamelCase__ ( self , *__A , **__A ) -> Optional[int]:
return self.tokenizer.decode(*__A , **__A )
@property
def UpperCamelCase__ ( self ) -> Tuple:
_lowerCAmelCase =self.tokenizer.model_input_names
_lowerCAmelCase =self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def UpperCamelCase__ ( self ) -> Optional[int]:
warnings.warn(
'`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' , __A , )
return self.image_processor_class
@property
def UpperCamelCase__ ( self ) -> Optional[Any]:
warnings.warn(
'`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , __A , )
return self.image_processor
| 58
| 1
|
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
lowercase_ = 16
lowercase_ = 32
def UpperCamelCase__ ( a__ , a__ = 1_6 ):
'''simple docstring'''
_lowerCAmelCase =AutoTokenizer.from_pretrained('bert-base-cased' )
_lowerCAmelCase =load_dataset('glue' , 'mrpc' )
def tokenize_function(a__ ):
# max_length=None => use the model max length (it's actually the default)
_lowerCAmelCase =tokenizer(examples['sentence1'] , examples['sentence2'] , truncation=a__ , max_length=a__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
_lowerCAmelCase =datasets.map(
a__ , batched=a__ , remove_columns=['idx', 'sentence1', 'sentence2'] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
_lowerCAmelCase =tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(a__ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
_lowerCAmelCase =1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
_lowerCAmelCase =1_6
elif accelerator.mixed_precision != "no":
_lowerCAmelCase =8
else:
_lowerCAmelCase =None
return tokenizer.pad(
a__ , padding='longest' , max_length=a__ , pad_to_multiple_of=a__ , return_tensors='pt' , )
# Instantiate dataloaders.
_lowerCAmelCase =DataLoader(
tokenized_datasets['train'] , shuffle=a__ , collate_fn=a__ , batch_size=a__ )
_lowerCAmelCase =DataLoader(
tokenized_datasets['validation'] , shuffle=a__ , collate_fn=a__ , batch_size=a__ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
lowercase_ = mocked_dataloaders # noqa: F811
def UpperCamelCase__ ( a__ , a__ ):
'''simple docstring'''
if os.environ.get('TESTING_MOCKED_DATALOADERS' , a__ ) == "1":
_lowerCAmelCase =2
# Initialize accelerator
_lowerCAmelCase =Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_lowerCAmelCase =config['lr']
_lowerCAmelCase =int(config['num_epochs'] )
_lowerCAmelCase =int(config['seed'] )
_lowerCAmelCase =int(config['batch_size'] )
_lowerCAmelCase =evaluate.load('glue' , 'mrpc' )
# If the batch size is too big we use gradient accumulation
_lowerCAmelCase =1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
_lowerCAmelCase =batch_size // MAX_GPU_BATCH_SIZE
_lowerCAmelCase =MAX_GPU_BATCH_SIZE
set_seed(a__ )
_lowerCAmelCase , _lowerCAmelCase =get_dataloaders(a__ , a__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_lowerCAmelCase =AutoModelForSequenceClassification.from_pretrained('bert-base-cased' , return_dict=a__ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
_lowerCAmelCase =model.to(accelerator.device )
# Instantiate optimizer
_lowerCAmelCase =AdamW(params=model.parameters() , lr=a__ )
# Instantiate scheduler
_lowerCAmelCase =get_linear_schedule_with_warmup(
optimizer=a__ , num_warmup_steps=1_0_0 , num_training_steps=(len(a__ ) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase =accelerator.prepare(
a__ , a__ , a__ , a__ , a__ )
# Now we train the model
for epoch in range(a__ ):
model.train()
for step, batch in enumerate(a__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
_lowerCAmelCase =model(**a__ )
_lowerCAmelCase =outputs.loss
_lowerCAmelCase =loss / gradient_accumulation_steps
accelerator.backward(a__ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
_lowerCAmelCase =0
for step, batch in enumerate(a__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
_lowerCAmelCase =model(**a__ )
_lowerCAmelCase =outputs.logits.argmax(dim=-1 )
_lowerCAmelCase , _lowerCAmelCase =accelerator.gather((predictions, batch['labels']) )
# New Code #
# First we check if it's a distributed system
if accelerator.use_distributed:
# Then see if we're on the last batch of our eval dataloader
if step == len(a__ ) - 1:
# Last batch needs to be truncated on distributed systems as it contains additional samples
_lowerCAmelCase =predictions[: len(eval_dataloader.dataset ) - samples_seen]
_lowerCAmelCase =references[: len(eval_dataloader.dataset ) - samples_seen]
else:
# Otherwise we add the number of samples seen
samples_seen += references.shape[0]
# All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
# accelerator.gather_for_metrics((predictions, batch["labels"]))
metric.add_batch(
predictions=a__ , references=a__ , )
_lowerCAmelCase =metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'''epoch {epoch}:''' , a__ )
def UpperCamelCase__ ( ):
'''simple docstring'''
_lowerCAmelCase =argparse.ArgumentParser(description='Simple example of training script.' )
parser.add_argument(
'--mixed_precision' , type=a__ , default=a__ , choices=['no', 'fp16', 'bf16', 'fp8'] , help='Whether to use mixed precision. Choose'
'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
'and an Nvidia Ampere GPU.' , )
parser.add_argument('--cpu' , action='store_true' , help='If passed, will train on the CPU.' )
_lowerCAmelCase =parser.parse_args()
_lowerCAmelCase ={'lr': 2E-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
training_function(a__ , a__ )
if __name__ == "__main__":
main()
| 58
|
'''simple docstring'''
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class SCREAMING_SNAKE_CASE ( __lowercase , __lowercase):
"""simple docstring"""
@register_to_config
def __init__( self , __A = 128 , __A = 256 , __A = 2_000.0 , __A = 768 , __A = 12 , __A = 12 , __A = 64 , __A = 2048 , __A = 0.1 , ) -> str:
super().__init__()
_lowerCAmelCase =nn.Sequential(
nn.Linear(__A , d_model * 4 , bias=__A ) , nn.SiLU() , nn.Linear(d_model * 4 , d_model * 4 , bias=__A ) , nn.SiLU() , )
_lowerCAmelCase =nn.Embedding(__A , __A )
_lowerCAmelCase =False
_lowerCAmelCase =nn.Linear(__A , __A , bias=__A )
_lowerCAmelCase =nn.Dropout(p=__A )
_lowerCAmelCase =nn.ModuleList()
for lyr_num in range(__A ):
# FiLM conditional T5 decoder
_lowerCAmelCase =DecoderLayer(d_model=__A , d_kv=__A , num_heads=__A , d_ff=__A , dropout_rate=__A )
self.decoders.append(__A )
_lowerCAmelCase =TaLayerNorm(__A )
_lowerCAmelCase =nn.Dropout(p=__A )
_lowerCAmelCase =nn.Linear(__A , __A , bias=__A )
def UpperCamelCase__ ( self , __A , __A ) -> Any:
_lowerCAmelCase =torch.mul(query_input.unsqueeze(-1 ) , key_input.unsqueeze(-2 ) )
return mask.unsqueeze(-3 )
def UpperCamelCase__ ( self , __A , __A , __A ) -> Optional[Any]:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase =decoder_input_tokens.shape
assert decoder_noise_time.shape == (batch,)
# decoder_noise_time is in [0, 1), so rescale to expected timing range.
_lowerCAmelCase =get_timestep_embedding(
decoder_noise_time * self.config.max_decoder_noise_time , embedding_dim=self.config.d_model , max_period=self.config.max_decoder_noise_time , ).to(dtype=self.dtype )
_lowerCAmelCase =self.conditioning_emb(__A ).unsqueeze(1 )
assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)
_lowerCAmelCase =decoder_input_tokens.shape[1]
# If we want to use relative positions for audio context, we can just offset
# this sequence by the length of encodings_and_masks.
_lowerCAmelCase =torch.broadcast_to(
torch.arange(__A , device=decoder_input_tokens.device ) , (batch, seq_length) , )
_lowerCAmelCase =self.position_encoding(__A )
_lowerCAmelCase =self.continuous_inputs_projection(__A )
inputs += position_encodings
_lowerCAmelCase =self.dropout(__A )
# decoder: No padding present.
_lowerCAmelCase =torch.ones(
decoder_input_tokens.shape[:2] , device=decoder_input_tokens.device , dtype=inputs.dtype )
# Translate encoding masks to encoder-decoder masks.
_lowerCAmelCase =[(x, self.encoder_decoder_mask(__A , __A )) for x, y in encodings_and_masks]
# cross attend style: concat encodings
_lowerCAmelCase =torch.cat([x[0] for x in encodings_and_encdec_masks] , dim=1 )
_lowerCAmelCase =torch.cat([x[1] for x in encodings_and_encdec_masks] , dim=-1 )
for lyr in self.decoders:
_lowerCAmelCase =lyr(
__A , conditioning_emb=__A , encoder_hidden_states=__A , encoder_attention_mask=__A , )[0]
_lowerCAmelCase =self.decoder_norm(__A )
_lowerCAmelCase =self.post_dropout(__A )
_lowerCAmelCase =self.spec_out(__A )
return spec_out
class SCREAMING_SNAKE_CASE ( nn.Module):
"""simple docstring"""
def __init__( self , __A , __A , __A , __A , __A , __A=1E-6 ) -> Union[str, Any]:
super().__init__()
_lowerCAmelCase =nn.ModuleList()
# cond self attention: layer 0
self.layer.append(
TaLayerSelfAttentionCond(d_model=__A , d_kv=__A , num_heads=__A , dropout_rate=__A ) )
# cross attention: layer 1
self.layer.append(
TaLayerCrossAttention(
d_model=__A , d_kv=__A , num_heads=__A , dropout_rate=__A , layer_norm_epsilon=__A , ) )
# Film Cond MLP + dropout: last layer
self.layer.append(
TaLayerFFCond(d_model=__A , d_ff=__A , dropout_rate=__A , layer_norm_epsilon=__A ) )
def UpperCamelCase__ ( self , __A , __A=None , __A=None , __A=None , __A=None , __A=None , ) -> Any:
_lowerCAmelCase =self.layer[0](
__A , conditioning_emb=__A , attention_mask=__A , )
if encoder_hidden_states is not None:
_lowerCAmelCase =torch.where(encoder_attention_mask > 0 , 0 , -1E10 ).to(
encoder_hidden_states.dtype )
_lowerCAmelCase =self.layer[1](
__A , key_value_states=__A , attention_mask=__A , )
# Apply Film Conditional Feed Forward layer
_lowerCAmelCase =self.layer[-1](__A , __A )
return (hidden_states,)
class SCREAMING_SNAKE_CASE ( nn.Module):
"""simple docstring"""
def __init__( self , __A , __A , __A , __A ) -> Optional[Any]:
super().__init__()
_lowerCAmelCase =TaLayerNorm(__A )
_lowerCAmelCase =TaFiLMLayer(in_features=d_model * 4 , out_features=__A )
_lowerCAmelCase =Attention(query_dim=__A , heads=__A , dim_head=__A , out_bias=__A , scale_qk=__A )
_lowerCAmelCase =nn.Dropout(__A )
def UpperCamelCase__ ( self , __A , __A=None , __A=None , ) -> List[Any]:
# pre_self_attention_layer_norm
_lowerCAmelCase =self.layer_norm(__A )
if conditioning_emb is not None:
_lowerCAmelCase =self.FiLMLayer(__A , __A )
# Self-attention block
_lowerCAmelCase =self.attention(__A )
_lowerCAmelCase =hidden_states + self.dropout(__A )
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module):
"""simple docstring"""
def __init__( self , __A , __A , __A , __A , __A ) -> Optional[int]:
super().__init__()
_lowerCAmelCase =Attention(query_dim=__A , heads=__A , dim_head=__A , out_bias=__A , scale_qk=__A )
_lowerCAmelCase =TaLayerNorm(__A , eps=__A )
_lowerCAmelCase =nn.Dropout(__A )
def UpperCamelCase__ ( self , __A , __A=None , __A=None , ) -> Tuple:
_lowerCAmelCase =self.layer_norm(__A )
_lowerCAmelCase =self.attention(
__A , encoder_hidden_states=__A , attention_mask=attention_mask.squeeze(1 ) , )
_lowerCAmelCase =hidden_states + self.dropout(__A )
return layer_output
class SCREAMING_SNAKE_CASE ( nn.Module):
"""simple docstring"""
def __init__( self , __A , __A , __A , __A ) -> Optional[Any]:
super().__init__()
_lowerCAmelCase =TaDenseGatedActDense(d_model=__A , d_ff=__A , dropout_rate=__A )
_lowerCAmelCase =TaFiLMLayer(in_features=d_model * 4 , out_features=__A )
_lowerCAmelCase =TaLayerNorm(__A , eps=__A )
_lowerCAmelCase =nn.Dropout(__A )
def UpperCamelCase__ ( self , __A , __A=None ) -> List[Any]:
_lowerCAmelCase =self.layer_norm(__A )
if conditioning_emb is not None:
_lowerCAmelCase =self.film(__A , __A )
_lowerCAmelCase =self.DenseReluDense(__A )
_lowerCAmelCase =hidden_states + self.dropout(__A )
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module):
"""simple docstring"""
def __init__( self , __A , __A , __A ) -> Union[str, Any]:
super().__init__()
_lowerCAmelCase =nn.Linear(__A , __A , bias=__A )
_lowerCAmelCase =nn.Linear(__A , __A , bias=__A )
_lowerCAmelCase =nn.Linear(__A , __A , bias=__A )
_lowerCAmelCase =nn.Dropout(__A )
_lowerCAmelCase =NewGELUActivation()
def UpperCamelCase__ ( self , __A ) -> List[Any]:
_lowerCAmelCase =self.act(self.wi_a(__A ) )
_lowerCAmelCase =self.wi_a(__A )
_lowerCAmelCase =hidden_gelu * hidden_linear
_lowerCAmelCase =self.dropout(__A )
_lowerCAmelCase =self.wo(__A )
return hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module):
"""simple docstring"""
def __init__( self , __A , __A=1E-6 ) -> int:
super().__init__()
_lowerCAmelCase =nn.Parameter(torch.ones(__A ) )
_lowerCAmelCase =eps
def UpperCamelCase__ ( self , __A ) -> Dict:
# T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
# Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
# w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
# half-precision inputs is done in fp32
_lowerCAmelCase =hidden_states.to(torch.floataa ).pow(2 ).mean(-1 , keepdim=__A )
_lowerCAmelCase =hidden_states * torch.rsqrt(variance + self.variance_epsilon )
# convert into half-precision if necessary
if self.weight.dtype in [torch.floataa, torch.bfloataa]:
_lowerCAmelCase =hidden_states.to(self.weight.dtype )
return self.weight * hidden_states
class SCREAMING_SNAKE_CASE ( nn.Module):
"""simple docstring"""
def UpperCamelCase__ ( self , __A ) -> torch.Tensor:
return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi ) * (input + 0.044_715 * torch.pow(__A , 3.0 )) ))
class SCREAMING_SNAKE_CASE ( nn.Module):
"""simple docstring"""
def __init__( self , __A , __A ) -> Optional[Any]:
super().__init__()
_lowerCAmelCase =nn.Linear(__A , out_features * 2 , bias=__A )
def UpperCamelCase__ ( self , __A , __A ) -> Optional[Any]:
_lowerCAmelCase =self.scale_bias(__A )
_lowerCAmelCase , _lowerCAmelCase =torch.chunk(__A , 2 , -1 )
_lowerCAmelCase =x * (1 + scale) + shift
return x
| 58
| 1
|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
'''shi-labs/dinat-mini-in1k-224''': '''https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json''',
# See all Dinat models at https://huggingface.co/models?filter=dinat
}
class SCREAMING_SNAKE_CASE ( __lowercase , __lowercase):
"""simple docstring"""
lowercase : Optional[Any] = 'dinat'
lowercase : str = {
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self , __A=4 , __A=3 , __A=64 , __A=[3, 4, 6, 5] , __A=[2, 4, 8, 16] , __A=7 , __A=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]] , __A=3.0 , __A=True , __A=0.0 , __A=0.0 , __A=0.1 , __A="gelu" , __A=0.02 , __A=1E-5 , __A=0.0 , __A=None , __A=None , **__A , ) -> Union[str, Any]:
super().__init__(**__A )
_lowerCAmelCase =patch_size
_lowerCAmelCase =num_channels
_lowerCAmelCase =embed_dim
_lowerCAmelCase =depths
_lowerCAmelCase =len(__A )
_lowerCAmelCase =num_heads
