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"""simple docstring"""
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
lowercase = logging.get_logger(__name__)
def UpperCAmelCase ( A : Optional[Any] , A : Optional[int] ):
'''simple docstring'''
try:
with open(A , 'rb' ) as flax_state_f:
_UpperCAmelCase = from_bytes(A , flax_state_f.read() )
except UnpicklingError as e:
try:
with open(A ) as f:
if f.read().startswith('version' ):
raise OSError(
'You seem to have cloned a repository without having git-lfs installed. Please'
' install git-lfs and run `git lfs install` followed by `git lfs pull` in the'
' folder you cloned.' )
else:
raise ValueError from e
except (UnicodeDecodeError, ValueError):
raise EnvironmentError(f'Unable to convert {model_file} to Flax deserializable object. ' )
return load_flax_weights_in_pytorch_model(A , A )
def UpperCAmelCase ( A : str , A : Union[str, Any] ):
'''simple docstring'''
try:
import torch # noqa: F401
except ImportError:
logger.error(
'Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see'
' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'
' instructions.' )
raise
# check if we have bf16 weights
_UpperCAmelCase = flatten_dict(jax.tree_util.tree_map(lambda A : x.dtype == jnp.bfloataa , A ) ).values()
if any(A ):
# convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16
# and bf16 is not fully supported in PT yet.
logger.warning(
'Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '
'before loading those in PyTorch model.' )
_UpperCAmelCase = jax.tree_util.tree_map(
lambda A : params.astype(np.floataa ) if params.dtype == jnp.bfloataa else params , A )
_UpperCAmelCase = ''
_UpperCAmelCase = flatten_dict(A , sep='.' )
_UpperCAmelCase = pt_model.state_dict()
# keep track of unexpected & missing keys
_UpperCAmelCase = []
_UpperCAmelCase = set(pt_model_dict.keys() )
for flax_key_tuple, flax_tensor in flax_state_dict.items():
_UpperCAmelCase = flax_key_tuple.split('.' )
if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
_UpperCAmelCase = flax_key_tuple_array[:-1] + ['weight']
_UpperCAmelCase = jnp.transpose(A , (3, 2, 0, 1) )
elif flax_key_tuple_array[-1] == "kernel":
_UpperCAmelCase = flax_key_tuple_array[:-1] + ['weight']
_UpperCAmelCase = flax_tensor.T
elif flax_key_tuple_array[-1] == "scale":
_UpperCAmelCase = flax_key_tuple_array[:-1] + ['weight']
if "time_embedding" not in flax_key_tuple_array:
for i, flax_key_tuple_string in enumerate(A ):
_UpperCAmelCase = (
flax_key_tuple_string.replace('_0' , '.0' )
.replace('_1' , '.1' )
.replace('_2' , '.2' )
.replace('_3' , '.3' )
.replace('_4' , '.4' )
.replace('_5' , '.5' )
.replace('_6' , '.6' )
.replace('_7' , '.7' )
.replace('_8' , '.8' )
.replace('_9' , '.9' )
)
_UpperCAmelCase = '.'.join(A )
if flax_key in pt_model_dict:
if flax_tensor.shape != pt_model_dict[flax_key].shape:
raise ValueError(
f'Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '
f'to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.' )
else:
# add weight to pytorch dict
_UpperCAmelCase = np.asarray(A ) if not isinstance(A , np.ndarray ) else flax_tensor
_UpperCAmelCase = torch.from_numpy(A )
# remove from missing keys
missing_keys.remove(A )
else:
# weight is not expected by PyTorch model
unexpected_keys.append(A )
pt_model.load_state_dict(A )
# re-transform missing_keys to list
_UpperCAmelCase = list(A )
if len(A ) > 0:
logger.warning(
'Some weights of the Flax model were not used when initializing the PyTorch model'
f' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'
f' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'
' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'
f' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'
' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'
' FlaxBertForSequenceClassification model).' )
if len(A ) > 0:
logger.warning(
f'Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'
f' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'
' use it for predictions and inference.' )
return pt_model
| 24
|
"""simple docstring"""
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # see https://github.com/huggingface/transformers/issues/11457
class lowercase__ ( A, unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = BarthezTokenizer
_UpperCAmelCase = BarthezTokenizerFast
_UpperCAmelCase = True
_UpperCAmelCase = True
def lowerCamelCase_ ( self ) -> Optional[int]:
super().setUp()
_UpperCAmelCase = BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez' )
tokenizer.save_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname , legacy_format=snake_case )
_UpperCAmelCase = tokenizer
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = '<pad>'
_UpperCAmelCase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case ) , snake_case )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case ) , snake_case )
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '<s>' )
self.assertEqual(vocab_keys[1] , '<pad>' )
self.assertEqual(vocab_keys[-1] , '<mask>' )
self.assertEqual(len(snake_case ) , 101122 )
def lowerCamelCase_ ( self ) -> List[Any]:
self.assertEqual(self.get_tokenizer().vocab_size , 101122 )
@require_torch
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
_UpperCAmelCase = [0, 57, 3018, 70307, 91, 2]
_UpperCAmelCase = self.tokenizer(
snake_case , max_length=len(snake_case ) , padding=snake_case , truncation=snake_case , return_tensors='pt' )
self.assertIsInstance(snake_case , snake_case )
self.assertEqual((2, 6) , batch.input_ids.shape )
self.assertEqual((2, 6) , batch.attention_mask.shape )
_UpperCAmelCase = batch.input_ids.tolist()[0]
self.assertListEqual(snake_case , snake_case )
def lowerCamelCase_ ( self ) -> Optional[Any]:
if not self.test_rust_tokenizer:
return
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = self.get_rust_tokenizer()
_UpperCAmelCase = 'I was born in 92000, and this is falsé.'
_UpperCAmelCase = tokenizer.tokenize(snake_case )
_UpperCAmelCase = rust_tokenizer.tokenize(snake_case )
self.assertListEqual(snake_case , snake_case )
_UpperCAmelCase = tokenizer.encode(snake_case , add_special_tokens=snake_case )
_UpperCAmelCase = rust_tokenizer.encode(snake_case , add_special_tokens=snake_case )
self.assertListEqual(snake_case , snake_case )
_UpperCAmelCase = self.get_rust_tokenizer()
_UpperCAmelCase = tokenizer.encode(snake_case )
_UpperCAmelCase = rust_tokenizer.encode(snake_case )
self.assertListEqual(snake_case , snake_case )
@slow
def lowerCamelCase_ ( self ) -> Optional[int]:
# fmt: off
_UpperCAmelCase = {'input_ids': [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 2, 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, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 5, 2]], '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, 0, 0, 0, 0, 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, 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]]} # noqa: E501
# fmt: on
# moussaKam/mbarthez is a french model. So we also use french texts.
_UpperCAmelCase = [
'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
'telles que la traduction et la synthèse de texte.',
]
self.tokenizer_integration_test_util(
expected_encoding=snake_case , model_name='moussaKam/mbarthez' , revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6' , sequences=snake_case , )
| 24
| 1
|
"""simple docstring"""
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'files' , [
['full:README.md', 'dataset_infos.json'],
['empty:README.md', 'dataset_infos.json'],
['dataset_infos.json'],
['full:README.md'],
] , )
def UpperCAmelCase ( A : str , A : Optional[int] ):
'''simple docstring'''
_UpperCAmelCase = tmp_path_factory.mktemp('dset_infos_dir' )
if "full:README.md" in files:
with open(dataset_infos_dir / 'README.md' , 'w' ) as f:
f.write('---\ndataset_info:\n dataset_size: 42\n---' )
if "empty:README.md" in files:
with open(dataset_infos_dir / 'README.md' , 'w' ) as f:
f.write('' )
# we want to support dataset_infos.json for backward compatibility
if "dataset_infos.json" in files:
with open(dataset_infos_dir / 'dataset_infos.json' , 'w' ) as f:
f.write('{"default": {"dataset_size": 42}}' )
_UpperCAmelCase = DatasetInfosDict.from_directory(A )
assert dataset_infos
assert dataset_infos["default"].dataset_size == 42
@pytest.mark.parametrize(
'dataset_info' , [
DatasetInfo(),
DatasetInfo(
description='foo' , features=Features({'a': Value('int32' )} ) , builder_name='builder' , config_name='config' , version='1.0.0' , splits=[{'name': 'train'}] , download_size=42 , ),
] , )
def UpperCAmelCase ( A : str , A : DatasetInfo ):
'''simple docstring'''
_UpperCAmelCase = str(A )
dataset_info.write_to_directory(A )
_UpperCAmelCase = DatasetInfo.from_directory(A )
assert dataset_info == reloaded
assert os.path.exists(os.path.join(A , 'dataset_info.json' ) )
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = DatasetInfo(
description='foo' , citation='bar' , homepage='https://foo.bar' , license='CC0' , features=Features({'a': Value('int32' )} ) , post_processed={} , supervised_keys=() , task_templates=[] , builder_name='builder' , config_name='config' , version='1.0.0' , splits=[{'name': 'train', 'num_examples': 42}] , download_checksums={} , download_size=1337 , post_processing_size=442 , dataset_size=1234 , size_in_bytes=1337 + 442 + 1234 , )
_UpperCAmelCase = dataset_info._to_yaml_dict()
assert sorted(A ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML )
for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
assert key in dataset_info_yaml_dict
assert isinstance(dataset_info_yaml_dict[key] , (list, dict, int, str) )
_UpperCAmelCase = yaml.safe_dump(A )
_UpperCAmelCase = yaml.safe_load(A )
assert dataset_info_yaml_dict == reloaded
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = DatasetInfo()
_UpperCAmelCase = dataset_info._to_yaml_dict()
assert dataset_info_yaml_dict == {}
@pytest.mark.parametrize(
'dataset_infos_dict' , [
DatasetInfosDict(),
DatasetInfosDict({'default': DatasetInfo()} ),
DatasetInfosDict({'my_config_name': DatasetInfo()} ),
DatasetInfosDict(
{
'default': DatasetInfo(
description='foo' , features=Features({'a': Value('int32' )} ) , builder_name='builder' , config_name='config' , version='1.0.0' , splits=[{'name': 'train'}] , download_size=42 , )
} ),
DatasetInfosDict(
{
'v1': DatasetInfo(dataset_size=42 ),
'v2': DatasetInfo(dataset_size=1337 ),
} ),
] , )
def UpperCAmelCase ( A : Tuple , A : DatasetInfosDict ):
'''simple docstring'''
_UpperCAmelCase = str(A )
dataset_infos_dict.write_to_directory(A )
_UpperCAmelCase = DatasetInfosDict.from_directory(A )
# the config_name of the dataset_infos_dict take over the attribute
for config_name, dataset_info in dataset_infos_dict.items():
_UpperCAmelCase = config_name
# the yaml representation doesn't include fields like description or citation
# so we just test that we can recover what we can from the yaml
_UpperCAmelCase = DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict() )
assert dataset_infos_dict == reloaded
if dataset_infos_dict:
assert os.path.exists(os.path.join(A , 'README.md' ) )
| 24
|
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class lowercase__ ( A, unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = DiTPipeline
_UpperCAmelCase = CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
_UpperCAmelCase = PipelineTesterMixin.required_optional_params - {
'''latents''',
'''num_images_per_prompt''',
'''callback''',
'''callback_steps''',
}
_UpperCAmelCase = CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
_UpperCAmelCase = False
def lowerCamelCase_ ( self ) -> str:
torch.manual_seed(0 )
_UpperCAmelCase = TransformeraDModel(
sample_size=16 , num_layers=2 , patch_size=4 , attention_head_dim=8 , num_attention_heads=2 , in_channels=4 , out_channels=8 , attention_bias=snake_case , activation_fn='gelu-approximate' , num_embeds_ada_norm=1000 , norm_type='ada_norm_zero' , norm_elementwise_affine=snake_case , )
_UpperCAmelCase = AutoencoderKL()
_UpperCAmelCase = DDIMScheduler()
_UpperCAmelCase = {'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
return components
def lowerCamelCase_ ( self , snake_case , snake_case=0 ) -> Optional[Any]:
if str(snake_case ).startswith('mps' ):
_UpperCAmelCase = torch.manual_seed(snake_case )
else:
_UpperCAmelCase = torch.Generator(device=snake_case ).manual_seed(snake_case )
_UpperCAmelCase = {
'class_labels': [1],
'generator': generator,
'num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
def lowerCamelCase_ ( self ) -> List[Any]:
_UpperCAmelCase = 'cpu'
_UpperCAmelCase = self.get_dummy_components()
_UpperCAmelCase = self.pipeline_class(**snake_case )
pipe.to(snake_case )
pipe.set_progress_bar_config(disable=snake_case )
_UpperCAmelCase = self.get_dummy_inputs(snake_case )
_UpperCAmelCase = pipe(**snake_case ).images
_UpperCAmelCase = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 16, 16, 3) )
_UpperCAmelCase = np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457] )
_UpperCAmelCase = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(snake_case , 1E-3 )
def lowerCamelCase_ ( self ) -> Any:
self._test_inference_batch_single_identical(relax_max_difference=snake_case , expected_max_diff=1E-3 )
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def lowerCamelCase_ ( self ) -> Optional[int]:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
@require_torch_gpu
@slow
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> int:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = DiTPipeline.from_pretrained('facebook/DiT-XL-2-256' )
pipe.to('cuda' )
_UpperCAmelCase = ['vase', 'umbrella', 'white shark', 'white wolf']
_UpperCAmelCase = pipe.get_label_ids(snake_case )
_UpperCAmelCase = pipe(snake_case , generator=snake_case , num_inference_steps=40 , output_type='np' ).images
for word, image in zip(snake_case , snake_case ):
_UpperCAmelCase = load_numpy(
f'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy' )
assert np.abs((expected_image - image).max() ) < 1E-2
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = DiTPipeline.from_pretrained('facebook/DiT-XL-2-512' )
_UpperCAmelCase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.to('cuda' )
_UpperCAmelCase = ['vase', 'umbrella']
_UpperCAmelCase = pipe.get_label_ids(snake_case )
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = pipe(snake_case , generator=snake_case , num_inference_steps=25 , output_type='np' ).images
for word, image in zip(snake_case , snake_case ):
_UpperCAmelCase = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
f'/dit/{word}_512.npy' )
assert np.abs((expected_image - image).max() ) < 1E-1
| 24
| 1
|
"""simple docstring"""
import os
import jsonlines
import numpy as np
from tqdm import tqdm
lowercase = 20_48
lowercase = 40_96
lowercase = 42
lowercase = os.environ.pop('''PROCESS_TRAIN''', '''false''')
lowercase = {'''null''': 0, '''short''': 1, '''long''': 2, '''yes''': 3, '''no''': 4}
def UpperCAmelCase ( A : str ):
'''simple docstring'''
def choose_first(A : int , A : Tuple=False ):
assert isinstance(A , A )
if len(A ) == 1:
_UpperCAmelCase = answer[0]
return {k: [answer[k]] for k in answer} if is_long_answer else answer
for a in answer:
if is_long_answer:
_UpperCAmelCase = {k: [a[k]] for k in a}
if len(a['start_token'] ) > 0:
break
return a
_UpperCAmelCase = {'id': example['id']}
_UpperCAmelCase = example['annotations']
_UpperCAmelCase = annotation['yes_no_answer']
if 0 in yes_no_answer or 1 in yes_no_answer:
_UpperCAmelCase = ['yes'] if 1 in yes_no_answer else ['no']
_UpperCAmelCase = _UpperCAmelCase = []
_UpperCAmelCase = _UpperCAmelCase = []
_UpperCAmelCase = ['<cls>']
else:
_UpperCAmelCase = ['short']
_UpperCAmelCase = choose_first(annotation['short_answers'] )
if len(out['start_token'] ) == 0:
# answer will be long if short is not available
_UpperCAmelCase = ['long']
_UpperCAmelCase = choose_first(annotation['long_answer'] , is_long_answer=A )
_UpperCAmelCase = []
answer.update(A )
# disregard some samples
if len(answer['start_token'] ) > 1 or answer["start_token"] == answer["end_token"]:
_UpperCAmelCase = True
else:
_UpperCAmelCase = False
_UpperCAmelCase = ['start_token', 'end_token', 'start_byte', 'end_byte', 'text']
if not all(isinstance(answer[k] , A ) for k in cols ):
raise ValueError('Issue in ID' , example['id'] )
return answer
def UpperCAmelCase ( A : Dict , A : str=False ):
'''simple docstring'''
_UpperCAmelCase = _get_single_answer(A )
# bytes are of no use
del answer["start_byte"]
del answer["end_byte"]
# handle yes_no answers explicitly
if answer["category"][0] in ["yes", "no"]: # category is list with one element
_UpperCAmelCase = example['document']['tokens']
_UpperCAmelCase = []
for i in range(len(doc['token'] ) ):
if not doc["is_html"][i]:
context.append(doc['token'][i] )
return {
"context": " ".join(A ),
"answer": {
"start_token": -100, # ignore index in cross-entropy
"end_token": -100, # ignore index in cross-entropy
"category": answer["category"],
"span": answer["category"], # extra
},
}
# later, help in removing all no answers
if answer["start_token"] == [-1]:
return {
"context": "None",
"answer": {
"start_token": -1,
"end_token": -1,
"category": "null",
"span": "None", # extra
},
}
# handling normal samples
_UpperCAmelCase = ['start_token', 'end_token']
answer.update({k: answer[k][0] if len(answer[k] ) > 0 else answer[k] for k in cols} ) # e.g. [10] == 10
_UpperCAmelCase = example['document']['tokens']
_UpperCAmelCase = answer['start_token']
_UpperCAmelCase = answer['end_token']
_UpperCAmelCase = []
for i in range(len(doc['token'] ) ):
if not doc["is_html"][i]:
context.append(doc['token'][i] )
else:
if answer["start_token"] > i:
start_token -= 1
if answer["end_token"] > i:
end_token -= 1
_UpperCAmelCase = ' '.join(context[start_token:end_token] )
# checking above code
if assertion:
_UpperCAmelCase = doc['is_html'][answer['start_token'] : answer['end_token']]
_UpperCAmelCase = doc['token'][answer['start_token'] : answer['end_token']]
_UpperCAmelCase = ' '.join([old[i] for i in range(len(A ) ) if not is_html[i]] )
if new != old:
print('ID:' , example['id'] )
print('New:' , A , end='\n' )
print('Old:' , A , end='\n\n' )
return {
"context": " ".join(A ),
"answer": {
"start_token": start_token,
"end_token": end_token - 1, # this makes it inclusive
"category": answer["category"], # either long or short
"span": new, # extra
},
}
def UpperCAmelCase ( A : int , A : Any , A : str=2048 , A : List[Any]=4096 , A : Any=True ):
'''simple docstring'''
_UpperCAmelCase = get_context_and_ans(A , assertion=A )
_UpperCAmelCase = out['answer']
# later, removing these samples
if answer["start_token"] == -1:
return {
"example_id": example["id"],
"input_ids": [[-1]],
"labels": {
"start_token": [-1],
"end_token": [-1],
"category": ["null"],
},
}
_UpperCAmelCase = tokenizer(example['question']['text'] , out['context'] ).input_ids
_UpperCAmelCase = input_ids.index(tokenizer.sep_token_id ) + 1
# return yes/no
if answer["category"][0] in ["yes", "no"]: # category is list with one element
_UpperCAmelCase = []
_UpperCAmelCase = []
_UpperCAmelCase = input_ids[:q_len]
_UpperCAmelCase = range(A , len(A ) , max_length - doc_stride )
for i in doc_start_indices:
_UpperCAmelCase = i + max_length - q_len
_UpperCAmelCase = input_ids[i:end_index]
inputs.append(q_indices + slice )
category.append(answer['category'][0] )
if slice[-1] == tokenizer.sep_token_id:
break
return {
"example_id": example["id"],
"input_ids": inputs,
"labels": {
"start_token": [-100] * len(A ),
"end_token": [-100] * len(A ),
"category": category,
},
}
_UpperCAmelCase = out['context'].split()
_UpperCAmelCase = splitted_context[answer['end_token']]
_UpperCAmelCase = len(
tokenizer(
' '.join(splitted_context[: answer['start_token']] ) , add_special_tokens=A , ).input_ids )
_UpperCAmelCase = len(
tokenizer(' '.join(splitted_context[: answer['end_token']] ) , add_special_tokens=A ).input_ids )
answer["start_token"] += q_len
answer["end_token"] += q_len
# fixing end token
_UpperCAmelCase = len(tokenizer(A , add_special_tokens=A ).input_ids )
if num_sub_tokens > 1:
answer["end_token"] += num_sub_tokens - 1
_UpperCAmelCase = input_ids[answer['start_token'] : answer['end_token'] + 1] # right & left are inclusive
_UpperCAmelCase = answer['start_token']
_UpperCAmelCase = answer['end_token']
if assertion:
_UpperCAmelCase = tokenizer.decode(A )
if answer["span"] != new:
print('ISSUE IN TOKENIZATION' )
print('OLD:' , answer['span'] )
print('NEW:' , A , end='\n\n' )
if len(A ) <= max_length:
return {
"example_id": example["id"],
"input_ids": [input_ids],
"labels": {
"start_token": [answer["start_token"]],
"end_token": [answer["end_token"]],
"category": answer["category"],
},
}
_UpperCAmelCase = input_ids[:q_len]
_UpperCAmelCase = range(A , len(A ) , max_length - doc_stride )
_UpperCAmelCase = []
_UpperCAmelCase = []
_UpperCAmelCase = []
_UpperCAmelCase = [] # null, yes, no, long, short
for i in doc_start_indices:
_UpperCAmelCase = i + max_length - q_len
_UpperCAmelCase = input_ids[i:end_index]
inputs.append(q_indices + slice )
assert len(inputs[-1] ) <= max_length, "Issue in truncating length"
if start_token >= i and end_token <= end_index - 1:
_UpperCAmelCase = start_token - i + q_len
_UpperCAmelCase = end_token - i + q_len
answers_category.append(answer['category'][0] ) # ["short"] -> "short"
else:
_UpperCAmelCase = -100
_UpperCAmelCase = -100
answers_category.append('null' )
_UpperCAmelCase = inputs[-1][start_token : end_token + 1]
answers_start_token.append(A )
answers_end_token.append(A )
if assertion:
if new != old and new != [tokenizer.cls_token_id]:
print('ISSUE in strided for ID:' , example['id'] )
print('New:' , tokenizer.decode(A ) )
print('Old:' , tokenizer.decode(A ) , end='\n\n' )
if slice[-1] == tokenizer.sep_token_id:
break
return {
"example_id": example["id"],
"input_ids": inputs,
"labels": {
"start_token": answers_start_token,
"end_token": answers_end_token,
"category": answers_category,
},
}
def UpperCAmelCase ( A : List[Any] , A : int , A : Any=2048 , A : Optional[int]=4096 , A : Optional[Any]=False ):
'''simple docstring'''
_UpperCAmelCase = get_strided_contexts_and_ans(
A , A , doc_stride=A , max_length=A , assertion=A , )
return example
def UpperCAmelCase ( A : int , A : Dict ):
'''simple docstring'''
with jsonlines.open(A , 'a' ) as writer:
for example in tqdm(A , total=len(A ) , desc='Saving samples ... ' ):
_UpperCAmelCase = example['labels']
for ids, start, end, cat in zip(
example['input_ids'] , labels['start_token'] , labels['end_token'] , labels['category'] , ):
if start == -1 and end == -1:
continue # leave waste samples with no answer
if cat == "null" and np.random.rand() < 0.6:
continue # removing 50 % samples
writer.write(
{
'input_ids': ids,
'start_token': start,
'end_token': end,
'category': CATEGORY_MAPPING[cat],
} )
if __name__ == "__main__":
from datasets import load_dataset
from transformers import BigBirdTokenizer
lowercase = load_dataset('''natural_questions''')
lowercase = BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base''')
lowercase = data['''train''' if PROCESS_TRAIN == '''true''' else '''validation''']
lowercase = {
'''tokenizer''': tokenizer,
'''doc_stride''': DOC_STRIDE,
'''max_length''': MAX_LENGTH,
'''assertion''': False,
}
lowercase = data.map(prepare_inputs, fn_kwargs=fn_kwargs)
lowercase = data.remove_columns(['''annotations''', '''document''', '''id''', '''question'''])
print(data)
np.random.seed(SEED)
lowercase = '''nq-training.jsonl''' if PROCESS_TRAIN == '''true''' else '''nq-validation.jsonl'''
save_to_disk(data, file_name=cache_file_name)
| 24
|
"""simple docstring"""
def UpperCAmelCase ( A : int ):
'''simple docstring'''
_UpperCAmelCase = abs(A )
_UpperCAmelCase = 0
while n > 0:
res += n % 10
n //= 10
return res
def UpperCAmelCase ( A : int ):
'''simple docstring'''
_UpperCAmelCase = abs(A )
return n if n < 10 else n % 10 + sum_of_digits(n // 10 )
def UpperCAmelCase ( A : int ):
'''simple docstring'''
return sum(int(A ) for c in str(abs(A ) ) )
def UpperCAmelCase ( ):
'''simple docstring'''
from collections.abc import Callable
from timeit import timeit
def benchmark_a_function(A : Callable , A : int ) -> None:
_UpperCAmelCase = f'{func.__name__}({value})'
_UpperCAmelCase = timeit(f'__main__.{call}' , setup='import __main__' )
print(f'{call:56} = {func(A )} -- {timing:.4f} seconds' )
for value in (26_2144, 1125_8999_0684_2624, 126_7650_6002_2822_9401_4967_0320_5376):
for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
benchmark_a_function(A , A )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 24
| 1
|
"""simple docstring"""
import string
from math import logaa
def UpperCAmelCase ( A : str , A : str ):
'''simple docstring'''
_UpperCAmelCase = document.translate(
str.maketrans('' , '' , string.punctuation ) ).replace('\n' , '' )
_UpperCAmelCase = document_without_punctuation.split(' ' ) # word tokenization
return len([word for word in tokenize_document if word.lower() == term.lower()] )
def UpperCAmelCase ( A : str , A : str ):
'''simple docstring'''
_UpperCAmelCase = corpus.lower().translate(
str.maketrans('' , '' , string.punctuation ) ) # strip all punctuation and replace it with ''
_UpperCAmelCase = corpus_without_punctuation.split('\n' )
_UpperCAmelCase = term.lower()
return (len([doc for doc in docs if term in doc] ), len(A ))
def UpperCAmelCase ( A : int , A : int , A : Union[str, Any]=False ):
'''simple docstring'''
if smoothing:
if n == 0:
raise ValueError('log10(0) is undefined.' )
return round(1 + logaa(n / (1 + df) ) , 3 )
if df == 0:
raise ZeroDivisionError('df must be > 0' )
elif n == 0:
raise ValueError('log10(0) is undefined.' )
return round(logaa(n / df ) , 3 )
def UpperCAmelCase ( A : int , A : int ):
'''simple docstring'''
return round(tf * idf , 3 )
| 24
|
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase ( A : int , A : int ):
'''simple docstring'''
_UpperCAmelCase = []
create_all_state(1 , A , A , [] , A )
return result
def UpperCAmelCase ( A : int , A : int , A : int , A : list[int] , A : list[list[int]] , ):
'''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 : list[list[int]] ):
'''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)
| 24
| 1
|
"""simple docstring"""
from __future__ import annotations
import math
def UpperCAmelCase ( A : int ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(A ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def UpperCAmelCase ( A : int ):
'''simple docstring'''
_UpperCAmelCase = str(A )
_UpperCAmelCase = [n]
for i in range(1 , len(A ) ):
list_nums.append(int(str_num[i:] ) )
list_nums.append(int(str_num[:-i] ) )
return list_nums
def UpperCAmelCase ( A : int ):
'''simple docstring'''
if len(str(A ) ) > 3:
if not is_prime(int(str(A )[-3:] ) ) or not is_prime(int(str(A )[:3] ) ):
return False
return True
def UpperCAmelCase ( A : int = 11 ):
'''simple docstring'''
_UpperCAmelCase = []
_UpperCAmelCase = 13
while len(A ) != count:
if validate(A ):
_UpperCAmelCase = list_truncated_nums(A )
if all(is_prime(A ) for i in list_nums ):
list_truncated_primes.append(A )
num += 2
return list_truncated_primes
def UpperCAmelCase ( ):
'''simple docstring'''
return sum(compute_truncated_primes(11 ) )
if __name__ == "__main__":
print(F'''{sum(compute_truncated_primes(11)) = }''')
| 24
|
"""simple docstring"""