_lowerCAmelCase =kernel_size
_lowerCAmelCase =dilations
_lowerCAmelCase =mlp_ratio
_lowerCAmelCase =qkv_bias
_lowerCAmelCase =hidden_dropout_prob
_lowerCAmelCase =attention_probs_dropout_prob
_lowerCAmelCase =drop_path_rate
_lowerCAmelCase =hidden_act
_lowerCAmelCase =layer_norm_eps
_lowerCAmelCase =initializer_range
# we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
_lowerCAmelCase =int(embed_dim * 2 ** (len(__A ) - 1) )
_lowerCAmelCase =layer_scale_init_value
_lowerCAmelCase =['stem'] + [F'''stage{idx}''' for idx in range(1 , len(__A ) + 1 )]
_lowerCAmelCase , _lowerCAmelCase =get_aligned_output_features_output_indices(
out_features=__A , out_indices=__A , stage_names=self.stage_names )
| 58
|
'''simple docstring'''
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_available() and not is_torch_available():
raise RuntimeError('''At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training''')
# TF training parameters
lowercase_ = False
lowercase_ = False
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
return TrainCommand(a__ )
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
@staticmethod
def UpperCamelCase__ ( __A ) -> Tuple:
_lowerCAmelCase =parser.add_parser('train' , help='CLI tool to train a model on a task.' )
train_parser.add_argument(
'--train_data' , type=__A , required=__A , help='path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.' , )
train_parser.add_argument(
'--column_label' , type=__A , default=0 , help='Column of the dataset csv file with example labels.' )
train_parser.add_argument(
'--column_text' , type=__A , default=1 , help='Column of the dataset csv file with example texts.' )
train_parser.add_argument(
'--column_id' , type=__A , default=2 , help='Column of the dataset csv file with example ids.' )
train_parser.add_argument(
'--skip_first_row' , action='store_true' , help='Skip the first row of the csv file (headers).' )
train_parser.add_argument('--validation_data' , type=__A , default='' , help='path to validation dataset.' )
train_parser.add_argument(
'--validation_split' , type=__A , default=0.1 , help='if validation dataset is not provided, fraction of train dataset to use as validation dataset.' , )
train_parser.add_argument('--output' , type=__A , default='./' , help='path to saved the trained model.' )
train_parser.add_argument(
'--task' , type=__A , default='text_classification' , help='Task to train the model on.' )
train_parser.add_argument(
'--model' , type=__A , default='bert-base-uncased' , help='Model\'s name or path to stored model.' )
train_parser.add_argument('--train_batch_size' , type=__A , default=32 , help='Batch size for training.' )
train_parser.add_argument('--valid_batch_size' , type=__A , default=64 , help='Batch size for validation.' )
train_parser.add_argument('--learning_rate' , type=__A , default=3E-5 , help='Learning rate.' )
train_parser.add_argument('--adam_epsilon' , type=__A , default=1E-08 , help='Epsilon for Adam optimizer.' )
train_parser.set_defaults(func=__A )
def __init__( self , __A ) -> List[str]:
_lowerCAmelCase =logging.get_logger('transformers-cli/training' )
_lowerCAmelCase ='tf' if is_tf_available() else 'torch'
os.makedirs(args.output , exist_ok=__A )
_lowerCAmelCase =args.output
_lowerCAmelCase =args.column_label
_lowerCAmelCase =args.column_text
_lowerCAmelCase =args.column_id
self.logger.info(F'''Loading {args.task} pipeline for {args.model}''' )
if args.task == "text_classification":
_lowerCAmelCase =TextClassificationPipeline.from_pretrained(args.model )
elif args.task == "token_classification":
raise NotImplementedError
elif args.task == "question_answering":
raise NotImplementedError
self.logger.info(F'''Loading dataset from {args.train_data}''' )
_lowerCAmelCase =Processor.create_from_csv(
args.train_data , column_label=args.column_label , column_text=args.column_text , column_id=args.column_id , skip_first_row=args.skip_first_row , )
_lowerCAmelCase =None
if args.validation_data:
self.logger.info(F'''Loading validation dataset from {args.validation_data}''' )
_lowerCAmelCase =Processor.create_from_csv(
args.validation_data , column_label=args.column_label , column_text=args.column_text , column_id=args.column_id , skip_first_row=args.skip_first_row , )
_lowerCAmelCase =args.validation_split
_lowerCAmelCase =args.train_batch_size
_lowerCAmelCase =args.valid_batch_size
_lowerCAmelCase =args.learning_rate
_lowerCAmelCase =args.adam_epsilon
def UpperCamelCase__ ( self ) -> List[str]:
if self.framework == "tf":
return self.run_tf()
return self.run_torch()
def UpperCamelCase__ ( self ) -> Union[str, Any]:
raise NotImplementedError
def UpperCamelCase__ ( self ) -> List[Any]:
self.pipeline.fit(
self.train_dataset , validation_data=self.valid_dataset , validation_split=self.validation_split , learning_rate=self.learning_rate , adam_epsilon=self.adam_epsilon , train_batch_size=self.train_batch_size , valid_batch_size=self.valid_batch_size , )
# Save trained pipeline
self.pipeline.save_pretrained(self.output )
| 58
| 1
|
'''simple docstring'''
import numpy as np
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 58
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowercase_ = {'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
'''VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ViTMAEForPreTraining''',
'''ViTMAELayer''',
'''ViTMAEModel''',
'''ViTMAEPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
'''TFViTMAEForPreTraining''',
'''TFViTMAEModel''',
'''TFViTMAEPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit_mae import (
VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTMAEForPreTraining,
ViTMAELayer,
ViTMAEModel,
ViTMAEPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel
else:
import sys
lowercase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 58
| 1
|
'''simple docstring'''
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
import torch
from transformers.modeling_outputs import BaseModelOutput
from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
lowercase_ = logging.get_logger(__name__)
@add_end_docstrings(__lowercase)
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
def __init__( self , **__A ) -> Optional[int]:
super().__init__(**__A )
if self.framework == "tf":
raise ValueError(F'''The {self.__class__} is only available in PyTorch.''' )
requires_backends(self , 'vision' )
self.check_model_type(__A )
def __call__( self , __A , __A = None , **__A , ) -> Tuple:
if "text_queries" in kwargs:
_lowerCAmelCase =kwargs.pop('text_queries' )
if isinstance(__A , (str, Image.Image) ):
_lowerCAmelCase ={'image': image, 'candidate_labels': candidate_labels}
else:
_lowerCAmelCase =image
_lowerCAmelCase =super().__call__(__A , **__A )
return results
def UpperCamelCase__ ( self , **__A ) -> Optional[Any]:
_lowerCAmelCase ={}
if "threshold" in kwargs:
_lowerCAmelCase =kwargs['threshold']
if "top_k" in kwargs:
_lowerCAmelCase =kwargs['top_k']
return {}, {}, postprocess_params
def UpperCamelCase__ ( self , __A ) -> Union[str, Any]:
_lowerCAmelCase =load_image(inputs['image'] )
_lowerCAmelCase =inputs['candidate_labels']
if isinstance(__A , __A ):
_lowerCAmelCase =candidate_labels.split(',' )
_lowerCAmelCase =torch.tensor([[image.height, image.width]] , dtype=torch.intaa )
for i, candidate_label in enumerate(__A ):
_lowerCAmelCase =self.tokenizer(__A , return_tensors=self.framework )
_lowerCAmelCase =self.image_processor(__A , return_tensors=self.framework )
yield {
"is_last": i == len(__A ) - 1,
"target_size": target_size,
"candidate_label": candidate_label,
**text_inputs,
**image_features,
}
def UpperCamelCase__ ( self , __A ) -> Optional[Any]:
_lowerCAmelCase =model_inputs.pop('target_size' )
_lowerCAmelCase =model_inputs.pop('candidate_label' )
_lowerCAmelCase =model_inputs.pop('is_last' )
_lowerCAmelCase =self.model(**__A )
_lowerCAmelCase ={'target_size': target_size, 'candidate_label': candidate_label, 'is_last': is_last, **outputs}
return model_outputs
def UpperCamelCase__ ( self , __A , __A=0.1 , __A=None ) -> Any:
_lowerCAmelCase =[]
for model_output in model_outputs:
_lowerCAmelCase =model_output['candidate_label']
_lowerCAmelCase =BaseModelOutput(__A )
_lowerCAmelCase =self.image_processor.post_process_object_detection(
outputs=__A , threshold=__A , target_sizes=model_output['target_size'] )[0]
for index in outputs["scores"].nonzero():
_lowerCAmelCase =outputs['scores'][index].item()
_lowerCAmelCase =self._get_bounding_box(outputs['boxes'][index][0] )
_lowerCAmelCase ={'score': score, 'label': label, 'box': box}
results.append(__A )
_lowerCAmelCase =sorted(__A , key=lambda __A : x["score"] , reverse=__A )
if top_k:
_lowerCAmelCase =results[:top_k]
return results
def UpperCamelCase__ ( self , __A ) -> Dict[str, int]:
if self.framework != "pt":
raise ValueError('The ZeroShotObjectDetectionPipeline is only available in PyTorch.' )
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase =box.int().tolist()
_lowerCAmelCase ={
'xmin': xmin,
'ymin': ymin,
'xmax': xmax,
'ymax': ymax,
}
return bbox
| 58
|
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
_lowerCAmelCase =os.path.join(args.tf_model_dir , 'parameters.json' )
_lowerCAmelCase =json.loads(open(a__ ).read() )
if not params:
raise ValueError(
F'''It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file.''' )
if not args.output.endswith('.pt' ):
_lowerCAmelCase =args.output + '.pt'
_lowerCAmelCase =OrderedDict()
with tf.device('/CPU:0' ):
_lowerCAmelCase =tf.train.load_checkpoint(args.tf_model_dir )
_lowerCAmelCase =reader.get_variable_to_shape_map()
for key_name in shapes.keys():
_lowerCAmelCase =reader.get_tensor(a__ ).astype(np.floataa )
if key_name.endswith('/adam_m' ) or key_name.endswith('/adam_v' ):
continue
if key_name.startswith('pasts/' ):
if key_name.startswith('pasts/mlp' ):
_lowerCAmelCase =int(key_name[9] )
elif key_name.startswith('pasts/out' ):
_lowerCAmelCase =8
_lowerCAmelCase ='model.sqout.%d.weight' % (player * 2) # enter to nn.Sequencial with Tanh, so 2 at a time
_lowerCAmelCase =vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.startswith('model/moe' ):
_lowerCAmelCase =int(key_name[9:].split('/' )[0] )
if key_name.endswith('/switch_gating/kernel' ):
_lowerCAmelCase ='model.blocks.%d.feed_forward.mlp.router.classifier.weight' % player
_lowerCAmelCase =vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.endswith('/softmlp/kernel' ):
_lowerCAmelCase ='model.blocks.%d.feed_forward.soft_bypass_mlp.weight' % player
_lowerCAmelCase =vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.endswith('/wo/kernel' ) or key_name.endswith('/wi/kernel' ):
_lowerCAmelCase =key_name[-9:-7]
for i in range(1_6 ):
_lowerCAmelCase ='model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight' % (player, i, nlayer)
_lowerCAmelCase =(
vnp[i].transpose([1, 0] ).copy()
) # In Mesh-Tensorflow, it is one array, so it is divided
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.startswith('model/mlp' ):
_lowerCAmelCase =int(key_name[9:].split('/' )[0] )
if key_name.endswith('/p1/kernel' ):
_lowerCAmelCase ='model.blocks.%d.feed_forward.mlp.wi.weight' % player
_lowerCAmelCase =vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.endswith('/p1/bias' ):
_lowerCAmelCase ='model.blocks.%d.feed_forward.mlp.wi.bias' % player
_lowerCAmelCase =vnp.copy() # same because it is one dimensional
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.endswith('/p2/kernel' ):
_lowerCAmelCase ='model.blocks.%d.feed_forward.mlp.wo.weight' % player
_lowerCAmelCase =vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.endswith('/p2/bias' ):
_lowerCAmelCase ='model.blocks.%d.feed_forward.mlp.wo.bias' % player
_lowerCAmelCase =vnp.copy() # same because it is one dimensional
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.startswith('model/ln' ):
_lowerCAmelCase =int(key_name[8:].split('/' )[0] )
if key_name.endswith('/b' ):
_lowerCAmelCase ='model.blocks.%d.feed_forward.norm.bias' % player
_lowerCAmelCase =vnp.copy() # same because it is one dimensional
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.endswith('/g' ):
_lowerCAmelCase ='model.blocks.%d.feed_forward.norm.weight' % player
_lowerCAmelCase =vnp.copy() # same because it is one dimensional
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.startswith('model/att' ):
_lowerCAmelCase =int(key_name[9:].split('/' )[0] )
if key_name.endswith('/qkv/kernel' ):
_lowerCAmelCase =vnp.copy() # Compute same dimension as Mesh-tensorflow using einsum
_lowerCAmelCase =state[:, 0, :, :]
_lowerCAmelCase =state[:, 1, :, :]
_lowerCAmelCase =state[:, 2, :, :]
_lowerCAmelCase =(
state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
_lowerCAmelCase =(
state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
_lowerCAmelCase =(
state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
_lowerCAmelCase ='model.blocks.%d.self_attn.self_attn.q_proj.weight' % player
_lowerCAmelCase =torch.tensor(a__ )
_lowerCAmelCase ='model.blocks.%d.self_attn.self_attn.k_proj.weight' % player
_lowerCAmelCase =torch.tensor(a__ )
_lowerCAmelCase ='model.blocks.%d.self_attn.self_attn.v_proj.weight' % player
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.endswith('/o/kernel' ):
_lowerCAmelCase ='model.blocks.%d.self_attn.self_attn.out_proj.weight' % player
_lowerCAmelCase =(
vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]] ).transpose([1, 0] ).copy()
) # Mesh-Tensorflow is a diagonal matrix
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.startswith('model/an' ):
_lowerCAmelCase =int(key_name[8:].split('/' )[0] )
if key_name.endswith('/b' ):
_lowerCAmelCase ='model.blocks.%d.self_attn.norm.bias' % player
_lowerCAmelCase =vnp.copy() # same because it is one dimensional
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.endswith('/g' ):
_lowerCAmelCase ='model.blocks.%d.self_attn.norm.weight' % player
_lowerCAmelCase =vnp.copy() # same because it is one dimensional
_lowerCAmelCase =torch.tensor(a__ )
elif (
key_name.startswith('model/wte' )
or key_name.startswith('model/wpe' )
or key_name.startswith('model/ete' )
):
_lowerCAmelCase ={'wte': 'embed_tokens', 'wpe': 'position_embeddings', 'ete': 'extra_position_embeddings'}[
key_name[-3:]
]
_lowerCAmelCase ='model.%s.weight' % nlayer
_lowerCAmelCase =vnp.copy() # same in embedded
_lowerCAmelCase =torch.tensor(a__ )
if key_name.startswith('model/wte' ):
_lowerCAmelCase ='lm_head.weight'
_lowerCAmelCase =vnp.copy() # same in embedded
_lowerCAmelCase =torch.tensor(a__ )
elif key_name.startswith('model/wob' ):
_lowerCAmelCase ='final_logits_bias'
_lowerCAmelCase =vnp.copy() # same in embedded
_lowerCAmelCase =state.reshape((1, -1) )
_lowerCAmelCase =torch.tensor(a__ )
elif key_name == "model/dense/kernel":
_lowerCAmelCase ='model.last_project.weight'
_lowerCAmelCase =vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_lowerCAmelCase =torch.tensor(a__ )
elif key_name == "model/dense_1/bias":
_lowerCAmelCase ='model.last_project.bias'
_lowerCAmelCase =vnp.copy() # same because it is one dimensional
_lowerCAmelCase =torch.tensor(a__ )
torch.save(a__ , args.output )
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser(
description='''model converter.''', formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument('''--tf_model_dir''', metavar='''PATH''', type=str, required=True, help='''import model''')
parser.add_argument('''--output''', metavar='''PATH''', type=str, required=True, help='''output model''')
lowercase_ = parser.parse_args()
convert_tf_gptsan_to_pt(args)
| 58
| 1
|
'''simple docstring'''
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
def __init__( self , __A , __A = None , __A = None , __A = False , __A = False , __A = None , __A = None , **__A , ) -> List[str]:
super().__init__(
features=__A , cache_dir=__A , keep_in_memory=__A , streaming=__A , num_proc=__A , **__A , )
_lowerCAmelCase =Generator(
cache_dir=__A , features=__A , generator=__A , gen_kwargs=__A , **__A , )
def UpperCamelCase__ ( self ) -> List[str]:
# Build iterable dataset
if self.streaming:
_lowerCAmelCase =self.builder.as_streaming_dataset(split='train' )
# Build regular (map-style) dataset
else:
_lowerCAmelCase =None
_lowerCAmelCase =None
_lowerCAmelCase =None
_lowerCAmelCase =None
self.builder.download_and_prepare(
download_config=__A , download_mode=__A , verification_mode=__A , base_path=__A , num_proc=self.num_proc , )
_lowerCAmelCase =self.builder.as_dataset(
split='train' , verification_mode=__A , in_memory=self.keep_in_memory )
return dataset
| 58
|
'''simple docstring'''
def UpperCamelCase__ ( a__ = 1_0_0_0 ):
'''simple docstring'''
_lowerCAmelCase =2**power
_lowerCAmelCase =0
while n:
_lowerCAmelCase , _lowerCAmelCase =r + n % 1_0, n // 1_0
return r
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 58
| 1
|
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
'''kssteven/ibert-roberta-base''': '''https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json''',
'''kssteven/ibert-roberta-large''': '''https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json''',
'''kssteven/ibert-roberta-large-mnli''': (
'''https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json'''
),
}
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : List[str] = 'ibert'
def __init__( self , __A=3_0522 , __A=768 , __A=12 , __A=12 , __A=3072 , __A="gelu" , __A=0.1 , __A=0.1 , __A=512 , __A=2 , __A=0.02 , __A=1E-12 , __A=1 , __A=0 , __A=2 , __A="absolute" , __A=False , __A="none" , **__A , ) -> Any:
super().__init__(pad_token_id=__A , bos_token_id=__A , eos_token_id=__A , **__A )
_lowerCAmelCase =vocab_size
_lowerCAmelCase =hidden_size
_lowerCAmelCase =num_hidden_layers
_lowerCAmelCase =num_attention_heads
_lowerCAmelCase =hidden_act
_lowerCAmelCase =intermediate_size
_lowerCAmelCase =hidden_dropout_prob
_lowerCAmelCase =attention_probs_dropout_prob
_lowerCAmelCase =max_position_embeddings
_lowerCAmelCase =type_vocab_size
_lowerCAmelCase =initializer_range
_lowerCAmelCase =layer_norm_eps
_lowerCAmelCase =position_embedding_type
_lowerCAmelCase =quant_mode
_lowerCAmelCase =force_dequant
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
@property
def UpperCamelCase__ ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_lowerCAmelCase ={0: 'batch', 1: 'choice', 2: 'sequence'}
else:
_lowerCAmelCase ={0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
] )
| 58
|
'''simple docstring'''
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
_lowerCAmelCase =set()
# To detect a back edge, keep track of vertices currently in the recursion stack
_lowerCAmelCase =set()
return any(
node not in visited and depth_first_search(a__ , a__ , a__ , a__ )
for node in graph )
def UpperCamelCase__ ( a__ , a__ , a__ , a__ ):
'''simple docstring'''
visited.add(a__ )
rec_stk.add(a__ )
for node in graph[vertex]:
if node not in visited:
if depth_first_search(a__ , a__ , a__ , a__ ):
return True
elif node in rec_stk:
return True
# The node needs to be removed from recursion stack before function ends
rec_stk.remove(a__ )
return False
if __name__ == "__main__":
from doctest import testmod
testmod()
| 58
| 1
|
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
lowercase_ = logging.get_logger(__name__)
lowercase_ = {'''vocab_file''': '''vocab.txt'''}
lowercase_ = {
'''vocab_file''': {
'''YituTech/conv-bert-base''': '''https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt''',
'''YituTech/conv-bert-medium-small''': (
'''https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt'''
),
'''YituTech/conv-bert-small''': '''https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt''',
}
}
lowercase_ = {
'''YituTech/conv-bert-base''': 512,
'''YituTech/conv-bert-medium-small''': 512,
'''YituTech/conv-bert-small''': 512,
}
lowercase_ = {
'''YituTech/conv-bert-base''': {'''do_lower_case''': True},
'''YituTech/conv-bert-medium-small''': {'''do_lower_case''': True},
'''YituTech/conv-bert-small''': {'''do_lower_case''': True},
}
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : Union[str, Any] = VOCAB_FILES_NAMES
lowercase : Tuple = PRETRAINED_VOCAB_FILES_MAP
lowercase : Optional[int] = PRETRAINED_INIT_CONFIGURATION
lowercase : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase : List[str] = ConvBertTokenizer
def __init__( self , __A=None , __A=None , __A=True , __A="[UNK]" , __A="[SEP]" , __A="[PAD]" , __A="[CLS]" , __A="[MASK]" , __A=True , __A=None , **__A , ) -> Union[str, Any]:
super().__init__(
__A , tokenizer_file=__A , do_lower_case=__A , unk_token=__A , sep_token=__A , pad_token=__A , cls_token=__A , mask_token=__A , tokenize_chinese_chars=__A , strip_accents=__A , **__A , )
_lowerCAmelCase =json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('lowercase' , __A ) != do_lower_case
or normalizer_state.get('strip_accents' , __A ) != strip_accents
or normalizer_state.get('handle_chinese_chars' , __A ) != tokenize_chinese_chars
):
_lowerCAmelCase =getattr(__A , normalizer_state.pop('type' ) )
_lowerCAmelCase =do_lower_case
_lowerCAmelCase =strip_accents
_lowerCAmelCase =tokenize_chinese_chars
_lowerCAmelCase =normalizer_class(**__A )
_lowerCAmelCase =do_lower_case
def UpperCamelCase__ ( self , __A , __A=None ) -> int:
_lowerCAmelCase =[self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def UpperCamelCase__ ( self , __A , __A = None ) -> List[int]:
_lowerCAmelCase =[self.sep_token_id]
_lowerCAmelCase =[self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def UpperCamelCase__ ( self , __A , __A = None ) -> Tuple[str]:
_lowerCAmelCase =self._tokenizer.model.save(__A , name=__A )
return tuple(__A )
| 58
|
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
'''salesforce/blip2-opt-2.7b''': '''https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json''',
}
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : Tuple = 'blip_2_vision_model'
def __init__( self , __A=1408 , __A=6144 , __A=39 , __A=16 , __A=224 , __A=14 , __A="gelu" , __A=0.00_001 , __A=0.0 , __A=1E-10 , __A=True , **__A , ) -> int:
super().__init__(**__A )
_lowerCAmelCase =hidden_size
_lowerCAmelCase =intermediate_size
_lowerCAmelCase =num_hidden_layers
_lowerCAmelCase =num_attention_heads
_lowerCAmelCase =patch_size
_lowerCAmelCase =image_size
_lowerCAmelCase =initializer_range
_lowerCAmelCase =attention_dropout
_lowerCAmelCase =layer_norm_eps
_lowerCAmelCase =hidden_act
_lowerCAmelCase =qkv_bias
@classmethod
def UpperCamelCase__ ( cls , __A , **__A ) -> "PretrainedConfig":
cls._set_token_in_kwargs(__A )
_lowerCAmelCase , _lowerCAmelCase =cls.get_config_dict(__A , **__A )
# get the vision config dict if we are loading from Blip2Config
if config_dict.get('model_type' ) == "blip-2":
_lowerCAmelCase =config_dict['vision_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(__A , **__A )
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : int = 'blip_2_qformer'
def __init__( self , __A=3_0522 , __A=768 , __A=12 , __A=12 , __A=3072 , __A="gelu" , __A=0.1 , __A=0.1 , __A=512 , __A=0.02 , __A=1E-12 , __A=0 , __A="absolute" , __A=2 , __A=1408 , **__A , ) -> List[str]:
super().__init__(pad_token_id=__A , **__A )
_lowerCAmelCase =vocab_size
_lowerCAmelCase =hidden_size
_lowerCAmelCase =num_hidden_layers
_lowerCAmelCase =num_attention_heads
_lowerCAmelCase =hidden_act
_lowerCAmelCase =intermediate_size
_lowerCAmelCase =hidden_dropout_prob
_lowerCAmelCase =attention_probs_dropout_prob
_lowerCAmelCase =max_position_embeddings
_lowerCAmelCase =initializer_range
_lowerCAmelCase =layer_norm_eps
_lowerCAmelCase =position_embedding_type
_lowerCAmelCase =cross_attention_frequency
_lowerCAmelCase =encoder_hidden_size
@classmethod
def UpperCamelCase__ ( cls , __A , **__A ) -> "PretrainedConfig":
cls._set_token_in_kwargs(__A )
_lowerCAmelCase , _lowerCAmelCase =cls.get_config_dict(__A , **__A )
# get the qformer config dict if we are loading from Blip2Config
if config_dict.get('model_type' ) == "blip-2":
_lowerCAmelCase =config_dict['qformer_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(__A , **__A )
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : Optional[int] = 'blip-2'
lowercase : Any = True
def __init__( self , __A=None , __A=None , __A=None , __A=32 , **__A ) -> int:
super().__init__(**__A )
if vision_config is None:
_lowerCAmelCase ={}
logger.info('vision_config is None. initializing the Blip2VisionConfig with default values.' )
if qformer_config is None:
_lowerCAmelCase ={}
logger.info('qformer_config is None. Initializing the Blip2QFormerConfig with default values.' )
if text_config is None:
_lowerCAmelCase ={}
logger.info('text_config is None. Initializing the text config with default values (`OPTConfig`).' )
_lowerCAmelCase =BlipaVisionConfig(**__A )
_lowerCAmelCase =BlipaQFormerConfig(**__A )
_lowerCAmelCase =text_config['model_type'] if 'model_type' in text_config else 'opt'
_lowerCAmelCase =CONFIG_MAPPING[text_model_type](**__A )
_lowerCAmelCase =self.text_config.tie_word_embeddings
_lowerCAmelCase =self.text_config.is_encoder_decoder
_lowerCAmelCase =num_query_tokens
_lowerCAmelCase =self.vision_config.hidden_size
_lowerCAmelCase =self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
_lowerCAmelCase =1.0
_lowerCAmelCase =0.02
@classmethod
def UpperCamelCase__ ( cls , __A , __A , __A , **__A , ) -> Any:
return cls(
vision_config=vision_config.to_dict() , qformer_config=qformer_config.to_dict() , text_config=text_config.to_dict() , **__A , )
def UpperCamelCase__ ( self ) -> Tuple:
_lowerCAmelCase =copy.deepcopy(self.__dict__ )
_lowerCAmelCase =self.vision_config.to_dict()
_lowerCAmelCase =self.qformer_config.to_dict()
_lowerCAmelCase =self.text_config.to_dict()
_lowerCAmelCase =self.__class__.model_type
return output
| 58
| 1
|
'''simple docstring'''
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
return 1 / (1 + np.exp(-z ))
def UpperCamelCase__ ( a__ , a__ ):
'''simple docstring'''
return (-y * np.log(a__ ) - (1 - y) * np.log(1 - h )).mean()
def UpperCamelCase__ ( a__ , a__ , a__ ):
'''simple docstring'''
_lowerCAmelCase =np.dot(a__ , a__ )
return np.sum(y * scores - np.log(1 + np.exp(a__ ) ) )
def UpperCamelCase__ ( a__ , a__ , a__ , a__=7_0_0_0_0 ):
'''simple docstring'''
_lowerCAmelCase =np.zeros(x.shape[1] )
for iterations in range(a__ ):
_lowerCAmelCase =np.dot(a__ , a__ )
_lowerCAmelCase =sigmoid_function(a__ )
_lowerCAmelCase =np.dot(x.T , h - y ) / y.size
_lowerCAmelCase =theta - alpha * gradient # updating the weights
_lowerCAmelCase =np.dot(a__ , a__ )
_lowerCAmelCase =sigmoid_function(a__ )
_lowerCAmelCase =cost_function(a__ , a__ )
if iterations % 1_0_0 == 0:
print(F'''loss: {j} \t''' ) # printing the loss after every 100 iterations
return theta
# In[68]:
if __name__ == "__main__":
lowercase_ = datasets.load_iris()
lowercase_ = iris.data[:, :2]
lowercase_ = (iris.target != 0) * 1
lowercase_ = 0.1
lowercase_ = logistic_reg(alpha, x, y, max_iterations=7_0000)
print('''theta: ''', theta) # printing the theta i.e our weights vector
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
return sigmoid_function(
np.dot(a__ , a__ ) ) # predicting the value of probability from the logistic regression algorithm
plt.figure(figsize=(10, 6))
plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color='''b''', label='''0''')
plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color='''r''', label='''1''')
((lowercase_) , (lowercase_)) = (x[:, 0].min(), x[:, 0].max())
((lowercase_) , (lowercase_)) = (x[:, 1].min(), x[:, 1].max())
((lowercase_) , (lowercase_)) = np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
lowercase_ = np.c_[xxa.ravel(), xxa.ravel()]
lowercase_ = predict_prob(grid).reshape(xxa.shape)
plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='''black''')
plt.legend()
plt.show()
| 58
|
'''simple docstring'''
lowercase_ = {
'''A''': '''.-''', '''B''': '''-...''', '''C''': '''-.-.''', '''D''': '''-..''', '''E''': '''.''', '''F''': '''..-.''', '''G''': '''--.''',
'''H''': '''....''', '''I''': '''..''', '''J''': '''.---''', '''K''': '''-.-''', '''L''': '''.-..''', '''M''': '''--''', '''N''': '''-.''',
'''O''': '''---''', '''P''': '''.--.''', '''Q''': '''--.-''', '''R''': '''.-.''', '''S''': '''...''', '''T''': '''-''', '''U''': '''..-''',
'''V''': '''...-''', '''W''': '''.--''', '''X''': '''-..-''', '''Y''': '''-.--''', '''Z''': '''--..''', '''1''': '''.----''',
'''2''': '''..---''', '''3''': '''...--''', '''4''': '''....-''', '''5''': '''.....''', '''6''': '''-....''', '''7''': '''--...''',
'''8''': '''---..''', '''9''': '''----.''', '''0''': '''-----''', '''&''': '''.-...''', '''@''': '''.--.-.''',
''':''': '''---...''', ''',''': '''--..--''', '''.''': '''.-.-.-''', '''\'''': '''.----.''', '''"''': '''.-..-.''',
'''?''': '''..--..''', '''/''': '''-..-.''', '''=''': '''-...-''', '''+''': '''.-.-.''', '''-''': '''-....-''',
'''(''': '''-.--.''', ''')''': '''-.--.-''', '''!''': '''-.-.--''', ''' ''': '''/'''
} # Exclamation mark is not in ITU-R recommendation
# fmt: on
lowercase_ = {value: key for key, value in MORSE_CODE_DICT.items()}
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
return " ".join(MORSE_CODE_DICT[char] for char in message.upper() )
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
return "".join(REVERSE_DICT[char] for char in message.split() )
def UpperCamelCase__ ( ):
'''simple docstring'''
_lowerCAmelCase ='Morse code here!'