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS
logging.basicConfig(level=logging.DEBUG)
lowercase = logging.getLogger()
def UpperCAmelCase ( A : Path , A : list ):
'''simple docstring'''
_UpperCAmelCase = '\n'.join(A )
Path(A ).open('w' ).writelines(A )
lowercase = '''patrickvonplaten/t5-tiny-random'''
lowercase = '''sshleifer/bart-tiny-random'''
lowercase = '''sshleifer/tiny-mbart'''
lowercase = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL) # remove noisy download output from tracebacks
class lowercase__ ( A ):
'''simple docstring'''
def lowerCamelCase_ ( self , snake_case ) -> str:
_UpperCAmelCase = Path(self.get_auto_remove_tmp_dir() ) / 'utest_input.source'
_UpperCAmelCase = input_file_name.parent / 'utest_output.txt'
assert not output_file_name.exists()
_UpperCAmelCase = [' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
_dump_articles(snake_case , snake_case )
_UpperCAmelCase = str(Path(self.get_auto_remove_tmp_dir() ) / 'scores.json' )
_UpperCAmelCase = 'translation_en_to_de' if model == T5_TINY else 'summarization'
_UpperCAmelCase = f'\n run_eval_search.py\n {model}\n {input_file_name}\n {output_file_name}\n --score_path {score_path}\n --task {task}\n --num_beams 2\n --length_penalty 2.0\n '.split()
with patch.object(snake_case , 'argv' , snake_case ):
run_generate()
assert Path(snake_case ).exists()
# os.remove(Path(output_file_name))
def lowerCamelCase_ ( self ) -> str:
self.run_eval_tester(snake_case )
@parameterized.expand([BART_TINY, MBART_TINY] )
@slow
def lowerCamelCase_ ( self , snake_case ) -> List[Any]:
self.run_eval_tester(snake_case )
@parameterized.expand([T5_TINY, MBART_TINY] )
@slow
def lowerCamelCase_ ( self , snake_case ) -> Dict:
_UpperCAmelCase = Path(self.get_auto_remove_tmp_dir() ) / 'utest_input.source'
_UpperCAmelCase = input_file_name.parent / 'utest_output.txt'
assert not output_file_name.exists()
_UpperCAmelCase = {
'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
'de': [
'Maschinelles Lernen ist großartig, oder?',
'Ich esse gerne Bananen',
'Morgen ist wieder ein toller Tag!',
],
}
_UpperCAmelCase = Path(self.get_auto_remove_tmp_dir() )
_UpperCAmelCase = str(tmp_dir / 'scores.json' )
_UpperCAmelCase = str(tmp_dir / 'val.target' )
_dump_articles(snake_case , text['en'] )
_dump_articles(snake_case , text['de'] )
_UpperCAmelCase = 'translation_en_to_de' if model == T5_TINY else 'summarization'
_UpperCAmelCase = f'\n run_eval_search.py\n {model}\n {str(snake_case )}\n {str(snake_case )}\n --score_path {score_path}\n --reference_path {reference_path}\n --task {task}\n '.split()
testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0'] )
with patch.object(snake_case , 'argv' , snake_case ):
with CaptureStdout() as cs:
run_search()
_UpperCAmelCase = [' num_beams | length_penalty', model, 'Best score args']
_UpperCAmelCase = ['Info']
if "translation" in task:
expected_strings.append('bleu' )
else:
expected_strings.extend(snake_case )
for w in expected_strings:
assert w in cs.out
for w in un_expected_strings:
assert w not in cs.out
assert Path(snake_case ).exists()
os.remove(Path(snake_case ) )
| 24
| 1
|
"""simple docstring"""
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
lowercase = logging.getLogger(__name__)
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = argparse.ArgumentParser(
description='Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).' )
parser.add_argument('--file_path' , type=A , default='data/dump.txt' , help='The path to the data.' )
parser.add_argument('--tokenizer_type' , type=A , default='bert' , choices=['bert', 'roberta', 'gpt2'] )
parser.add_argument('--tokenizer_name' , type=A , default='bert-base-uncased' , help='The tokenizer to use.' )
parser.add_argument('--dump_file' , type=A , default='data/dump' , help='The dump file prefix.' )
_UpperCAmelCase = parser.parse_args()
logger.info(f'Loading Tokenizer ({args.tokenizer_name})' )
if args.tokenizer_type == "bert":
_UpperCAmelCase = BertTokenizer.from_pretrained(args.tokenizer_name )
_UpperCAmelCase = tokenizer.special_tokens_map['cls_token'] # `[CLS]`
_UpperCAmelCase = tokenizer.special_tokens_map['sep_token'] # `[SEP]`
elif args.tokenizer_type == "roberta":
_UpperCAmelCase = RobertaTokenizer.from_pretrained(args.tokenizer_name )
_UpperCAmelCase = tokenizer.special_tokens_map['cls_token'] # `<s>`
_UpperCAmelCase = tokenizer.special_tokens_map['sep_token'] # `</s>`
elif args.tokenizer_type == "gpt2":
_UpperCAmelCase = GPTaTokenizer.from_pretrained(args.tokenizer_name )
_UpperCAmelCase = tokenizer.special_tokens_map['bos_token'] # `<|endoftext|>`
_UpperCAmelCase = tokenizer.special_tokens_map['eos_token'] # `<|endoftext|>`
logger.info(f'Loading text from {args.file_path}' )
with open(args.file_path , 'r' , encoding='utf8' ) as fp:
_UpperCAmelCase = fp.readlines()
logger.info('Start encoding' )
logger.info(f'{len(A )} examples to process.' )
_UpperCAmelCase = []
_UpperCAmelCase = 0
_UpperCAmelCase = 1_0000
_UpperCAmelCase = time.time()
for text in data:
_UpperCAmelCase = f'{bos} {text.strip()} {sep}'
_UpperCAmelCase = tokenizer.encode(A , add_special_tokens=A )
rslt.append(A )
iter += 1
if iter % interval == 0:
_UpperCAmelCase = time.time()
logger.info(f'{iter} examples processed. - {(end-start):.2f}s/{interval}expl' )
_UpperCAmelCase = time.time()
logger.info('Finished binarization' )
logger.info(f'{len(A )} examples processed.' )
_UpperCAmelCase = f'{args.dump_file}.{args.tokenizer_name}.pickle'
_UpperCAmelCase = tokenizer.vocab_size
if vocab_size < (1 << 16):
_UpperCAmelCase = [np.uintaa(A ) for d in rslt]
else:
_UpperCAmelCase = [np.intaa(A ) for d in rslt]
random.shuffle(rslt_ )
logger.info(f'Dump to {dp_file}' )
with open(A , 'wb' ) as handle:
pickle.dump(rslt_ , A , protocol=pickle.HIGHEST_PROTOCOL )
if __name__ == "__main__":
main()
| 24
|
"""simple docstring"""
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal
lowercase = logging.get_logger(__name__)
lowercase = TypeVar('''DatasetType''', Dataset, IterableDataset)
def UpperCAmelCase ( A : List[DatasetType] , A : Optional[List[float]] = None , A : Optional[int] = None , A : Optional[DatasetInfo] = None , A : Optional[NamedSplit] = None , A : Literal["first_exhausted", "all_exhausted"] = "first_exhausted" , ):
'''simple docstring'''
from .arrow_dataset import Dataset
from .iterable_dataset import IterableDataset
if not datasets:
raise ValueError('Unable to interleave an empty list of datasets.' )
for i, dataset in enumerate(A ):
if not isinstance(A , (Dataset, IterableDataset) ):
if isinstance(A , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
'is an empty dataset dictionary.' )
raise ValueError(
f'Dataset at position {i} has at least one split: {list(A )}\n'
f'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(A ) )}\']' )
raise ValueError(
f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(A ).__name__}.' )
if i == 0:
_UpperCAmelCase , _UpperCAmelCase = (
(Dataset, IterableDataset) if isinstance(A , A ) else (IterableDataset, Dataset)
)
elif not isinstance(A , A ):
raise ValueError(
f'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.' )
if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
raise ValueError(f'{stopping_strategy} is not supported. Please enter a valid stopping_strategy.' )
if dataset_type is Dataset:
return _interleave_map_style_datasets(
A , A , A , info=A , split=A , stopping_strategy=A )
else:
return _interleave_iterable_datasets(
A , A , A , info=A , split=A , stopping_strategy=A )
def UpperCAmelCase ( A : List[DatasetType] , A : Optional[DatasetInfo] = None , A : Optional[NamedSplit] = None , A : int = 0 , ):
'''simple docstring'''
if not dsets:
raise ValueError('Unable to concatenate an empty list of datasets.' )
for i, dataset in enumerate(A ):
if not isinstance(A , (Dataset, IterableDataset) ):
if isinstance(A , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
'is an empty dataset dictionary.' )
raise ValueError(
f'Dataset at position {i} has at least one split: {list(A )}\n'
f'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(A ) )}\']' )
raise ValueError(
f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(A ).__name__}.' )
if i == 0:
_UpperCAmelCase , _UpperCAmelCase = (
(Dataset, IterableDataset) if isinstance(A , A ) else (IterableDataset, Dataset)
)
elif not isinstance(A , A ):
raise ValueError(
f'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.' )
if dataset_type is Dataset:
return _concatenate_map_style_datasets(A , info=A , split=A , axis=A )
else:
return _concatenate_iterable_datasets(A , info=A , split=A , axis=A )
| 24
| 1
|
"""simple docstring"""
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
@require_torch
@require_tf
class lowercase__ ( A ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = SMALL_MODEL_IDENTIFIER
_UpperCAmelCase = 'pt'
_UpperCAmelCase = 'tf'
def lowerCamelCase_ ( self , snake_case ) -> Union[str, Any]:
_UpperCAmelCase = AutoModel.from_pretrained(self.test_model )
model_pt.save_pretrained(snake_case )
def lowerCamelCase_ ( self , snake_case ) -> Optional[Any]:
_UpperCAmelCase = TFAutoModel.from_pretrained(self.test_model , from_pt=snake_case )
model_tf.save_pretrained(snake_case )
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = 'mock_framework'
# Framework provided - return whatever the user provides
_UpperCAmelCase = FeaturesManager.determine_framework(self.test_model , snake_case )
self.assertEqual(snake_case , snake_case )
# Local checkpoint and framework provided - return provided framework
# PyTorch checkpoint
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(snake_case )
_UpperCAmelCase = FeaturesManager.determine_framework(snake_case , snake_case )
self.assertEqual(snake_case , snake_case )
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(snake_case )
_UpperCAmelCase = FeaturesManager.determine_framework(snake_case , snake_case )
self.assertEqual(snake_case , snake_case )
def lowerCamelCase_ ( self ) -> List[Any]:
# PyTorch checkpoint
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(snake_case )
_UpperCAmelCase = FeaturesManager.determine_framework(snake_case )
self.assertEqual(snake_case , self.framework_pt )
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(snake_case )
_UpperCAmelCase = FeaturesManager.determine_framework(snake_case )
self.assertEqual(snake_case , self.framework_tf )
# Invalid local checkpoint
with TemporaryDirectory() as local_invalid_ckpt:
with self.assertRaises(snake_case ):
_UpperCAmelCase = FeaturesManager.determine_framework(snake_case )
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = MagicMock(return_value=snake_case )
with patch('transformers.onnx.features.is_tf_available' , snake_case ):
_UpperCAmelCase = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(snake_case , self.framework_pt )
# PyTorch not in environment -> use TensorFlow
_UpperCAmelCase = MagicMock(return_value=snake_case )
with patch('transformers.onnx.features.is_torch_available' , snake_case ):
_UpperCAmelCase = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(snake_case , self.framework_tf )
# Both in environment -> use PyTorch
_UpperCAmelCase = MagicMock(return_value=snake_case )
_UpperCAmelCase = MagicMock(return_value=snake_case )
with patch('transformers.onnx.features.is_tf_available' , snake_case ), patch(
'transformers.onnx.features.is_torch_available' , snake_case ):
_UpperCAmelCase = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(snake_case , self.framework_pt )
# Both not in environment -> raise error
_UpperCAmelCase = MagicMock(return_value=snake_case )
_UpperCAmelCase = MagicMock(return_value=snake_case )
with patch('transformers.onnx.features.is_tf_available' , snake_case ), patch(
'transformers.onnx.features.is_torch_available' , snake_case ):
with self.assertRaises(snake_case ):
_UpperCAmelCase = FeaturesManager.determine_framework(self.test_model )
| 24
|
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available
from .test_pipelines_common import ANY
if is_torch_available():
import torch
@is_pipeline_test
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
_UpperCAmelCase = TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case ) -> Dict:
_UpperCAmelCase = TextaTextGenerationPipeline(model=snake_case , tokenizer=snake_case )
return generator, ["Something to write", "Something else"]
def lowerCamelCase_ ( self , snake_case , snake_case ) -> Dict:
_UpperCAmelCase = generator('Something there' )
self.assertEqual(snake_case , [{'generated_text': ANY(snake_case )}] )
# These are encoder decoder, they don't just append to incoming string
self.assertFalse(outputs[0]['generated_text'].startswith('Something there' ) )
_UpperCAmelCase = generator(['This is great !', 'Something else'] , num_return_sequences=2 , do_sample=snake_case )
self.assertEqual(
snake_case , [
[{'generated_text': ANY(snake_case )}, {'generated_text': ANY(snake_case )}],
[{'generated_text': ANY(snake_case )}, {'generated_text': ANY(snake_case )}],
] , )
_UpperCAmelCase = generator(
['This is great !', 'Something else'] , num_return_sequences=2 , batch_size=2 , do_sample=snake_case )
self.assertEqual(
snake_case , [
[{'generated_text': ANY(snake_case )}, {'generated_text': ANY(snake_case )}],
[{'generated_text': ANY(snake_case )}, {'generated_text': ANY(snake_case )}],
] , )
with self.assertRaises(snake_case ):
generator(4 )
@require_torch
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = pipeline('text2text-generation' , model='patrickvonplaten/t5-tiny-random' , framework='pt' )
# do_sample=False necessary for reproducibility
_UpperCAmelCase = generator('Something there' , do_sample=snake_case )
self.assertEqual(snake_case , [{'generated_text': ''}] )
_UpperCAmelCase = 3
_UpperCAmelCase = generator(
'Something there' , num_return_sequences=snake_case , num_beams=snake_case , )
_UpperCAmelCase = [
{'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide Beide'},
{'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide'},
{'generated_text': ''},
]
self.assertEqual(snake_case , snake_case )
_UpperCAmelCase = generator('This is a test' , do_sample=snake_case , num_return_sequences=2 , return_tensors=snake_case )
self.assertEqual(
snake_case , [
{'generated_token_ids': ANY(torch.Tensor )},
{'generated_token_ids': ANY(torch.Tensor )},
] , )
_UpperCAmelCase = generator.model.config.eos_token_id
_UpperCAmelCase = '<pad>'
_UpperCAmelCase = generator(
['This is a test', 'This is a second test'] , do_sample=snake_case , num_return_sequences=2 , batch_size=2 , return_tensors=snake_case , )
self.assertEqual(
snake_case , [
[
{'generated_token_ids': ANY(torch.Tensor )},
{'generated_token_ids': ANY(torch.Tensor )},
],
[
{'generated_token_ids': ANY(torch.Tensor )},
{'generated_token_ids': ANY(torch.Tensor )},
],
] , )
@require_tf
def lowerCamelCase_ ( self ) -> Any:
_UpperCAmelCase = pipeline('text2text-generation' , model='patrickvonplaten/t5-tiny-random' , framework='tf' )
# do_sample=False necessary for reproducibility
_UpperCAmelCase = generator('Something there' , do_sample=snake_case )
self.assertEqual(snake_case , [{'generated_text': ''}] )
| 24
| 1
|
"""simple docstring"""
def UpperCAmelCase ( A : Tuple ):
'''simple docstring'''
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
7: [6, 8],
8: [5, 7],
},
{
0: [6],
1: [9],
2: [4, 5],
3: [4],
4: [2, 3],
5: [2],
6: [0, 7],
7: [6],
8: [],
9: [1],
},
{
0: [4],
1: [6],
2: [],
3: [5, 6, 7],
4: [0, 6],
5: [3, 8, 9],
6: [1, 3, 4, 7],
7: [3, 6, 8, 9],
8: [5, 7],
9: [5, 7],
},
{
0: [1, 3],
1: [0, 2, 4],
2: [1, 3, 4],
3: [0, 2, 4],
4: [1, 2, 3],
},
][index]
def UpperCAmelCase ( A : dict[int, list[int]] ):
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase = len(A ) # No of vertices in graph
_UpperCAmelCase = [0] * n
_UpperCAmelCase = [False] * n
def dfs(A : Dict , A : List[Any] , A : Any , A : Any ):
_UpperCAmelCase = True
_UpperCAmelCase = id_
id_ += 1
for to in graph[at]:
if to == parent:
pass
elif not visited[to]:
dfs(A , A , A , id_ )
_UpperCAmelCase = min(low[at] , low[to] )
if id_ <= low[to]:
bridges.append((at, to) if at < to else (to, at) )
else:
# This edge is a back edge and cannot be a bridge
_UpperCAmelCase = min(low[at] , low[to] )
_UpperCAmelCase = []
for i in range(A ):
if not visited[i]:
dfs(A , -1 , A , id_ )
return bridges
if __name__ == "__main__":
import doctest
doctest.testmod()
| 24
|
"""simple docstring"""
def UpperCAmelCase ( A : int ):
'''simple docstring'''
_UpperCAmelCase = [[0 for _ in range(A )] for _ in range(m + 1 )]
for i in range(m + 1 ):
_UpperCAmelCase = 1
for n in range(m + 1 ):
for k in range(1 , A ):
memo[n][k] += memo[n][k - 1]
if n - k > 0:
memo[n][k] += memo[n - k - 1][k]
return memo[m][m - 1]
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
try:
lowercase = int(input('''Enter a number: ''').strip())
print(partition(n))
except ValueError:
print('''Please enter a number.''')
else:
try:
lowercase = int(sys.argv[1])
print(partition(n))
except ValueError:
print('''Please pass a number.''')
| 24
| 1
|
"""simple docstring"""
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoolingAndNoAttention,
ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_resnet import ResNetConfig
lowercase = logging.get_logger(__name__)
# General docstring
lowercase = '''ResNetConfig'''
# Base docstring
lowercase = '''microsoft/resnet-50'''
lowercase = [1, 20_48, 7, 7]
# Image classification docstring
lowercase = '''microsoft/resnet-50'''
lowercase = '''tiger cat'''
lowercase = [
'''microsoft/resnet-50''',
# See all resnet models at https://huggingface.co/models?filter=resnet
]
class lowercase__ ( nn.Module ):
'''simple docstring'''
def __init__( self , snake_case , snake_case , snake_case = 3 , snake_case = 1 , snake_case = "relu" ) -> Dict:
super().__init__()
_UpperCAmelCase = nn.Convad(
snake_case , snake_case , kernel_size=snake_case , stride=snake_case , padding=kernel_size // 2 , bias=snake_case )
_UpperCAmelCase = nn.BatchNormad(snake_case )
_UpperCAmelCase = ACTaFN[activation] if activation is not None else nn.Identity()
def lowerCamelCase_ ( self , snake_case ) -> Tensor:
_UpperCAmelCase = self.convolution(snake_case )
_UpperCAmelCase = self.normalization(snake_case )
_UpperCAmelCase = self.activation(snake_case )
return hidden_state
class lowercase__ ( nn.Module ):
'''simple docstring'''
def __init__( self , snake_case ) -> Dict:
super().__init__()
_UpperCAmelCase = ResNetConvLayer(
config.num_channels , config.embedding_size , kernel_size=7 , stride=2 , activation=config.hidden_act )
_UpperCAmelCase = nn.MaxPoolad(kernel_size=3 , stride=2 , padding=1 )
_UpperCAmelCase = config.num_channels
def lowerCamelCase_ ( self , snake_case ) -> Tensor:
_UpperCAmelCase = pixel_values.shape[1]
if num_channels != self.num_channels:
raise ValueError(
'Make sure that the channel dimension of the pixel values match with the one set in the configuration.' )
_UpperCAmelCase = self.embedder(snake_case )
_UpperCAmelCase = self.pooler(snake_case )
return embedding
class lowercase__ ( nn.Module ):
'''simple docstring'''
def __init__( self , snake_case , snake_case , snake_case = 2 ) -> Union[str, Any]:
super().__init__()
_UpperCAmelCase = nn.Convad(snake_case , snake_case , kernel_size=1 , stride=snake_case , bias=snake_case )
_UpperCAmelCase = nn.BatchNormad(snake_case )
def lowerCamelCase_ ( self , snake_case ) -> Tensor:
_UpperCAmelCase = self.convolution(snake_case )
_UpperCAmelCase = self.normalization(snake_case )
return hidden_state
class lowercase__ ( nn.Module ):
'''simple docstring'''
def __init__( self , snake_case , snake_case , snake_case = 1 , snake_case = "relu" ) -> List[str]:
super().__init__()
_UpperCAmelCase = in_channels != out_channels or stride != 1
_UpperCAmelCase = (
ResNetShortCut(snake_case , snake_case , stride=snake_case ) if should_apply_shortcut else nn.Identity()
)
_UpperCAmelCase = nn.Sequential(
ResNetConvLayer(snake_case , snake_case , stride=snake_case ) , ResNetConvLayer(snake_case , snake_case , activation=snake_case ) , )
_UpperCAmelCase = ACTaFN[activation]
def lowerCamelCase_ ( self , snake_case ) -> List[Any]:
_UpperCAmelCase = hidden_state
_UpperCAmelCase = self.layer(snake_case )
_UpperCAmelCase = self.shortcut(snake_case )
hidden_state += residual
_UpperCAmelCase = self.activation(snake_case )
return hidden_state
class lowercase__ ( nn.Module ):
'''simple docstring'''
def __init__( self , snake_case , snake_case , snake_case = 1 , snake_case = "relu" , snake_case = 4 ) -> Optional[int]:
super().__init__()
_UpperCAmelCase = in_channels != out_channels or stride != 1
_UpperCAmelCase = out_channels // reduction
_UpperCAmelCase = (
ResNetShortCut(snake_case , snake_case , stride=snake_case ) if should_apply_shortcut else nn.Identity()
)
_UpperCAmelCase = nn.Sequential(
ResNetConvLayer(snake_case , snake_case , kernel_size=1 ) , ResNetConvLayer(snake_case , snake_case , stride=snake_case ) , ResNetConvLayer(snake_case , snake_case , kernel_size=1 , activation=snake_case ) , )
_UpperCAmelCase = ACTaFN[activation]
def lowerCamelCase_ ( self , snake_case ) -> int:
_UpperCAmelCase = hidden_state
_UpperCAmelCase = self.layer(snake_case )
_UpperCAmelCase = self.shortcut(snake_case )
hidden_state += residual
_UpperCAmelCase = self.activation(snake_case )
return hidden_state
class lowercase__ ( nn.Module ):
'''simple docstring'''
def __init__( self , snake_case , snake_case , snake_case , snake_case = 2 , snake_case = 2 , ) -> Optional[int]:
super().__init__()
_UpperCAmelCase = ResNetBottleNeckLayer if config.layer_type == 'bottleneck' else ResNetBasicLayer
_UpperCAmelCase = nn.Sequential(
# downsampling is done in the first layer with stride of 2
layer(snake_case , snake_case , stride=snake_case , activation=config.hidden_act ) , *[layer(snake_case , snake_case , activation=config.hidden_act ) for _ in range(depth - 1 )] , )
def lowerCamelCase_ ( self , snake_case ) -> Tensor:
_UpperCAmelCase = input
for layer in self.layers:
_UpperCAmelCase = layer(snake_case )
return hidden_state
class lowercase__ ( nn.Module ):
'''simple docstring'''
def __init__( self , snake_case ) -> Optional[int]:
super().__init__()
_UpperCAmelCase = nn.ModuleList([] )
# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
self.stages.append(
ResNetStage(
snake_case , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , ) )
_UpperCAmelCase = zip(config.hidden_sizes , config.hidden_sizes[1:] )
for (in_channels, out_channels), depth in zip(snake_case , config.depths[1:] ):
self.stages.append(ResNetStage(snake_case , snake_case , snake_case , depth=snake_case ) )
def lowerCamelCase_ ( self , snake_case , snake_case = False , snake_case = True ) -> BaseModelOutputWithNoAttention:
_UpperCAmelCase = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
_UpperCAmelCase = hidden_states + (hidden_state,)
_UpperCAmelCase = stage_module(snake_case )
if output_hidden_states:
_UpperCAmelCase = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return BaseModelOutputWithNoAttention(
last_hidden_state=snake_case , hidden_states=snake_case , )
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = ResNetConfig
_UpperCAmelCase = '''resnet'''
_UpperCAmelCase = '''pixel_values'''
_UpperCAmelCase = True
def lowerCamelCase_ ( self , snake_case ) -> str:
if isinstance(snake_case , nn.Convad ):
nn.init.kaiming_normal_(module.weight , mode='fan_out' , nonlinearity='relu' )
elif isinstance(snake_case , (nn.BatchNormad, nn.GroupNorm) ):
nn.init.constant_(module.weight , 1 )
nn.init.constant_(module.bias , 0 )
def lowerCamelCase_ ( self , snake_case , snake_case=False ) -> Optional[Any]:
if isinstance(snake_case , snake_case ):
_UpperCAmelCase = value
lowercase = r'''
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''
lowercase = r'''
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`ConvNextImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
'''
@add_start_docstrings(
'''The bare ResNet model outputting raw features without any specific head on top.''', A, )
class lowercase__ ( A ):
'''simple docstring'''
def __init__( self , snake_case ) -> Tuple:
super().__init__(snake_case )
_UpperCAmelCase = config
_UpperCAmelCase = ResNetEmbeddings(snake_case )
_UpperCAmelCase = ResNetEncoder(snake_case )
_UpperCAmelCase = nn.AdaptiveAvgPoolad((1, 1) )
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(snake_case )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=snake_case , config_class=_CONFIG_FOR_DOC , modality='vision' , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def lowerCamelCase_ ( self , snake_case , snake_case = None , snake_case = None ) -> BaseModelOutputWithPoolingAndNoAttention:
_UpperCAmelCase = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
_UpperCAmelCase = return_dict if return_dict is not None else self.config.use_return_dict
_UpperCAmelCase = self.embedder(snake_case )
_UpperCAmelCase = self.encoder(
snake_case , output_hidden_states=snake_case , return_dict=snake_case )
_UpperCAmelCase = encoder_outputs[0]
_UpperCAmelCase = self.pooler(snake_case )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=snake_case , pooler_output=snake_case , hidden_states=encoder_outputs.hidden_states , )
@add_start_docstrings(
'''
ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.
''', A, )
class lowercase__ ( A ):
'''simple docstring'''
def __init__( self , snake_case ) -> str:
super().__init__(snake_case )
_UpperCAmelCase = config.num_labels
_UpperCAmelCase = ResNetModel(snake_case )
# classification head
_UpperCAmelCase = nn.Sequential(
nn.Flatten() , nn.Linear(config.hidden_sizes[-1] , config.num_labels ) if config.num_labels > 0 else nn.Identity() , )
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(snake_case )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=snake_case , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def lowerCamelCase_ ( self , snake_case = None , snake_case = None , snake_case = None , snake_case = None , ) -> ImageClassifierOutputWithNoAttention:
_UpperCAmelCase = return_dict if return_dict is not None else self.config.use_return_dict
_UpperCAmelCase = self.resnet(snake_case , output_hidden_states=snake_case , return_dict=snake_case )
_UpperCAmelCase = outputs.pooler_output if return_dict else outputs[1]
_UpperCAmelCase = self.classifier(snake_case )
_UpperCAmelCase = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
_UpperCAmelCase = 'regression'
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
_UpperCAmelCase = 'single_label_classification'
else:
_UpperCAmelCase = 'multi_label_classification'
if self.config.problem_type == "regression":
_UpperCAmelCase = MSELoss()
if self.num_labels == 1:
_UpperCAmelCase = loss_fct(logits.squeeze() , labels.squeeze() )
else:
_UpperCAmelCase = loss_fct(snake_case , snake_case )
elif self.config.problem_type == "single_label_classification":
_UpperCAmelCase = CrossEntropyLoss()
_UpperCAmelCase = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
_UpperCAmelCase = BCEWithLogitsLoss()
_UpperCAmelCase = loss_fct(snake_case , snake_case )
if not return_dict:
_UpperCAmelCase = (logits,) + outputs[2:]
return (loss,) + output if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=snake_case , logits=snake_case , hidden_states=outputs.hidden_states )
@add_start_docstrings(
'''
ResNet backbone, to be used with frameworks like DETR and MaskFormer.