print(a__ )
_lowerCAmelCase =encrypt(a__ )
print(a__ )
_lowerCAmelCase =decrypt(a__ )
print(a__ )
if __name__ == "__main__":
main()
| 58
| 1
|
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
'''deepmind/language-perceiver''': '''https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json''',
# See all Perceiver models at https://huggingface.co/models?filter=perceiver
}
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : List[str] = 'perceiver'
def __init__( self , __A=256 , __A=1280 , __A=768 , __A=1 , __A=26 , __A=8 , __A=8 , __A=None , __A=None , __A="kv" , __A=1 , __A=1 , __A="gelu" , __A=0.1 , __A=0.02 , __A=1E-12 , __A=True , __A=262 , __A=2048 , __A=56 , __A=[368, 496] , __A=16 , __A=1920 , __A=16 , __A=[1, 16, 224, 224] , **__A , ) -> Union[str, Any]:
super().__init__(**__A )
_lowerCAmelCase =num_latents
_lowerCAmelCase =d_latents
_lowerCAmelCase =d_model
_lowerCAmelCase =num_blocks
_lowerCAmelCase =num_self_attends_per_block
_lowerCAmelCase =num_self_attention_heads
_lowerCAmelCase =num_cross_attention_heads
_lowerCAmelCase =qk_channels
_lowerCAmelCase =v_channels
_lowerCAmelCase =cross_attention_shape_for_attention
_lowerCAmelCase =self_attention_widening_factor
_lowerCAmelCase =cross_attention_widening_factor
_lowerCAmelCase =hidden_act
_lowerCAmelCase =attention_probs_dropout_prob
_lowerCAmelCase =initializer_range
_lowerCAmelCase =layer_norm_eps
_lowerCAmelCase =use_query_residual
# masked language modeling attributes
_lowerCAmelCase =vocab_size
_lowerCAmelCase =max_position_embeddings
# image classification attributes
_lowerCAmelCase =image_size
# flow attributes
_lowerCAmelCase =train_size
# multimodal autoencoding attributes
_lowerCAmelCase =num_frames
_lowerCAmelCase =audio_samples_per_frame
_lowerCAmelCase =samples_per_patch
_lowerCAmelCase =output_shape
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
@property
def UpperCamelCase__ ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_lowerCAmelCase ={0: 'batch', 1: 'choice', 2: 'sequence'}
else:
_lowerCAmelCase ={0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('inputs', dynamic_axis),
('attention_mask', dynamic_axis),
] )
@property
def UpperCamelCase__ ( self ) -> float:
return 1E-4
def UpperCamelCase__ ( self , __A , __A = -1 , __A = -1 , __A = -1 , __A = False , __A = None , __A = 3 , __A = 40 , __A = 40 , ) -> Mapping[str, Any]:
# copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified
if isinstance(__A , __A ):
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
_lowerCAmelCase =compute_effective_axis_dimension(
__A , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
_lowerCAmelCase =preprocessor.num_special_tokens_to_add(__A )
_lowerCAmelCase =compute_effective_axis_dimension(
__A , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=__A )
# Generate dummy inputs according to compute batch and sequence
_lowerCAmelCase =[' '.join(['a'] ) * seq_length] * batch_size
_lowerCAmelCase =dict(preprocessor(__A , return_tensors=__A ) )
_lowerCAmelCase =inputs.pop('input_ids' )
return inputs
elif isinstance(__A , __A ) and preprocessor.model_input_names[0] == "pixel_values":
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
_lowerCAmelCase =compute_effective_axis_dimension(__A , fixed_dimension=OnnxConfig.default_fixed_batch )
_lowerCAmelCase =self._generate_dummy_images(__A , __A , __A , __A )
_lowerCAmelCase =dict(preprocessor(images=__A , return_tensors=__A ) )
_lowerCAmelCase =inputs.pop('pixel_values' )
return inputs
else:
raise ValueError(
'Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.' )
| 58
|
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
'''facebook/data2vec-text-base''': '''https://huggingface.co/data2vec/resolve/main/config.json''',
}
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : List[str] = 'data2vec-text'
def __init__( self , __A=3_0522 , __A=768 , __A=12 , __A=12 , __A=3072 , __A="gelu" , __A=0.1 , __A=0.1 , __A=512 , __A=2 , __A=0.02 , __A=1E-12 , __A=1 , __A=0 , __A=2 , __A="absolute" , __A=True , __A=None , **__A , ) -> List[Any]:
super().__init__(pad_token_id=__A , bos_token_id=__A , eos_token_id=__A , **__A )
_lowerCAmelCase =vocab_size
_lowerCAmelCase =hidden_size
_lowerCAmelCase =num_hidden_layers
_lowerCAmelCase =num_attention_heads
_lowerCAmelCase =hidden_act
_lowerCAmelCase =intermediate_size
_lowerCAmelCase =hidden_dropout_prob
_lowerCAmelCase =attention_probs_dropout_prob
_lowerCAmelCase =max_position_embeddings
_lowerCAmelCase =type_vocab_size
_lowerCAmelCase =initializer_range
_lowerCAmelCase =layer_norm_eps
_lowerCAmelCase =position_embedding_type
_lowerCAmelCase =use_cache
_lowerCAmelCase =classifier_dropout
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
@property
def UpperCamelCase__ ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_lowerCAmelCase ={0: 'batch', 1: 'choice', 2: 'sequence'}
else:
_lowerCAmelCase ={0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
] )
| 58
| 1
|
'''simple docstring'''
import datasets
from .evaluate import evaluate
lowercase_ = '''\
@article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
journal={arXiv preprint arXiv:2103.06268},
year={2021}
}
'''
lowercase_ = '''
This metric wrap the official scoring script for version 1 of the Contract
Understanding Atticus Dataset (CUAD).
Contract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510
commercial legal contracts that have been manually labeled to identify 41 categories of important
clauses that lawyers look for when reviewing contracts in connection with corporate transactions.
'''
lowercase_ = '''
Computes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).
Args:
predictions: List of question-answers dictionaries with the following key-values:
- \'id\': id of the question-answer pair as given in the references (see below)
- \'prediction_text\': list of possible texts for the answer, as a list of strings
depending on a threshold on the confidence probability of each prediction.
references: List of question-answers dictionaries with the following key-values:
- \'id\': id of the question-answer pair (see above),
- \'answers\': a Dict in the CUAD dataset format
{
\'text\': list of possible texts for the answer, as a list of strings
\'answer_start\': list of start positions for the answer, as a list of ints
}
Note that answer_start values are not taken into account to compute the metric.
Returns:
\'exact_match\': Exact match (the normalized answer exactly match the gold answer)
\'f1\': The F-score of predicted tokens versus the gold answer
\'aupr\': Area Under the Precision-Recall curve
\'prec_at_80_recall\': Precision at 80% recall
\'prec_at_90_recall\': Precision at 90% recall
Examples:
>>> predictions = [{\'prediction_text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\'], \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]
>>> references = [{\'answers\': {\'answer_start\': [143, 49], \'text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\']}, \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]
>>> cuad_metric = datasets.load_metric("cuad")
>>> results = cuad_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'exact_match\': 100.0, \'f1\': 100.0, \'aupr\': 0.0, \'prec_at_80_recall\': 1.0, \'prec_at_90_recall\': 1.0}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class SCREAMING_SNAKE_CASE ( datasets.Metric):
"""simple docstring"""
def UpperCamelCase__ ( self ) -> str:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': {
'id': datasets.Value('string' ),
'prediction_text': datasets.features.Sequence(datasets.Value('string' ) ),
},
'references': {
'id': datasets.Value('string' ),
'answers': datasets.features.Sequence(
{
'text': datasets.Value('string' ),
'answer_start': datasets.Value('int32' ),
} ),
},
} ) , codebase_urls=['https://www.atticusprojectai.org/cuad'] , reference_urls=['https://www.atticusprojectai.org/cuad'] , )
def UpperCamelCase__ ( self , __A , __A ) -> List[Any]:
_lowerCAmelCase ={prediction['id']: prediction['prediction_text'] for prediction in predictions}
_lowerCAmelCase =[
{
'paragraphs': [
{
'qas': [
{
'answers': [{'text': answer_text} for answer_text in ref['answers']['text']],
'id': ref['id'],
}
for ref in references
]
}
]
}
]
_lowerCAmelCase =evaluate(dataset=__A , predictions=__A )
return score
| 58
|
'''simple docstring'''
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin
@skip_mps
class SCREAMING_SNAKE_CASE ( __lowercase , __lowercase , unittest.TestCase):
"""simple docstring"""
lowercase : List[Any] = IFPipeline
lowercase : Tuple = TEXT_TO_IMAGE_PARAMS - {'width', 'height', 'latents'}
lowercase : Union[str, Any] = TEXT_TO_IMAGE_BATCH_PARAMS
lowercase : int = PipelineTesterMixin.required_optional_params - {'latents'}
def UpperCamelCase__ ( self ) -> str:
return self._get_dummy_components()
def UpperCamelCase__ ( self , __A , __A=0 ) -> int:
if str(__A ).startswith('mps' ):
_lowerCAmelCase =torch.manual_seed(__A )
else:
_lowerCAmelCase =torch.Generator(device=__A ).manual_seed(__A )
_lowerCAmelCase ={
'prompt': 'A painting of a squirrel eating a burger',
'generator': generator,
'num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
def UpperCamelCase__ ( self ) -> Optional[Any]:
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != 'cuda' , reason='float16 requires CUDA' )
def UpperCamelCase__ ( self ) -> Tuple:
# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
super().test_save_load_floataa(expected_max_diff=1E-1 )
def UpperCamelCase__ ( self ) -> List[Any]:
self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )
def UpperCamelCase__ ( self ) -> str:
self._test_save_load_local()
def UpperCamelCase__ ( self ) -> Union[str, Any]:
self._test_inference_batch_single_identical(
expected_max_diff=1E-2 , )
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def UpperCamelCase__ ( self ) -> List[str]:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE ( unittest.TestCase):
"""simple docstring"""
def UpperCamelCase__ ( self ) -> Optional[int]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase__ ( self ) -> Optional[Any]:
# if
_lowerCAmelCase =IFPipeline.from_pretrained('DeepFloyd/IF-I-XL-v1.0' , variant='fp16' , torch_dtype=torch.floataa )
_lowerCAmelCase =IFSuperResolutionPipeline.from_pretrained(
'DeepFloyd/IF-II-L-v1.0' , variant='fp16' , torch_dtype=torch.floataa , text_encoder=__A , tokenizer=__A )
# pre compute text embeddings and remove T5 to save memory
pipe_a.text_encoder.to('cuda' )
_lowerCAmelCase , _lowerCAmelCase =pipe_a.encode_prompt('anime turtle' , device='cuda' )
del pipe_a.tokenizer
del pipe_a.text_encoder
gc.collect()
_lowerCAmelCase =None
_lowerCAmelCase =None
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if(__A , __A , __A , __A )
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# img2img
_lowerCAmelCase =IFImgaImgPipeline(**pipe_a.components )
_lowerCAmelCase =IFImgaImgSuperResolutionPipeline(**pipe_a.components )
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if_imgaimg(__A , __A , __A , __A )
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# inpainting
_lowerCAmelCase =IFInpaintingPipeline(**pipe_a.components )
_lowerCAmelCase =IFInpaintingSuperResolutionPipeline(**pipe_a.components )
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if_inpainting(__A , __A , __A , __A )
def UpperCamelCase__ ( self , __A , __A , __A , __A ) -> str:
# pipeline 1
_start_torch_memory_measurement()
_lowerCAmelCase =torch.Generator(device='cpu' ).manual_seed(0 )
_lowerCAmelCase =pipe_a(
prompt_embeds=__A , negative_prompt_embeds=__A , num_inference_steps=2 , generator=__A , output_type='np' , )
_lowerCAmelCase =output.images[0]
assert image.shape == (64, 64, 3)
_lowerCAmelCase =torch.cuda.max_memory_allocated()
assert mem_bytes < 13 * 10**9
_lowerCAmelCase =load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy' )
assert_mean_pixel_difference(__A , __A )
# pipeline 2
_start_torch_memory_measurement()
_lowerCAmelCase =torch.Generator(device='cpu' ).manual_seed(0 )
_lowerCAmelCase =floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(__A )
_lowerCAmelCase =pipe_a(
prompt_embeds=__A , negative_prompt_embeds=__A , image=__A , generator=__A , num_inference_steps=2 , output_type='np' , )
_lowerCAmelCase =output.images[0]
assert image.shape == (256, 256, 3)
_lowerCAmelCase =torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
_lowerCAmelCase =load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy' )
assert_mean_pixel_difference(__A , __A )
def UpperCamelCase__ ( self , __A , __A , __A , __A ) -> Optional[int]:
# pipeline 1
_start_torch_memory_measurement()
_lowerCAmelCase =floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(__A )
_lowerCAmelCase =torch.Generator(device='cpu' ).manual_seed(0 )
_lowerCAmelCase =pipe_a(
prompt_embeds=__A , negative_prompt_embeds=__A , image=__A , num_inference_steps=2 , generator=__A , output_type='np' , )
_lowerCAmelCase =output.images[0]
assert image.shape == (64, 64, 3)
_lowerCAmelCase =torch.cuda.max_memory_allocated()
assert mem_bytes < 10 * 10**9
_lowerCAmelCase =load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy' )
assert_mean_pixel_difference(__A , __A )
# pipeline 2
_start_torch_memory_measurement()
_lowerCAmelCase =torch.Generator(device='cpu' ).manual_seed(0 )
_lowerCAmelCase =floats_tensor((1, 3, 256, 256) , rng=random.Random(0 ) ).to(__A )
_lowerCAmelCase =floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(__A )
_lowerCAmelCase =pipe_a(
prompt_embeds=__A , negative_prompt_embeds=__A , image=__A , original_image=__A , generator=__A , num_inference_steps=2 , output_type='np' , )
_lowerCAmelCase =output.images[0]
assert image.shape == (256, 256, 3)
_lowerCAmelCase =torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
_lowerCAmelCase =load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy' )
assert_mean_pixel_difference(__A , __A )
def UpperCamelCase__ ( self , __A , __A , __A , __A ) -> Dict:
# pipeline 1
_start_torch_memory_measurement()
_lowerCAmelCase =floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(__A )
_lowerCAmelCase =floats_tensor((1, 3, 64, 64) , rng=random.Random(1 ) ).to(__A )
_lowerCAmelCase =torch.Generator(device='cpu' ).manual_seed(0 )
_lowerCAmelCase =pipe_a(
prompt_embeds=__A , negative_prompt_embeds=__A , image=__A , mask_image=__A , num_inference_steps=2 , generator=__A , output_type='np' , )
_lowerCAmelCase =output.images[0]
assert image.shape == (64, 64, 3)
_lowerCAmelCase =torch.cuda.max_memory_allocated()
assert mem_bytes < 10 * 10**9
_lowerCAmelCase =load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy' )
assert_mean_pixel_difference(__A , __A )
# pipeline 2
_start_torch_memory_measurement()
_lowerCAmelCase =torch.Generator(device='cpu' ).manual_seed(0 )
_lowerCAmelCase =floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(__A )
_lowerCAmelCase =floats_tensor((1, 3, 256, 256) , rng=random.Random(0 ) ).to(__A )
_lowerCAmelCase =floats_tensor((1, 3, 256, 256) , rng=random.Random(1 ) ).to(__A )
_lowerCAmelCase =pipe_a(
prompt_embeds=__A , negative_prompt_embeds=__A , image=__A , mask_image=__A , original_image=__A , generator=__A , num_inference_steps=2 , output_type='np' , )
_lowerCAmelCase =output.images[0]
assert image.shape == (256, 256, 3)
_lowerCAmelCase =torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
_lowerCAmelCase =load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy' )
assert_mean_pixel_difference(__A , __A )
def UpperCamelCase__ ( ):
'''simple docstring'''
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
| 58
| 1
|
'''simple docstring'''
import copy
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
'''microsoft/conditional-detr-resnet-50''': (
'''https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'''
),
}
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : List[Any] = 'conditional_detr'
lowercase : Dict = ['past_key_values']
lowercase : Optional[Any] = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self , __A=True , __A=None , __A=3 , __A=300 , __A=6 , __A=2048 , __A=8 , __A=6 , __A=2048 , __A=8 , __A=0.0 , __A=0.0 , __A=True , __A="relu" , __A=256 , __A=0.1 , __A=0.0 , __A=0.0 , __A=0.02 , __A=1.0 , __A=False , __A="sine" , __A="resnet50" , __A=True , __A=False , __A=2 , __A=5 , __A=2 , __A=1 , __A=1 , __A=2 , __A=5 , __A=2 , __A=0.25 , **__A , ) -> Optional[Any]:
if backbone_config is not None and use_timm_backbone:
raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.' )