''', A, )
class lowercase__ ( A, A ):
'''simple docstring'''
def __init__( self , snake_case ) -> List[str]:
super().__init__(snake_case )
super()._init_backbone(snake_case )
_UpperCAmelCase = [config.embedding_size] + config.hidden_sizes
_UpperCAmelCase = ResNetEmbeddings(snake_case )
_UpperCAmelCase = ResNetEncoder(snake_case )
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(snake_case )
@replace_return_docstrings(output_type=snake_case , config_class=_CONFIG_FOR_DOC )
def lowerCamelCase_ ( self , snake_case , snake_case = None , snake_case = None ) -> BackboneOutput:
_UpperCAmelCase = return_dict if return_dict is not None else self.config.use_return_dict
_UpperCAmelCase = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
_UpperCAmelCase = self.embedder(snake_case )
_UpperCAmelCase = self.encoder(snake_case , output_hidden_states=snake_case , return_dict=snake_case )
_UpperCAmelCase = outputs.hidden_states
_UpperCAmelCase = ()
for idx, stage in enumerate(self.stage_names ):
if stage in self.out_features:
feature_maps += (hidden_states[idx],)
if not return_dict:
_UpperCAmelCase = (feature_maps,)
if output_hidden_states:
output += (outputs.hidden_states,)
return output
return BackboneOutput(
feature_maps=snake_case , hidden_states=outputs.hidden_states if output_hidden_states else None , attentions=snake_case , )
| 24
|
"""simple docstring"""
import os
lowercase = {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 1_00, '''D''': 5_00, '''M''': 10_00}
def UpperCAmelCase ( A : str ):
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase = 0
while index < len(A ) - 1:
_UpperCAmelCase = SYMBOLS[numerals[index]]
_UpperCAmelCase = 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 : int ):
'''simple docstring'''
_UpperCAmelCase = ''
_UpperCAmelCase = num // 1000
numerals += m_count * "M"
num %= 1000
_UpperCAmelCase = num // 100
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 %= 100
_UpperCAmelCase = num // 10
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 %= 10
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 : str = "/p089_roman.txt" ):
'''simple docstring'''
_UpperCAmelCase = 0
with open(os.path.dirname(A ) + roman_numerals_filename ) as filea:
_UpperCAmelCase = filea.readlines()
for line in lines:
_UpperCAmelCase = line.strip()
_UpperCAmelCase = parse_roman_numerals(A )
_UpperCAmelCase = generate_roman_numerals(A )
savings += len(A ) - len(A )
return savings
if __name__ == "__main__":
print(F'''{solution() = }''')
| 24
| 1
|
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = 42
_UpperCAmelCase = 42
def __init__( self , snake_case , snake_case ) -> Optional[Any]:
super().__init__()
self.register_modules(unet=snake_case , scheduler=snake_case )
@torch.no_grad()
def __call__( self , snake_case = 1 , snake_case = 2000 , snake_case = None , snake_case = "pil" , snake_case = True , **snake_case , ) -> Union[ImagePipelineOutput, Tuple]:
_UpperCAmelCase = self.unet.config.sample_size
_UpperCAmelCase = (batch_size, 3, img_size, img_size)
_UpperCAmelCase = self.unet
_UpperCAmelCase = randn_tensor(snake_case , generator=snake_case ) * self.scheduler.init_noise_sigma
_UpperCAmelCase = sample.to(self.device )
self.scheduler.set_timesteps(snake_case )
self.scheduler.set_sigmas(snake_case )
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
_UpperCAmelCase = self.scheduler.sigmas[i] * torch.ones(shape[0] , device=self.device )
# correction step
for _ in range(self.scheduler.config.correct_steps ):
_UpperCAmelCase = self.unet(snake_case , snake_case ).sample
_UpperCAmelCase = self.scheduler.step_correct(snake_case , snake_case , generator=snake_case ).prev_sample
# prediction step
_UpperCAmelCase = model(snake_case , snake_case ).sample
_UpperCAmelCase = self.scheduler.step_pred(snake_case , snake_case , snake_case , generator=snake_case )
_UpperCAmelCase , _UpperCAmelCase = output.prev_sample, output.prev_sample_mean
_UpperCAmelCase = sample_mean.clamp(0 , 1 )
_UpperCAmelCase = sample.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
_UpperCAmelCase = self.numpy_to_pil(snake_case )
if not return_dict:
return (sample,)
return ImagePipelineOutput(images=snake_case )
| 24
|
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_available():
import jax.numpy as jnp
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = {
'task_specific_params': {
'summarization': {'length_penalty': 1.0, 'max_length': 128, 'min_length': 12, 'num_beams': 4},
'summarization_cnn': {'length_penalty': 2.0, 'max_length': 142, 'min_length': 56, 'num_beams': 4},
'summarization_xsum': {'length_penalty': 1.0, 'max_length': 62, 'min_length': 11, 'num_beams': 6},
}
}
_UpperCAmelCase = {
'task_specific_params.summarization.length_penalty': 1.0,
'task_specific_params.summarization.max_length': 128,
'task_specific_params.summarization.min_length': 12,
'task_specific_params.summarization.num_beams': 4,
'task_specific_params.summarization_cnn.length_penalty': 2.0,
'task_specific_params.summarization_cnn.max_length': 142,
'task_specific_params.summarization_cnn.min_length': 56,
'task_specific_params.summarization_cnn.num_beams': 4,
'task_specific_params.summarization_xsum.length_penalty': 1.0,
'task_specific_params.summarization_xsum.max_length': 62,
'task_specific_params.summarization_xsum.min_length': 11,
'task_specific_params.summarization_xsum.num_beams': 6,
}
self.assertEqual(flatten_dict(snake_case ) , snake_case )
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(transpose(snake_case ) , x.transpose() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(transpose(snake_case , axes=(1, 2, 0) ) , x.transpose((1, 2, 0) ) ) )
@require_torch
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(transpose(snake_case ) , transpose(snake_case ).numpy() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(transpose(snake_case , axes=(1, 2, 0) ) , transpose(snake_case , axes=(1, 2, 0) ).numpy() ) )
@require_tf
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(transpose(snake_case ) , transpose(snake_case ).numpy() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(transpose(snake_case , axes=(1, 2, 0) ) , transpose(snake_case , axes=(1, 2, 0) ).numpy() ) )
@require_flax
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(transpose(snake_case ) , np.asarray(transpose(snake_case ) ) ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(transpose(snake_case , axes=(1, 2, 0) ) , np.asarray(transpose(snake_case , axes=(1, 2, 0) ) ) ) )
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(reshape(snake_case , (4, 3) ) , np.reshape(snake_case , (4, 3) ) ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(reshape(snake_case , (12, 5) ) , np.reshape(snake_case , (12, 5) ) ) )
@require_torch
def lowerCamelCase_ ( self ) -> Optional[Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (4, 3) ) , reshape(snake_case , (4, 3) ).numpy() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (12, 5) ) , reshape(snake_case , (12, 5) ).numpy() ) )
@require_tf
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (4, 3) ) , reshape(snake_case , (4, 3) ).numpy() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (12, 5) ) , reshape(snake_case , (12, 5) ).numpy() ) )
@require_flax
def lowerCamelCase_ ( self ) -> Tuple:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (4, 3) ) , np.asarray(reshape(snake_case , (4, 3) ) ) ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (12, 5) ) , np.asarray(reshape(snake_case , (12, 5) ) ) ) )
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = np.random.randn(1 , 3 , 4 )
self.assertTrue(np.allclose(squeeze(snake_case ) , np.squeeze(snake_case ) ) )
_UpperCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
self.assertTrue(np.allclose(squeeze(snake_case , axis=2 ) , np.squeeze(snake_case , axis=2 ) ) )
@require_torch
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(1 , 3 , 4 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case ) , squeeze(snake_case ).numpy() ) )
_UpperCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case , axis=2 ) , squeeze(snake_case , axis=2 ).numpy() ) )
@require_tf
def lowerCamelCase_ ( self ) -> Optional[int]:
_UpperCAmelCase = np.random.randn(1 , 3 , 4 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case ) , squeeze(snake_case ).numpy() ) )
_UpperCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case , axis=2 ) , squeeze(snake_case , axis=2 ).numpy() ) )
@require_flax
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = np.random.randn(1 , 3 , 4 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case ) , np.asarray(squeeze(snake_case ) ) ) )
_UpperCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case , axis=2 ) , np.asarray(squeeze(snake_case , axis=2 ) ) ) )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(expand_dims(snake_case , axis=1 ) , np.expand_dims(snake_case , axis=1 ) ) )
@require_torch
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(expand_dims(snake_case , axis=1 ) , expand_dims(snake_case , axis=1 ).numpy() ) )
@require_tf
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(expand_dims(snake_case , axis=1 ) , expand_dims(snake_case , axis=1 ).numpy() ) )
@require_flax
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(expand_dims(snake_case , axis=1 ) , np.asarray(expand_dims(snake_case , axis=1 ) ) ) )
| 24
| 1
|
"""simple docstring"""
def UpperCAmelCase ( A : int , A : float , A : float ):
'''simple docstring'''
return round(float(moles / volume ) * nfactor )
def UpperCAmelCase ( A : float , A : float , A : float ):
'''simple docstring'''
return round(float((moles * 0.0821 * temperature) / (volume) ) )
def UpperCAmelCase ( A : float , A : float , A : float ):
'''simple docstring'''
return round(float((moles * 0.0821 * temperature) / (pressure) ) )
def UpperCAmelCase ( A : float , A : float , A : float ):
'''simple docstring'''
return round(float((pressure * volume) / (0.0821 * moles) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 24
|
"""simple docstring"""
import os
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = os.path.join(os.path.dirname(A ) , 'num.txt' )
with open(A ) as file_hand:
return str(sum(int(A ) for line in file_hand ) )[:10]
if __name__ == "__main__":
print(solution())
| 24
| 1
|
"""simple docstring"""
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher,
EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
def UpperCAmelCase ( A : List[Any] , A : Dict ):
'''simple docstring'''
_UpperCAmelCase = old_name
if "patch_embed" in old_name:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = old_name.split('.' )
if layer == "0":
_UpperCAmelCase = old_name.replace('0' , 'convolution1' )
elif layer == "1":
_UpperCAmelCase = old_name.replace('1' , 'batchnorm_before' )
elif layer == "3":
_UpperCAmelCase = old_name.replace('3' , 'convolution2' )
else:
_UpperCAmelCase = old_name.replace('4' , 'batchnorm_after' )
if "network" in old_name and re.search(r'\d\.\d' , A ):
_UpperCAmelCase = r'\b\d{2}\b'
if bool(re.search(A , A ) ):
_UpperCAmelCase = re.search(r'\d\.\d\d.' , A ).group()
else:
_UpperCAmelCase = re.search(r'\d\.\d.' , A ).group()
if int(match[0] ) < 6:
_UpperCAmelCase = old_name.replace(A , '' )
_UpperCAmelCase = trimmed_name.replace('network' , match[0] + '.meta4D_layers.blocks.' + match[2:-1] )
_UpperCAmelCase = 'intermediate_stages.' + trimmed_name
else:
_UpperCAmelCase = old_name.replace(A , '' )
if int(match[2] ) < num_meta4D_last_stage:
_UpperCAmelCase = trimmed_name.replace('network' , 'meta4D_layers.blocks.' + match[2] )
else:
_UpperCAmelCase = str(int(match[2] ) - num_meta4D_last_stage )
_UpperCAmelCase = trimmed_name.replace('network' , 'meta3D_layers.blocks.' + layer_index )
if "norm1" in old_name:
_UpperCAmelCase = trimmed_name.replace('norm1' , 'layernorm1' )
elif "norm2" in old_name:
_UpperCAmelCase = trimmed_name.replace('norm2' , 'layernorm2' )
elif "fc1" in old_name:
_UpperCAmelCase = trimmed_name.replace('fc1' , 'linear_in' )
elif "fc2" in old_name:
_UpperCAmelCase = trimmed_name.replace('fc2' , 'linear_out' )
_UpperCAmelCase = 'last_stage.' + trimmed_name
elif "network" in old_name and re.search(r'.\d.' , A ):
_UpperCAmelCase = old_name.replace('network' , 'intermediate_stages' )
if "fc" in new_name:
_UpperCAmelCase = new_name.replace('fc' , 'convolution' )
elif ("norm1" in new_name) and ("layernorm1" not in new_name):
_UpperCAmelCase = new_name.replace('norm1' , 'batchnorm_before' )
elif ("norm2" in new_name) and ("layernorm2" not in new_name):
_UpperCAmelCase = new_name.replace('norm2' , 'batchnorm_after' )
if "proj" in new_name:
_UpperCAmelCase = new_name.replace('proj' , 'projection' )
if "dist_head" in new_name:
_UpperCAmelCase = new_name.replace('dist_head' , 'distillation_classifier' )
elif "head" in new_name:
_UpperCAmelCase = new_name.replace('head' , 'classifier' )
elif "patch_embed" in new_name:
_UpperCAmelCase = 'efficientformer.' + new_name
elif new_name == "norm.weight" or new_name == "norm.bias":
_UpperCAmelCase = new_name.replace('norm' , 'layernorm' )
_UpperCAmelCase = 'efficientformer.' + new_name
else:
_UpperCAmelCase = 'efficientformer.encoder.' + new_name
return new_name
def UpperCAmelCase ( A : List[Any] , A : List[str] ):
'''simple docstring'''
for key in checkpoint.copy().keys():
_UpperCAmelCase = checkpoint.pop(A )
_UpperCAmelCase = val
return checkpoint
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = 'http://images.cocodataset.org/val2017/000000039769.jpg'
_UpperCAmelCase = Image.open(requests.get(A , stream=A ).raw )
return image
def UpperCAmelCase ( A : Path , A : Path , A : Path , A : bool ):
'''simple docstring'''
_UpperCAmelCase = torch.load(A , map_location='cpu' )['model']
_UpperCAmelCase = EfficientFormerConfig.from_json_file(A )
_UpperCAmelCase = EfficientFormerForImageClassificationWithTeacher(A )
_UpperCAmelCase = '_'.join(checkpoint_path.split('/' )[-1].split('.' )[0].split('_' )[:-1] )
_UpperCAmelCase = config.depths[-1] - config.num_metaad_blocks + 1
_UpperCAmelCase = convert_torch_checkpoint(A , A )
model.load_state_dict(A )
model.eval()
_UpperCAmelCase = {
'bilinear': PILImageResampling.BILINEAR,
'bicubic': PILImageResampling.BICUBIC,
'nearest': PILImageResampling.NEAREST,
}
# prepare image
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = 256
_UpperCAmelCase = 224
_UpperCAmelCase = EfficientFormerImageProcessor(
size={'shortest_edge': image_size} , crop_size={'height': crop_size, 'width': crop_size} , resample=pillow_resamplings['bicubic'] , )
_UpperCAmelCase = processor(images=A , return_tensors='pt' ).pixel_values
# original processing pipeline
_UpperCAmelCase = Compose(
[
Resize(A , interpolation=pillow_resamplings['bicubic'] ),
CenterCrop(A ),
ToTensor(),
Normalize(A , A ),
] )
_UpperCAmelCase = image_transforms(A ).unsqueeze(0 )
assert torch.allclose(A , A )
_UpperCAmelCase = model(A )
_UpperCAmelCase = outputs.logits
_UpperCAmelCase = (1, 1000)
if "l1" in model_name:
_UpperCAmelCase = torch.Tensor(
[-0.1312, 0.4353, -1.0499, -0.5124, 0.4183, -0.6793, -1.3777, -0.0893, -0.7358, -2.4328] )
assert torch.allclose(logits[0, :10] , A , atol=1e-3 )
assert logits.shape == expected_shape
elif "l3" in model_name:
_UpperCAmelCase = torch.Tensor(
[-1.3150, -1.5456, -1.2556, -0.8496, -0.7127, -0.7897, -0.9728, -0.3052, 0.3751, -0.3127] )
assert torch.allclose(logits[0, :10] , A , atol=1e-3 )
assert logits.shape == expected_shape
elif "l7" in model_name:
_UpperCAmelCase = torch.Tensor(
[-1.0283, -1.4131, -0.5644, -1.3115, -0.5785, -1.2049, -0.7528, 0.1992, -0.3822, -0.0878] )
assert logits.shape == expected_shape
else:
raise ValueError(
f'Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7' )
# Save Checkpoints
Path(A ).mkdir(exist_ok=A )
model.save_pretrained(A )
print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}' )
processor.save_pretrained(A )
print(f'Processor successfuly saved at {pytorch_dump_path}' )
if push_to_hub:
print('Pushing model to the hub...' )
model.push_to_hub(
repo_id=f'Bearnardd/{pytorch_dump_path}' , commit_message='Add model' , use_temp_dir=A , )
processor.push_to_hub(
repo_id=f'Bearnardd/{pytorch_dump_path}' , commit_message='Add image processor' , use_temp_dir=A , )
if __name__ == "__main__":
lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--pytorch_model_path''',
default=None,
type=str,
required=True,
help='''Path to EfficientFormer pytorch checkpoint.''',
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help='''The json file for EfficientFormer model config.''',
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')
parser.add_argument(
'''--no-push_to_hub''',
dest='''push_to_hub''',
action='''store_false''',
help='''Do not push model and image processor to the hub''',
)
parser.set_defaults(push_to_hub=True)
lowercase = parser.parse_args()
convert_efficientformer_checkpoint(
checkpoint_path=args.pytorch_model_path,
efficientformer_config_file=args.config_file,
pytorch_dump_path=args.pytorch_dump_path,
push_to_hub=args.push_to_hub,
)
| 24
|
"""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_roberta''': ['''ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RobertaConfig''', '''RobertaOnnxConfig'''],
'''tokenization_roberta''': ['''RobertaTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = ['''RobertaTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''RobertaForCausalLM''',
'''RobertaForMaskedLM''',
'''RobertaForMultipleChoice''',
'''RobertaForQuestionAnswering''',
'''RobertaForSequenceClassification''',
'''RobertaForTokenClassification''',
'''RobertaModel''',
'''RobertaPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFRobertaForCausalLM''',
'''TFRobertaForMaskedLM''',
'''TFRobertaForMultipleChoice''',
'''TFRobertaForQuestionAnswering''',
'''TFRobertaForSequenceClassification''',
'''TFRobertaForTokenClassification''',
'''TFRobertaMainLayer''',
'''TFRobertaModel''',
'''TFRobertaPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''FlaxRobertaForCausalLM''',
'''FlaxRobertaForMaskedLM''',
'''FlaxRobertaForMultipleChoice''',
'''FlaxRobertaForQuestionAnswering''',
'''FlaxRobertaForSequenceClassification''',
'''FlaxRobertaForTokenClassification''',
'''FlaxRobertaModel''',
'''FlaxRobertaPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
from .tokenization_roberta import RobertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_roberta_fast import RobertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta import (
ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaForCausalLM,
RobertaForMaskedLM,
RobertaForMultipleChoice,
RobertaForQuestionAnswering,
RobertaForSequenceClassification,
RobertaForTokenClassification,
RobertaModel,
RobertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta import (
TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
TFRobertaForMultipleChoice,
TFRobertaForQuestionAnswering,
TFRobertaForSequenceClassification,
TFRobertaForTokenClassification,
TFRobertaMainLayer,
TFRobertaModel,
TFRobertaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
FlaxRobertaPreTrainedModel,
)
else:
import sys
lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 24
| 1
|
"""simple docstring"""
import math
from numpy import inf
from scipy.integrate import quad
def UpperCAmelCase ( A : float ):
'''simple docstring'''
if num <= 0:
raise ValueError('math domain error' )
return quad(A , 0 , A , args=(A) )[0]
def UpperCAmelCase ( A : float , A : float ):
'''simple docstring'''
return math.pow(A , z - 1 ) * math.exp(-x )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 24
|
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
lowercase = logging.get_logger(__name__)
class lowercase__ ( A ):
'''simple docstring'''
def __init__( self , *snake_case , **snake_case ) -> None:
warnings.warn(
'The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
' use YolosImageProcessor instead.' , snake_case , )
super().__init__(*snake_case , **snake_case )
| 24
| 1
|
"""simple docstring"""
from scipy.stats import pearsonr
import datasets
lowercase = '''
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.
The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.
'''
lowercase = '''
Args:
predictions (`list` of `int`): Predicted class labels, as returned by a model.
references (`list` of `int`): Ground truth labels.
return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.
Returns:
pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.
p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.
Examples:
Example 1-A simple example using only predictions and references.
>>> pearsonr_metric = datasets.load_metric("pearsonr")
>>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])
>>> print(round(results[\'pearsonr\'], 2))
-0.74
Example 2-The same as Example 1, but that also returns the `p-value`.
>>> pearsonr_metric = datasets.load_metric("pearsonr")
>>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)
>>> print(sorted(list(results.keys())))
[\'p-value\', \'pearsonr\']
>>> print(round(results[\'pearsonr\'], 2))
-0.74
>>> print(round(results[\'p-value\'], 2))
0.15
'''
lowercase = '''
@article{2020SciPy-NMeth,
author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
Haberland, Matt and Reddy, Tyler and Cournapeau, David and
Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
Kern, Robert and Larson, Eric and Carey, C J and
Polat, Ilhan and Feng, Yu and Moore, Eric W. and
{VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
Harris, Charles R. and Archibald, Anne M. and
Ribeiro, Antonio H. and Pedregosa, Fabian and
{van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
Computing in Python}},
journal = {Nature Methods},
year = {2020},
volume = {17},
pages = {261--272},
adsurl = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION )
class lowercase__ ( datasets.Metric ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> Dict:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('float' ),
'references': datasets.Value('float' ),
} ) , reference_urls=['https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html'] , )
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case=False ) -> List[str]:
if return_pvalue:
_UpperCAmelCase = pearsonr(snake_case , snake_case )
return {"pearsonr": results[0], "p-value": results[1]}
else:
return {"pearsonr": float(pearsonr(snake_case , snake_case )[0] )}
| 24
|
"""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
lowercase = logging.get_logger(__name__)
lowercase = {
'''microsoft/beit-base-patch16-224-pt22k''': (
'''https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'''
),
# See all BEiT models at https://huggingface.co/models?filter=beit
}
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = '''beit'''
def __init__( self , snake_case=8192 , snake_case=768 , snake_case=12 , snake_case=12 , snake_case=3072 , snake_case="gelu" , snake_case=0.0 , snake_case=0.0 , snake_case=0.02 , snake_case=1E-12 , snake_case=224 , snake_case=16 , snake_case=3 , snake_case=False , snake_case=False , snake_case=False , snake_case=False , snake_case=0.1 , snake_case=0.1 , snake_case=True , snake_case=[3, 5, 7, 11] , snake_case=[1, 2, 3, 6] , snake_case=True , snake_case=0.4 , snake_case=256 , snake_case=1 , snake_case=False , snake_case=255 , **snake_case , ) -> str:
super().__init__(**snake_case )
_UpperCAmelCase = vocab_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_act
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = image_size
_UpperCAmelCase = patch_size
_UpperCAmelCase = num_channels
_UpperCAmelCase = use_mask_token
_UpperCAmelCase = use_absolute_position_embeddings
_UpperCAmelCase = use_relative_position_bias
_UpperCAmelCase = use_shared_relative_position_bias
_UpperCAmelCase = layer_scale_init_value
_UpperCAmelCase = drop_path_rate
_UpperCAmelCase = use_mean_pooling
# decode head attributes (semantic segmentation)
_UpperCAmelCase = out_indices
_UpperCAmelCase = pool_scales
# auxiliary head attributes (semantic segmentation)
_UpperCAmelCase = use_auxiliary_head
_UpperCAmelCase = auxiliary_loss_weight
_UpperCAmelCase = auxiliary_channels
_UpperCAmelCase = auxiliary_num_convs
_UpperCAmelCase = auxiliary_concat_input
_UpperCAmelCase = semantic_loss_ignore_index
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = version.parse('''1.11''' )
@property
def lowerCamelCase_ ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def lowerCamelCase_ ( self ) -> float:
return 1E-4
| 24
| 1
|
"""simple docstring"""
class lowercase__ :
'''simple docstring'''
def __init__( self , snake_case ) -> int:
_UpperCAmelCase = n
_UpperCAmelCase = [None] * self.n
_UpperCAmelCase = 0 # index of the first element
_UpperCAmelCase = 0
_UpperCAmelCase = 0
def __len__( self ) -> int:
return self.size
def lowerCamelCase_ ( self ) -> bool:
return self.size == 0
def lowerCamelCase_ ( self ) -> int:
return False if self.is_empty() else self.array[self.front]
def lowerCamelCase_ ( self , snake_case ) -> Union[str, Any]:
if self.size >= self.n:
raise Exception('QUEUE IS FULL' )
_UpperCAmelCase = data
_UpperCAmelCase = (self.rear + 1) % self.n
self.size += 1
return self
def lowerCamelCase_ ( self ) -> List[str]:
if self.size == 0:
raise Exception('UNDERFLOW' )
_UpperCAmelCase = self.array[self.front]
_UpperCAmelCase = None
_UpperCAmelCase = (self.front + 1) % self.n
self.size -= 1
return temp
| 24
|
"""simple docstring"""
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
lowercase = logging.getLogger(__name__)
if __name__ == "__main__":
lowercase = argparse.ArgumentParser(
description='''Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'''
)
parser.add_argument(
'''--data_file''', type=str, default='''data/dump.bert-base-uncased.pickle''', help='''The binarized dataset.'''
)
parser.add_argument(
'''--token_counts_dump''', type=str, default='''data/token_counts.bert-base-uncased.pickle''', help='''The dump file.'''
)
parser.add_argument('''--vocab_size''', default=3_05_22, type=int)
lowercase = parser.parse_args()
logger.info(F'''Loading data from {args.data_file}''')
with open(args.data_file, '''rb''') as fp:
lowercase = pickle.load(fp)
logger.info('''Counting occurrences for MLM.''')
lowercase = Counter()
for tk_ids in data:
counter.update(tk_ids)
lowercase = [0] * args.vocab_size
for k, v in counter.items():
lowercase = v
logger.info(F'''Dump to {args.token_counts_dump}''')
with open(args.token_counts_dump, '''wb''') as handle:
pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)
| 24
| 1
|
"""simple docstring"""
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionInpaintPipeline` instead.'''