if not use_timm_backbone:
if backbone_config is None:
logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.' )
_lowerCAmelCase =CONFIG_MAPPING['resnet'](out_features=['stage4'] )
elif isinstance(__A , __A ):
_lowerCAmelCase =backbone_config.get('model_type' )
_lowerCAmelCase =CONFIG_MAPPING[backbone_model_type]
_lowerCAmelCase =config_class.from_dict(__A )
_lowerCAmelCase =use_timm_backbone
_lowerCAmelCase =backbone_config
_lowerCAmelCase =num_channels
_lowerCAmelCase =num_queries
_lowerCAmelCase =d_model
_lowerCAmelCase =encoder_ffn_dim
_lowerCAmelCase =encoder_layers
_lowerCAmelCase =encoder_attention_heads
_lowerCAmelCase =decoder_ffn_dim
_lowerCAmelCase =decoder_layers
_lowerCAmelCase =decoder_attention_heads
_lowerCAmelCase =dropout
_lowerCAmelCase =attention_dropout
_lowerCAmelCase =activation_dropout
_lowerCAmelCase =activation_function
_lowerCAmelCase =init_std
_lowerCAmelCase =init_xavier_std
_lowerCAmelCase =encoder_layerdrop
_lowerCAmelCase =decoder_layerdrop
_lowerCAmelCase =encoder_layers
_lowerCAmelCase =auxiliary_loss
_lowerCAmelCase =position_embedding_type
_lowerCAmelCase =backbone
_lowerCAmelCase =use_pretrained_backbone
_lowerCAmelCase =dilation
# Hungarian matcher
_lowerCAmelCase =class_cost
_lowerCAmelCase =bbox_cost
_lowerCAmelCase =giou_cost
# Loss coefficients
_lowerCAmelCase =mask_loss_coefficient
_lowerCAmelCase =dice_loss_coefficient
_lowerCAmelCase =cls_loss_coefficient
_lowerCAmelCase =bbox_loss_coefficient
_lowerCAmelCase =giou_loss_coefficient
_lowerCAmelCase =focal_alpha
super().__init__(is_encoder_decoder=__A , **__A )
@property
def UpperCamelCase__ ( self ) -> int:
return self.encoder_attention_heads
@property
def UpperCamelCase__ ( self ) -> int:
return self.d_model
def UpperCamelCase__ ( self ) -> List[Any]:
_lowerCAmelCase =copy.deepcopy(self.__dict__ )
if self.backbone_config is not None:
_lowerCAmelCase =self.backbone_config.to_dict()
_lowerCAmelCase =self.__class__.model_type
return output
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : Optional[Any] = version.parse('1.11')
@property
def UpperCamelCase__ ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
('pixel_mask', {0: 'batch'}),
] )
@property
def UpperCamelCase__ ( self ) -> float:
return 1E-5
@property
def UpperCamelCase__ ( self ) -> int:
return 12
| 58
|
'''simple docstring'''
import unittest
from knapsack import knapsack as k
class SCREAMING_SNAKE_CASE ( unittest.TestCase):
"""simple docstring"""
def UpperCamelCase__ ( self ) -> Optional[Any]:
_lowerCAmelCase =0
_lowerCAmelCase =[0]
_lowerCAmelCase =[0]
_lowerCAmelCase =len(__A )
self.assertEqual(k.knapsack(__A , __A , __A , __A ) , 0 )
_lowerCAmelCase =[60]
_lowerCAmelCase =[10]
_lowerCAmelCase =len(__A )
self.assertEqual(k.knapsack(__A , __A , __A , __A ) , 0 )
def UpperCamelCase__ ( self ) -> Tuple:
_lowerCAmelCase =3
_lowerCAmelCase =[1, 2, 3]
_lowerCAmelCase =[3, 2, 1]
_lowerCAmelCase =len(__A )
self.assertEqual(k.knapsack(__A , __A , __A , __A ) , 5 )
def UpperCamelCase__ ( self ) -> Union[str, Any]:
_lowerCAmelCase =50
_lowerCAmelCase =[60, 100, 120]
_lowerCAmelCase =[10, 20, 30]
_lowerCAmelCase =len(__A )
self.assertEqual(k.knapsack(__A , __A , __A , __A ) , 220 )
if __name__ == "__main__":
unittest.main()
| 58
| 1
|
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
'''facebook/data2vec-text-base''': '''https://huggingface.co/data2vec/resolve/main/config.json''',
}
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : List[str] = 'data2vec-text'
def __init__( self , __A=3_0522 , __A=768 , __A=12 , __A=12 , __A=3072 , __A="gelu" , __A=0.1 , __A=0.1 , __A=512 , __A=2 , __A=0.02 , __A=1E-12 , __A=1 , __A=0 , __A=2 , __A="absolute" , __A=True , __A=None , **__A , ) -> List[Any]:
super().__init__(pad_token_id=__A , bos_token_id=__A , eos_token_id=__A , **__A )
_lowerCAmelCase =vocab_size
_lowerCAmelCase =hidden_size
_lowerCAmelCase =num_hidden_layers
_lowerCAmelCase =num_attention_heads
_lowerCAmelCase =hidden_act
_lowerCAmelCase =intermediate_size
_lowerCAmelCase =hidden_dropout_prob
_lowerCAmelCase =attention_probs_dropout_prob
_lowerCAmelCase =max_position_embeddings
_lowerCAmelCase =type_vocab_size
_lowerCAmelCase =initializer_range
_lowerCAmelCase =layer_norm_eps
_lowerCAmelCase =position_embedding_type
_lowerCAmelCase =use_cache
_lowerCAmelCase =classifier_dropout
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
@property
def UpperCamelCase__ ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_lowerCAmelCase ={0: 'batch', 1: 'choice', 2: 'sequence'}
else:
_lowerCAmelCase ={0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
] )
| 58
|
'''simple docstring'''
lowercase_ = '''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
lowercase_ = [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
lowercase_ = {
'''{processor_class}''': '''FakeProcessorClass''',
'''{model_class}''': '''FakeModelClass''',
'''{object_class}''': '''FakeObjectClass''',
}
| 58
| 1
|
'''simple docstring'''
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors
lowercase_ = logging.getLogger(__name__)
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : int = 'sequence-classification'
def __init__( self , __A ) -> List[Any]:
if type(__A ) == dict:
_lowerCAmelCase =Namespace(**__A )
_lowerCAmelCase =glue_output_modes[hparams.task]
_lowerCAmelCase =glue_tasks_num_labels[hparams.task]
super().__init__(__A , __A , self.mode )
def UpperCamelCase__ ( self , **__A ) -> Any:
return self.model(**__A )
def UpperCamelCase__ ( self , __A , __A ) -> Union[str, Any]:
_lowerCAmelCase ={'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
_lowerCAmelCase =batch[2] if self.config.model_type in ['bert', 'xlnet', 'albert'] else None
_lowerCAmelCase =self(**__A )
_lowerCAmelCase =outputs[0]
_lowerCAmelCase =self.trainer.lr_schedulers[0]['scheduler']
_lowerCAmelCase ={'loss': loss, 'rate': lr_scheduler.get_last_lr()[-1]}
return {"loss": loss, "log": tensorboard_logs}
def UpperCamelCase__ ( self ) -> Any:
_lowerCAmelCase =self.hparams
_lowerCAmelCase =processors[args.task]()
_lowerCAmelCase =processor.get_labels()
for mode in ["train", "dev"]:
_lowerCAmelCase =self._feature_file(__A )
if os.path.exists(__A ) and not args.overwrite_cache:
logger.info('Loading features from cached file %s' , __A )
else:
logger.info('Creating features from dataset file at %s' , args.data_dir )
_lowerCAmelCase =(
processor.get_dev_examples(args.data_dir )
if mode == 'dev'
else processor.get_train_examples(args.data_dir )
)
_lowerCAmelCase =convert_examples_to_features(
__A , self.tokenizer , max_length=args.max_seq_length , label_list=self.labels , output_mode=args.glue_output_mode , )
logger.info('Saving features into cached file %s' , __A )
torch.save(__A , __A )
def UpperCamelCase__ ( self , __A , __A , __A = False ) -> DataLoader:
_lowerCAmelCase ='dev' if mode == 'test' else mode
_lowerCAmelCase =self._feature_file(__A )
logger.info('Loading features from cached file %s' , __A )
_lowerCAmelCase =torch.load(__A )
_lowerCAmelCase =torch.tensor([f.input_ids for f in features] , dtype=torch.long )
_lowerCAmelCase =torch.tensor([f.attention_mask for f in features] , dtype=torch.long )
_lowerCAmelCase =torch.tensor([f.token_type_ids for f in features] , dtype=torch.long )
if self.hparams.glue_output_mode == "classification":
_lowerCAmelCase =torch.tensor([f.label for f in features] , dtype=torch.long )
elif self.hparams.glue_output_mode == "regression":
_lowerCAmelCase =torch.tensor([f.label for f in features] , dtype=torch.float )
return DataLoader(
TensorDataset(__A , __A , __A , __A ) , batch_size=__A , shuffle=__A , )
def UpperCamelCase__ ( self , __A , __A ) -> List[str]:
_lowerCAmelCase ={'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
_lowerCAmelCase =batch[2] if self.config.model_type in ['bert', 'xlnet', 'albert'] else None
_lowerCAmelCase =self(**__A )
_lowerCAmelCase , _lowerCAmelCase =outputs[:2]
_lowerCAmelCase =logits.detach().cpu().numpy()
_lowerCAmelCase =inputs['labels'].detach().cpu().numpy()
return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
def UpperCamelCase__ ( self , __A ) -> tuple:
_lowerCAmelCase =torch.stack([x['val_loss'] for x in outputs] ).mean().detach().cpu().item()
_lowerCAmelCase =np.concatenate([x['pred'] for x in outputs] , axis=0 )
if self.hparams.glue_output_mode == "classification":
_lowerCAmelCase =np.argmax(__A , axis=1 )
elif self.hparams.glue_output_mode == "regression":
_lowerCAmelCase =np.squeeze(__A )
_lowerCAmelCase =np.concatenate([x['target'] for x in outputs] , axis=0 )
_lowerCAmelCase =[[] for _ in range(out_label_ids.shape[0] )]
_lowerCAmelCase =[[] for _ in range(out_label_ids.shape[0] )]
_lowerCAmelCase ={**{'val_loss': val_loss_mean}, **compute_metrics(self.hparams.task , __A , __A )}
_lowerCAmelCase =dict(results.items() )
_lowerCAmelCase =results
return ret, preds_list, out_label_list
def UpperCamelCase__ ( self , __A ) -> dict:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase =self._eval_end(__A )
_lowerCAmelCase =ret['log']
return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
def UpperCamelCase__ ( self , __A ) -> dict:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase =self._eval_end(__A )
_lowerCAmelCase =ret['log']
# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
@staticmethod
def UpperCamelCase__ ( __A , __A ) -> Any:
BaseTransformer.add_model_specific_args(__A , __A )
parser.add_argument(
'--max_seq_length' , default=128 , type=__A , help=(
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
) , )
parser.add_argument(
'--task' , default='' , type=__A , required=__A , help='The GLUE task to run' , )
parser.add_argument(
'--gpus' , default=0 , type=__A , help='The number of GPUs allocated for this, it is by default 0 meaning none' , )
parser.add_argument(
'--overwrite_cache' , action='store_true' , help='Overwrite the cached training and evaluation sets' )
return parser
def UpperCamelCase__ ( ):
'''simple docstring'''
_lowerCAmelCase =argparse.ArgumentParser()
add_generic_args(a__ , os.getcwd() )
_lowerCAmelCase =GLUETransformer.add_model_specific_args(a__ , os.getcwd() )
_lowerCAmelCase =parser.parse_args()
# If output_dir not provided, a folder will be generated in pwd
if args.output_dir is None:
_lowerCAmelCase =os.path.join(
'./results' , F'''{args.task}_{time.strftime('%Y%m%d_%H%M%S' )}''' , )
os.makedirs(args.output_dir )
_lowerCAmelCase =GLUETransformer(a__ )
_lowerCAmelCase =generic_train(a__ , a__ )
# Optionally, predict on dev set and write to output_dir
if args.do_predict:
_lowerCAmelCase =sorted(glob.glob(os.path.join(args.output_dir , 'checkpoint-epoch=*.ckpt' ) , recursive=a__ ) )
_lowerCAmelCase =model.load_from_checkpoint(checkpoints[-1] )
return trainer.test(a__ )
if __name__ == "__main__":
main()
| 58
|
'''simple docstring'''
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json
lowercase_ = '''sshleifer/mar_enro_6_3_student'''
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
def UpperCamelCase__ ( self ) -> Optional[Any]:
super().setUp()
_lowerCAmelCase =cached_path(
'https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz' , extract_compressed_file=__A , )
_lowerCAmelCase =F'''{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'''
@slow
@require_torch_gpu
def UpperCamelCase__ ( self ) -> Union[str, Any]:
MarianMTModel.from_pretrained(__A )
@slow
@require_torch_gpu
def UpperCamelCase__ ( self ) -> Union[str, Any]:
_lowerCAmelCase ={
'$MAX_LEN': 64,
'$BS': 64,
'$GAS': 1,
'$ENRO_DIR': self.data_dir,
'facebook/mbart-large-cc25': MARIAN_MODEL,
# "val_check_interval=0.25": "val_check_interval=1.0",
'--learning_rate=3e-5': '--learning_rate 3e-4',
'--num_train_epochs 6': '--num_train_epochs 1',
}
# Clean up bash script
_lowerCAmelCase =(self.test_file_dir / 'train_mbart_cc25_enro.sh').open().read().split('finetune.py' )[1].strip()
_lowerCAmelCase =bash_script.replace('\\\n' , '' ).strip().replace('"$@"' , '' )
for k, v in env_vars_to_replace.items():
_lowerCAmelCase =bash_script.replace(__A , str(__A ) )
_lowerCAmelCase =self.get_auto_remove_tmp_dir()
# bash_script = bash_script.replace("--fp16 ", "")
_lowerCAmelCase =F'''
--output_dir {output_dir}
--tokenizer_name Helsinki-NLP/opus-mt-en-ro
--sortish_sampler
--do_predict
--gpus 1
--freeze_encoder
--n_train 40000
--n_val 500
--n_test 500
--fp16_opt_level O1
--num_sanity_val_steps 0
--eval_beams 2
'''.split()
# XXX: args.gpus > 1 : handle multi_gpu in the future
_lowerCAmelCase =['finetune.py'] + bash_script.split() + args
with patch.object(__A , 'argv' , __A ):
_lowerCAmelCase =argparse.ArgumentParser()
_lowerCAmelCase =pl.Trainer.add_argparse_args(__A )
_lowerCAmelCase =SummarizationModule.add_model_specific_args(__A , os.getcwd() )
_lowerCAmelCase =parser.parse_args()
_lowerCAmelCase =main(__A )
# Check metrics
_lowerCAmelCase =load_json(model.metrics_save_path )
_lowerCAmelCase =metrics['val'][0]
_lowerCAmelCase =metrics['val'][-1]
self.assertEqual(len(metrics['val'] ) , (args.max_epochs / args.val_check_interval) )
assert isinstance(last_step_stats[F'''val_avg_{model.val_metric}'''] , __A )
self.assertGreater(last_step_stats['val_avg_gen_time'] , 0.01 )
# model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
self.assertLessEqual(last_step_stats['val_avg_gen_time'] , 1.0 )
# test learning requirements:
# 1. BLEU improves over the course of training by more than 2 pts
self.assertGreater(last_step_stats['val_avg_bleu'] - first_step_stats['val_avg_bleu'] , 2 )
# 2. BLEU finishes above 17
self.assertGreater(last_step_stats['val_avg_bleu'] , 17 )
# 3. test BLEU and val BLEU within ~1.1 pt.
self.assertLess(abs(metrics['val'][-1]['val_avg_bleu'] - metrics['test'][-1]['test_avg_bleu'] ) , 1.1 )
# check lightning ckpt can be loaded and has a reasonable statedict
_lowerCAmelCase =os.listdir(__A )
_lowerCAmelCase =[x for x in contents if x.endswith('.ckpt' )][0]
_lowerCAmelCase =os.path.join(args.output_dir , __A )
_lowerCAmelCase =torch.load(__A , map_location='cpu' )
_lowerCAmelCase ='model.model.decoder.layers.0.encoder_attn_layer_norm.weight'
assert expected_key in ckpt["state_dict"]
assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa
# TODO: turn on args.do_predict when PL bug fixed.
if args.do_predict:
_lowerCAmelCase ={os.path.basename(__A ) for p in contents}
assert "test_generations.txt" in contents
assert "test_results.txt" in contents
# assert len(metrics["val"]) == desired_n_evals
assert len(metrics['test'] ) == 1
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
@timeout_decorator.timeout(600 )
@slow
@require_torch_gpu
def UpperCamelCase__ ( self ) -> Tuple:
_lowerCAmelCase =F'''{self.test_file_dir_str}/test_data/wmt_en_ro'''
_lowerCAmelCase ={
'--fp16_opt_level=O1': '',
'$MAX_LEN': 128,
'$BS': 16,
'$GAS': 1,
'$ENRO_DIR': data_dir,
'$m': 'sshleifer/student_marian_en_ro_6_1',
'val_check_interval=0.25': 'val_check_interval=1.0',
}
# Clean up bash script
_lowerCAmelCase =(
(self.test_file_dir / 'distil_marian_no_teacher.sh').open().read().split('distillation.py' )[1].strip()
)
_lowerCAmelCase =bash_script.replace('\\\n' , '' ).strip().replace('"$@"' , '' )
_lowerCAmelCase =bash_script.replace('--fp16 ' , ' ' )
for k, v in env_vars_to_replace.items():
_lowerCAmelCase =bash_script.replace(__A , str(__A ) )
_lowerCAmelCase =self.get_auto_remove_tmp_dir()
_lowerCAmelCase =bash_script.replace('--fp16' , '' )
_lowerCAmelCase =6
_lowerCAmelCase =(
['distillation.py']
+ bash_script.split()
+ [
F'''--output_dir={output_dir}''',
'--gpus=1',
'--learning_rate=1e-3',
F'''--num_train_epochs={epochs}''',
'--warmup_steps=10',
'--val_check_interval=1.0',
'--do_predict',
]
)
with patch.object(__A , 'argv' , __A ):
_lowerCAmelCase =argparse.ArgumentParser()
_lowerCAmelCase =pl.Trainer.add_argparse_args(__A )
_lowerCAmelCase =SummarizationDistiller.add_model_specific_args(__A , os.getcwd() )
_lowerCAmelCase =parser.parse_args()
# assert args.gpus == gpus THIS BREAKS for multi_gpu
_lowerCAmelCase =distill_main(__A )
# Check metrics
_lowerCAmelCase =load_json(model.metrics_save_path )
_lowerCAmelCase =metrics['val'][0]
_lowerCAmelCase =metrics['val'][-1]
assert len(metrics['val'] ) >= (args.max_epochs / args.val_check_interval) # +1 accounts for val_sanity_check
assert last_step_stats["val_avg_gen_time"] >= 0.01
assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"] # model learned nothing
assert 1.0 >= last_step_stats["val_avg_gen_time"] # model hanging on generate. Maybe bad config was saved.