)
| 24
|
"""simple docstring"""
from itertools import permutations
def UpperCAmelCase ( A : tuple ):
'''simple docstring'''
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
_UpperCAmelCase = [7, 11, 13, 17]
for i, test in enumerate(A ):
if (num[i + 4] * 100 + num[i + 5] * 10 + num[i + 6]) % test != 0:
return False
return True
def UpperCAmelCase ( A : int = 10 ):
'''simple docstring'''
return sum(
int(''.join(map(A , A ) ) )
for num in permutations(range(A ) )
if is_substring_divisible(A ) )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 24
| 1
|
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def UpperCAmelCase ( A : Callable , A : float , A : float , A : float , A : float ):
'''simple docstring'''
_UpperCAmelCase = int(np.ceil((x_end - xa) / step_size ) )
_UpperCAmelCase = np.zeros((n + 1,) )
_UpperCAmelCase = ya
_UpperCAmelCase = xa
for k in range(A ):
_UpperCAmelCase = y[k] + step_size * ode_func(A , y[k] )
_UpperCAmelCase = y[k] + (
(step_size / 2) * (ode_func(A , y[k] ) + ode_func(x + step_size , A ))
)
x += step_size
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| 24
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase = {
'''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
'''tokenization_mvp''': ['''MvpTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = ['''MvpTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''MVP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MvpForCausalLM''',
'''MvpForConditionalGeneration''',
'''MvpForQuestionAnswering''',
'''MvpForSequenceClassification''',
'''MvpModel''',
'''MvpPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
from .tokenization_mvp import MvpTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mvp_fast import MvpTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mvp import (
MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
MvpForCausalLM,
MvpForConditionalGeneration,
MvpForQuestionAnswering,
MvpForSequenceClassification,
MvpModel,
MvpPreTrainedModel,
)
else:
import sys
lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 24
| 1
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase = logging.get_logger(__name__)
lowercase = {
'''microsoft/cvt-13''': '''https://huggingface.co/microsoft/cvt-13/resolve/main/config.json''',
# See all Cvt models at https://huggingface.co/models?filter=cvt
}
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = '''cvt'''
def __init__( self , snake_case=3 , snake_case=[7, 3, 3] , snake_case=[4, 2, 2] , snake_case=[2, 1, 1] , snake_case=[64, 192, 384] , snake_case=[1, 3, 6] , snake_case=[1, 2, 10] , snake_case=[4.0, 4.0, 4.0] , snake_case=[0.0, 0.0, 0.0] , snake_case=[0.0, 0.0, 0.0] , snake_case=[0.0, 0.0, 0.1] , snake_case=[True, True, True] , snake_case=[False, False, True] , snake_case=["dw_bn", "dw_bn", "dw_bn"] , snake_case=[3, 3, 3] , snake_case=[1, 1, 1] , snake_case=[2, 2, 2] , snake_case=[1, 1, 1] , snake_case=[1, 1, 1] , snake_case=0.02 , snake_case=1E-12 , **snake_case , ) -> Optional[Any]:
super().__init__(**snake_case )
_UpperCAmelCase = num_channels
_UpperCAmelCase = patch_sizes
_UpperCAmelCase = patch_stride
_UpperCAmelCase = patch_padding
_UpperCAmelCase = embed_dim
_UpperCAmelCase = num_heads
_UpperCAmelCase = depth
_UpperCAmelCase = mlp_ratio
_UpperCAmelCase = attention_drop_rate
_UpperCAmelCase = drop_rate
_UpperCAmelCase = drop_path_rate
_UpperCAmelCase = qkv_bias
_UpperCAmelCase = cls_token
_UpperCAmelCase = qkv_projection_method
_UpperCAmelCase = kernel_qkv
_UpperCAmelCase = padding_kv
_UpperCAmelCase = stride_kv
_UpperCAmelCase = padding_q
_UpperCAmelCase = stride_q
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
| 24
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase = {
'''configuration_clipseg''': [
'''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPSegConfig''',
'''CLIPSegTextConfig''',
'''CLIPSegVisionConfig''',
],
'''processing_clipseg''': ['''CLIPSegProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''CLIPSegModel''',
'''CLIPSegPreTrainedModel''',
'''CLIPSegTextModel''',
'''CLIPSegVisionModel''',
'''CLIPSegForImageSegmentation''',
]
if TYPE_CHECKING:
from .configuration_clipseg import (
CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIPSegConfig,
CLIPSegTextConfig,
CLIPSegVisionConfig,
)
from .processing_clipseg import CLIPSegProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clipseg import (
CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPSegForImageSegmentation,
CLIPSegModel,
CLIPSegPreTrainedModel,
CLIPSegTextModel,
CLIPSegVisionModel,
)
else:
import sys
lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 24
| 1
|
"""simple docstring"""
def UpperCAmelCase ( A : int , A : int ):
'''simple docstring'''
return number | (1 << position)
def UpperCAmelCase ( A : int , A : int ):
'''simple docstring'''
return number & ~(1 << position)
def UpperCAmelCase ( A : int , A : int ):
'''simple docstring'''
return number ^ (1 << position)
def UpperCAmelCase ( A : int , A : int ):
'''simple docstring'''
return ((number >> position) & 1) == 1
def UpperCAmelCase ( A : int , A : int ):
'''simple docstring'''
return int((number & (1 << position)) != 0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 24
|
"""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/swin-tiny-patch4-window7-224''': (
'''https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'''
),
# See all Swin models at https://huggingface.co/models?filter=swin
}
class lowercase__ ( A, A ):
'''simple docstring'''
_UpperCAmelCase = '''swin'''
_UpperCAmelCase = {
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self , snake_case=224 , snake_case=4 , snake_case=3 , snake_case=96 , snake_case=[2, 2, 6, 2] , snake_case=[3, 6, 12, 24] , snake_case=7 , snake_case=4.0 , snake_case=True , snake_case=0.0 , snake_case=0.0 , snake_case=0.1 , snake_case="gelu" , snake_case=False , snake_case=0.02 , snake_case=1E-5 , snake_case=32 , snake_case=None , snake_case=None , **snake_case , ) -> List[Any]:
super().__init__(**snake_case )
_UpperCAmelCase = image_size
_UpperCAmelCase = patch_size
_UpperCAmelCase = num_channels
_UpperCAmelCase = embed_dim
_UpperCAmelCase = depths
_UpperCAmelCase = len(snake_case )
_UpperCAmelCase = num_heads
_UpperCAmelCase = window_size
_UpperCAmelCase = mlp_ratio
_UpperCAmelCase = qkv_bias
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = drop_path_rate
_UpperCAmelCase = hidden_act
_UpperCAmelCase = use_absolute_embeddings
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = initializer_range
_UpperCAmelCase = encoder_stride
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
_UpperCAmelCase = int(embed_dim * 2 ** (len(snake_case ) - 1) )
_UpperCAmelCase = ['stem'] + [f'stage{idx}' for idx in range(1 , len(snake_case ) + 1 )]
_UpperCAmelCase , _UpperCAmelCase = get_aligned_output_features_output_indices(
out_features=snake_case , out_indices=snake_case , stage_names=self.stage_names )
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = version.parse('''1.11''' )
@property
def lowerCamelCase_ ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def lowerCamelCase_ ( self ) -> float:
return 1E-4
| 24
| 1
|
"""simple docstring"""
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class lowercase__ ( nn.Module ):
'''simple docstring'''
def __init__( self , snake_case = 16 , snake_case = 88 , snake_case = None , snake_case = 1 , snake_case = 0.0 , snake_case = 32 , snake_case = None , snake_case = False , snake_case = None , snake_case = None , snake_case = "geglu" , snake_case = None , ) -> str:
super().__init__()
_UpperCAmelCase = nn.ModuleList(
[
TransformeraDModel(
num_attention_heads=snake_case , attention_head_dim=snake_case , in_channels=snake_case , num_layers=snake_case , dropout=snake_case , norm_num_groups=snake_case , cross_attention_dim=snake_case , attention_bias=snake_case , sample_size=snake_case , num_vector_embeds=snake_case , activation_fn=snake_case , num_embeds_ada_norm=snake_case , )
for _ in range(2 )
] )
# Variables that can be set by a pipeline:
# The ratio of transformer1 to transformer2's output states to be combined during inference
_UpperCAmelCase = 0.5
# The shape of `encoder_hidden_states` is expected to be
# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
_UpperCAmelCase = [77, 257]
# Which transformer to use to encode which condition.
# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
_UpperCAmelCase = [1, 0]
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case=None , snake_case=None , snake_case=None , snake_case = True , ) -> Any:
_UpperCAmelCase = hidden_states
_UpperCAmelCase = []
_UpperCAmelCase = 0
# attention_mask is not used yet
for i in range(2 ):
# for each of the two transformers, pass the corresponding condition tokens
_UpperCAmelCase = encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
_UpperCAmelCase = self.transformer_index_for_condition[i]
_UpperCAmelCase = self.transformers[transformer_index](
snake_case , encoder_hidden_states=snake_case , timestep=snake_case , cross_attention_kwargs=snake_case , return_dict=snake_case , )[0]
encoded_states.append(encoded_state - input_states )
tokens_start += self.condition_lengths[i]
_UpperCAmelCase = encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
_UpperCAmelCase = output_states + input_states
if not return_dict:
return (output_states,)
return TransformeraDModelOutput(sample=snake_case )
| 24
|
"""simple docstring"""
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class lowercase__ ( nn.Module ):
'''simple docstring'''
def __init__( self , snake_case = 16 , snake_case = 88 , snake_case = None , snake_case = 1 , snake_case = 0.0 , snake_case = 32 , snake_case = None , snake_case = False , snake_case = None , snake_case = None , snake_case = "geglu" , snake_case = None , ) -> str:
super().__init__()
_UpperCAmelCase = nn.ModuleList(
[
TransformeraDModel(
num_attention_heads=snake_case , attention_head_dim=snake_case , in_channels=snake_case , num_layers=snake_case , dropout=snake_case , norm_num_groups=snake_case , cross_attention_dim=snake_case , attention_bias=snake_case , sample_size=snake_case , num_vector_embeds=snake_case , activation_fn=snake_case , num_embeds_ada_norm=snake_case , )
for _ in range(2 )
] )
# Variables that can be set by a pipeline:
# The ratio of transformer1 to transformer2's output states to be combined during inference
_UpperCAmelCase = 0.5
# The shape of `encoder_hidden_states` is expected to be
# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
_UpperCAmelCase = [77, 257]
# Which transformer to use to encode which condition.
# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
_UpperCAmelCase = [1, 0]
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case=None , snake_case=None , snake_case=None , snake_case = True , ) -> Any:
_UpperCAmelCase = hidden_states
_UpperCAmelCase = []
_UpperCAmelCase = 0
# attention_mask is not used yet
for i in range(2 ):
# for each of the two transformers, pass the corresponding condition tokens
_UpperCAmelCase = encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
_UpperCAmelCase = self.transformer_index_for_condition[i]
_UpperCAmelCase = self.transformers[transformer_index](
snake_case , encoder_hidden_states=snake_case , timestep=snake_case , cross_attention_kwargs=snake_case , return_dict=snake_case , )[0]
encoded_states.append(encoded_state - input_states )
tokens_start += self.condition_lengths[i]
_UpperCAmelCase = encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
_UpperCAmelCase = output_states + input_states
if not return_dict:
return (output_states,)
return TransformeraDModelOutput(sample=snake_case )
| 24
| 1
|
"""simple docstring"""
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def UpperCAmelCase ( A : List[str] ):
'''simple docstring'''
_UpperCAmelCase = tmp_path / 'file.csv'
_UpperCAmelCase = textwrap.dedent(
'\\n header1,header2\n 1,2\n 10,20\n ' )
with open(A , 'w' ) as f:
f.write(A )
return str(A )
@pytest.fixture
def UpperCAmelCase ( A : Any ):
'''simple docstring'''
_UpperCAmelCase = tmp_path / 'malformed_file.csv'
_UpperCAmelCase = textwrap.dedent(
'\\n header1,header2\n 1,2\n 10,20,\n ' )
with open(A , 'w' ) as f:
f.write(A )
return str(A )
@pytest.fixture
def UpperCAmelCase ( A : Any , A : int ):
'''simple docstring'''
_UpperCAmelCase = tmp_path / 'csv_with_image.csv'
_UpperCAmelCase = textwrap.dedent(
f'\\n image\n {image_file}\n ' )
with open(A , 'w' ) as f:
f.write(A )
return str(A )
@pytest.fixture
def UpperCAmelCase ( A : Union[str, Any] ):
'''simple docstring'''
_UpperCAmelCase = tmp_path / 'csv_with_label.csv'
_UpperCAmelCase = textwrap.dedent(
'\\n label\n good\n bad\n good\n ' )
with open(A , 'w' ) as f:
f.write(A )
return str(A )
@pytest.fixture
def UpperCAmelCase ( A : Optional[Any] ):
'''simple docstring'''
_UpperCAmelCase = tmp_path / 'csv_with_int_list.csv'
_UpperCAmelCase = textwrap.dedent(
'\\n int_list\n 1 2 3\n 4 5 6\n 7 8 9\n ' )
with open(A , 'w' ) as f:
f.write(A )
return str(A )
def UpperCAmelCase ( A : Dict , A : Dict , A : str ):
'''simple docstring'''
_UpperCAmelCase = Csv()
_UpperCAmelCase = csv._generate_tables([[csv_file, malformed_csv_file]] )
with pytest.raises(A , match='Error tokenizing data' ):
for _ in generator:
pass
assert any(
record.levelname == 'ERROR'
and 'Failed to read file' in record.message
and os.path.basename(A ) in record.message
for record in caplog.records )
@require_pil
def UpperCAmelCase ( A : Union[str, Any] ):
'''simple docstring'''
with open(A , encoding='utf-8' ) as f:
_UpperCAmelCase = f.read().splitlines()[1]
_UpperCAmelCase = Csv(encoding='utf-8' , features=Features({'image': Image()} ) )
_UpperCAmelCase = csv._generate_tables([[csv_file_with_image]] )
_UpperCAmelCase = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field('image' ).type == Image()()
_UpperCAmelCase = pa_table.to_pydict()['image']
assert generated_content == [{"path": image_file, "bytes": None}]
def UpperCAmelCase ( A : Dict ):
'''simple docstring'''
with open(A , encoding='utf-8' ) as f:
_UpperCAmelCase = f.read().splitlines()[1:]
_UpperCAmelCase = Csv(encoding='utf-8' , features=Features({'label': ClassLabel(names=['good', 'bad'] )} ) )
_UpperCAmelCase = csv._generate_tables([[csv_file_with_label]] )
_UpperCAmelCase = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field('label' ).type == ClassLabel(names=['good', 'bad'] )()
_UpperCAmelCase = pa_table.to_pydict()['label']
assert generated_content == [ClassLabel(names=['good', 'bad'] ).straint(A ) for label in labels]
def UpperCAmelCase ( A : int ):
'''simple docstring'''
_UpperCAmelCase = Csv(encoding='utf-8' , sep=',' , converters={'int_list': lambda A : [int(A ) for i in x.split()]} )
_UpperCAmelCase = csv._generate_tables([[csv_file_with_int_list]] )
_UpperCAmelCase = pa.concat_tables([table for _, table in generator] )
assert pa.types.is_list(pa_table.schema.field('int_list' ).type )
_UpperCAmelCase = pa_table.to_pydict()['int_list']
assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
| 24
|
"""simple docstring"""
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import CvtForImageClassification, CvtModel
from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class lowercase__ ( A ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(snake_case , 'embed_dim' ) )
self.parent.assertTrue(hasattr(snake_case , 'num_heads' ) )
class lowercase__ :
'''simple docstring'''
def __init__( self , snake_case , snake_case=13 , snake_case=64 , snake_case=3 , snake_case=[16, 48, 96] , snake_case=[1, 3, 6] , snake_case=[1, 2, 10] , snake_case=[7, 3, 3] , snake_case=[4, 2, 2] , snake_case=[2, 1, 1] , snake_case=[2, 2, 2] , snake_case=[False, False, True] , snake_case=[0.0, 0.0, 0.0] , snake_case=0.02 , snake_case=1E-12 , snake_case=True , snake_case=True , snake_case=2 , ) -> Tuple:
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = image_size
_UpperCAmelCase = patch_sizes
_UpperCAmelCase = patch_stride
_UpperCAmelCase = patch_padding
_UpperCAmelCase = is_training
_UpperCAmelCase = use_labels
_UpperCAmelCase = num_labels
_UpperCAmelCase = num_channels
_UpperCAmelCase = embed_dim
_UpperCAmelCase = num_heads
_UpperCAmelCase = stride_kv
_UpperCAmelCase = depth
_UpperCAmelCase = cls_token
_UpperCAmelCase = attention_drop_rate
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_UpperCAmelCase = None
if self.use_labels:
_UpperCAmelCase = ids_tensor([self.batch_size] , self.num_labels )
_UpperCAmelCase = self.get_config()
return config, pixel_values, labels
def lowerCamelCase_ ( self ) -> List[str]:
return CvtConfig(
image_size=self.image_size , num_labels=self.num_labels , num_channels=self.num_channels , embed_dim=self.embed_dim , num_heads=self.num_heads , patch_sizes=self.patch_sizes , patch_padding=self.patch_padding , patch_stride=self.patch_stride , stride_kv=self.stride_kv , depth=self.depth , cls_token=self.cls_token , attention_drop_rate=self.attention_drop_rate , initializer_range=self.initializer_range , )
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case ) -> Optional[int]:
_UpperCAmelCase = CvtModel(config=snake_case )
model.to(snake_case )
model.eval()
_UpperCAmelCase = model(snake_case )
_UpperCAmelCase = (self.image_size, self.image_size)
_UpperCAmelCase , _UpperCAmelCase = image_size[0], image_size[1]
for i in range(len(self.depth ) ):
_UpperCAmelCase = floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
_UpperCAmelCase = floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dim[-1], height, width) )
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case ) -> Optional[Any]:
_UpperCAmelCase = self.num_labels
_UpperCAmelCase = CvtForImageClassification(snake_case )
model.to(snake_case )
model.eval()
_UpperCAmelCase = model(snake_case , labels=snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = self.prepare_config_and_inputs()
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = config_and_inputs
_UpperCAmelCase = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class lowercase__ ( A, A, unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = (CvtModel, CvtForImageClassification) if is_torch_available() else ()
_UpperCAmelCase = (
{'''feature-extraction''': CvtModel, '''image-classification''': CvtForImageClassification}
if is_torch_available()
else {}
)
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = CvtModelTester(self )
_UpperCAmelCase = ConfigTester(self , config_class=snake_case , has_text_modality=snake_case , hidden_size=37 )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def lowerCamelCase_ ( self ) -> Union[str, Any]:
return
@unittest.skip(reason='Cvt does not output attentions' )
def lowerCamelCase_ ( self ) -> str:
pass
@unittest.skip(reason='Cvt does not use inputs_embeds' )
def lowerCamelCase_ ( self ) -> int:
pass
@unittest.skip(reason='Cvt does not support input and output embeddings' )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
pass
def lowerCamelCase_ ( self ) -> Any:
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(snake_case )
_UpperCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCAmelCase = [*signature.parameters.keys()]
_UpperCAmelCase = ['pixel_values']
self.assertListEqual(arg_names[:1] , snake_case )
def lowerCamelCase_ ( self ) -> Optional[int]:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case )
def lowerCamelCase_ ( self ) -> Optional[int]:
def check_hidden_states_output(snake_case , snake_case , snake_case ):
_UpperCAmelCase = model_class(snake_case )
model.to(snake_case )
model.eval()
with torch.no_grad():
_UpperCAmelCase = model(**self._prepare_for_class(snake_case , snake_case ) )
_UpperCAmelCase = outputs.hidden_states
_UpperCAmelCase = len(self.model_tester.depth )
self.assertEqual(len(snake_case ) , snake_case )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:] ) , [
self.model_tester.embed_dim[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
] , )
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = True
check_hidden_states_output(snake_case , snake_case , snake_case )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_UpperCAmelCase = True
check_hidden_states_output(snake_case , snake_case , snake_case )
def lowerCamelCase_ ( self ) -> Any:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*snake_case )
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def lowerCamelCase_ ( self ) -> Dict:
pass
@slow
def lowerCamelCase_ ( self ) -> Dict:
for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase = CvtModel.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def lowerCamelCase_ ( self ) -> List[Any]:
return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
@slow
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(snake_case )
_UpperCAmelCase = self.default_image_processor
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = image_processor(images=snake_case , return_tensors='pt' ).to(snake_case )
# forward pass
with torch.no_grad():
_UpperCAmelCase = model(**snake_case )
# verify the logits
_UpperCAmelCase = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , snake_case )
_UpperCAmelCase = torch.tensor([0.9285, 0.9015, -0.3150] ).to(snake_case )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , snake_case , atol=1E-4 ) )
| 24
| 1
|
"""simple docstring"""
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def UpperCAmelCase ( A : Dict ):
'''simple docstring'''
_UpperCAmelCase = filter(lambda A : p.requires_grad , model.parameters() )
_UpperCAmelCase = sum([np.prod(p.size() ) for p in model_parameters] )
return params
lowercase = logging.getLogger(__name__)
def UpperCAmelCase ( A : str , A : int ):
'''simple docstring'''
if metric == "rouge2":
_UpperCAmelCase = '{val_avg_rouge2:.4f}-{step_count}'
elif metric == "bleu":
_UpperCAmelCase = '{val_avg_bleu:.4f}-{step_count}'
elif metric == "em":
_UpperCAmelCase = '{val_avg_em:.4f}-{step_count}'
elif metric == "loss":
_UpperCAmelCase = '{val_avg_loss:.4f}-{step_count}'
else:
raise NotImplementedError(
f'seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'
' function.' )
_UpperCAmelCase = ModelCheckpoint(
dirpath=A , filename=A , monitor=f'val_{metric}' , mode='max' , save_top_k=1 , every_n_epochs=1 , )
return checkpoint_callback
def UpperCAmelCase ( A : Union[str, Any] , A : Optional[int] ):
'''simple docstring'''
return EarlyStopping(
monitor=f'val_{metric}' , mode='min' if 'loss' in metric else 'max' , patience=A , verbose=A , )
class lowercase__ ( pl.Callback ):
'''simple docstring'''
def lowerCamelCase_ ( self , snake_case , snake_case ) -> Optional[int]:
_UpperCAmelCase = {f'lr_group_{i}': param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
pl_module.logger.log_metrics(snake_case )
@rank_zero_only
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case , snake_case=True ) -> None:
logger.info(f'***** {type_path} results at step {trainer.global_step:05d} *****' )
_UpperCAmelCase = trainer.callback_metrics
trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['log', 'progress_bar', 'preds']} )
# Log results
_UpperCAmelCase = Path(pl_module.hparams.output_dir )
if type_path == "test":
_UpperCAmelCase = od / 'test_results.txt'
_UpperCAmelCase = od / 'test_generations.txt'
else:
# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
# If people want this it will be easy enough to add back.
_UpperCAmelCase = od / f'{type_path}_results/{trainer.global_step:05d}.txt'
_UpperCAmelCase = od / f'{type_path}_generations/{trainer.global_step:05d}.txt'
results_file.parent.mkdir(exist_ok=snake_case )
generations_file.parent.mkdir(exist_ok=snake_case )
with open(snake_case , 'a+' ) as writer:
for key in sorted(snake_case ):
if key in ["log", "progress_bar", "preds"]:
continue
_UpperCAmelCase = metrics[key]
if isinstance(snake_case , torch.Tensor ):
_UpperCAmelCase = val.item()
_UpperCAmelCase = f'{key}: {val:.6f}\n'
writer.write(snake_case )
if not save_generations:
return
if "preds" in metrics:
_UpperCAmelCase = '\n'.join(metrics['preds'] )
generations_file.open('w+' ).write(snake_case )
@rank_zero_only
def lowerCamelCase_ ( self , snake_case , snake_case ) -> List[Any]:
try:
_UpperCAmelCase = pl_module.model.model.num_parameters()
except AttributeError:
_UpperCAmelCase = pl_module.model.num_parameters()
_UpperCAmelCase = count_trainable_parameters(snake_case )
# mp stands for million parameters
trainer.logger.log_metrics({'n_params': npars, 'mp': npars / 1E6, 'grad_mp': n_trainable_pars / 1E6} )
@rank_zero_only
def lowerCamelCase_ ( self , snake_case , snake_case ) -> int:
save_json(pl_module.metrics , pl_module.metrics_save_path )
return self._write_logs(snake_case , snake_case , 'test' )
@rank_zero_only
def lowerCamelCase_ ( self , snake_case , snake_case ) -> List[Any]:
save_json(pl_module.metrics , pl_module.metrics_save_path )
# Uncommenting this will save val generations
# return self._write_logs(trainer, pl_module, "valid")
| 24
|
"""simple docstring"""
from __future__ import annotations
from cmath import sqrt
def UpperCAmelCase ( A : int , A : int , A : int ):
'''simple docstring'''
if a == 0:
raise ValueError('Coefficient \'a\' must not be zero.' )
_UpperCAmelCase = b * b - 4 * a * c
_UpperCAmelCase = (-b + sqrt(A )) / (2 * a)
_UpperCAmelCase = (-b - sqrt(A )) / (2 * a)
return (
root_a.real if not root_a.imag else root_a,
root_a.real if not root_a.imag else root_a,
)
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = quadratic_roots(a=5 , b=6 , c=1 )
print(f'The solutions are: {solutiona} and {solutiona}' )
if __name__ == "__main__":
main()
| 24
| 1
|
"""simple docstring"""
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version
from .. import __version__
from . import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging
lowercase = (
'''https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py'''
)
lowercase = logging.get_logger(__name__) # pylint: disable=invalid-name
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = 'https://pypi.org/pypi/diffusers/json'
_UpperCAmelCase = json.loads(request.urlopen(A ).read() )['releases'].keys()
return sorted(A , key=lambda A : version.Version(A ) )
def UpperCAmelCase ( ):
'''simple docstring'''
if HF_MODULES_CACHE in sys.path:
return
sys.path.append(A )
os.makedirs(A , exist_ok=A )
_UpperCAmelCase = Path(A ) / '__init__.py'
if not init_path.exists():
init_path.touch()
def UpperCAmelCase ( A : Union[str, os.PathLike] ):
'''simple docstring'''
init_hf_modules()
_UpperCAmelCase = Path(A ) / name
# If the parent module does not exist yet, recursively create it.
if not dynamic_module_path.parent.exists():
create_dynamic_module(dynamic_module_path.parent )
os.makedirs(A , exist_ok=A )
_UpperCAmelCase = dynamic_module_path / '__init__.py'
if not init_path.exists():
init_path.touch()
def UpperCAmelCase ( A : List[Any] ):
'''simple docstring'''
with open(A , 'r' , encoding='utf-8' ) as f:
_UpperCAmelCase = f.read()
# Imports of the form `import .xxx`
_UpperCAmelCase = re.findall('^\s*import\s+\.(\S+)\s*$' , A , flags=re.MULTILINE )
# Imports of the form `from .xxx import yyy`
relative_imports += re.findall('^\s*from\s+\.(\S+)\s+import' , A , flags=re.MULTILINE )
# Unique-ify
return list(set(A ) )
def UpperCAmelCase ( A : List[str] ):
'''simple docstring'''
_UpperCAmelCase = False
_UpperCAmelCase = [module_file]
_UpperCAmelCase = []
# Let's recurse through all relative imports
while not no_change:
_UpperCAmelCase = []
for f in files_to_check:
new_imports.extend(get_relative_imports(A ) )
_UpperCAmelCase = Path(A ).parent
_UpperCAmelCase = [str(module_path / m ) for m in new_imports]
_UpperCAmelCase = [f for f in new_import_files if f not in all_relative_imports]
_UpperCAmelCase = [f'{f}.py' for f in new_import_files]
_UpperCAmelCase = len(A ) == 0
all_relative_imports.extend(A )
return all_relative_imports
def UpperCAmelCase ( A : str ):
'''simple docstring'''
with open(A , 'r' , encoding='utf-8' ) as f:
_UpperCAmelCase = f.read()
# Imports of the form `import xxx`
_UpperCAmelCase = re.findall('^\s*import\s+(\S+)\s*$' , A , flags=re.MULTILINE )
# Imports of the form `from xxx import yyy`
imports += re.findall('^\s*from\s+(\S+)\s+import' , A , flags=re.MULTILINE )
# Only keep the top-level module
_UpperCAmelCase = [imp.split('.' )[0] for imp in imports if not imp.startswith('.' )]
# Unique-ify and test we got them all
_UpperCAmelCase = list(set(A ) )
_UpperCAmelCase = []
for imp in imports:
try:
importlib.import_module(A )
except ImportError:
missing_packages.append(A )
if len(A ) > 0:
raise ImportError(
'This modeling file requires the following packages that were not found in your environment: '
f'{", ".join(A )}. Run `pip install {" ".join(A )}`' )
return get_relative_imports(A )
def UpperCAmelCase ( A : Dict , A : Tuple ):
'''simple docstring'''
_UpperCAmelCase = module_path.replace(os.path.sep , '.' )
_UpperCAmelCase = importlib.import_module(A )
if class_name is None:
return find_pipeline_class(A )
return getattr(A , A )
def UpperCAmelCase ( A : Tuple ):
'''simple docstring'''
from ..pipelines import DiffusionPipeline
_UpperCAmelCase = dict(inspect.getmembers(A , inspect.isclass ) )
_UpperCAmelCase = None
for cls_name, cls in cls_members.items():
if (
cls_name != DiffusionPipeline.__name__
and issubclass(cls , A )
and cls.__module__.split('.' )[0] != "diffusers"
):
if pipeline_class is not None:
raise ValueError(
f'Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:'
f' {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in'
f' {loaded_module}.' )
_UpperCAmelCase = cls
return pipeline_class
def UpperCAmelCase ( A : Union[str, os.PathLike] , A : str , A : Optional[Union[str, os.PathLike]] = None , A : bool = False , A : bool = False , A : Optional[Dict[str, str]] = None , A : Optional[Union[bool, str]] = None , A : Optional[str] = None , A : bool = False , ):
'''simple docstring'''
_UpperCAmelCase = str(A )
_UpperCAmelCase = os.path.join(A , A )
if os.path.isfile(A ):
_UpperCAmelCase = module_file_or_url
_UpperCAmelCase = 'local'
elif pretrained_model_name_or_path.count('/' ) == 0:
_UpperCAmelCase = get_diffusers_versions()
# cut ".dev0"
_UpperCAmelCase = 'v' + '.'.join(__version__.split('.' )[:3] )
# retrieve github version that matches
if revision is None:
_UpperCAmelCase = latest_version if latest_version[1:] in available_versions else 'main'
logger.info(f'Defaulting to latest_version: {revision}.' )
elif revision in available_versions:
_UpperCAmelCase = f'v{revision}'
elif revision == "main":
_UpperCAmelCase = revision
else:
raise ValueError(
f'`custom_revision`: {revision} does not exist. Please make sure to choose one of'
f' {", ".join(available_versions + ["main"] )}.' )
# community pipeline on GitHub
_UpperCAmelCase = COMMUNITY_PIPELINES_URL.format(revision=A , pipeline=A )
try:
_UpperCAmelCase = cached_download(
A , cache_dir=A , force_download=A , proxies=A , resume_download=A , local_files_only=A , use_auth_token=A , )
_UpperCAmelCase = 'git'
_UpperCAmelCase = pretrained_model_name_or_path + '.py'
except EnvironmentError:
logger.error(f'Could not locate the {module_file} inside {pretrained_model_name_or_path}.' )
raise
else:
try:
# Load from URL or cache if already cached
_UpperCAmelCase = hf_hub_download(
A , A , cache_dir=A , force_download=A , proxies=A , resume_download=A , local_files_only=A , use_auth_token=A , )
_UpperCAmelCase = os.path.join('local' , '--'.join(pretrained_model_name_or_path.split('/' ) ) )
except EnvironmentError:
logger.error(f'Could not locate the {module_file} inside {pretrained_model_name_or_path}.' )
raise
# Check we have all the requirements in our environment
_UpperCAmelCase = check_imports(A )