assert isinstance(last_step_stats[F'''val_avg_{model.val_metric}'''] , __A )
# check lightning ckpt can be loaded and has a reasonable statedict
_lowerCAmelCase =os.listdir(__A )
_lowerCAmelCase =[x for x in contents if x.endswith('.ckpt' )][0]
_lowerCAmelCase =os.path.join(args.output_dir , __A )
_lowerCAmelCase =torch.load(__A , map_location='cpu' )
_lowerCAmelCase ='model.model.decoder.layers.0.encoder_attn_layer_norm.weight'
assert expected_key in ckpt["state_dict"]
assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa
# TODO: turn on args.do_predict when PL bug fixed.
if args.do_predict:
_lowerCAmelCase ={os.path.basename(__A ) for p in contents}
assert "test_generations.txt" in contents
assert "test_results.txt" in contents
# assert len(metrics["val"]) == desired_n_evals
assert len(metrics['test'] ) == 1
| 58
| 1
|
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class SCREAMING_SNAKE_CASE ( __lowercase , unittest.TestCase):
"""simple docstring"""
lowercase : str = ShapEPipeline
lowercase : int = ['prompt']
lowercase : str = ['prompt']
lowercase : Any = [
'num_images_per_prompt',
'num_inference_steps',
'generator',
'latents',
'guidance_scale',
'frame_size',
'output_type',
'return_dict',
]
lowercase : Optional[int] = False
@property
def UpperCamelCase__ ( self ) -> str:
return 32
@property
def UpperCamelCase__ ( self ) -> List[Any]:
return 32
@property
def UpperCamelCase__ ( self ) -> Optional[Any]:
return self.time_input_dim * 4
@property
def UpperCamelCase__ ( self ) -> Optional[Any]:
return 8
@property
def UpperCamelCase__ ( self ) -> List[str]:
_lowerCAmelCase =CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
return tokenizer
@property
def UpperCamelCase__ ( self ) -> Optional[Any]:
torch.manual_seed(0 )
_lowerCAmelCase =CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModelWithProjection(__A )
@property
def UpperCamelCase__ ( self ) -> int:
torch.manual_seed(0 )
_lowerCAmelCase ={
'num_attention_heads': 2,
'attention_head_dim': 16,
'embedding_dim': self.time_input_dim,
'num_embeddings': 32,
'embedding_proj_dim': self.text_embedder_hidden_size,
'time_embed_dim': self.time_embed_dim,
'num_layers': 1,
'clip_embed_dim': self.time_input_dim * 2,
'additional_embeddings': 0,
'time_embed_act_fn': 'gelu',
'norm_in_type': 'layer',
'encoder_hid_proj_type': None,
'added_emb_type': None,
}
_lowerCAmelCase =PriorTransformer(**__A )
return model
@property
def UpperCamelCase__ ( self ) -> int:
torch.manual_seed(0 )
_lowerCAmelCase ={
'param_shapes': (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
'd_latent': self.time_input_dim,
'd_hidden': self.renderer_dim,
'n_output': 12,
'background': (
0.1,
0.1,
0.1,
),
}
_lowerCAmelCase =ShapERenderer(**__A )
return model
def UpperCamelCase__ ( self ) -> int:
_lowerCAmelCase =self.dummy_prior
_lowerCAmelCase =self.dummy_text_encoder
_lowerCAmelCase =self.dummy_tokenizer
_lowerCAmelCase =self.dummy_renderer
_lowerCAmelCase =HeunDiscreteScheduler(
beta_schedule='exp' , num_train_timesteps=1024 , prediction_type='sample' , use_karras_sigmas=__A , clip_sample=__A , clip_sample_range=1.0 , )
_lowerCAmelCase ={
'prior': prior,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'renderer': renderer,
'scheduler': scheduler,
}
return components
def UpperCamelCase__ ( self , __A , __A=0 ) -> Dict:
if str(__A ).startswith('mps' ):
_lowerCAmelCase =torch.manual_seed(__A )
else:
_lowerCAmelCase =torch.Generator(device=__A ).manual_seed(__A )
_lowerCAmelCase ={
'prompt': 'horse',
'generator': generator,
'num_inference_steps': 1,
'frame_size': 32,
'output_type': 'np',
}
return inputs
def UpperCamelCase__ ( self ) -> int:
_lowerCAmelCase ='cpu'
_lowerCAmelCase =self.get_dummy_components()
_lowerCAmelCase =self.pipeline_class(**__A )
_lowerCAmelCase =pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
_lowerCAmelCase =pipe(**self.get_dummy_inputs(__A ) )
_lowerCAmelCase =output.images[0]
_lowerCAmelCase =image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
_lowerCAmelCase =np.array(
[
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
0.00_039_216,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def UpperCamelCase__ ( self ) -> List[Any]:
# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def UpperCamelCase__ ( self ) -> int:
_lowerCAmelCase =torch_device == 'cpu'
_lowerCAmelCase =True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=__A , relax_max_difference=__A , )
def UpperCamelCase__ ( self ) -> Dict:
_lowerCAmelCase =self.get_dummy_components()
_lowerCAmelCase =self.pipeline_class(**__A )
_lowerCAmelCase =pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
_lowerCAmelCase =1
_lowerCAmelCase =2
_lowerCAmelCase =self.get_dummy_inputs(__A )
for key in inputs.keys():
if key in self.batch_params:
_lowerCAmelCase =batch_size * [inputs[key]]
_lowerCAmelCase =pipe(**__A , num_images_per_prompt=__A )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE ( unittest.TestCase):
"""simple docstring"""
def UpperCamelCase__ ( self ) -> Union[str, Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase__ ( self ) -> Tuple:
_lowerCAmelCase =load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/shap_e/test_shap_e_np_out.npy' )
_lowerCAmelCase =ShapEPipeline.from_pretrained('openai/shap-e' )
_lowerCAmelCase =pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
_lowerCAmelCase =torch.Generator(device=__A ).manual_seed(0 )
_lowerCAmelCase =pipe(
'a shark' , generator=__A , guidance_scale=15.0 , num_inference_steps=64 , frame_size=64 , output_type='np' , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(__A , __A )
| 58
|
'''simple docstring'''
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors
lowercase_ = logging.getLogger(__name__)
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : int = 'sequence-classification'
def __init__( self , __A ) -> List[Any]:
if type(__A ) == dict:
_lowerCAmelCase =Namespace(**__A )
_lowerCAmelCase =glue_output_modes[hparams.task]
_lowerCAmelCase =glue_tasks_num_labels[hparams.task]
super().__init__(__A , __A , self.mode )
def UpperCamelCase__ ( self , **__A ) -> Any:
return self.model(**__A )
def UpperCamelCase__ ( self , __A , __A ) -> Union[str, Any]:
_lowerCAmelCase ={'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
_lowerCAmelCase =batch[2] if self.config.model_type in ['bert', 'xlnet', 'albert'] else None
_lowerCAmelCase =self(**__A )
_lowerCAmelCase =outputs[0]
_lowerCAmelCase =self.trainer.lr_schedulers[0]['scheduler']
_lowerCAmelCase ={'loss': loss, 'rate': lr_scheduler.get_last_lr()[-1]}
return {"loss": loss, "log": tensorboard_logs}
def UpperCamelCase__ ( self ) -> Any:
_lowerCAmelCase =self.hparams
_lowerCAmelCase =processors[args.task]()
_lowerCAmelCase =processor.get_labels()
for mode in ["train", "dev"]:
_lowerCAmelCase =self._feature_file(__A )
if os.path.exists(__A ) and not args.overwrite_cache:
logger.info('Loading features from cached file %s' , __A )
else:
logger.info('Creating features from dataset file at %s' , args.data_dir )
_lowerCAmelCase =(
processor.get_dev_examples(args.data_dir )
if mode == 'dev'
else processor.get_train_examples(args.data_dir )
)
_lowerCAmelCase =convert_examples_to_features(
__A , self.tokenizer , max_length=args.max_seq_length , label_list=self.labels , output_mode=args.glue_output_mode , )
logger.info('Saving features into cached file %s' , __A )
torch.save(__A , __A )
def UpperCamelCase__ ( self , __A , __A , __A = False ) -> DataLoader:
_lowerCAmelCase ='dev' if mode == 'test' else mode
_lowerCAmelCase =self._feature_file(__A )
logger.info('Loading features from cached file %s' , __A )
_lowerCAmelCase =torch.load(__A )
_lowerCAmelCase =torch.tensor([f.input_ids for f in features] , dtype=torch.long )
_lowerCAmelCase =torch.tensor([f.attention_mask for f in features] , dtype=torch.long )
_lowerCAmelCase =torch.tensor([f.token_type_ids for f in features] , dtype=torch.long )
if self.hparams.glue_output_mode == "classification":
_lowerCAmelCase =torch.tensor([f.label for f in features] , dtype=torch.long )
elif self.hparams.glue_output_mode == "regression":
_lowerCAmelCase =torch.tensor([f.label for f in features] , dtype=torch.float )
return DataLoader(
TensorDataset(__A , __A , __A , __A ) , batch_size=__A , shuffle=__A , )
def UpperCamelCase__ ( self , __A , __A ) -> List[str]:
_lowerCAmelCase ={'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
if self.config.model_type not in ["distilbert", "bart"]:
_lowerCAmelCase =batch[2] if self.config.model_type in ['bert', 'xlnet', 'albert'] else None
_lowerCAmelCase =self(**__A )
_lowerCAmelCase , _lowerCAmelCase =outputs[:2]
_lowerCAmelCase =logits.detach().cpu().numpy()
_lowerCAmelCase =inputs['labels'].detach().cpu().numpy()
return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
def UpperCamelCase__ ( self , __A ) -> tuple:
_lowerCAmelCase =torch.stack([x['val_loss'] for x in outputs] ).mean().detach().cpu().item()
_lowerCAmelCase =np.concatenate([x['pred'] for x in outputs] , axis=0 )
if self.hparams.glue_output_mode == "classification":
_lowerCAmelCase =np.argmax(__A , axis=1 )
elif self.hparams.glue_output_mode == "regression":
_lowerCAmelCase =np.squeeze(__A )
_lowerCAmelCase =np.concatenate([x['target'] for x in outputs] , axis=0 )
_lowerCAmelCase =[[] for _ in range(out_label_ids.shape[0] )]
_lowerCAmelCase =[[] for _ in range(out_label_ids.shape[0] )]
_lowerCAmelCase ={**{'val_loss': val_loss_mean}, **compute_metrics(self.hparams.task , __A , __A )}
_lowerCAmelCase =dict(results.items() )
_lowerCAmelCase =results
return ret, preds_list, out_label_list
def UpperCamelCase__ ( self , __A ) -> dict:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase =self._eval_end(__A )
_lowerCAmelCase =ret['log']
return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
def UpperCamelCase__ ( self , __A ) -> dict:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase =self._eval_end(__A )
_lowerCAmelCase =ret['log']
# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
@staticmethod
def UpperCamelCase__ ( __A , __A ) -> Any:
BaseTransformer.add_model_specific_args(__A , __A )
parser.add_argument(
'--max_seq_length' , default=128 , type=__A , help=(
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
) , )
parser.add_argument(
'--task' , default='' , type=__A , required=__A , help='The GLUE task to run' , )
parser.add_argument(
'--gpus' , default=0 , type=__A , help='The number of GPUs allocated for this, it is by default 0 meaning none' , )
parser.add_argument(
'--overwrite_cache' , action='store_true' , help='Overwrite the cached training and evaluation sets' )
return parser
def UpperCamelCase__ ( ):
'''simple docstring'''
_lowerCAmelCase =argparse.ArgumentParser()
add_generic_args(a__ , os.getcwd() )
_lowerCAmelCase =GLUETransformer.add_model_specific_args(a__ , os.getcwd() )
_lowerCAmelCase =parser.parse_args()
# If output_dir not provided, a folder will be generated in pwd
if args.output_dir is None:
_lowerCAmelCase =os.path.join(
'./results' , F'''{args.task}_{time.strftime('%Y%m%d_%H%M%S' )}''' , )
os.makedirs(args.output_dir )
_lowerCAmelCase =GLUETransformer(a__ )
_lowerCAmelCase =generic_train(a__ , a__ )
# Optionally, predict on dev set and write to output_dir
if args.do_predict:
_lowerCAmelCase =sorted(glob.glob(os.path.join(args.output_dir , 'checkpoint-epoch=*.ckpt' ) , recursive=a__ ) )
_lowerCAmelCase =model.load_from_checkpoint(checkpoints[-1] )
return trainer.test(a__ )
if __name__ == "__main__":
main()
| 58
| 1
|
'''simple docstring'''
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
@property
def UpperCamelCase__ ( self ) -> Tuple:
return self.get_dummy_input()
@property
def UpperCamelCase__ ( self ) -> Optional[int]:
if self.block_type == "down":
return (4, 32, 16, 16)
elif self.block_type == "mid":
return (4, 32, 32, 32)
elif self.block_type == "up":
return (4, 32, 64, 64)
raise ValueError(F'''\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'.''' )
def UpperCamelCase__ ( self , __A=True , __A=False , __A=False , __A=False , ) -> List[Any]:
_lowerCAmelCase =4
_lowerCAmelCase =32
_lowerCAmelCase =(32, 32)
_lowerCAmelCase =torch.manual_seed(0 )
_lowerCAmelCase =torch.device(__A )
_lowerCAmelCase =(batch_size, num_channels) + sizes
_lowerCAmelCase =randn_tensor(__A , generator=__A , device=__A )
_lowerCAmelCase ={'hidden_states': hidden_states}
if include_temb:
_lowerCAmelCase =128
_lowerCAmelCase =randn_tensor((batch_size, temb_channels) , generator=__A , device=__A )
if include_res_hidden_states_tuple:
_lowerCAmelCase =torch.manual_seed(1 )
_lowerCAmelCase =(randn_tensor(__A , generator=__A , device=__A ),)
if include_encoder_hidden_states:
_lowerCAmelCase =floats_tensor((batch_size, 32, 32) ).to(__A )
if include_skip_sample:
_lowerCAmelCase =randn_tensor(((batch_size, 3) + sizes) , generator=__A , device=__A )
return dummy_input
def UpperCamelCase__ ( self ) -> List[str]:
_lowerCAmelCase ={
'in_channels': 32,
'out_channels': 32,
'temb_channels': 128,
}
if self.block_type == "up":
_lowerCAmelCase =32
if self.block_type == "mid":
init_dict.pop('out_channels' )
_lowerCAmelCase =self.dummy_input
return init_dict, inputs_dict
def UpperCamelCase__ ( self , __A ) -> Any:
_lowerCAmelCase , _lowerCAmelCase =self.prepare_init_args_and_inputs_for_common()
_lowerCAmelCase =self.block_class(**__A )
unet_block.to(__A )
unet_block.eval()
with torch.no_grad():
_lowerCAmelCase =unet_block(**__A )
if isinstance(__A , __A ):
_lowerCAmelCase =output[0]
self.assertEqual(output.shape , self.output_shape )
_lowerCAmelCase =output[0, -1, -3:, -3:]
_lowerCAmelCase =torch.tensor(__A ).to(__A )
assert torch_all_close(output_slice.flatten() , __A , atol=5E-3 )
@unittest.skipIf(torch_device == 'mps' , 'Training is not supported in mps' )
def UpperCamelCase__ ( self ) -> Optional[Any]:
_lowerCAmelCase , _lowerCAmelCase =self.prepare_init_args_and_inputs_for_common()
_lowerCAmelCase =self.block_class(**__A )
model.to(__A )
model.train()
_lowerCAmelCase =model(**__A )
if isinstance(__A , __A ):
_lowerCAmelCase =output[0]
_lowerCAmelCase =torch.device(__A )
_lowerCAmelCase =randn_tensor(output.shape , device=__A )
_lowerCAmelCase =torch.nn.functional.mse_loss(__A , __A )
loss.backward()
| 58
|
'''simple docstring'''