# Now we move the module inside our cached dynamic modules.
_UpperCAmelCase = DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
create_dynamic_module(A )
_UpperCAmelCase = Path(A ) / full_submodule
if submodule == "local" or submodule == "git":
# We always copy local files (we could hash the file to see if there was a change, and give them the name of
# that hash, to only copy when there is a modification but it seems overkill for now).
# The only reason we do the copy is to avoid putting too many folders in sys.path.
shutil.copy(A , submodule_path / module_file )
for module_needed in modules_needed:
_UpperCAmelCase = f'{module_needed}.py'
shutil.copy(os.path.join(A , A ) , submodule_path / module_needed )
else:
# Get the commit hash
# TODO: we will get this info in the etag soon, so retrieve it from there and not here.
if isinstance(A , A ):
_UpperCAmelCase = use_auth_token
elif use_auth_token is True:
_UpperCAmelCase = HfFolder.get_token()
else:
_UpperCAmelCase = None
_UpperCAmelCase = model_info(A , revision=A , token=A ).sha
# The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
# benefit of versioning.
_UpperCAmelCase = submodule_path / commit_hash
_UpperCAmelCase = full_submodule + os.path.sep + commit_hash
create_dynamic_module(A )
if not (submodule_path / module_file).exists():
shutil.copy(A , submodule_path / module_file )
# Make sure we also have every file with relative
for module_needed in modules_needed:
if not (submodule_path / module_needed).exists():
get_cached_module_file(
A , f'{module_needed}.py' , cache_dir=A , force_download=A , resume_download=A , proxies=A , use_auth_token=A , revision=A , local_files_only=A , )
return os.path.join(A , A )
def UpperCAmelCase ( A : Union[str, os.PathLike] , A : str , A : Optional[str] = None , A : Optional[Union[str, os.PathLike]] = None , A : bool = False , A : bool = False , A : Optional[Dict[str, str]] = None , A : Optional[Union[bool, str]] = None , A : Optional[str] = None , A : bool = False , **A : Dict , ):
'''simple docstring'''
_UpperCAmelCase = get_cached_module_file(
A , A , cache_dir=A , force_download=A , resume_download=A , proxies=A , use_auth_token=A , revision=A , local_files_only=A , )
return get_class_in_module(A , final_module.replace('.py' , '' ) )
| 24
|
"""simple docstring"""
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # see https://github.com/huggingface/transformers/issues/11457
class lowercase__ ( A, unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = BarthezTokenizer
_UpperCAmelCase = BarthezTokenizerFast
_UpperCAmelCase = True
_UpperCAmelCase = True
def lowerCamelCase_ ( self ) -> Optional[int]:
super().setUp()
_UpperCAmelCase = BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez' )
tokenizer.save_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname , legacy_format=snake_case )
_UpperCAmelCase = tokenizer
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = '<pad>'
_UpperCAmelCase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case ) , snake_case )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case ) , snake_case )
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '<s>' )
self.assertEqual(vocab_keys[1] , '<pad>' )
self.assertEqual(vocab_keys[-1] , '<mask>' )
self.assertEqual(len(snake_case ) , 101122 )
def lowerCamelCase_ ( self ) -> List[Any]:
self.assertEqual(self.get_tokenizer().vocab_size , 101122 )
@require_torch
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
_UpperCAmelCase = [0, 57, 3018, 70307, 91, 2]
_UpperCAmelCase = self.tokenizer(
snake_case , max_length=len(snake_case ) , padding=snake_case , truncation=snake_case , return_tensors='pt' )
self.assertIsInstance(snake_case , snake_case )
self.assertEqual((2, 6) , batch.input_ids.shape )
self.assertEqual((2, 6) , batch.attention_mask.shape )
_UpperCAmelCase = batch.input_ids.tolist()[0]
self.assertListEqual(snake_case , snake_case )
def lowerCamelCase_ ( self ) -> Optional[Any]:
if not self.test_rust_tokenizer:
return
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = self.get_rust_tokenizer()
_UpperCAmelCase = 'I was born in 92000, and this is falsé.'
_UpperCAmelCase = tokenizer.tokenize(snake_case )
_UpperCAmelCase = rust_tokenizer.tokenize(snake_case )
self.assertListEqual(snake_case , snake_case )
_UpperCAmelCase = tokenizer.encode(snake_case , add_special_tokens=snake_case )
_UpperCAmelCase = rust_tokenizer.encode(snake_case , add_special_tokens=snake_case )
self.assertListEqual(snake_case , snake_case )
_UpperCAmelCase = self.get_rust_tokenizer()
_UpperCAmelCase = tokenizer.encode(snake_case )
_UpperCAmelCase = rust_tokenizer.encode(snake_case )
self.assertListEqual(snake_case , snake_case )
@slow
def lowerCamelCase_ ( self ) -> Optional[int]:
# fmt: off
_UpperCAmelCase = {'input_ids': [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 2, 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, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 5, 2]], '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, 0, 0, 0, 0, 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, 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]]} # noqa: E501
# fmt: on
# moussaKam/mbarthez is a french model. So we also use french texts.
_UpperCAmelCase = [
'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
'telles que la traduction et la synthèse de texte.',
]
self.tokenizer_integration_test_util(
expected_encoding=snake_case , model_name='moussaKam/mbarthez' , revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6' , sequences=snake_case , )
| 24
| 1
|
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
lowercase = logging.get_logger(__name__)
lowercase = {
'''post_extract_proj''': '''feature_projection.projection''',
'''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
'''self_attn.linear_k''': '''encoder.layers.*.self_attn.linear_k''',
'''self_attn.linear_v''': '''encoder.layers.*.self_attn.linear_v''',
'''self_attn.linear_q''': '''encoder.layers.*.self_attn.linear_q''',
'''self_attn.pos_bias_u''': '''encoder.layers.*.self_attn.pos_bias_u''',
'''self_attn.pos_bias_v''': '''encoder.layers.*.self_attn.pos_bias_v''',
'''self_attn.linear_out''': '''encoder.layers.*.self_attn.linear_out''',
'''self_attn.linear_pos''': '''encoder.layers.*.self_attn.linear_pos''',
'''self_attn.rotary_emb''': '''encoder.embed_positions''',
'''self_attn_layer_norm''': '''encoder.layers.*.self_attn_layer_norm''',
'''conv_module.pointwise_conv1''': '''encoder.layers.*.conv_module.pointwise_conv1''',
'''conv_module.pointwise_conv2''': '''encoder.layers.*.conv_module.pointwise_conv2''',
'''conv_module.depthwise_conv''': '''encoder.layers.*.conv_module.depthwise_conv''',
'''conv_module.batch_norm''': '''encoder.layers.*.conv_module.batch_norm''',
'''conv_module.layer_norm''': '''encoder.layers.*.conv_module.layer_norm''',
'''ffn1.w_1''': '''encoder.layers.*.ffn1.intermediate_dense''',
'''ffn1.w_2''': '''encoder.layers.*.ffn1.output_dense''',
'''ffn1.layer_norm''': '''encoder.layers.*.ffn1_layer_norm''',
'''ffn2.w_1''': '''encoder.layers.*.ffn2.intermediate_dense''',
'''ffn2.w_2''': '''encoder.layers.*.ffn2.output_dense''',
'''ffn2.layer_norm''': '''encoder.layers.*.ffn2_layer_norm''',
'''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
'''encoder.layer_norm''': '''encoder.layer_norm''',
'''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
'''quantizer.weight_proj''': '''quantizer.weight_proj''',
'''quantizer.vars''': '''quantizer.codevectors''',
'''project_q''': '''project_q''',
'''final_proj''': '''project_hid''',
'''w2v_encoder.proj''': '''lm_head''',
'''mask_emb''': '''masked_spec_embed''',
}
lowercase = [
'''lm_head''',
'''quantizer.weight_proj''',
'''quantizer.codevectors''',
'''project_q''',
'''project_hid''',
]
def UpperCAmelCase ( A : Optional[Any] , A : Optional[Any] , A : List[str] , A : Dict , A : Tuple ):
'''simple docstring'''
for attribute in key.split('.' ):
_UpperCAmelCase = getattr(A , A )
if weight_type is not None:
_UpperCAmelCase = getattr(A , A ).shape
else:
_UpperCAmelCase = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
f' {value.shape} for {full_name}' )
if weight_type == "weight":
_UpperCAmelCase = value
elif weight_type == "weight_g":
_UpperCAmelCase = value
elif weight_type == "weight_v":
_UpperCAmelCase = value
elif weight_type == "bias":
_UpperCAmelCase = value
elif weight_type == "running_mean":
_UpperCAmelCase = value
elif weight_type == "running_var":
_UpperCAmelCase = value
elif weight_type == "num_batches_tracked":
_UpperCAmelCase = value
elif weight_type == "inv_freq":
_UpperCAmelCase = value
else:
_UpperCAmelCase = value
logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.' )
def UpperCAmelCase ( A : Any , A : List[Any] , A : Union[str, Any] ):
'''simple docstring'''
_UpperCAmelCase = []
_UpperCAmelCase = fairseq_model.state_dict()
_UpperCAmelCase = hf_model.wavaveca_conformer.feature_extractor
for name, value in fairseq_dict.items():
_UpperCAmelCase = False
if "conv_layers" in name:
load_conv_layer(
A , A , A , A , hf_model.config.feat_extract_norm == 'group' , )
_UpperCAmelCase = True
else:
for key, mapped_key in MAPPING.items():
_UpperCAmelCase = 'wav2vec2_conformer.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]:
_UpperCAmelCase = True
if "*" in mapped_key:
_UpperCAmelCase = name.split(A )[0].split('.' )[-2]
_UpperCAmelCase = mapped_key.replace('*' , A )
if "pos_bias_u" in name:
_UpperCAmelCase = None
elif "pos_bias_v" in name:
_UpperCAmelCase = None
elif "weight_g" in name:
_UpperCAmelCase = 'weight_g'
elif "weight_v" in name:
_UpperCAmelCase = 'weight_v'
elif "bias" in name:
_UpperCAmelCase = 'bias'
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
_UpperCAmelCase = 'weight'
elif "running_mean" in name:
_UpperCAmelCase = 'running_mean'
elif "inv_freq" in name:
_UpperCAmelCase = 'inv_freq'
elif "running_var" in name:
_UpperCAmelCase = 'running_var'
elif "num_batches_tracked" in name:
_UpperCAmelCase = 'num_batches_tracked'
else:
_UpperCAmelCase = None
set_recursively(A , A , A , A , A )
continue
if not is_used:
unused_weights.append(A )
logger.warning(f'Unused weights: {unused_weights}' )
def UpperCAmelCase ( A : Dict , A : List[Any] , A : List[Any] , A : Dict , A : Optional[Any] ):
'''simple docstring'''
_UpperCAmelCase = full_name.split('conv_layers.' )[-1]
_UpperCAmelCase = name.split('.' )
_UpperCAmelCase = int(items[0] )
_UpperCAmelCase = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.' )
_UpperCAmelCase = value
logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.' )
_UpperCAmelCase = value
logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.' )
_UpperCAmelCase = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.' )
_UpperCAmelCase = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
else:
unused_weights.append(A )
@torch.no_grad()
def UpperCAmelCase ( A : List[str] , A : Dict , A : Dict=None , A : Optional[Any]=None , A : int=True ):
'''simple docstring'''
if config_path is not None:
_UpperCAmelCase = WavaVecaConformerConfig.from_pretrained(A , hidden_act='swish' )
else:
_UpperCAmelCase = WavaVecaConformerConfig()
if "rope" in checkpoint_path:
_UpperCAmelCase = 'rotary'
if is_finetuned:
if dict_path:
_UpperCAmelCase = Dictionary.load(A )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
_UpperCAmelCase = target_dict.pad_index
_UpperCAmelCase = target_dict.bos_index
_UpperCAmelCase = target_dict.eos_index
_UpperCAmelCase = len(target_dict.symbols )
_UpperCAmelCase = os.path.join(A , 'vocab.json' )
if not os.path.isdir(A ):
logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(A ) )
return
os.makedirs(A , exist_ok=A )
_UpperCAmelCase = target_dict.indices
# fairseq has the <pad> and <s> switched
_UpperCAmelCase = 0
_UpperCAmelCase = 1
with open(A , 'w' , encoding='utf-8' ) as vocab_handle:
json.dump(A , A )
_UpperCAmelCase = WavaVecaCTCTokenizer(
A , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token='|' , do_lower_case=A , )
_UpperCAmelCase = True if config.feat_extract_norm == 'layer' else False
_UpperCAmelCase = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_6000 , padding_value=0 , do_normalize=A , return_attention_mask=A , )
_UpperCAmelCase = WavaVecaProcessor(feature_extractor=A , tokenizer=A )
processor.save_pretrained(A )
_UpperCAmelCase = WavaVecaConformerForCTC(A )
else:
_UpperCAmelCase = WavaVecaConformerForPreTraining(A )
if is_finetuned:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'data': '/'.join(dict_path.split('/' )[:-1] )} )
else:
_UpperCAmelCase = argparse.Namespace(task='audio_pretraining' )
_UpperCAmelCase = fairseq.tasks.setup_task(A )
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=A )
_UpperCAmelCase = model[0].eval()
recursively_load_weights(A , A , not is_finetuned )
hf_wavavec.save_pretrained(A )
if __name__ == "__main__":
lowercase = argparse.ArgumentParser()
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
parser.add_argument(
'''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
)
lowercase = parser.parse_args()
convert_wavaveca_conformer_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 24
|
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class lowercase__ ( A, unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = DiTPipeline
_UpperCAmelCase = CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
_UpperCAmelCase = PipelineTesterMixin.required_optional_params - {
'''latents''',
'''num_images_per_prompt''',
'''callback''',
'''callback_steps''',
}
_UpperCAmelCase = CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
_UpperCAmelCase = False
def lowerCamelCase_ ( self ) -> str:
torch.manual_seed(0 )
_UpperCAmelCase = TransformeraDModel(
sample_size=16 , num_layers=2 , patch_size=4 , attention_head_dim=8 , num_attention_heads=2 , in_channels=4 , out_channels=8 , attention_bias=snake_case , activation_fn='gelu-approximate' , num_embeds_ada_norm=1000 , norm_type='ada_norm_zero' , norm_elementwise_affine=snake_case , )
_UpperCAmelCase = AutoencoderKL()
_UpperCAmelCase = DDIMScheduler()
_UpperCAmelCase = {'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
return components
def lowerCamelCase_ ( self , snake_case , snake_case=0 ) -> Optional[Any]:
if str(snake_case ).startswith('mps' ):
_UpperCAmelCase = torch.manual_seed(snake_case )
else:
_UpperCAmelCase = torch.Generator(device=snake_case ).manual_seed(snake_case )
_UpperCAmelCase = {
'class_labels': [1],
'generator': generator,
'num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
def lowerCamelCase_ ( self ) -> List[Any]:
_UpperCAmelCase = 'cpu'
_UpperCAmelCase = self.get_dummy_components()
_UpperCAmelCase = self.pipeline_class(**snake_case )
pipe.to(snake_case )
pipe.set_progress_bar_config(disable=snake_case )
_UpperCAmelCase = self.get_dummy_inputs(snake_case )
_UpperCAmelCase = pipe(**snake_case ).images
_UpperCAmelCase = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 16, 16, 3) )
_UpperCAmelCase = np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457] )
_UpperCAmelCase = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(snake_case , 1E-3 )
def lowerCamelCase_ ( self ) -> Any:
self._test_inference_batch_single_identical(relax_max_difference=snake_case , expected_max_diff=1E-3 )
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def lowerCamelCase_ ( self ) -> Optional[int]:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
@require_torch_gpu
@slow
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> int:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = DiTPipeline.from_pretrained('facebook/DiT-XL-2-256' )
pipe.to('cuda' )
_UpperCAmelCase = ['vase', 'umbrella', 'white shark', 'white wolf']
_UpperCAmelCase = pipe.get_label_ids(snake_case )
_UpperCAmelCase = pipe(snake_case , generator=snake_case , num_inference_steps=40 , output_type='np' ).images
for word, image in zip(snake_case , snake_case ):
_UpperCAmelCase = load_numpy(
f'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy' )
assert np.abs((expected_image - image).max() ) < 1E-2
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = DiTPipeline.from_pretrained('facebook/DiT-XL-2-512' )
_UpperCAmelCase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.to('cuda' )
_UpperCAmelCase = ['vase', 'umbrella']
_UpperCAmelCase = pipe.get_label_ids(snake_case )
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = pipe(snake_case , generator=snake_case , num_inference_steps=25 , output_type='np' ).images
for word, image in zip(snake_case , snake_case ):
_UpperCAmelCase = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
f'/dit/{word}_512.npy' )
assert np.abs((expected_image - image).max() ) < 1E-1
| 24
| 1
|
"""simple docstring"""
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokenizer,
BlipaConfig,
BlipaForConditionalGeneration,
BlipaProcessor,
BlipaVisionConfig,
BlipImageProcessor,
OPTConfig,
TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = 'https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png'
_UpperCAmelCase = Image.open(requests.get(A , stream=A ).raw ).convert('RGB' )
return image
def UpperCAmelCase ( A : Any ):
'''simple docstring'''
_UpperCAmelCase = []
# fmt: off
# vision encoder
rename_keys.append(('visual_encoder.cls_token', 'vision_model.embeddings.class_embedding') )
rename_keys.append(('visual_encoder.pos_embed', 'vision_model.embeddings.position_embedding') )
rename_keys.append(('visual_encoder.patch_embed.proj.weight', 'vision_model.embeddings.patch_embedding.weight') )
rename_keys.append(('visual_encoder.patch_embed.proj.bias', 'vision_model.embeddings.patch_embedding.bias') )
rename_keys.append(('ln_vision.weight', 'vision_model.post_layernorm.weight') )
rename_keys.append(('ln_vision.bias', 'vision_model.post_layernorm.bias') )
for i in range(config.vision_config.num_hidden_layers ):
rename_keys.append((f'visual_encoder.blocks.{i}.norm1.weight', f'vision_model.encoder.layers.{i}.layer_norm1.weight') )
rename_keys.append((f'visual_encoder.blocks.{i}.norm1.bias', f'vision_model.encoder.layers.{i}.layer_norm1.bias') )
rename_keys.append((f'visual_encoder.blocks.{i}.norm2.weight', f'vision_model.encoder.layers.{i}.layer_norm2.weight') )
rename_keys.append((f'visual_encoder.blocks.{i}.norm2.bias', f'vision_model.encoder.layers.{i}.layer_norm2.bias') )
rename_keys.append((f'visual_encoder.blocks.{i}.attn.qkv.weight', f'vision_model.encoder.layers.{i}.self_attn.qkv.weight') )
rename_keys.append((f'visual_encoder.blocks.{i}.attn.proj.weight', f'vision_model.encoder.layers.{i}.self_attn.projection.weight',) )
rename_keys.append((f'visual_encoder.blocks.{i}.attn.proj.bias', f'vision_model.encoder.layers.{i}.self_attn.projection.bias') )
rename_keys.append((f'visual_encoder.blocks.{i}.mlp.fc1.weight', f'vision_model.encoder.layers.{i}.mlp.fc1.weight') )
rename_keys.append((f'visual_encoder.blocks.{i}.mlp.fc1.bias', f'vision_model.encoder.layers.{i}.mlp.fc1.bias') )
rename_keys.append((f'visual_encoder.blocks.{i}.mlp.fc2.weight', f'vision_model.encoder.layers.{i}.mlp.fc2.weight') )
rename_keys.append((f'visual_encoder.blocks.{i}.mlp.fc2.bias', f'vision_model.encoder.layers.{i}.mlp.fc2.bias') )
# QFormer
rename_keys.append(('Qformer.bert.embeddings.LayerNorm.weight', 'qformer.layernorm.weight') )
rename_keys.append(('Qformer.bert.embeddings.LayerNorm.bias', 'qformer.layernorm.bias') )
# fmt: on
return rename_keys
def UpperCAmelCase ( A : Dict , A : str , A : List[Any] ):
'''simple docstring'''
_UpperCAmelCase = dct.pop(A )
_UpperCAmelCase = val
def UpperCAmelCase ( A : Optional[Any] , A : Optional[Any] ):
'''simple docstring'''
for i in range(config.vision_config.num_hidden_layers ):
# read in original q and v biases
_UpperCAmelCase = state_dict.pop(f'visual_encoder.blocks.{i}.attn.q_bias' )
_UpperCAmelCase = state_dict.pop(f'visual_encoder.blocks.{i}.attn.v_bias' )
# next, set bias in the state dict
_UpperCAmelCase = torch.cat((q_bias, torch.zeros_like(A , requires_grad=A ), v_bias) )
_UpperCAmelCase = qkv_bias
def UpperCAmelCase ( A : Optional[Any] , A : List[Any] ):
'''simple docstring'''
_UpperCAmelCase = 364 if 'coco' in model_name else 224
_UpperCAmelCase = BlipaVisionConfig(image_size=A ).to_dict()
# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
# seems like flan-T5 models don't have bos_token_id properly set?
if "opt-2.7b" in model_name:
_UpperCAmelCase = OPTConfig.from_pretrained('facebook/opt-2.7b' , eos_token_id=A ).to_dict()
elif "opt-6.7b" in model_name:
_UpperCAmelCase = OPTConfig.from_pretrained('facebook/opt-6.7b' , eos_token_id=A ).to_dict()
elif "t5-xl" in model_name:
_UpperCAmelCase = TaConfig.from_pretrained('google/flan-t5-xl' , dense_act_fn='gelu' , bos_token_id=1 ).to_dict()
elif "t5-xxl" in model_name:
_UpperCAmelCase = TaConfig.from_pretrained('google/flan-t5-xxl' , dense_act_fn='gelu' , bos_token_id=1 ).to_dict()
_UpperCAmelCase = BlipaConfig(vision_config=A , text_config=A )
return config, image_size
@torch.no_grad()
def UpperCAmelCase ( A : str , A : List[Any]=None , A : Any=False ):
'''simple docstring'''
_UpperCAmelCase = (
AutoTokenizer.from_pretrained('facebook/opt-2.7b' )
if 'opt' in model_name
else AutoTokenizer.from_pretrained('google/flan-t5-xl' )
)
_UpperCAmelCase = tokenizer('\n' , add_special_tokens=A ).input_ids[0]
_UpperCAmelCase , _UpperCAmelCase = get_blipa_config(A , eos_token_id=A )
_UpperCAmelCase = BlipaForConditionalGeneration(A ).eval()
_UpperCAmelCase = {
'blip2-opt-2.7b': ('blip2_opt', 'pretrain_opt2.7b'),
'blip2-opt-6.7b': ('blip2_opt', 'pretrain_opt6.7b'),
'blip2-opt-2.7b-coco': ('blip2_opt', 'caption_coco_opt2.7b'),
'blip2-opt-6.7b-coco': ('blip2_opt', 'caption_coco_opt6.7b'),
'blip2-flan-t5-xl': ('blip2_t5', 'pretrain_flant5xl'),
'blip2-flan-t5-xl-coco': ('blip2_t5', 'caption_coco_flant5xl'),
'blip2-flan-t5-xxl': ('blip2_t5', 'pretrain_flant5xxl'),
}
_UpperCAmelCase , _UpperCAmelCase = model_name_to_original[model_name]
# load original model
print('Loading original model...' )
_UpperCAmelCase = 'cuda' if torch.cuda.is_available() else 'cpu'
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = load_model_and_preprocess(
name=A , model_type=A , is_eval=A , device=A )
original_model.eval()
print('Done!' )
# update state dict keys
_UpperCAmelCase = original_model.state_dict()
_UpperCAmelCase = create_rename_keys(A )
for src, dest in rename_keys:
rename_key(A , A , A )
# some keys can be renamed efficiently
for key, val in state_dict.copy().items():
_UpperCAmelCase = state_dict.pop(A )
if key.startswith('Qformer.bert' ):
_UpperCAmelCase = key.replace('Qformer.bert' , 'qformer' )
if "attention.self" in key:
_UpperCAmelCase = key.replace('self' , 'attention' )
if "opt_proj" in key:
_UpperCAmelCase = key.replace('opt_proj' , 'language_projection' )
if "t5_proj" in key:
_UpperCAmelCase = key.replace('t5_proj' , 'language_projection' )
if key.startswith('opt' ):
_UpperCAmelCase = key.replace('opt' , 'language' )
if key.startswith('t5' ):
_UpperCAmelCase = key.replace('t5' , 'language' )
_UpperCAmelCase = val
# read in qv biases
read_in_q_v_bias(A , A )
_UpperCAmelCase , _UpperCAmelCase = hf_model.load_state_dict(A , strict=A )
assert len(A ) == 0
assert unexpected_keys == ["qformer.embeddings.position_ids"]
_UpperCAmelCase = load_demo_image()
_UpperCAmelCase = vis_processors['eval'](A ).unsqueeze(0 ).to(A )
_UpperCAmelCase = tokenizer(['\n'] , return_tensors='pt' ).input_ids.to(A )
# create processor
_UpperCAmelCase = BlipImageProcessor(
size={'height': image_size, 'width': image_size} , image_mean=A , image_std=A )
_UpperCAmelCase = BlipaProcessor(image_processor=A , tokenizer=A )
_UpperCAmelCase = processor(images=A , return_tensors='pt' ).pixel_values.to(A )
# make sure processor creates exact same pixel values
assert torch.allclose(A , A )
original_model.to(A )
hf_model.to(A )
with torch.no_grad():
if "opt" in model_name:
_UpperCAmelCase = original_model({'image': original_pixel_values, 'text_input': ['']} ).logits
_UpperCAmelCase = hf_model(A , A ).logits
else:
_UpperCAmelCase = original_model(
{'image': original_pixel_values, 'text_input': ['\n'], 'text_output': ['\n']} ).logits
_UpperCAmelCase = input_ids.masked_fill(input_ids == tokenizer.pad_token_id , -100 )
_UpperCAmelCase = hf_model(A , A , labels=A ).logits
assert original_logits.shape == logits.shape
print('First values of original logits:' , original_logits[0, :3, :3] )
print('First values of HF logits:' , logits[0, :3, :3] )
# assert values
if model_name == "blip2-flan-t5-xl":
_UpperCAmelCase = torch.tensor(
[[-41.5850, -4.4440, -8.9922], [-47.4322, -5.9143, -1.7340]] , device=A )
assert torch.allclose(logits[0, :3, :3] , A , atol=1e-4 )
elif model_name == "blip2-flan-t5-xl-coco":
_UpperCAmelCase = torch.tensor(
[[-57.0109, -9.8967, -12.6280], [-68.6578, -12.7191, -10.5065]] , device=A )
else:
# cast to same type
_UpperCAmelCase = logits.dtype
assert torch.allclose(original_logits.to(A ) , A , atol=1e-2 )
print('Looks ok!' )
print('Generating a caption...' )
_UpperCAmelCase = ''
_UpperCAmelCase = tokenizer(A , return_tensors='pt' ).input_ids.to(A )
_UpperCAmelCase = original_model.generate({'image': original_pixel_values} )
_UpperCAmelCase = hf_model.generate(
A , A , do_sample=A , num_beams=5 , max_length=30 , min_length=1 , top_p=0.9 , repetition_penalty=1.0 , length_penalty=1.0 , temperature=1 , )
print('Original generation:' , A )
_UpperCAmelCase = input_ids.shape[1]
_UpperCAmelCase = processor.batch_decode(outputs[:, prompt_length:] , skip_special_tokens=A )
_UpperCAmelCase = [text.strip() for text in output_text]
print('HF generation:' , A )
if pytorch_dump_folder_path is not None:
processor.save_pretrained(A )
hf_model.save_pretrained(A )
if push_to_hub:
processor.push_to_hub(f'nielsr/{model_name}' )
hf_model.push_to_hub(f'nielsr/{model_name}' )
if __name__ == "__main__":
lowercase = argparse.ArgumentParser()
lowercase = [
'''blip2-opt-2.7b''',
'''blip2-opt-6.7b''',
'''blip2-opt-2.7b-coco''',
'''blip2-opt-6.7b-coco''',
'''blip2-flan-t5-xl''',
'''blip2-flan-t5-xl-coco''',
'''blip2-flan-t5-xxl''',
]
parser.add_argument(
'''--model_name''',
default='''blip2-opt-2.7b''',
choices=choices,
type=str,
help='''Path to hf config.json of model to convert''',
)
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument(
'''--push_to_hub''',
action='''store_true''',
help='''Whether to push the model and processor to the hub after converting''',
)
lowercase = parser.parse_args()
convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 24
|
"""simple docstring"""
def UpperCAmelCase ( A : int ):
'''simple docstring'''
_UpperCAmelCase = abs(A )
_UpperCAmelCase = 0
while n > 0:
res += n % 10
n //= 10
return res
def UpperCAmelCase ( A : int ):
'''simple docstring'''
_UpperCAmelCase = abs(A )
return n if n < 10 else n % 10 + sum_of_digits(n // 10 )
def UpperCAmelCase ( A : int ):
'''simple docstring'''
return sum(int(A ) for c in str(abs(A ) ) )
def UpperCAmelCase ( ):
'''simple docstring'''
from collections.abc import Callable
from timeit import timeit
def benchmark_a_function(A : Callable , A : int ) -> None:
_UpperCAmelCase = f'{func.__name__}({value})'
_UpperCAmelCase = timeit(f'__main__.{call}' , setup='import __main__' )
print(f'{call:56} = {func(A )} -- {timing:.4f} seconds' )
for value in (26_2144, 1125_8999_0684_2624, 126_7650_6002_2822_9401_4967_0320_5376):
for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
benchmark_a_function(A , A )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 24
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowercase = {
'''configuration_efficientformer''': [
'''EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''EfficientFormerConfig''',
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = ['''EfficientFormerImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''EfficientFormerForImageClassification''',
'''EfficientFormerForImageClassificationWithTeacher''',
'''EfficientFormerModel''',
'''EfficientFormerPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFEfficientFormerForImageClassification''',
'''TFEfficientFormerForImageClassificationWithTeacher''',
'''TFEfficientFormerModel''',
'''TFEfficientFormerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_efficientformer import EfficientFormerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_efficientformer import (
EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
EfficientFormerForImageClassification,
EfficientFormerForImageClassificationWithTeacher,
EfficientFormerModel,
EfficientFormerPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_efficientformer import (
TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFEfficientFormerForImageClassification,
TFEfficientFormerForImageClassificationWithTeacher,
TFEfficientFormerModel,
TFEfficientFormerPreTrainedModel,
)
else:
import sys
lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 24
|
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase ( A : int , A : int ):
'''simple docstring'''
_UpperCAmelCase = []
create_all_state(1 , A , A , [] , A )
return result
def UpperCAmelCase ( A : int , A : int , A : int , A : list[int] , A : list[list[int]] , ):
'''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 : list[list[int]] ):
'''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)
| 24
| 1
|
"""simple docstring"""
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def UpperCAmelCase ( A : Dict , A : str , A : Union[str, Any] ):
'''simple docstring'''
_UpperCAmelCase = OmegaConf.load(A )
_UpperCAmelCase = torch.load(A , map_location='cpu' )['model']
_UpperCAmelCase = list(state_dict.keys() )
# extract state_dict for VQVAE
_UpperCAmelCase = {}
_UpperCAmelCase = 'first_stage_model.'
for key in keys:
if key.startswith(A ):
_UpperCAmelCase = state_dict[key]
# extract state_dict for UNetLDM
_UpperCAmelCase = {}
_UpperCAmelCase = 'model.diffusion_model.'
for key in keys:
if key.startswith(A ):
_UpperCAmelCase = state_dict[key]
_UpperCAmelCase = config.model.params.first_stage_config.params
_UpperCAmelCase = config.model.params.unet_config.params
_UpperCAmelCase = VQModel(**A ).eval()
vqvae.load_state_dict(A )
_UpperCAmelCase = UNetLDMModel(**A ).eval()
unet.load_state_dict(A )
_UpperCAmelCase = DDIMScheduler(
timesteps=config.model.params.timesteps , beta_schedule='scaled_linear' , beta_start=config.model.params.linear_start , beta_end=config.model.params.linear_end , clip_sample=A , )
_UpperCAmelCase = LDMPipeline(A , A , A )
pipeline.save_pretrained(A )
if __name__ == "__main__":
lowercase = argparse.ArgumentParser()
parser.add_argument('''--checkpoint_path''', type=str, required=True)
parser.add_argument('''--config_path''', type=str, required=True)
parser.add_argument('''--output_path''', type=str, required=True)
lowercase = parser.parse_args()
convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)
| 24
|
"""simple docstring"""
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS
logging.basicConfig(level=logging.DEBUG)
lowercase = logging.getLogger()
def UpperCAmelCase ( A : Path , A : list ):
'''simple docstring'''
_UpperCAmelCase = '\n'.join(A )
Path(A ).open('w' ).writelines(A )
lowercase = '''patrickvonplaten/t5-tiny-random'''
lowercase = '''sshleifer/bart-tiny-random'''
lowercase = '''sshleifer/tiny-mbart'''
lowercase = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL) # remove noisy download output from tracebacks
class lowercase__ ( A ):
'''simple docstring'''
def lowerCamelCase_ ( self , snake_case ) -> str:
_UpperCAmelCase = Path(self.get_auto_remove_tmp_dir() ) / 'utest_input.source'
_UpperCAmelCase = input_file_name.parent / 'utest_output.txt'
assert not output_file_name.exists()
_UpperCAmelCase = [' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
_dump_articles(snake_case , snake_case )
_UpperCAmelCase = str(Path(self.get_auto_remove_tmp_dir() ) / 'scores.json' )
_UpperCAmelCase = 'translation_en_to_de' if model == T5_TINY else 'summarization'
_UpperCAmelCase = f'\n run_eval_search.py\n {model}\n {input_file_name}\n {output_file_name}\n --score_path {score_path}\n --task {task}\n --num_beams 2\n --length_penalty 2.0\n '.split()
with patch.object(snake_case , 'argv' , snake_case ):
run_generate()
assert Path(snake_case ).exists()
# os.remove(Path(output_file_name))
def lowerCamelCase_ ( self ) -> str:
self.run_eval_tester(snake_case )
@parameterized.expand([BART_TINY, MBART_TINY] )
@slow
def lowerCamelCase_ ( self , snake_case ) -> List[Any]:
self.run_eval_tester(snake_case )
@parameterized.expand([T5_TINY, MBART_TINY] )
@slow
def lowerCamelCase_ ( self , snake_case ) -> Dict:
_UpperCAmelCase = Path(self.get_auto_remove_tmp_dir() ) / 'utest_input.source'
_UpperCAmelCase = input_file_name.parent / 'utest_output.txt'
assert not output_file_name.exists()
_UpperCAmelCase = {
'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
'de': [
'Maschinelles Lernen ist großartig, oder?',
'Ich esse gerne Bananen',
'Morgen ist wieder ein toller Tag!',
],
}
_UpperCAmelCase = Path(self.get_auto_remove_tmp_dir() )
_UpperCAmelCase = str(tmp_dir / 'scores.json' )
_UpperCAmelCase = str(tmp_dir / 'val.target' )
_dump_articles(snake_case , text['en'] )
_dump_articles(snake_case , text['de'] )
_UpperCAmelCase = 'translation_en_to_de' if model == T5_TINY else 'summarization'
_UpperCAmelCase = f'\n run_eval_search.py\n {model}\n {str(snake_case )}\n {str(snake_case )}\n --score_path {score_path}\n --reference_path {reference_path}\n --task {task}\n '.split()
testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0'] )
with patch.object(snake_case , 'argv' , snake_case ):
with CaptureStdout() as cs:
run_search()
_UpperCAmelCase = [' num_beams | length_penalty', model, 'Best score args']
_UpperCAmelCase = ['Info']
if "translation" in task:
expected_strings.append('bleu' )
else:
expected_strings.extend(snake_case )
for w in expected_strings:
assert w in cs.out
for w in un_expected_strings:
assert w not in cs.out
assert Path(snake_case ).exists()
os.remove(Path(snake_case ) )
| 24
| 1
|
"""simple docstring"""
from __future__ import annotations
from random import random
class lowercase__ :
'''simple docstring'''
def __init__( self , snake_case = None ) -> List[str]:
_UpperCAmelCase = value
_UpperCAmelCase = random()
_UpperCAmelCase = None
_UpperCAmelCase = None
def __repr__( self ) -> str:
from pprint import pformat
if self.left is None and self.right is None:
return f'\'{self.value}: {self.prior:.5}\''
else:
return pformat(
{f'{self.value}: {self.prior:.5}': (self.left, self.right)} , indent=1 )
def __str__( self ) -> str:
_UpperCAmelCase = str(self.value ) + ' '
_UpperCAmelCase = str(self.left or '' )
_UpperCAmelCase = str(self.right or '' )
return value + left + right
def UpperCAmelCase ( A : Node | None , A : int ):
'''simple docstring'''
if root is None: # None tree is split into 2 Nones
return None, None
elif root.value is None:
return None, None
else:
if value < root.value:
_UpperCAmelCase , _UpperCAmelCase = split(root.left , A )
return left, root
else:
_UpperCAmelCase , _UpperCAmelCase = split(root.right , A )
return root, right
def UpperCAmelCase ( A : Node | None , A : Node | None ):
'''simple docstring'''
if (not left) or (not right): # If one node is None, return the other
return left or right
elif left.prior < right.prior:
_UpperCAmelCase = merge(left.right , A )
return left
else:
_UpperCAmelCase = merge(A , right.left )
return right
def UpperCAmelCase ( A : Node | None , A : int ):
'''simple docstring'''
_UpperCAmelCase = Node(A )
_UpperCAmelCase , _UpperCAmelCase = split(A , A )
return merge(merge(A , A ) , A )
def UpperCAmelCase ( A : Node | None , A : int ):
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = split(A , value - 1 )
_UpperCAmelCase , _UpperCAmelCase = split(A , A )
return merge(A , A )
def UpperCAmelCase ( A : Node | None ):
'''simple docstring'''
if not root: # None
return
else:
inorder(root.left )
print(root.value , end=',' )
inorder(root.right )
def UpperCAmelCase ( A : Node | None , A : str ):
'''simple docstring'''
for arg in args.split():
if arg[0] == "+":
_UpperCAmelCase = insert(A , int(arg[1:] ) )
elif arg[0] == "-":
_UpperCAmelCase = erase(A , int(arg[1:] ) )
else:
print('Unknown command' )
return root
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = None
print(
'enter numbers to create a tree, + value to add value into treap, '
'- value to erase all nodes with value. \'q\' to quit. ' )
_UpperCAmelCase = input()
while args != "q":
_UpperCAmelCase = interact_treap(A , A )
print(A )
_UpperCAmelCase = input()
print('good by!' )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 24
|
"""simple docstring"""
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal
lowercase = logging.get_logger(__name__)
lowercase = TypeVar('''DatasetType''', Dataset, IterableDataset)
def UpperCAmelCase ( A : List[DatasetType] , A : Optional[List[float]] = None , A : Optional[int] = None , A : Optional[DatasetInfo] = None , A : Optional[NamedSplit] = None , A : Literal["first_exhausted", "all_exhausted"] = "first_exhausted" , ):
'''simple docstring'''
from .arrow_dataset import Dataset
from .iterable_dataset import IterableDataset
if not datasets:
raise ValueError('Unable to interleave an empty list of datasets.' )
for i, dataset in enumerate(A ):
if not isinstance(A , (Dataset, IterableDataset) ):
if isinstance(A , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
'is an empty dataset dictionary.' )
raise ValueError(
f'Dataset at position {i} has at least one split: {list(A )}\n'
f'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(A ) )}\']' )
raise ValueError(
f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(A ).__name__}.' )
if i == 0:
_UpperCAmelCase , _UpperCAmelCase = (
(Dataset, IterableDataset) if isinstance(A , A ) else (IterableDataset, Dataset)
)
elif not isinstance(A , A ):
raise ValueError(
f'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.' )
if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
raise ValueError(f'{stopping_strategy} is not supported. Please enter a valid stopping_strategy.' )
if dataset_type is Dataset:
return _interleave_map_style_datasets(
A , A , A , info=A , split=A , stopping_strategy=A )
else:
return _interleave_iterable_datasets(
A , A , A , info=A , split=A , stopping_strategy=A )
def UpperCAmelCase ( A : List[DatasetType] , A : Optional[DatasetInfo] = None , A : Optional[NamedSplit] = None , A : int = 0 , ):
'''simple docstring'''
if not dsets:
raise ValueError('Unable to concatenate an empty list of datasets.' )
for i, dataset in enumerate(A ):
if not isinstance(A , (Dataset, IterableDataset) ):
if isinstance(A , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
'is an empty dataset dictionary.' )
raise ValueError(
f'Dataset at position {i} has at least one split: {list(A )}\n'
f'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(A ) )}\']' )
raise ValueError(
f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(A ).__name__}.' )
if i == 0:
_UpperCAmelCase , _UpperCAmelCase = (
(Dataset, IterableDataset) if isinstance(A , A ) else (IterableDataset, Dataset)
)
elif not isinstance(A , A ):
raise ValueError(
f'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.' )
if dataset_type is Dataset:
return _concatenate_map_style_datasets(A , info=A , split=A , axis=A )
else:
return _concatenate_iterable_datasets(A , info=A , split=A , axis=A )
| 24
| 1
|
"""simple docstring"""
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
lowercase = '''\
@inproceedings{kakwani2020indicnlpsuite,
title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},
author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},
year={2020},
booktitle={Findings of EMNLP},
}
'''
lowercase = '''\
IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide
variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te.
'''
lowercase = '''
Compute IndicGLUE evaluation metric associated to each IndicGLUE dataset.
Args:
predictions: list of predictions to score (as int64),
except for \'cvit-mkb-clsr\' where each prediction is a vector (of float32).
references: list of ground truth labels corresponding to the predictions (as int64),
except for \'cvit-mkb-clsr\' where each reference is a vector (of float32).
Returns: depending on the IndicGLUE subset, one or several of:
"accuracy": Accuracy
"f1": F1 score
"precision": Precision@10
Examples:
>>> indic_glue_metric = datasets.load_metric(\'indic_glue\', \'wnli\') # \'wnli\' or any of ["copa", "sna", "csqa", "wstp", "inltkh", "bbca", "iitp-mr", "iitp-pr", "actsa-sc", "md"]
>>> references = [0, 1]
>>> predictions = [0, 1]
>>> results = indic_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'accuracy\': 1.0}
>>> indic_glue_metric = datasets.load_metric(\'indic_glue\', \'wiki-ner\')
>>> references = [0, 1]
>>> predictions = [0, 1]
>>> results = indic_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'accuracy\': 1.0, \'f1\': 1.0}
>>> indic_glue_metric = datasets.load_metric(\'indic_glue\', \'cvit-mkb-clsr\')
>>> references = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]
>>> predictions = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]
>>> results = indic_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'precision@10\': 1.0}
'''
def UpperCAmelCase ( A : Optional[int] , A : List[str] ):
'''simple docstring'''
return float((preds == labels).mean() )
def UpperCAmelCase ( A : List[str] , A : Dict ):
'''simple docstring'''
_UpperCAmelCase = simple_accuracy(A , A )
_UpperCAmelCase = float(fa_score(y_true=A , y_pred=A ) )
return {
"accuracy": acc,
"f1": fa,
}
def UpperCAmelCase ( A : Optional[int] , A : Optional[int] ):
'''simple docstring'''
_UpperCAmelCase = np.array(A )
_UpperCAmelCase = np.array(A )
_UpperCAmelCase = en_sentvecs.shape[0]
# mean centering
_UpperCAmelCase = en_sentvecs - np.mean(A , axis=0 )
_UpperCAmelCase = in_sentvecs - np.mean(A , axis=0 )
_UpperCAmelCase = cdist(A , A , 'cosine' )
_UpperCAmelCase = np.array(range(A ) )
_UpperCAmelCase = sim.argsort(axis=1 )[:, :10]
_UpperCAmelCase = np.any(preds == actual[:, None] , axis=1 )
return float(matches.mean() )
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION )
class lowercase__ ( datasets.Metric ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> Optional[int]:
if self.config_name not in [
"wnli",
"copa",
"sna",
"csqa",
"wstp",
"inltkh",
"bbca",
"cvit-mkb-clsr",
"iitp-mr",
"iitp-pr",
"actsa-sc",
"md",
"wiki-ner",
]:
raise KeyError(
'You should supply a configuration name selected in '
'["wnli", "copa", "sna", "csqa", "wstp", "inltkh", "bbca", '
'"cvit-mkb-clsr", "iitp-mr", "iitp-pr", "actsa-sc", "md", '
'"wiki-ner"]' )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('int64' )
if self.config_name != 'cvit-mkb-clsr'
else datasets.Sequence(datasets.Value('float32' ) ),
'references': datasets.Value('int64' )
if self.config_name != 'cvit-mkb-clsr'
else datasets.Sequence(datasets.Value('float32' ) ),
} ) , codebase_urls=[] , reference_urls=[] , format='numpy' if self.config_name != 'cvit-mkb-clsr' else None , )
def lowerCamelCase_ ( self , snake_case , snake_case ) -> Optional[Any]:
if self.config_name == "cvit-mkb-clsr":
return {"precision@10": precision_at_aa(snake_case , snake_case )}
elif self.config_name in ["wiki-ner"]:
return acc_and_fa(snake_case , snake_case )
elif self.config_name in [
"wnli",
"copa",
"sna",
"csqa",
"wstp",
"inltkh",
"bbca",
"iitp-mr",
"iitp-pr",
"actsa-sc",
"md",
]:
return {"accuracy": simple_accuracy(snake_case , snake_case )}
else:
raise KeyError(
'You should supply a configuration name selected in '
'["wnli", "copa", "sna", "csqa", "wstp", "inltkh", "bbca", '
'"cvit-mkb-clsr", "iitp-mr", "iitp-pr", "actsa-sc", "md", '
'"wiki-ner"]' )
| 24
|
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available
from .test_pipelines_common import ANY
if is_torch_available():
import torch
@is_pipeline_test
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
_UpperCAmelCase = TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case ) -> Dict:
_UpperCAmelCase = TextaTextGenerationPipeline(model=snake_case , tokenizer=snake_case )
return generator, ["Something to write", "Something else"]
def lowerCamelCase_ ( self , snake_case , snake_case ) -> Dict:
_UpperCAmelCase = generator('Something there' )
self.assertEqual(snake_case , [{'generated_text': ANY(snake_case )}] )
# These are encoder decoder, they don't just append to incoming string
self.assertFalse(outputs[0]['generated_text'].startswith('Something there' ) )
_UpperCAmelCase = generator(['This is great !', 'Something else'] , num_return_sequences=2 , do_sample=snake_case )
self.assertEqual(
snake_case , [
[{'generated_text': ANY(snake_case )}, {'generated_text': ANY(snake_case )}],
[{'generated_text': ANY(snake_case )}, {'generated_text': ANY(snake_case )}],
] , )
_UpperCAmelCase = generator(
['This is great !', 'Something else'] , num_return_sequences=2 , batch_size=2 , do_sample=snake_case )
self.assertEqual(
snake_case , [
[{'generated_text': ANY(snake_case )}, {'generated_text': ANY(snake_case )}],
[{'generated_text': ANY(snake_case )}, {'generated_text': ANY(snake_case )}],
] , )
with self.assertRaises(snake_case ):
generator(4 )
@require_torch
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = pipeline('text2text-generation' , model='patrickvonplaten/t5-tiny-random' , framework='pt' )
# do_sample=False necessary for reproducibility
_UpperCAmelCase = generator('Something there' , do_sample=snake_case )
self.assertEqual(snake_case , [{'generated_text': ''}] )
_UpperCAmelCase = 3
_UpperCAmelCase = generator(
'Something there' , num_return_sequences=snake_case , num_beams=snake_case , )
_UpperCAmelCase = [
{'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide Beide'},
{'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide'},
{'generated_text': ''},
]
self.assertEqual(snake_case , snake_case )
_UpperCAmelCase = generator('This is a test' , do_sample=snake_case , num_return_sequences=2 , return_tensors=snake_case )
self.assertEqual(
snake_case , [
{'generated_token_ids': ANY(torch.Tensor )},
{'generated_token_ids': ANY(torch.Tensor )},
] , )
_UpperCAmelCase = generator.model.config.eos_token_id
_UpperCAmelCase = '<pad>'
_UpperCAmelCase = generator(
['This is a test', 'This is a second test'] , do_sample=snake_case , num_return_sequences=2 , batch_size=2 , return_tensors=snake_case , )
self.assertEqual(
snake_case , [
[
{'generated_token_ids': ANY(torch.Tensor )},
{'generated_token_ids': ANY(torch.Tensor )},
],
[
{'generated_token_ids': ANY(torch.Tensor )},
{'generated_token_ids': ANY(torch.Tensor )},
],
] , )
@require_tf
def lowerCamelCase_ ( self ) -> Any:
_UpperCAmelCase = pipeline('text2text-generation' , model='patrickvonplaten/t5-tiny-random' , framework='tf' )
# do_sample=False necessary for reproducibility
_UpperCAmelCase = generator('Something there' , do_sample=snake_case )
self.assertEqual(snake_case , [{'generated_text': ''}] )
| 24
| 1
|
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_available():
import jax.numpy as jnp
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = {
'task_specific_params': {
'summarization': {'length_penalty': 1.0, 'max_length': 128, 'min_length': 12, 'num_beams': 4},
'summarization_cnn': {'length_penalty': 2.0, 'max_length': 142, 'min_length': 56, 'num_beams': 4},
'summarization_xsum': {'length_penalty': 1.0, 'max_length': 62, 'min_length': 11, 'num_beams': 6},
}
}
_UpperCAmelCase = {
'task_specific_params.summarization.length_penalty': 1.0,
'task_specific_params.summarization.max_length': 128,
'task_specific_params.summarization.min_length': 12,
'task_specific_params.summarization.num_beams': 4,
'task_specific_params.summarization_cnn.length_penalty': 2.0,
'task_specific_params.summarization_cnn.max_length': 142,
'task_specific_params.summarization_cnn.min_length': 56,
'task_specific_params.summarization_cnn.num_beams': 4,
'task_specific_params.summarization_xsum.length_penalty': 1.0,
'task_specific_params.summarization_xsum.max_length': 62,
'task_specific_params.summarization_xsum.min_length': 11,
'task_specific_params.summarization_xsum.num_beams': 6,
}
self.assertEqual(flatten_dict(snake_case ) , snake_case )
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(transpose(snake_case ) , x.transpose() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(transpose(snake_case , axes=(1, 2, 0) ) , x.transpose((1, 2, 0) ) ) )
@require_torch
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(transpose(snake_case ) , transpose(snake_case ).numpy() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(transpose(snake_case , axes=(1, 2, 0) ) , transpose(snake_case , axes=(1, 2, 0) ).numpy() ) )
@require_tf
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(transpose(snake_case ) , transpose(snake_case ).numpy() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(transpose(snake_case , axes=(1, 2, 0) ) , transpose(snake_case , axes=(1, 2, 0) ).numpy() ) )
@require_flax
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(transpose(snake_case ) , np.asarray(transpose(snake_case ) ) ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(transpose(snake_case , axes=(1, 2, 0) ) , np.asarray(transpose(snake_case , axes=(1, 2, 0) ) ) ) )
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(reshape(snake_case , (4, 3) ) , np.reshape(snake_case , (4, 3) ) ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(reshape(snake_case , (12, 5) ) , np.reshape(snake_case , (12, 5) ) ) )
@require_torch
def lowerCamelCase_ ( self ) -> Optional[Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (4, 3) ) , reshape(snake_case , (4, 3) ).numpy() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (12, 5) ) , reshape(snake_case , (12, 5) ).numpy() ) )
@require_tf
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (4, 3) ) , reshape(snake_case , (4, 3) ).numpy() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (12, 5) ) , reshape(snake_case , (12, 5) ).numpy() ) )
@require_flax
def lowerCamelCase_ ( self ) -> Tuple:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (4, 3) ) , np.asarray(reshape(snake_case , (4, 3) ) ) ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (12, 5) ) , np.asarray(reshape(snake_case , (12, 5) ) ) ) )
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = np.random.randn(1 , 3 , 4 )
self.assertTrue(np.allclose(squeeze(snake_case ) , np.squeeze(snake_case ) ) )
_UpperCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
self.assertTrue(np.allclose(squeeze(snake_case , axis=2 ) , np.squeeze(snake_case , axis=2 ) ) )
@require_torch
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(1 , 3 , 4 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case ) , squeeze(snake_case ).numpy() ) )
_UpperCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case , axis=2 ) , squeeze(snake_case , axis=2 ).numpy() ) )
@require_tf
def lowerCamelCase_ ( self ) -> Optional[int]:
_UpperCAmelCase = np.random.randn(1 , 3 , 4 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case ) , squeeze(snake_case ).numpy() ) )
_UpperCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case , axis=2 ) , squeeze(snake_case , axis=2 ).numpy() ) )
@require_flax
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = np.random.randn(1 , 3 , 4 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case ) , np.asarray(squeeze(snake_case ) ) ) )
_UpperCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case , axis=2 ) , np.asarray(squeeze(snake_case , axis=2 ) ) ) )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(expand_dims(snake_case , axis=1 ) , np.expand_dims(snake_case , axis=1 ) ) )
@require_torch
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(expand_dims(snake_case , axis=1 ) , expand_dims(snake_case , axis=1 ).numpy() ) )
@require_tf
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(expand_dims(snake_case , axis=1 ) , expand_dims(snake_case , axis=1 ).numpy() ) )
@require_flax
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(expand_dims(snake_case , axis=1 ) , np.asarray(expand_dims(snake_case , axis=1 ) ) ) )
| 24
|
"""simple docstring"""
def UpperCAmelCase ( A : int ):
'''simple docstring'''
_UpperCAmelCase = [[0 for _ in range(A )] for _ in range(m + 1 )]
for i in range(m + 1 ):
_UpperCAmelCase = 1
for n in range(m + 1 ):
for k in range(1 , A ):
memo[n][k] += memo[n][k - 1]
if n - k > 0:
memo[n][k] += memo[n - k - 1][k]
return memo[m][m - 1]
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
try:
lowercase = int(input('''Enter a number: ''').strip())
print(partition(n))
except ValueError:
print('''Please enter a number.''')
else:
try:
lowercase = int(sys.argv[1])
print(partition(n))
except ValueError:
print('''Please pass a number.''')
| 24
| 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/swin-tiny-patch4-window7-224''': (
'''https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'''
),
# See all Swin models at https://huggingface.co/models?filter=swin
}
class lowercase__ ( A, A ):
'''simple docstring'''
_UpperCAmelCase = '''swin'''
_UpperCAmelCase = {
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self , snake_case=224 , snake_case=4 , snake_case=3 , snake_case=96 , snake_case=[2, 2, 6, 2] , snake_case=[3, 6, 12, 24] , snake_case=7 , snake_case=4.0 , snake_case=True , snake_case=0.0 , snake_case=0.0 , snake_case=0.1 , snake_case="gelu" , snake_case=False , snake_case=0.02 , snake_case=1E-5 , snake_case=32 , snake_case=None , snake_case=None , **snake_case , ) -> List[Any]:
super().__init__(**snake_case )
_UpperCAmelCase = image_size
_UpperCAmelCase = patch_size
_UpperCAmelCase = num_channels
_UpperCAmelCase = embed_dim
_UpperCAmelCase = depths
_UpperCAmelCase = len(snake_case )
_UpperCAmelCase = num_heads
_UpperCAmelCase = window_size
_UpperCAmelCase = mlp_ratio
_UpperCAmelCase = qkv_bias
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = drop_path_rate
_UpperCAmelCase = hidden_act
_UpperCAmelCase = use_absolute_embeddings
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = initializer_range
_UpperCAmelCase = encoder_stride
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
_UpperCAmelCase = int(embed_dim * 2 ** (len(snake_case ) - 1) )
_UpperCAmelCase = ['stem'] + [f'stage{idx}' for idx in range(1 , len(snake_case ) + 1 )]
_UpperCAmelCase , _UpperCAmelCase = get_aligned_output_features_output_indices(
out_features=snake_case , out_indices=snake_case , stage_names=self.stage_names )
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = version.parse('''1.11''' )
@property
def lowerCamelCase_ ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def lowerCamelCase_ ( self ) -> float:
return 1E-4
| 24
|
"""simple docstring"""
import os
lowercase = {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 1_00, '''D''': 5_00, '''M''': 10_00}
def UpperCAmelCase ( A : str ):
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase = 0
while index < len(A ) - 1:
_UpperCAmelCase = SYMBOLS[numerals[index]]
_UpperCAmelCase = 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 : int ):
'''simple docstring'''
_UpperCAmelCase = ''
_UpperCAmelCase = num // 1000
numerals += m_count * "M"
num %= 1000
_UpperCAmelCase = num // 100
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 %= 100
_UpperCAmelCase = num // 10
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 %= 10
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 : str = "/p089_roman.txt" ):
'''simple docstring'''
_UpperCAmelCase = 0
with open(os.path.dirname(A ) + roman_numerals_filename ) as filea:
_UpperCAmelCase = filea.readlines()
for line in lines:
_UpperCAmelCase = line.strip()
_UpperCAmelCase = parse_roman_numerals(A )
_UpperCAmelCase = generate_roman_numerals(A )
savings += len(A ) - len(A )
return savings
if __name__ == "__main__":
print(F'''{solution() = }''')
| 24
| 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_roberta''': ['''ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RobertaConfig''', '''RobertaOnnxConfig'''],
'''tokenization_roberta''': ['''RobertaTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = ['''RobertaTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''RobertaForCausalLM''',
'''RobertaForMaskedLM''',
'''RobertaForMultipleChoice''',
'''RobertaForQuestionAnswering''',
'''RobertaForSequenceClassification''',
'''RobertaForTokenClassification''',
'''RobertaModel''',
'''RobertaPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFRobertaForCausalLM''',
'''TFRobertaForMaskedLM''',
'''TFRobertaForMultipleChoice''',
'''TFRobertaForQuestionAnswering''',
'''TFRobertaForSequenceClassification''',
'''TFRobertaForTokenClassification''',
'''TFRobertaMainLayer''',
'''TFRobertaModel''',
'''TFRobertaPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''FlaxRobertaForCausalLM''',
'''FlaxRobertaForMaskedLM''',
'''FlaxRobertaForMultipleChoice''',
'''FlaxRobertaForQuestionAnswering''',
'''FlaxRobertaForSequenceClassification''',
'''FlaxRobertaForTokenClassification''',
'''FlaxRobertaModel''',
'''FlaxRobertaPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
from .tokenization_roberta import RobertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_roberta_fast import RobertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta import (
ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaForCausalLM,
RobertaForMaskedLM,
RobertaForMultipleChoice,
RobertaForQuestionAnswering,
RobertaForSequenceClassification,
RobertaForTokenClassification,
RobertaModel,
RobertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta import (
TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
TFRobertaForMultipleChoice,
TFRobertaForQuestionAnswering,
TFRobertaForSequenceClassification,
TFRobertaForTokenClassification,
TFRobertaMainLayer,
TFRobertaModel,
TFRobertaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
FlaxRobertaPreTrainedModel,
)
else:
import sys
lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 24
|
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_available():
import jax.numpy as jnp
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = {
'task_specific_params': {
'summarization': {'length_penalty': 1.0, 'max_length': 128, 'min_length': 12, 'num_beams': 4},
'summarization_cnn': {'length_penalty': 2.0, 'max_length': 142, 'min_length': 56, 'num_beams': 4},
'summarization_xsum': {'length_penalty': 1.0, 'max_length': 62, 'min_length': 11, 'num_beams': 6},
}
}
_UpperCAmelCase = {
'task_specific_params.summarization.length_penalty': 1.0,
'task_specific_params.summarization.max_length': 128,
'task_specific_params.summarization.min_length': 12,
'task_specific_params.summarization.num_beams': 4,
'task_specific_params.summarization_cnn.length_penalty': 2.0,
'task_specific_params.summarization_cnn.max_length': 142,
'task_specific_params.summarization_cnn.min_length': 56,
'task_specific_params.summarization_cnn.num_beams': 4,
'task_specific_params.summarization_xsum.length_penalty': 1.0,
'task_specific_params.summarization_xsum.max_length': 62,
'task_specific_params.summarization_xsum.min_length': 11,
'task_specific_params.summarization_xsum.num_beams': 6,
}
self.assertEqual(flatten_dict(snake_case ) , snake_case )
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(transpose(snake_case ) , x.transpose() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(transpose(snake_case , axes=(1, 2, 0) ) , x.transpose((1, 2, 0) ) ) )
@require_torch
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(transpose(snake_case ) , transpose(snake_case ).numpy() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(transpose(snake_case , axes=(1, 2, 0) ) , transpose(snake_case , axes=(1, 2, 0) ).numpy() ) )
@require_tf
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(transpose(snake_case ) , transpose(snake_case ).numpy() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(transpose(snake_case , axes=(1, 2, 0) ) , transpose(snake_case , axes=(1, 2, 0) ).numpy() ) )
@require_flax
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(transpose(snake_case ) , np.asarray(transpose(snake_case ) ) ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(transpose(snake_case , axes=(1, 2, 0) ) , np.asarray(transpose(snake_case , axes=(1, 2, 0) ) ) ) )
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(reshape(snake_case , (4, 3) ) , np.reshape(snake_case , (4, 3) ) ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(reshape(snake_case , (12, 5) ) , np.reshape(snake_case , (12, 5) ) ) )
@require_torch
def lowerCamelCase_ ( self ) -> Optional[Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (4, 3) ) , reshape(snake_case , (4, 3) ).numpy() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (12, 5) ) , reshape(snake_case , (12, 5) ).numpy() ) )
@require_tf
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (4, 3) ) , reshape(snake_case , (4, 3) ).numpy() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (12, 5) ) , reshape(snake_case , (12, 5) ).numpy() ) )
@require_flax
def lowerCamelCase_ ( self ) -> Tuple:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (4, 3) ) , np.asarray(reshape(snake_case , (4, 3) ) ) ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (12, 5) ) , np.asarray(reshape(snake_case , (12, 5) ) ) ) )
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = np.random.randn(1 , 3 , 4 )
self.assertTrue(np.allclose(squeeze(snake_case ) , np.squeeze(snake_case ) ) )
_UpperCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
self.assertTrue(np.allclose(squeeze(snake_case , axis=2 ) , np.squeeze(snake_case , axis=2 ) ) )
@require_torch
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(1 , 3 , 4 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case ) , squeeze(snake_case ).numpy() ) )
_UpperCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case , axis=2 ) , squeeze(snake_case , axis=2 ).numpy() ) )
@require_tf
def lowerCamelCase_ ( self ) -> Optional[int]:
_UpperCAmelCase = np.random.randn(1 , 3 , 4 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case ) , squeeze(snake_case ).numpy() ) )
_UpperCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case , axis=2 ) , squeeze(snake_case , axis=2 ).numpy() ) )
@require_flax
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = np.random.randn(1 , 3 , 4 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case ) , np.asarray(squeeze(snake_case ) ) ) )
_UpperCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case , axis=2 ) , np.asarray(squeeze(snake_case , axis=2 ) ) ) )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(expand_dims(snake_case , axis=1 ) , np.expand_dims(snake_case , axis=1 ) ) )
@require_torch
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(expand_dims(snake_case , axis=1 ) , expand_dims(snake_case , axis=1 ).numpy() ) )
@require_tf
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(expand_dims(snake_case , axis=1 ) , expand_dims(snake_case , axis=1 ).numpy() ) )
@require_flax
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(expand_dims(snake_case , axis=1 ) , np.asarray(expand_dims(snake_case , axis=1 ) ) ) )
| 24
| 1
|
"""simple docstring"""
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
lowercase = get_tests_dir('''fixtures/test_sentencepiece.model''')
@require_sentencepiece
@require_tokenizers
class lowercase__ ( A, unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = XGLMTokenizer
_UpperCAmelCase = XGLMTokenizerFast
_UpperCAmelCase = True
_UpperCAmelCase = True
def lowerCamelCase_ ( self ) -> int:
super().setUp()
# We have a SentencePiece fixture for testing
_UpperCAmelCase = XGLMTokenizer(snake_case , keep_accents=snake_case )
tokenizer.save_pretrained(self.tmpdirname )
def lowerCamelCase_ ( self ) -> Optional[int]:
_UpperCAmelCase = '<pad>'
_UpperCAmelCase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case ) , snake_case )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case ) , snake_case )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '<s>' )
self.assertEqual(vocab_keys[1] , '<pad>' )
self.assertEqual(len(snake_case ) , 1008 )
def lowerCamelCase_ ( self ) -> Tuple:
self.assertEqual(self.get_tokenizer().vocab_size , 1008 )
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = XGLMTokenizer(snake_case , keep_accents=snake_case )
_UpperCAmelCase = tokenizer.tokenize('This is a test' )
self.assertListEqual(snake_case , ['▁This', '▁is', '▁a', '▁t', 'est'] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(snake_case ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
_UpperCAmelCase = tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
snake_case , [
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',
'.',
] , )
_UpperCAmelCase = tokenizer.convert_tokens_to_ids(snake_case )
self.assertListEqual(
snake_case , [
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]
] , )
_UpperCAmelCase = tokenizer.convert_ids_to_tokens(snake_case )
self.assertListEqual(
snake_case , [
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>',
'.',
] , )
@cached_property
def lowerCamelCase_ ( self ) -> int:
return XGLMTokenizer.from_pretrained('facebook/xglm-564M' )
def lowerCamelCase_ ( self ) -> Optional[int]:
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(snake_case , f.name )
_UpperCAmelCase = XGLMTokenizer(f.name , keep_accents=snake_case )
_UpperCAmelCase = pickle.dumps(snake_case )
pickle.loads(snake_case )
def lowerCamelCase_ ( self ) -> int:
if not self.test_rust_tokenizer:
return
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = self.get_rust_tokenizer()
_UpperCAmelCase = 'I was born in 92000, and this is falsé.'
_UpperCAmelCase = tokenizer.tokenize(snake_case )
_UpperCAmelCase = rust_tokenizer.tokenize(snake_case )
self.assertListEqual(snake_case , snake_case )
_UpperCAmelCase = tokenizer.encode(snake_case , add_special_tokens=snake_case )
_UpperCAmelCase = rust_tokenizer.encode(snake_case , add_special_tokens=snake_case )
self.assertListEqual(snake_case , snake_case )
_UpperCAmelCase = self.get_rust_tokenizer()
_UpperCAmelCase = tokenizer.encode(snake_case )
_UpperCAmelCase = rust_tokenizer.encode(snake_case )
self.assertListEqual(snake_case , snake_case )
@slow
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = 'Hello World!'
_UpperCAmelCase = [2, 31227, 4447, 35]
self.assertListEqual(snake_case , self.big_tokenizer.encode(snake_case ) )
@slow
def lowerCamelCase_ ( self ) -> Optional[Any]:
_UpperCAmelCase = (
'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
' add words that should not exsist and be tokenized to unk, such as saoneuhaoesuth'
)
# fmt: off
_UpperCAmelCase = [2, 1018, 67, 11, 1988, 2617, 5631, 278, 11, 3407, 48, 71630, 28085, 4, 3234, 157, 13, 6, 5, 6, 4, 3526, 768, 15, 659, 57, 298, 3983, 864, 129, 21, 6, 5, 13675, 377, 652, 7580, 10341, 155, 2817, 422, 1666, 7, 1674, 53, 113, 202277, 17892, 33, 60, 87, 4, 3234, 157, 61, 2667, 52376, 19, 88, 23, 735]
# fmt: on
self.assertListEqual(snake_case , self.big_tokenizer.encode(snake_case ) )
@slow
def lowerCamelCase_ ( self ) -> str:
# fmt: off
_UpperCAmelCase = {
'input_ids': [[2, 108825, 1163, 15, 88010, 473, 15898, 157, 13672, 1857, 312, 8, 238021, 1163, 53, 13672, 1857, 312, 8, 53283, 182396, 8, 18566, 16, 36733, 4101, 8, 230, 244017, 122553, 7, 15, 132597, 4, 293, 12511, 7610, 4, 3414, 132597, 9, 4, 32361, 362, 4, 734, 28512, 32569, 18, 4, 32361, 26096, 14982, 73, 18715, 21433, 235261, 15, 492, 12427, 16, 53, 18715, 21433, 65454, 15, 23659, 563, 16, 278, 597, 2843, 595, 7931, 182396, 64186, 22, 886, 595, 132981, 53, 25540, 3449, 43982, 39901, 5951, 878, 330, 4, 27694, 80269, 312, 53, 6517, 11780, 611, 20408, 5], [2, 6, 132597, 67, 42897, 33, 592, 8, 163729, 25540, 361, 136997, 109514, 173230, 7, 501, 60, 102913, 196, 5631, 235, 63243, 473, 6, 231757, 74, 5277, 7905, 53, 3095, 37317, 22, 454, 183874, 5], [2, 268, 31298, 46530, 6, 132935, 43831, 7, 597, 32, 24, 3688, 9865, 5]],
'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, 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]]
} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=snake_case , model_name='facebook/xglm-564M' , padding=snake_case , )
| 24
|
"""simple docstring"""
import os
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = os.path.join(os.path.dirname(A ) , 'num.txt' )
with open(A ) as file_hand:
return str(sum(int(A ) for line in file_hand ) )[:10]
if __name__ == "__main__":
print(solution())
| 24
| 1
|
"""simple docstring"""
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, TrainingArguments
from transformers.hf_argparser import make_choice_type_function, string_to_bool
# Since Python 3.10, we can use the builtin `|` operator for Union types
# See PEP 604: https://peps.python.org/pep-0604
lowercase = sys.version_info >= (3, 10)
def UpperCAmelCase ( A : str=None , A : Optional[int]=None ):
'''simple docstring'''
return field(default_factory=lambda: default , metadata=A )
@dataclass
class lowercase__ :
'''simple docstring'''
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
@dataclass
class lowercase__ :
'''simple docstring'''
_UpperCAmelCase = 42
_UpperCAmelCase = field(default='''toto''', metadata={'''help''': '''help message'''} )
@dataclass
class lowercase__ :
'''simple docstring'''
_UpperCAmelCase = False
_UpperCAmelCase = True
_UpperCAmelCase = None
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = '''titi'''
_UpperCAmelCase = '''toto'''
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = '''titi'''
_UpperCAmelCase = '''toto'''
_UpperCAmelCase = 42
@dataclass
class lowercase__ :
'''simple docstring'''
_UpperCAmelCase = "toto"
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = BasicEnum(self.foo )
@dataclass
class lowercase__ :
'''simple docstring'''
_UpperCAmelCase = "toto"
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = MixedTypeEnum(self.foo )
@dataclass
class lowercase__ :
'''simple docstring'''
_UpperCAmelCase = None
_UpperCAmelCase = field(default=A, metadata={'''help''': '''help message'''} )
_UpperCAmelCase = None
_UpperCAmelCase = list_field(default=[] )
_UpperCAmelCase = list_field(default=[] )
@dataclass
class lowercase__ :
'''simple docstring'''
_UpperCAmelCase = list_field(default=[] )
_UpperCAmelCase = list_field(default=[1, 2, 3] )
_UpperCAmelCase = list_field(default=['''Hallo''', '''Bonjour''', '''Hello'''] )
_UpperCAmelCase = list_field(default=[0.1, 0.2, 0.3] )
@dataclass
class lowercase__ :
'''simple docstring'''
_UpperCAmelCase = field()
_UpperCAmelCase = field()
_UpperCAmelCase = field()
def lowerCamelCase_ ( self ) -> Tuple:
_UpperCAmelCase = BasicEnum(self.required_enum )
@dataclass
class lowercase__ :
'''simple docstring'''
_UpperCAmelCase = 42
_UpperCAmelCase = field()
_UpperCAmelCase = None
_UpperCAmelCase = field(default='''toto''', metadata={'''help''': '''help message'''} )
_UpperCAmelCase = list_field(default=['''Hallo''', '''Bonjour''', '''Hello'''] )
if is_python_no_less_than_3_10:
@dataclass
class lowercase__ :
'''simple docstring'''
_UpperCAmelCase = False
_UpperCAmelCase = True
_UpperCAmelCase = None
@dataclass
class lowercase__ :
'''simple docstring'''
_UpperCAmelCase = None
_UpperCAmelCase = field(default=A, metadata={'''help''': '''help message'''} )
_UpperCAmelCase = None
_UpperCAmelCase = list_field(default=[] )
_UpperCAmelCase = list_field(default=[] )
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase_ ( self , snake_case , snake_case ) -> Optional[int]:
self.assertEqual(len(a._actions ) , len(b._actions ) )
for x, y in zip(a._actions , b._actions ):
_UpperCAmelCase = {k: v for k, v in vars(snake_case ).items() if k != 'container'}
_UpperCAmelCase = {k: v for k, v in vars(snake_case ).items() if k != 'container'}
# Choices with mixed type have custom function as "type"
# So we need to compare results directly for equality
if xx.get('choices' , snake_case ) and yy.get('choices' , snake_case ):
for expected_choice in yy["choices"] + xx["choices"]:
self.assertEqual(xx['type'](snake_case ) , yy['type'](snake_case ) )
del xx["type"], yy["type"]
self.assertEqual(snake_case , snake_case )
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = HfArgumentParser(snake_case )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('--foo' , type=snake_case , required=snake_case )
expected.add_argument('--bar' , type=snake_case , required=snake_case )
expected.add_argument('--baz' , type=snake_case , required=snake_case )
expected.add_argument('--flag' , type=snake_case , default=snake_case , const=snake_case , nargs='?' )
self.argparsersEqual(snake_case , snake_case )
_UpperCAmelCase = ['--foo', '1', '--baz', 'quux', '--bar', '0.5']
((_UpperCAmelCase) , ) = parser.parse_args_into_dataclasses(snake_case , look_for_args_file=snake_case )
self.assertFalse(example.flag )
def lowerCamelCase_ ( self ) -> Optional[int]:
_UpperCAmelCase = HfArgumentParser(snake_case )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('--foo' , default=42 , type=snake_case )
expected.add_argument('--baz' , default='toto' , type=snake_case , help='help message' )
self.argparsersEqual(snake_case , snake_case )
def lowerCamelCase_ ( self ) -> Optional[int]:
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('--foo' , type=snake_case , default=snake_case , const=snake_case , nargs='?' )
expected.add_argument('--baz' , type=snake_case , default=snake_case , const=snake_case , nargs='?' )
# A boolean no_* argument always has to come after its "default: True" regular counter-part
# and its default must be set to False
expected.add_argument('--no_baz' , action='store_false' , default=snake_case , dest='baz' )
expected.add_argument('--opt' , type=snake_case , default=snake_case )
_UpperCAmelCase = [WithDefaultBoolExample]
if is_python_no_less_than_3_10:
dataclass_types.append(snake_case )
for dataclass_type in dataclass_types:
_UpperCAmelCase = HfArgumentParser(snake_case )
self.argparsersEqual(snake_case , snake_case )
_UpperCAmelCase = parser.parse_args([] )
self.assertEqual(snake_case , Namespace(foo=snake_case , baz=snake_case , opt=snake_case ) )
_UpperCAmelCase = parser.parse_args(['--foo', '--no_baz'] )
self.assertEqual(snake_case , Namespace(foo=snake_case , baz=snake_case , opt=snake_case ) )
_UpperCAmelCase = parser.parse_args(['--foo', '--baz'] )
self.assertEqual(snake_case , Namespace(foo=snake_case , baz=snake_case , opt=snake_case ) )
_UpperCAmelCase = parser.parse_args(['--foo', 'True', '--baz', 'True', '--opt', 'True'] )
self.assertEqual(snake_case , Namespace(foo=snake_case , baz=snake_case , opt=snake_case ) )
_UpperCAmelCase = parser.parse_args(['--foo', 'False', '--baz', 'False', '--opt', 'False'] )
self.assertEqual(snake_case , Namespace(foo=snake_case , baz=snake_case , opt=snake_case ) )
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = HfArgumentParser(snake_case )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument(
'--foo' , default='toto' , choices=['titi', 'toto', 42] , type=make_choice_type_function(['titi', 'toto', 42] ) , )
self.argparsersEqual(snake_case , snake_case )
_UpperCAmelCase = parser.parse_args([] )
self.assertEqual(args.foo , 'toto' )
_UpperCAmelCase = parser.parse_args_into_dataclasses([] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.toto )
_UpperCAmelCase = parser.parse_args(['--foo', 'titi'] )
self.assertEqual(args.foo , 'titi' )
_UpperCAmelCase = parser.parse_args_into_dataclasses(['--foo', 'titi'] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.titi )
_UpperCAmelCase = parser.parse_args(['--foo', '42'] )
self.assertEqual(args.foo , 42 )
_UpperCAmelCase = parser.parse_args_into_dataclasses(['--foo', '42'] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.fourtytwo )
def lowerCamelCase_ ( self ) -> Optional[int]:
@dataclass
class lowercase__ :
'''simple docstring'''
_UpperCAmelCase = "toto"
_UpperCAmelCase = HfArgumentParser(snake_case )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument(
'--foo' , default='toto' , choices=('titi', 'toto', 42) , type=make_choice_type_function(['titi', 'toto', 42] ) , )
self.argparsersEqual(snake_case , snake_case )
_UpperCAmelCase = parser.parse_args([] )
self.assertEqual(args.foo , 'toto' )
_UpperCAmelCase = parser.parse_args(['--foo', 'titi'] )
self.assertEqual(args.foo , 'titi' )
_UpperCAmelCase = parser.parse_args(['--foo', '42'] )
self.assertEqual(args.foo , 42 )
def lowerCamelCase_ ( self ) -> Tuple:
_UpperCAmelCase = HfArgumentParser(snake_case )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('--foo_int' , nargs='+' , default=[] , type=snake_case )
expected.add_argument('--bar_int' , nargs='+' , default=[1, 2, 3] , type=snake_case )
expected.add_argument('--foo_str' , nargs='+' , default=['Hallo', 'Bonjour', 'Hello'] , type=snake_case )
expected.add_argument('--foo_float' , nargs='+' , default=[0.1, 0.2, 0.3] , type=snake_case )
self.argparsersEqual(snake_case , snake_case )
_UpperCAmelCase = parser.parse_args([] )
self.assertEqual(
snake_case , Namespace(foo_int=[] , bar_int=[1, 2, 3] , foo_str=['Hallo', 'Bonjour', 'Hello'] , foo_float=[0.1, 0.2, 0.3] ) , )
_UpperCAmelCase = parser.parse_args('--foo_int 1 --bar_int 2 3 --foo_str a b c --foo_float 0.1 0.7'.split() )
self.assertEqual(snake_case , Namespace(foo_int=[1] , bar_int=[2, 3] , foo_str=['a', 'b', 'c'] , foo_float=[0.1, 0.7] ) )
def lowerCamelCase_ ( self ) -> Optional[int]:
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('--foo' , default=snake_case , type=snake_case )
expected.add_argument('--bar' , default=snake_case , type=snake_case , help='help message' )
expected.add_argument('--baz' , default=snake_case , type=snake_case )
expected.add_argument('--ces' , nargs='+' , default=[] , type=snake_case )
expected.add_argument('--des' , nargs='+' , default=[] , type=snake_case )
_UpperCAmelCase = [OptionalExample]
if is_python_no_less_than_3_10:
dataclass_types.append(snake_case )
for dataclass_type in dataclass_types:
_UpperCAmelCase = HfArgumentParser(snake_case )
self.argparsersEqual(snake_case , snake_case )
_UpperCAmelCase = parser.parse_args([] )
self.assertEqual(snake_case , Namespace(foo=snake_case , bar=snake_case , baz=snake_case , ces=[] , des=[] ) )
_UpperCAmelCase = parser.parse_args('--foo 12 --bar 3.14 --baz 42 --ces a b c --des 1 2 3'.split() )
self.assertEqual(snake_case , Namespace(foo=12 , bar=3.14 , baz='42' , ces=['a', 'b', 'c'] , des=[1, 2, 3] ) )
def lowerCamelCase_ ( self ) -> Any:
_UpperCAmelCase = HfArgumentParser(snake_case )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('--required_list' , nargs='+' , type=snake_case , required=snake_case )
expected.add_argument('--required_str' , type=snake_case , required=snake_case )
expected.add_argument(
'--required_enum' , type=make_choice_type_function(['titi', 'toto'] ) , choices=['titi', 'toto'] , required=snake_case , )
self.argparsersEqual(snake_case , snake_case )
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = HfArgumentParser(snake_case )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('--foo' , type=snake_case , required=snake_case )
expected.add_argument(
'--required_enum' , type=make_choice_type_function(['titi', 'toto'] ) , choices=['titi', 'toto'] , required=snake_case , )
expected.add_argument('--opt' , type=snake_case , default=snake_case )
expected.add_argument('--baz' , default='toto' , type=snake_case , help='help message' )
expected.add_argument('--foo_str' , nargs='+' , default=['Hallo', 'Bonjour', 'Hello'] , type=snake_case )
self.argparsersEqual(snake_case , snake_case )
def lowerCamelCase_ ( self ) -> Optional[Any]:
_UpperCAmelCase = HfArgumentParser(snake_case )
_UpperCAmelCase = {
'foo': 12,
'bar': 3.14,
'baz': '42',
'flag': True,
}
_UpperCAmelCase = parser.parse_dict(snake_case )[0]
_UpperCAmelCase = BasicExample(**snake_case )
self.assertEqual(snake_case , snake_case )
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = HfArgumentParser(snake_case )
_UpperCAmelCase = {
'foo': 12,
'bar': 3.14,
'baz': '42',
'flag': True,
'extra': 42,
}
self.assertRaises(snake_case , parser.parse_dict , snake_case , allow_extra_keys=snake_case )
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = HfArgumentParser(snake_case )
_UpperCAmelCase = {
'foo': 12,
'bar': 3.14,
'baz': '42',
'flag': True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
_UpperCAmelCase = os.path.join(snake_case , 'temp_json' )
os.mkdir(snake_case )
with open(temp_local_path + '.json' , 'w+' ) as f:
json.dump(snake_case , snake_case )
_UpperCAmelCase = parser.parse_yaml_file(Path(temp_local_path + '.json' ) )[0]
_UpperCAmelCase = BasicExample(**snake_case )
self.assertEqual(snake_case , snake_case )
def lowerCamelCase_ ( self ) -> Tuple:
_UpperCAmelCase = HfArgumentParser(snake_case )
_UpperCAmelCase = {
'foo': 12,
'bar': 3.14,
'baz': '42',
'flag': True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
_UpperCAmelCase = os.path.join(snake_case , 'temp_yaml' )
os.mkdir(snake_case )
with open(temp_local_path + '.yaml' , 'w+' ) as f:
yaml.dump(snake_case , snake_case )
_UpperCAmelCase = parser.parse_yaml_file(Path(temp_local_path + '.yaml' ) )[0]
_UpperCAmelCase = BasicExample(**snake_case )
self.assertEqual(snake_case , snake_case )
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = HfArgumentParser(snake_case )
self.assertIsNotNone(snake_case )
| 24
|
"""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_roberta''': ['''ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RobertaConfig''', '''RobertaOnnxConfig'''],
'''tokenization_roberta''': ['''RobertaTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = ['''RobertaTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''RobertaForCausalLM''',
'''RobertaForMaskedLM''',
'''RobertaForMultipleChoice''',
'''RobertaForQuestionAnswering''',
'''RobertaForSequenceClassification''',
'''RobertaForTokenClassification''',
'''RobertaModel''',
'''RobertaPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFRobertaForCausalLM''',
'''TFRobertaForMaskedLM''',
'''TFRobertaForMultipleChoice''',
'''TFRobertaForQuestionAnswering''',
'''TFRobertaForSequenceClassification''',
'''TFRobertaForTokenClassification''',
'''TFRobertaMainLayer''',
'''TFRobertaModel''',
'''TFRobertaPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''FlaxRobertaForCausalLM''',
'''FlaxRobertaForMaskedLM''',
'''FlaxRobertaForMultipleChoice''',
'''FlaxRobertaForQuestionAnswering''',
'''FlaxRobertaForSequenceClassification''',
'''FlaxRobertaForTokenClassification''',
'''FlaxRobertaModel''',
'''FlaxRobertaPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
from .tokenization_roberta import RobertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_roberta_fast import RobertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta import (
ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaForCausalLM,
RobertaForMaskedLM,
RobertaForMultipleChoice,
RobertaForQuestionAnswering,
RobertaForSequenceClassification,
RobertaForTokenClassification,
RobertaModel,
RobertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta import (
TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
TFRobertaForMultipleChoice,
TFRobertaForQuestionAnswering,
TFRobertaForSequenceClassification,
TFRobertaForTokenClassification,
TFRobertaMainLayer,
TFRobertaModel,
TFRobertaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
FlaxRobertaPreTrainedModel,
)
else:
import sys
lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 24
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase = {
'''configuration_time_series_transformer''': [
'''TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''TimeSeriesTransformerConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TimeSeriesTransformerForPrediction''',
'''TimeSeriesTransformerModel''',
'''TimeSeriesTransformerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_time_series_transformer import (
TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
TimeSeriesTransformerConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_time_series_transformer import (
TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TimeSeriesTransformerForPrediction,
TimeSeriesTransformerModel,
TimeSeriesTransformerPreTrainedModel,
)
else:
import sys
lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 24
|
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
lowercase = logging.get_logger(__name__)
class lowercase__ ( A ):
'''simple docstring'''
def __init__( self , *snake_case , **snake_case ) -> None:
warnings.warn(
'The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
' use YolosImageProcessor instead.' , snake_case , )
super().__init__(*snake_case , **snake_case )
| 24
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase = {
'''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
'''tokenization_mvp''': ['''MvpTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = ['''MvpTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''MVP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MvpForCausalLM''',
'''MvpForConditionalGeneration''',
'''MvpForQuestionAnswering''',
'''MvpForSequenceClassification''',
'''MvpModel''',
'''MvpPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
from .tokenization_mvp import MvpTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mvp_fast import MvpTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mvp import (
MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
MvpForCausalLM,
MvpForConditionalGeneration,
MvpForQuestionAnswering,
MvpForSequenceClassification,
MvpModel,
MvpPreTrainedModel,
)
else:
import sys
lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 24
|
"""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
lowercase = logging.get_logger(__name__)
lowercase = {
'''microsoft/beit-base-patch16-224-pt22k''': (
'''https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'''
),
# See all BEiT models at https://huggingface.co/models?filter=beit
}
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = '''beit'''
def __init__( self , snake_case=8192 , snake_case=768 , snake_case=12 , snake_case=12 , snake_case=3072 , snake_case="gelu" , snake_case=0.0 , snake_case=0.0 , snake_case=0.02 , snake_case=1E-12 , snake_case=224 , snake_case=16 , snake_case=3 , snake_case=False , snake_case=False , snake_case=False , snake_case=False , snake_case=0.1 , snake_case=0.1 , snake_case=True , snake_case=[3, 5, 7, 11] , snake_case=[1, 2, 3, 6] , snake_case=True , snake_case=0.4 , snake_case=256 , snake_case=1 , snake_case=False , snake_case=255 , **snake_case , ) -> str:
super().__init__(**snake_case )
_UpperCAmelCase = vocab_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_act
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = image_size
_UpperCAmelCase = patch_size
_UpperCAmelCase = num_channels
_UpperCAmelCase = use_mask_token
_UpperCAmelCase = use_absolute_position_embeddings
_UpperCAmelCase = use_relative_position_bias
_UpperCAmelCase = use_shared_relative_position_bias
_UpperCAmelCase = layer_scale_init_value
_UpperCAmelCase = drop_path_rate
_UpperCAmelCase = use_mean_pooling
# decode head attributes (semantic segmentation)
_UpperCAmelCase = out_indices
_UpperCAmelCase = pool_scales
# auxiliary head attributes (semantic segmentation)
_UpperCAmelCase = use_auxiliary_head
_UpperCAmelCase = auxiliary_loss_weight
_UpperCAmelCase = auxiliary_channels
_UpperCAmelCase = auxiliary_num_convs
_UpperCAmelCase = auxiliary_concat_input
_UpperCAmelCase = semantic_loss_ignore_index
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = version.parse('''1.11''' )
@property
def lowerCamelCase_ ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def lowerCamelCase_ ( self ) -> float:
return 1E-4
| 24
| 1
|
"""simple docstring"""
import os
lowercase = {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 1_00, '''D''': 5_00, '''M''': 10_00}
def UpperCAmelCase ( A : str ):
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase = 0
while index < len(A ) - 1:
_UpperCAmelCase = SYMBOLS[numerals[index]]
_UpperCAmelCase = 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 : int ):
'''simple docstring'''
_UpperCAmelCase = ''
_UpperCAmelCase = num // 1000
numerals += m_count * "M"
num %= 1000
_UpperCAmelCase = num // 100
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 %= 100
_UpperCAmelCase = num // 10
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 %= 10
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 : str = "/p089_roman.txt" ):
'''simple docstring'''
_UpperCAmelCase = 0
with open(os.path.dirname(A ) + roman_numerals_filename ) as filea:
_UpperCAmelCase = filea.readlines()
for line in lines:
_UpperCAmelCase = line.strip()
_UpperCAmelCase = parse_roman_numerals(A )
_UpperCAmelCase = generate_roman_numerals(A )
savings += len(A ) - len(A )
return savings
if __name__ == "__main__":
print(F'''{solution() = }''')
| 24
|
"""simple docstring"""
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
lowercase = logging.getLogger(__name__)
if __name__ == "__main__":
lowercase = argparse.ArgumentParser(
description='''Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'''
)
parser.add_argument(
'''--data_file''', type=str, default='''data/dump.bert-base-uncased.pickle''', help='''The binarized dataset.'''