from __future__ import annotations
from typing import Any
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __A ) -> None:
_lowerCAmelCase =num_of_nodes
_lowerCAmelCase =[]
_lowerCAmelCase ={}
def UpperCamelCase__ ( self , __A , __A , __A ) -> None:
self.m_edges.append([u_node, v_node, weight] )
def UpperCamelCase__ ( self , __A ) -> int:
if self.m_component[u_node] == u_node:
return u_node
return self.find_component(self.m_component[u_node] )
def UpperCamelCase__ ( self , __A ) -> None:
if self.m_component[u_node] != u_node:
for k in self.m_component:
_lowerCAmelCase =self.find_component(__A )
def UpperCamelCase__ ( self , __A , __A , __A ) -> None:
if component_size[u_node] <= component_size[v_node]:
_lowerCAmelCase =v_node
component_size[v_node] += component_size[u_node]
self.set_component(__A )
elif component_size[u_node] >= component_size[v_node]:
_lowerCAmelCase =self.find_component(__A )
component_size[u_node] += component_size[v_node]
self.set_component(__A )
def UpperCamelCase__ ( self ) -> None:
_lowerCAmelCase =[]
_lowerCAmelCase =0
_lowerCAmelCase =[-1] * self.m_num_of_nodes
# A list of components (initialized to all of the nodes)
for node in range(self.m_num_of_nodes ):
self.m_component.update({node: node} )
component_size.append(1 )
_lowerCAmelCase =self.m_num_of_nodes
while num_of_components > 1:
for edge in self.m_edges:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase =edge
_lowerCAmelCase =self.m_component[u]
_lowerCAmelCase =self.m_component[v]
if u_component != v_component:
for component in (u_component, v_component):
if (
minimum_weight_edge[component] == -1
or minimum_weight_edge[component][2] > w
):
_lowerCAmelCase =[u, v, w]
for edge in minimum_weight_edge:
if isinstance(__A , __A ):
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase =edge
_lowerCAmelCase =self.m_component[u]
_lowerCAmelCase =self.m_component[v]
if u_component != v_component:
mst_weight += w
self.union(__A , __A , __A )
print(F'''Added edge [{u} - {v}]\nAdded weight: {w}\n''' )
num_of_components -= 1
_lowerCAmelCase =[-1] * self.m_num_of_nodes
print(F'''The total weight of the minimal spanning tree is: {mst_weight}''' )
def UpperCamelCase__ ( ):
'''simple docstring'''
if __name__ == "__main__":
import doctest
doctest.testmod()
| 58
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase_ = {
'''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
'''tokenization_roformer''': ['''RoFormerTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = ['''RoFormerTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
'''ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''RoFormerForCausalLM''',
'''RoFormerForMaskedLM''',
'''RoFormerForMultipleChoice''',
'''RoFormerForQuestionAnswering''',
'''RoFormerForSequenceClassification''',
'''RoFormerForTokenClassification''',
'''RoFormerLayer''',
'''RoFormerModel''',
'''RoFormerPreTrainedModel''',
'''load_tf_weights_in_roformer''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
'''TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFRoFormerForCausalLM''',
'''TFRoFormerForMaskedLM''',
'''TFRoFormerForMultipleChoice''',
'''TFRoFormerForQuestionAnswering''',
'''TFRoFormerForSequenceClassification''',
'''TFRoFormerForTokenClassification''',
'''TFRoFormerLayer''',
'''TFRoFormerModel''',
'''TFRoFormerPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
'''FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''FlaxRoFormerForMaskedLM''',
'''FlaxRoFormerForMultipleChoice''',
'''FlaxRoFormerForQuestionAnswering''',
'''FlaxRoFormerForSequenceClassification''',
'''FlaxRoFormerForTokenClassification''',
'''FlaxRoFormerModel''',
'''FlaxRoFormerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
from .tokenization_roformer import RoFormerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_roformer_fast import RoFormerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roformer import (
ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
RoFormerForCausalLM,
RoFormerForMaskedLM,
RoFormerForMultipleChoice,
RoFormerForQuestionAnswering,
RoFormerForSequenceClassification,
RoFormerForTokenClassification,
RoFormerLayer,
RoFormerModel,
RoFormerPreTrainedModel,
load_tf_weights_in_roformer,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roformer import (
TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForMultipleChoice,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerLayer,
TFRoFormerModel,
TFRoFormerPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roformer import (
FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
FlaxRoFormerPreTrainedModel,
)
else:
import sys
lowercase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 58
|
'''simple docstring'''
from PIL import Image
def UpperCamelCase__ ( a__ , a__ ):
'''simple docstring'''
def brightness(a__ ) -> float:
return 1_2_8 + level + (c - 1_2_8)
if not -255.0 <= level <= 255.0:
raise ValueError('level must be between -255.0 (black) and 255.0 (white)' )
return img.point(a__ )
if __name__ == "__main__":
# Load image
with Image.open('''image_data/lena.jpg''') as img:
# Change brightness to 100
lowercase_ = change_brightness(img, 100)
brigt_img.save('''image_data/lena_brightness.png''', format='''png''')
| 58
| 1
|
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool
if is_datasets_available():
from datasets import load_dataset
class SCREAMING_SNAKE_CASE ( __lowercase):
"""simple docstring"""
lowercase : List[Any] = 'microsoft/speecht5_tts'
lowercase : Tuple = (
'This is a tool that reads an English text out loud. It takes an input named `text` which should contain the '
'text to read (in English) and returns a waveform object containing the sound.'
)
lowercase : Any = 'text_reader'
lowercase : str = SpeechTaProcessor
lowercase : List[str] = SpeechTaForTextToSpeech
lowercase : List[Any] = SpeechTaHifiGan
lowercase : List[str] = ['text']
lowercase : Any = ['audio']
def UpperCamelCase__ ( self ) -> Dict:
if self.post_processor is None:
_lowerCAmelCase ='microsoft/speecht5_hifigan'
super().setup()
def UpperCamelCase__ ( self , __A , __A=None ) -> str:
_lowerCAmelCase =self.pre_processor(text=__A , return_tensors='pt' , truncation=__A )
if speaker_embeddings is None:
if not is_datasets_available():
raise ImportError('Datasets needs to be installed if not passing speaker embeddings.' )
_lowerCAmelCase =load_dataset('Matthijs/cmu-arctic-xvectors' , split='validation' )
_lowerCAmelCase =torch.tensor(embeddings_dataset[7305]['xvector'] ).unsqueeze(0 )
return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}
def UpperCamelCase__ ( self , __A ) -> Any:
with torch.no_grad():
return self.model.generate_speech(**__A )
def UpperCamelCase__ ( self , __A ) -> Dict:
with torch.no_grad():
return self.post_processor(__A ).cpu().detach()
| 58
|
'''simple docstring'''
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / '''utils'''))
from test_module.custom_configuration import CustomConfig # noqa E402
lowercase_ = {
'''return_dict''': False,
'''output_hidden_states''': True,
'''output_attentions''': True,
'''torchscript''': True,
'''torch_dtype''': '''float16''',
'''use_bfloat16''': True,
'''tf_legacy_loss''': True,
'''pruned_heads''': {'''a''': 1},
'''tie_word_embeddings''': False,
'''is_decoder''': True,
'''cross_attention_hidden_size''': 128,
'''add_cross_attention''': True,
'''tie_encoder_decoder''': True,
'''max_length''': 50,
'''min_length''': 3,
'''do_sample''': True,
'''early_stopping''': True,
'''num_beams''': 3,
'''num_beam_groups''': 3,
'''diversity_penalty''': 0.5,
'''temperature''': 2.0,
'''top_k''': 10,
'''top_p''': 0.7,
'''typical_p''': 0.2,
'''repetition_penalty''': 0.8,
'''length_penalty''': 0.8,
'''no_repeat_ngram_size''': 5,
'''encoder_no_repeat_ngram_size''': 5,
'''bad_words_ids''': [1, 2, 3],
'''num_return_sequences''': 3,
'''chunk_size_feed_forward''': 5,
'''output_scores''': True,
'''return_dict_in_generate''': True,
'''forced_bos_token_id''': 2,
'''forced_eos_token_id''': 3,
'''remove_invalid_values''': True,
'''architectures''': ['''BertModel'''],
'''finetuning_task''': '''translation''',
'''id2label''': {0: '''label'''},
'''label2id''': {'''label''': '''0'''},
'''tokenizer_class''': '''BertTokenizerFast''',
'''prefix''': '''prefix''',
'''bos_token_id''': 6,
'''pad_token_id''': 7,
'''eos_token_id''': 8,
'''sep_token_id''': 9,
'''decoder_start_token_id''': 10,
'''exponential_decay_length_penalty''': (5, 1.01),
'''suppress_tokens''': [0, 1],
'''begin_suppress_tokens''': 2,
'''task_specific_params''': {'''translation''': '''some_params'''},
'''problem_type''': '''regression''',
}
@is_staging_test
class SCREAMING_SNAKE_CASE ( unittest.TestCase):
"""simple docstring"""
@classmethod
def UpperCamelCase__ ( cls ) -> Optional[Any]:
_lowerCAmelCase =TOKEN
HfFolder.save_token(__A )
@classmethod
def UpperCamelCase__ ( cls ) -> List[str]:
try:
delete_repo(token=cls._token , repo_id='test-config' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='valid_org/test-config-org' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='test-dynamic-config' )
except HTTPError:
pass
def UpperCamelCase__ ( self ) -> str:
_lowerCAmelCase =BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
config.push_to_hub('test-config' , use_auth_token=self._token )
_lowerCAmelCase =BertConfig.from_pretrained(F'''{USER}/test-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__A , getattr(__A , __A ) )
# Reset repo
delete_repo(token=self._token , repo_id='test-config' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(__A , repo_id='test-config' , push_to_hub=__A , use_auth_token=self._token )
_lowerCAmelCase =BertConfig.from_pretrained(F'''{USER}/test-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__A , getattr(__A , __A ) )
def UpperCamelCase__ ( self ) -> Dict:
_lowerCAmelCase =BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
config.push_to_hub('valid_org/test-config-org' , use_auth_token=self._token )
_lowerCAmelCase =BertConfig.from_pretrained('valid_org/test-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__A , getattr(__A , __A ) )
# Reset repo
delete_repo(token=self._token , repo_id='valid_org/test-config-org' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
__A , repo_id='valid_org/test-config-org' , push_to_hub=__A , use_auth_token=self._token )
_lowerCAmelCase =BertConfig.from_pretrained('valid_org/test-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(__A , getattr(__A , __A ) )
def UpperCamelCase__ ( self ) -> List[str]:
CustomConfig.register_for_auto_class()
_lowerCAmelCase =CustomConfig(attribute=42 )
config.push_to_hub('test-dynamic-config' , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(config.auto_map , {'AutoConfig': 'custom_configuration.CustomConfig'} )
_lowerCAmelCase =AutoConfig.from_pretrained(F'''{USER}/test-dynamic-config''' , trust_remote_code=__A )
# Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
self.assertEqual(new_config.__class__.__name__ , 'CustomConfig' )
self.assertEqual(new_config.attribute , 42 )
class SCREAMING_SNAKE_CASE ( unittest.TestCase):
"""simple docstring"""
def UpperCamelCase__ ( self ) -> List[Any]:
_lowerCAmelCase =GPTaConfig()
# attempt to modify each of int/float/bool/str config records and verify they were updated
_lowerCAmelCase =c.n_embd + 1 # int
_lowerCAmelCase =c.resid_pdrop + 1.0 # float
_lowerCAmelCase =not c.scale_attn_weights # bool
_lowerCAmelCase =c.summary_type + 'foo' # str
c.update_from_string(
F'''n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}''' )
self.assertEqual(__A , c.n_embd , 'mismatch for key: n_embd' )
self.assertEqual(__A , c.resid_pdrop , 'mismatch for key: resid_pdrop' )
self.assertEqual(__A , c.scale_attn_weights , 'mismatch for key: scale_attn_weights' )
self.assertEqual(__A , c.summary_type , 'mismatch for key: summary_type' )
def UpperCamelCase__ ( self ) -> List[str]:
_lowerCAmelCase =PretrainedConfig()
_lowerCAmelCase =[key for key in base_config.__dict__ if key not in config_common_kwargs]
# If this part of the test fails, you have arguments to addin config_common_kwargs above.
self.assertListEqual(
__A , ['is_encoder_decoder', '_name_or_path', '_commit_hash', 'transformers_version'] )
_lowerCAmelCase =[key for key, value in config_common_kwargs.items() if value == getattr(__A , __A )]
if len(__A ) > 0:
raise ValueError(
'The following keys are set with the default values in'
' `test_configuration_common.config_common_kwargs` pick another value for them:'
F''' {', '.join(__A )}.''' )
def UpperCamelCase__ ( self ) -> Optional[int]:
with self.assertRaises(__A ):
# config is in subfolder, the following should not work without specifying the subfolder
_lowerCAmelCase =BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder' )
_lowerCAmelCase =BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder' , subfolder='bert' )
self.assertIsNotNone(__A )
def UpperCamelCase__ ( self ) -> List[str]:
# A mock response for an HTTP head request to emulate server down
_lowerCAmelCase =mock.Mock()
_lowerCAmelCase =500
_lowerCAmelCase ={}
_lowerCAmelCase =HTTPError
_lowerCAmelCase ={}
# Download this model to make sure it's in the cache.
_lowerCAmelCase =BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert' )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch('requests.Session.request' , return_value=__A ) as mock_head:
_lowerCAmelCase =BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert' )
# This check we did call the fake head request
mock_head.assert_called()
def UpperCamelCase__ ( self ) -> Optional[int]:
# This test is for deprecated behavior and can be removed in v5
_lowerCAmelCase =BertConfig.from_pretrained(
'https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json' )
def UpperCamelCase__ ( self ) -> Any:
_lowerCAmelCase =AutoConfig.from_pretrained('bert-base-cased' )
_lowerCAmelCase =['config.4.0.0.json']
with tempfile.TemporaryDirectory() as tmp_dir:
configuration.save_pretrained(__A )
_lowerCAmelCase =2
json.dump(configuration.to_dict() , open(os.path.join(__A , 'config.4.0.0.json' ) , 'w' ) )
# This should pick the new configuration file as the version of Transformers is > 4.0.0
_lowerCAmelCase =AutoConfig.from_pretrained(__A )
self.assertEqual(new_configuration.hidden_size , 2 )
# Will need to be adjusted if we reach v42 and this test is still here.
# Should pick the old configuration file as the version of Transformers is < 4.42.0
_lowerCAmelCase =['config.42.0.0.json']
_lowerCAmelCase =768
configuration.save_pretrained(__A )
shutil.move(os.path.join(__A , 'config.4.0.0.json' ) , os.path.join(__A , 'config.42.0.0.json' ) )
_lowerCAmelCase =AutoConfig.from_pretrained(__A )
self.assertEqual(new_configuration.hidden_size , 768 )
def UpperCamelCase__ ( self ) -> Any:
# This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
_lowerCAmelCase ='hf-internal-testing/test-two-configs'
import transformers as new_transformers
_lowerCAmelCase ='v4.0.0'
_lowerCAmelCase , _lowerCAmelCase =new_transformers.models.auto.AutoConfig.from_pretrained(
__A , return_unused_kwargs=__A )
self.assertEqual(new_configuration.hidden_size , 2 )
# This checks `_configuration_file` ia not kept in the kwargs by mistake.
self.assertDictEqual(__A , {} )
# Testing an older version by monkey-patching the version in the module it's used.
import transformers as old_transformers
_lowerCAmelCase ='v3.0.0'
_lowerCAmelCase =old_transformers.models.auto.AutoConfig.from_pretrained(__A )
self.assertEqual(old_configuration.hidden_size , 768 )
| 58
| 1
|
'''simple docstring'''
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
FEATURE_EXTRACTOR_NAME,
FLAX_WEIGHTS_NAME,
HF_MODULES_CACHE,
HUGGINGFACE_CO_PREFIX,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
MODEL_CARD_NAME,
MULTIPLE_CHOICE_DUMMY_INPUTS,
PYTORCH_PRETRAINED_BERT_CACHE,
PYTORCH_TRANSFORMERS_CACHE,
S3_BUCKET_PREFIX,
SENTENCEPIECE_UNDERLINE,
SPIECE_UNDERLINE,
TF2_WEIGHTS_NAME,
TF_WEIGHTS_NAME,
TORCH_FX_REQUIRED_VERSION,
TRANSFORMERS_CACHE,
TRANSFORMERS_DYNAMIC_MODULE_NAME,
USE_JAX,
USE_TF,
USE_TORCH,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
ContextManagers,
DummyObject,
EntryNotFoundError,
ExplicitEnum,
ModelOutput,
PaddingStrategy,
PushToHubMixin,
RepositoryNotFoundError,
RevisionNotFoundError,
TensorType,
_LazyModule,
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
cached_property,
copy_func,
default_cache_path,
define_sagemaker_information,
get_cached_models,
get_file_from_repo,
get_full_repo_name,
get_torch_version,
has_file,
http_user_agent,
is_apex_available,
is_bsa_available,
is_coloredlogs_available,
is_datasets_available,
is_detectrona_available,
is_faiss_available,
is_flax_available,
is_ftfy_available,
is_in_notebook,
is_ipex_available,
is_librosa_available,
is_offline_mode,
is_onnx_available,
is_pandas_available,
is_phonemizer_available,
is_protobuf_available,
is_psutil_available,
is_pyanvml_available,
is_pyctcdecode_available,
is_pytesseract_available,
is_pytorch_quantization_available,
is_rjieba_available,
is_sagemaker_dp_enabled,
is_sagemaker_mp_enabled,
is_scipy_available,
is_sentencepiece_available,
is_seqio_available,
is_sklearn_available,
is_soundfile_availble,
is_spacy_available,
is_speech_available,
is_tensor,
is_tensorflow_probability_available,
is_tfaonnx_available,
is_tf_available,
is_timm_available,
is_tokenizers_available,
is_torch_available,
is_torch_bfaa_available,
is_torch_cuda_available,
is_torch_fx_available,
is_torch_fx_proxy,
is_torch_mps_available,
is_torch_tfaa_available,
is_torch_tpu_available,
is_torchaudio_available,
is_training_run_on_sagemaker,
is_vision_available,
replace_return_docstrings,
requires_backends,
to_numpy,
to_py_obj,
torch_only_method,
)
| 58
|
'''simple docstring'''
from __future__ import annotations
lowercase_ = 10
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
_lowerCAmelCase =1
_lowerCAmelCase =max(a__ )
while placement <= max_digit:
# declare and initialize empty buckets
_lowerCAmelCase =[[] for _ in range(a__ )]
# split list_of_ints between the buckets
for i in list_of_ints:
_lowerCAmelCase =int((i / placement) % RADIX )
buckets[tmp].append(a__ )
# put each buckets' contents into list_of_ints
_lowerCAmelCase =0
for b in range(a__ ):
for i in buckets[b]:
_lowerCAmelCase =i
a += 1
# move to next
placement *= RADIX
return list_of_ints
if __name__ == "__main__":
import doctest
doctest.testmod()
| 58
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowercase_ = {
'''configuration_mobilevit''': ['''MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MobileViTConfig''', '''MobileViTOnnxConfig'''],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = ['''MobileViTFeatureExtractor''']
lowercase_ = ['''MobileViTImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
'''MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MobileViTForImageClassification''',
'''MobileViTForSemanticSegmentation''',
'''MobileViTModel''',
'''MobileViTPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
'''TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFMobileViTForImageClassification''',
'''TFMobileViTForSemanticSegmentation''',
'''TFMobileViTModel''',
'''TFMobileViTPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_mobilevit import MobileViTFeatureExtractor
from .image_processing_mobilevit import MobileViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mobilevit import (
MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTModel,
MobileViTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mobilevit import (
TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFMobileViTForImageClassification,
TFMobileViTForSemanticSegmentation,
TFMobileViTModel,
TFMobileViTPreTrainedModel,
)
else:
import sys
lowercase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 58
|
'''simple docstring'''
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
FEATURE_EXTRACTOR_NAME,
FLAX_WEIGHTS_NAME,
HF_MODULES_CACHE,
HUGGINGFACE_CO_PREFIX,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
MODEL_CARD_NAME,
MULTIPLE_CHOICE_DUMMY_INPUTS,
PYTORCH_PRETRAINED_BERT_CACHE,
PYTORCH_TRANSFORMERS_CACHE,
S3_BUCKET_PREFIX,
SENTENCEPIECE_UNDERLINE,
SPIECE_UNDERLINE,
TF2_WEIGHTS_NAME,
TF_WEIGHTS_NAME,
TORCH_FX_REQUIRED_VERSION,
TRANSFORMERS_CACHE,
TRANSFORMERS_DYNAMIC_MODULE_NAME,
USE_JAX,
USE_TF,
USE_TORCH,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
ContextManagers,
DummyObject,
EntryNotFoundError,
ExplicitEnum,
ModelOutput,
PaddingStrategy,
PushToHubMixin,
RepositoryNotFoundError,
RevisionNotFoundError,
TensorType,
_LazyModule,
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
cached_property,
copy_func,
default_cache_path,
define_sagemaker_information,
get_cached_models,
get_file_from_repo,
get_full_repo_name,
get_torch_version,
has_file,
http_user_agent,
is_apex_available,
is_bsa_available,
is_coloredlogs_available,
is_datasets_available,
is_detectrona_available,
is_faiss_available,
is_flax_available,
is_ftfy_available,
is_in_notebook,
is_ipex_available,
is_librosa_available,
is_offline_mode,
is_onnx_available,
is_pandas_available,
is_phonemizer_available,
is_protobuf_available,
is_psutil_available,
is_pyanvml_available,
is_pyctcdecode_available,
is_pytesseract_available,
is_pytorch_quantization_available,
is_rjieba_available,
is_sagemaker_dp_enabled,
is_sagemaker_mp_enabled,
is_scipy_available,
is_sentencepiece_available,
is_seqio_available,
is_sklearn_available,
is_soundfile_availble,
is_spacy_available,
is_speech_available,
is_tensor,
is_tensorflow_probability_available,
is_tfaonnx_available,
is_tf_available,
is_timm_available,
is_tokenizers_available,
is_torch_available,
is_torch_bfaa_available,
is_torch_cuda_available,
is_torch_fx_available,
is_torch_fx_proxy,
is_torch_mps_available,
is_torch_tfaa_available,
is_torch_tpu_available,
is_torchaudio_available,
is_training_run_on_sagemaker,
is_vision_available,
replace_return_docstrings,
requires_backends,
to_numpy,
to_py_obj,
torch_only_method,
)
| 58
| 1
|
'''simple docstring'''
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def UpperCamelCase__ ( a__ , a__=0.999 , a__="cosine" , ):
'''simple docstring'''
if alpha_transform_type == "cosine":
def alpha_bar_fn(a__ ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(a__ ):
return math.exp(t * -12.0 )
else:
raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}''' )
_lowerCAmelCase =[]
for i in range(a__ ):
_lowerCAmelCase =i / num_diffusion_timesteps
_lowerCAmelCase =(i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(a__ ) / alpha_bar_fn(a__ ) , a__ ) )
return torch.tensor(a__ , dtype=torch.floataa )
class SCREAMING_SNAKE_CASE ( __lowercase , __lowercase):
"""simple docstring"""
lowercase : List[str] = [e.name for e in KarrasDiffusionSchedulers]
lowercase : Any = 2
@register_to_config
def __init__( self , __A = 1000 , __A = 0.00_085 , __A = 0.012 , __A = "linear" , __A = None , __A = "epsilon" , __A = "linspace" , __A = 0 , ) -> str:
if trained_betas is not None:
_lowerCAmelCase =torch.tensor(__A , dtype=torch.floataa )
elif beta_schedule == "linear":
_lowerCAmelCase =torch.linspace(__A , __A , __A , dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
_lowerCAmelCase =(
torch.linspace(beta_start**0.5 , beta_end**0.5 , __A , dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
_lowerCAmelCase =betas_for_alpha_bar(__A )
else:
raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}''' )
_lowerCAmelCase =1.0 - self.betas
_lowerCAmelCase =torch.cumprod(self.alphas , dim=0 )
# set all values
self.set_timesteps(__A , __A , __A )
def UpperCamelCase__ ( self , __A , __A=None ) -> Optional[Any]:
if schedule_timesteps is None:
_lowerCAmelCase =self.timesteps
_lowerCAmelCase =(schedule_timesteps == timestep).nonzero()
# The sigma index that is taken for the **very** first `step`
# is always the second index (or the last index if there is only 1)
# This way we can ensure we don't accidentally skip a sigma in
# case we start in the middle of the denoising schedule (e.g. for image-to-image)
if len(self._index_counter ) == 0:
_lowerCAmelCase =1 if len(__A ) > 1 else 0
else:
_lowerCAmelCase =timestep.cpu().item() if torch.is_tensor(__A ) else timestep
_lowerCAmelCase =self._index_counter[timestep_int]
return indices[pos].item()
@property
def UpperCamelCase__ ( self ) -> List[str]:
# standard deviation of the initial noise distribution
if self.config.timestep_spacing in ["linspace", "trailing"]:
return self.sigmas.max()
return (self.sigmas.max() ** 2 + 1) ** 0.5
def UpperCamelCase__ ( self , __A , __A , ) -> torch.FloatTensor:
_lowerCAmelCase =self.index_for_timestep(__A )
if self.state_in_first_order:
_lowerCAmelCase =self.sigmas[step_index]
else:
_lowerCAmelCase =self.sigmas_interpol[step_index]
_lowerCAmelCase =sample / ((sigma**2 + 1) ** 0.5)
return sample
def UpperCamelCase__ ( self , __A , __A = None , __A = None , ) -> List[Any]:
_lowerCAmelCase =num_inference_steps
_lowerCAmelCase =num_train_timesteps or self.config.num_train_timesteps
# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
if self.config.timestep_spacing == "linspace":
_lowerCAmelCase =np.linspace(0 , num_train_timesteps - 1 , __A , dtype=__A )[::-1].copy()
elif self.config.timestep_spacing == "leading":
_lowerCAmelCase =num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
_lowerCAmelCase =(np.arange(0 , __A ) * step_ratio).round()[::-1].copy().astype(__A )
timesteps += self.config.steps_offset
elif self.config.timestep_spacing == "trailing":
_lowerCAmelCase =num_train_timesteps / self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
_lowerCAmelCase =(np.arange(__A , 0 , -step_ratio )).round().copy().astype(__A )
timesteps -= 1
else:
raise ValueError(
F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.''' )
_lowerCAmelCase =np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
_lowerCAmelCase =torch.from_numpy(np.log(__A ) ).to(__A )
_lowerCAmelCase =np.interp(__A , np.arange(0 , len(__A ) ) , __A )
_lowerCAmelCase =np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
_lowerCAmelCase =torch.from_numpy(__A ).to(device=__A )
# interpolate sigmas
_lowerCAmelCase =sigmas.log().lerp(sigmas.roll(1 ).log() , 0.5 ).exp()
_lowerCAmelCase =torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2 ), sigmas[-1:]] )
_lowerCAmelCase =torch.cat(
[sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2 ), sigmas_interpol[-1:]] )
if str(__A ).startswith('mps' ):
# mps does not support float64
_lowerCAmelCase =torch.from_numpy(__A ).to(__A , dtype=torch.floataa )
else:
_lowerCAmelCase =torch.from_numpy(__A ).to(__A )
# interpolate timesteps
_lowerCAmelCase =self.sigma_to_t(__A ).to(__A , dtype=timesteps.dtype )
_lowerCAmelCase =torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]) , dim=-1 ).flatten()
_lowerCAmelCase =torch.cat([timesteps[:1], interleaved_timesteps] )
_lowerCAmelCase =None
# for exp beta schedules, such as the one for `pipeline_shap_e.py`
# we need an index counter
_lowerCAmelCase =defaultdict(__A )
def UpperCamelCase__ ( self , __A ) -> int:
# get log sigma
_lowerCAmelCase =sigma.log()
# get distribution
_lowerCAmelCase =log_sigma - self.log_sigmas[:, None]
# get sigmas range
_lowerCAmelCase =dists.ge(0 ).cumsum(dim=0 ).argmax(dim=0 ).clamp(max=self.log_sigmas.shape[0] - 2 )
_lowerCAmelCase =low_idx + 1
_lowerCAmelCase =self.log_sigmas[low_idx]
_lowerCAmelCase =self.log_sigmas[high_idx]
# interpolate sigmas
_lowerCAmelCase =(low - log_sigma) / (low - high)
_lowerCAmelCase =w.clamp(0 , 1 )
# transform interpolation to time range
_lowerCAmelCase =(1 - w) * low_idx + w * high_idx
_lowerCAmelCase =t.view(sigma.shape )
return t
@property
def UpperCamelCase__ ( self ) -> Tuple:
return self.sample is None
def UpperCamelCase__ ( self , __A , __A , __A , __A = True , ) -> Union[SchedulerOutput, Tuple]:
_lowerCAmelCase =self.index_for_timestep(__A )
# advance index counter by 1
_lowerCAmelCase =timestep.cpu().item() if torch.is_tensor(__A ) else timestep
self._index_counter[timestep_int] += 1
if self.state_in_first_order:
_lowerCAmelCase =self.sigmas[step_index]
_lowerCAmelCase =self.sigmas_interpol[step_index + 1]
_lowerCAmelCase =self.sigmas[step_index + 1]
else:
# 2nd order / KDPM2's method
_lowerCAmelCase =self.sigmas[step_index - 1]
_lowerCAmelCase =self.sigmas_interpol[step_index]
_lowerCAmelCase =self.sigmas[step_index]
# currently only gamma=0 is supported. This usually works best anyways.
# We can support gamma in the future but then need to scale the timestep before
# passing it to the model which requires a change in API
_lowerCAmelCase =0
_lowerCAmelCase =sigma * (gamma + 1) # Note: sigma_hat == sigma for now
# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
if self.config.prediction_type == "epsilon":
_lowerCAmelCase =sigma_hat if self.state_in_first_order else sigma_interpol
_lowerCAmelCase =sample - sigma_input * model_output
elif self.config.prediction_type == "v_prediction":
_lowerCAmelCase =sigma_hat if self.state_in_first_order else sigma_interpol
_lowerCAmelCase =model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
sample / (sigma_input**2 + 1)
)
elif self.config.prediction_type == "sample":
raise NotImplementedError('prediction_type not implemented yet: sample' )
else:
raise ValueError(
F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`''' )
if self.state_in_first_order:
# 2. Convert to an ODE derivative for 1st order
_lowerCAmelCase =(sample - pred_original_sample) / sigma_hat
# 3. delta timestep
_lowerCAmelCase =sigma_interpol - sigma_hat
# store for 2nd order step
_lowerCAmelCase =sample
else:
# DPM-Solver-2
# 2. Convert to an ODE derivative for 2nd order
_lowerCAmelCase =(sample - pred_original_sample) / sigma_interpol
# 3. delta timestep
_lowerCAmelCase =sigma_next - sigma_hat
_lowerCAmelCase =self.sample
_lowerCAmelCase =None
_lowerCAmelCase =sample + derivative * dt
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=__A )
def UpperCamelCase__ ( self , __A , __A , __A , ) -> torch.FloatTensor:
# Make sure sigmas and timesteps have the same device and dtype as original_samples
_lowerCAmelCase =self.sigmas.to(device=original_samples.device , dtype=original_samples.dtype )
if original_samples.device.type == "mps" and torch.is_floating_point(__A ):
# mps does not support float64
_lowerCAmelCase =self.timesteps.to(original_samples.device , dtype=torch.floataa )
_lowerCAmelCase =timesteps.to(original_samples.device , dtype=torch.floataa )
else:
_lowerCAmelCase =self.timesteps.to(original_samples.device )
_lowerCAmelCase =timesteps.to(original_samples.device )
_lowerCAmelCase =[self.index_for_timestep(__A , __A ) for t in timesteps]
_lowerCAmelCase =sigmas[step_indices].flatten()
while len(sigma.shape ) < len(original_samples.shape ):
_lowerCAmelCase =sigma.unsqueeze(-1 )
_lowerCAmelCase =original_samples + noise * sigma
return noisy_samples
def __len__( self ) -> List[Any]:
return self.config.num_train_timesteps
| 58
|
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase__ ( a__ ):
'''simple docstring'''
_lowerCAmelCase =len(a__ ) // 2
# choose the middle 3 elements
_lowerCAmelCase =lst[m - 1 : m + 2]
# if middle element is peak
if three[1] > three[0] and three[1] > three[2]:
return three[1]
# if increasing, recurse on right
elif three[0] < three[2]:
if len(lst[:m] ) == 2:
m -= 1
return peak(lst[m:] )
# decreasing
else:
if len(lst[:m] ) == 2:
m += 1
return peak(lst[:m] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 58
| 1
|
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