)
parser.add_argument(
'''--token_counts_dump''', type=str, default='''data/token_counts.bert-base-uncased.pickle''', help='''The dump file.'''
)
parser.add_argument('''--vocab_size''', default=3_05_22, type=int)
lowercase = parser.parse_args()
logger.info(F'''Loading data from {args.data_file}''')
with open(args.data_file, '''rb''') as fp:
lowercase = pickle.load(fp)
logger.info('''Counting occurrences for MLM.''')
lowercase = Counter()
for tk_ids in data:
counter.update(tk_ids)
lowercase = [0] * args.vocab_size
for k, v in counter.items():
lowercase = v
logger.info(F'''Dump to {args.token_counts_dump}''')
with open(args.token_counts_dump, '''wb''') as handle:
pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)
| 24
| 1
|
"""simple docstring"""
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from accelerate.test_utils import require_multi_gpu
class lowercase__ ( nn.Module ):
'''simple docstring'''
def __init__( self ) -> Any:
super().__init__()
_UpperCAmelCase = nn.Linear(3 , 4 )
_UpperCAmelCase = nn.BatchNormad(4 )
_UpperCAmelCase = nn.Linear(4 , 5 )
def lowerCamelCase_ ( self , snake_case ) -> Any:
return self.lineara(self.batchnorm(self.lineara(snake_case ) ) )
class lowercase__ ( A ):
'''simple docstring'''
def lowerCamelCase_ ( self , snake_case , *snake_case , **snake_case ) -> int:
return (args[0] + 1,) + args[1:], kwargs
class lowercase__ ( A ):
'''simple docstring'''
def lowerCamelCase_ ( self , snake_case , snake_case ) -> str:
return output + 1
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> Any:
_UpperCAmelCase = ModelForTest()
_UpperCAmelCase = ModelHook()
add_hook_to_module(snake_case , snake_case )
self.assertEqual(test_model._hf_hook , snake_case )
self.assertTrue(hasattr(snake_case , '_old_forward' ) )
# Check adding the hook did not change the name or the signature
self.assertEqual(test_model.forward.__name__ , 'forward' )
self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ['x'] )
remove_hook_from_module(snake_case )
self.assertFalse(hasattr(snake_case , '_hf_hook' ) )
self.assertFalse(hasattr(snake_case , '_old_forward' ) )
def lowerCamelCase_ ( self ) -> Optional[int]:
_UpperCAmelCase = ModelForTest()
_UpperCAmelCase = ModelHook()
add_hook_to_module(snake_case , snake_case )
add_hook_to_module(snake_case , snake_case , append=snake_case )
self.assertEqual(isinstance(test_model._hf_hook , snake_case ) , snake_case )
self.assertEqual(len(test_model._hf_hook.hooks ) , 2 )
self.assertTrue(hasattr(snake_case , '_old_forward' ) )
# Check adding the hook did not change the name or the signature
self.assertEqual(test_model.forward.__name__ , 'forward' )
self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ['x'] )
remove_hook_from_module(snake_case )
self.assertFalse(hasattr(snake_case , '_hf_hook' ) )
self.assertFalse(hasattr(snake_case , '_old_forward' ) )
def lowerCamelCase_ ( self ) -> Tuple:
_UpperCAmelCase = ModelForTest()
_UpperCAmelCase = torch.randn(2 , 3 )
_UpperCAmelCase = test_model(x + 1 )
_UpperCAmelCase = test_model(x + 2 )
_UpperCAmelCase = PreForwardHook()
add_hook_to_module(snake_case , snake_case )
_UpperCAmelCase = test_model(snake_case )
self.assertTrue(torch.allclose(snake_case , snake_case , atol=1E-5 ) )
# Attaching a hook to a model when it already has one replaces, does not chain
_UpperCAmelCase = PreForwardHook()
add_hook_to_module(snake_case , snake_case )
_UpperCAmelCase = test_model(snake_case )
self.assertTrue(torch.allclose(snake_case , snake_case , atol=1E-5 ) )
# You need to use the sequential hook to chain two or more hooks
_UpperCAmelCase = SequentialHook(PreForwardHook() , PreForwardHook() )
add_hook_to_module(snake_case , snake_case )
_UpperCAmelCase = test_model(snake_case )
assert torch.allclose(snake_case , snake_case , atol=1E-5 )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = ModelForTest()
_UpperCAmelCase = torch.randn(2 , 3 )
_UpperCAmelCase = test_model(snake_case )
_UpperCAmelCase = PostForwardHook()
add_hook_to_module(snake_case , snake_case )
_UpperCAmelCase = test_model(snake_case )
self.assertTrue(torch.allclose(snake_case , output + 1 , atol=1E-5 ) )
# Attaching a hook to a model when it already has one replaces, does not chain
_UpperCAmelCase = PostForwardHook()
add_hook_to_module(snake_case , snake_case )
_UpperCAmelCase = test_model(snake_case )
self.assertTrue(torch.allclose(snake_case , output + 1 , atol=1E-5 ) )
# You need to use the sequential hook to chain two or more hooks
_UpperCAmelCase = SequentialHook(PostForwardHook() , PostForwardHook() )
add_hook_to_module(snake_case , snake_case )
_UpperCAmelCase = test_model(snake_case )
assert torch.allclose(snake_case , output + 2 , atol=1E-5 )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = ModelForTest()
_UpperCAmelCase = torch.randn(2 , 3 )
_UpperCAmelCase = test_model(snake_case )
_UpperCAmelCase = PostForwardHook()
add_hook_to_module(snake_case , snake_case )
_UpperCAmelCase = test_model(snake_case )
self.assertTrue(torch.allclose(snake_case , output + 1 ) )
self.assertTrue(outputa.requires_grad )
_UpperCAmelCase = True
_UpperCAmelCase = test_model(snake_case )
self.assertFalse(outputa.requires_grad )
@require_multi_gpu
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
# This will move each submodule on different devices
add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=0 ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(execution_device=0 ) )
add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=1 ) )
self.assertEqual(model.lineara.weight.device , torch.device(0 ) )
self.assertEqual(model.batchnorm.weight.device , torch.device(0 ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device(0 ) )
self.assertEqual(model.lineara.weight.device , torch.device(1 ) )
# We can still make a forward pass. The input does not need to be on any particular device
_UpperCAmelCase = torch.randn(2 , 3 )
_UpperCAmelCase = model(snake_case )
self.assertEqual(output.device , torch.device(1 ) )
# We can add a general hook to put back output on same device as input.
add_hook_to_module(snake_case , AlignDevicesHook(io_same_device=snake_case ) )
_UpperCAmelCase = torch.randn(2 , 3 ).to(0 )
_UpperCAmelCase = model(snake_case )
self.assertEqual(output.device , torch.device(0 ) )
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
# This will move each submodule on different devices
_UpperCAmelCase = {'execution_device': 0 if torch.cuda.is_available() else 'cpu', 'offload': True}
add_hook_to_module(model.lineara , AlignDevicesHook(**snake_case ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(**snake_case ) )
add_hook_to_module(model.lineara , AlignDevicesHook(**snake_case ) )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device('meta' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('meta' ) )
self.assertEqual(model.lineara.weight.device , torch.device('meta' ) )
# Buffers are not included in the offload by default, so are on the execution device
_UpperCAmelCase = torch.device(hook_kwargs['execution_device'] )
self.assertEqual(model.batchnorm.running_mean.device , snake_case )
_UpperCAmelCase = torch.randn(2 , 3 )
_UpperCAmelCase = model(snake_case )
self.assertEqual(output.device , snake_case )
# Removing hooks loads back the weights in the model.
remove_hook_from_module(model.lineara )
remove_hook_from_module(model.batchnorm )
remove_hook_from_module(model.lineara )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
# Now test with buffers included in the offload
_UpperCAmelCase = {
'execution_device': 0 if torch.cuda.is_available() else 'cpu',
'offload': True,
'offload_buffers': True,
}
add_hook_to_module(model.lineara , AlignDevicesHook(**snake_case ) )
add_hook_to_module(model.batchnorm , AlignDevicesHook(**snake_case ) )
add_hook_to_module(model.lineara , AlignDevicesHook(**snake_case ) )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device('meta' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('meta' ) )
self.assertEqual(model.lineara.weight.device , torch.device('meta' ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device('meta' ) )
_UpperCAmelCase = torch.randn(2 , 3 )
_UpperCAmelCase = model(snake_case )
self.assertEqual(output.device , snake_case )
# Removing hooks loads back the weights in the model.
remove_hook_from_module(model.lineara )
remove_hook_from_module(model.batchnorm )
remove_hook_from_module(model.lineara )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
def lowerCamelCase_ ( self ) -> List[Any]:
_UpperCAmelCase = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
# This will move each submodule on different devices
_UpperCAmelCase = 0 if torch.cuda.is_available() else 'cpu'
attach_align_device_hook(snake_case , execution_device=snake_case , offload=snake_case )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device('meta' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('meta' ) )
self.assertEqual(model.lineara.weight.device , torch.device('meta' ) )
# Buffers are not included in the offload by default, so are on the execution device
_UpperCAmelCase = torch.device(snake_case )
self.assertEqual(model.batchnorm.running_mean.device , snake_case )
_UpperCAmelCase = torch.randn(2 , 3 )
_UpperCAmelCase = model(snake_case )
self.assertEqual(output.device , snake_case )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(snake_case )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
# Now test with buffers included in the offload
attach_align_device_hook(snake_case , execution_device=snake_case , offload=snake_case , offload_buffers=snake_case )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device('meta' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('meta' ) )
self.assertEqual(model.lineara.weight.device , torch.device('meta' ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device('meta' ) )
_UpperCAmelCase = torch.randn(2 , 3 )
_UpperCAmelCase = model(snake_case )
self.assertEqual(output.device , snake_case )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(snake_case )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = ModelForTest()
# Everything is on CPU
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
# This will move each submodule on different devices
_UpperCAmelCase = 0 if torch.cuda.is_available() else 'cpu'
attach_align_device_hook(
snake_case , execution_device=snake_case , offload=snake_case , weights_map=model.state_dict() )
# Parameters have been offloaded, so on the meta device
self.assertEqual(model.lineara.weight.device , torch.device('meta' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('meta' ) )
self.assertEqual(model.lineara.weight.device , torch.device('meta' ) )
# Buffers are not included in the offload by default, so are on the execution device
_UpperCAmelCase = torch.device(snake_case )
self.assertEqual(model.batchnorm.running_mean.device , snake_case )
_UpperCAmelCase = torch.randn(2 , 3 )
_UpperCAmelCase = model(snake_case )
self.assertEqual(output.device , snake_case )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(snake_case )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
# Now test with buffers included in the offload
attach_align_device_hook(
snake_case , execution_device=snake_case , offload=snake_case , weights_map=model.state_dict() , offload_buffers=snake_case , )
# Parameters have been offloaded, so on the meta device, buffers included
self.assertEqual(model.lineara.weight.device , torch.device('meta' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('meta' ) )
self.assertEqual(model.lineara.weight.device , torch.device('meta' ) )
self.assertEqual(model.batchnorm.running_mean.device , torch.device('meta' ) )
_UpperCAmelCase = torch.randn(2 , 3 )
_UpperCAmelCase = model(snake_case )
self.assertEqual(output.device , snake_case )
# Removing hooks loads back the weights in the model.
remove_hook_from_submodules(snake_case )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) )
self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
| 24
|
"""simple docstring"""
from itertools import permutations
def UpperCAmelCase ( A : tuple ):
'''simple docstring'''
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
_UpperCAmelCase = [7, 11, 13, 17]
for i, test in enumerate(A ):
if (num[i + 4] * 100 + num[i + 5] * 10 + num[i + 6]) % test != 0:
return False
return True
def UpperCAmelCase ( A : int = 10 ):
'''simple docstring'''
return sum(
int(''.join(map(A , A ) ) )
for num in permutations(range(A ) )
if is_substring_divisible(A ) )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 24
| 1
|
"""simple docstring"""
from collections import UserDict
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_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
from ..tf_utils import stable_softmax
lowercase = logging.get_logger(__name__)
@add_end_docstrings(A )
class lowercase__ ( A ):
'''simple docstring'''
def __init__( self , **snake_case ) -> List[Any]:
super().__init__(**snake_case )
requires_backends(self , 'vision' )
self.check_model_type(
TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
if self.framework == 'tf'
else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING )
def __call__( self , snake_case , **snake_case ) -> Tuple:
return super().__call__(snake_case , **snake_case )
def lowerCamelCase_ ( self , **snake_case ) -> str:
_UpperCAmelCase = {}
if "candidate_labels" in kwargs:
_UpperCAmelCase = kwargs['candidate_labels']
if "hypothesis_template" in kwargs:
_UpperCAmelCase = kwargs['hypothesis_template']
return preprocess_params, {}, {}
def lowerCamelCase_ ( self , snake_case , snake_case=None , snake_case="This is a photo of {}." ) -> int:
_UpperCAmelCase = load_image(snake_case )
_UpperCAmelCase = self.image_processor(images=[image] , return_tensors=self.framework )
_UpperCAmelCase = candidate_labels
_UpperCAmelCase = [hypothesis_template.format(snake_case ) for x in candidate_labels]
_UpperCAmelCase = self.tokenizer(snake_case , return_tensors=self.framework , padding=snake_case )
_UpperCAmelCase = [text_inputs]
return inputs
def lowerCamelCase_ ( self , snake_case ) -> Optional[int]:
_UpperCAmelCase = model_inputs.pop('candidate_labels' )
_UpperCAmelCase = model_inputs.pop('text_inputs' )
if isinstance(text_inputs[0] , snake_case ):
_UpperCAmelCase = text_inputs[0]
else:
# Batching case.
_UpperCAmelCase = text_inputs[0][0]
_UpperCAmelCase = self.model(**snake_case , **snake_case )
_UpperCAmelCase = {
'candidate_labels': candidate_labels,
'logits': outputs.logits_per_image,
}
return model_outputs
def lowerCamelCase_ ( self , snake_case ) -> Optional[int]:
_UpperCAmelCase = model_outputs.pop('candidate_labels' )
_UpperCAmelCase = model_outputs['logits'][0]
if self.framework == "pt":
_UpperCAmelCase = logits.softmax(dim=-1 ).squeeze(-1 )
_UpperCAmelCase = probs.tolist()
if not isinstance(snake_case , snake_case ):
_UpperCAmelCase = [scores]
elif self.framework == "tf":
_UpperCAmelCase = stable_softmax(snake_case , axis=-1 )
_UpperCAmelCase = probs.numpy().tolist()
else:
raise ValueError(f'Unsupported framework: {self.framework}' )
_UpperCAmelCase = [
{'score': score, 'label': candidate_label}
for score, candidate_label in sorted(zip(snake_case , snake_case ) , key=lambda snake_case : -x[0] )
]
return result
| 24
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase = {
'''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
'''tokenization_mvp''': ['''MvpTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = ['''MvpTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''MVP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MvpForCausalLM''',
'''MvpForConditionalGeneration''',
'''MvpForQuestionAnswering''',
'''MvpForSequenceClassification''',
'''MvpModel''',
'''MvpPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
from .tokenization_mvp import MvpTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mvp_fast import MvpTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mvp import (
MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
MvpForCausalLM,
MvpForConditionalGeneration,
MvpForQuestionAnswering,
MvpForSequenceClassification,
MvpModel,
MvpPreTrainedModel,
)
else:
import sys
lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 24
| 1
|
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
lowercase = logging.get_logger(__name__)
lowercase = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}
# See all LED models at https://huggingface.co/models?filter=LED
lowercase = {
'''vocab_file''': {
'''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
},
'''merges_file''': {
'''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
},
'''tokenizer_file''': {
'''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
},
}
lowercase = {
'''allenai/led-base-16384''': 1_63_84,
}
@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = (
list(range(ord('!' ) , ord('~' ) + 1 ) ) + list(range(ord('¡' ) , ord('¬' ) + 1 ) ) + list(range(ord('®' ) , ord('ÿ' ) + 1 ) )
)
_UpperCAmelCase = bs[:]
_UpperCAmelCase = 0
for b in range(2**8 ):
if b not in bs:
bs.append(A )
cs.append(2**8 + n )
n += 1
_UpperCAmelCase = [chr(A ) for n in cs]
return dict(zip(A , A ) )
def UpperCAmelCase ( A : Union[str, Any] ):
'''simple docstring'''
_UpperCAmelCase = set()
_UpperCAmelCase = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
_UpperCAmelCase = char
return pairs
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = VOCAB_FILES_NAMES
_UpperCAmelCase = PRETRAINED_VOCAB_FILES_MAP
_UpperCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_UpperCAmelCase = ['''input_ids''', '''attention_mask''']
def __init__( self , snake_case , snake_case , snake_case="replace" , snake_case="<s>" , snake_case="</s>" , snake_case="</s>" , snake_case="<s>" , snake_case="<unk>" , snake_case="<pad>" , snake_case="<mask>" , snake_case=False , **snake_case , ) -> int:
_UpperCAmelCase = AddedToken(snake_case , lstrip=snake_case , rstrip=snake_case ) if isinstance(snake_case , snake_case ) else bos_token
_UpperCAmelCase = AddedToken(snake_case , lstrip=snake_case , rstrip=snake_case ) if isinstance(snake_case , snake_case ) else eos_token
_UpperCAmelCase = AddedToken(snake_case , lstrip=snake_case , rstrip=snake_case ) if isinstance(snake_case , snake_case ) else sep_token
_UpperCAmelCase = AddedToken(snake_case , lstrip=snake_case , rstrip=snake_case ) if isinstance(snake_case , snake_case ) else cls_token
_UpperCAmelCase = AddedToken(snake_case , lstrip=snake_case , rstrip=snake_case ) if isinstance(snake_case , snake_case ) else unk_token
_UpperCAmelCase = AddedToken(snake_case , lstrip=snake_case , rstrip=snake_case ) if isinstance(snake_case , snake_case ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
_UpperCAmelCase = AddedToken(snake_case , lstrip=snake_case , rstrip=snake_case ) if isinstance(snake_case , snake_case ) else mask_token
super().__init__(
errors=snake_case , bos_token=snake_case , eos_token=snake_case , unk_token=snake_case , sep_token=snake_case , cls_token=snake_case , pad_token=snake_case , mask_token=snake_case , add_prefix_space=snake_case , **snake_case , )
with open(snake_case , encoding='utf-8' ) as vocab_handle:
_UpperCAmelCase = json.load(snake_case )
_UpperCAmelCase = {v: k for k, v in self.encoder.items()}
_UpperCAmelCase = errors # how to handle errors in decoding
_UpperCAmelCase = bytes_to_unicode()
_UpperCAmelCase = {v: k for k, v in self.byte_encoder.items()}
with open(snake_case , encoding='utf-8' ) as merges_handle:
_UpperCAmelCase = merges_handle.read().split('\n' )[1:-1]
_UpperCAmelCase = [tuple(merge.split() ) for merge in bpe_merges]
_UpperCAmelCase = dict(zip(snake_case , range(len(snake_case ) ) ) )
_UpperCAmelCase = {}
_UpperCAmelCase = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
_UpperCAmelCase = re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+' )
@property
# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
def lowerCamelCase_ ( self ) -> Any:
return len(self.encoder )
def lowerCamelCase_ ( self ) -> Optional[int]:
return dict(self.encoder , **self.added_tokens_encoder )
def lowerCamelCase_ ( self , snake_case ) -> List[Any]:
if token in self.cache:
return self.cache[token]
_UpperCAmelCase = tuple(snake_case )
_UpperCAmelCase = get_pairs(snake_case )
if not pairs:
return token
while True:
_UpperCAmelCase = min(snake_case , key=lambda snake_case : self.bpe_ranks.get(snake_case , float('inf' ) ) )
if bigram not in self.bpe_ranks:
break
_UpperCAmelCase , _UpperCAmelCase = bigram
_UpperCAmelCase = []
_UpperCAmelCase = 0
while i < len(snake_case ):
try:
_UpperCAmelCase = word.index(snake_case , snake_case )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
_UpperCAmelCase = j
if word[i] == first and i < len(snake_case ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
_UpperCAmelCase = tuple(snake_case )
_UpperCAmelCase = new_word
if len(snake_case ) == 1:
break
else:
_UpperCAmelCase = get_pairs(snake_case )
_UpperCAmelCase = ' '.join(snake_case )
_UpperCAmelCase = word
return word
def lowerCamelCase_ ( self , snake_case ) -> int:
_UpperCAmelCase = []
for token in re.findall(self.pat , snake_case ):
_UpperCAmelCase = ''.join(
self.byte_encoder[b] for b in token.encode('utf-8' ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(snake_case ).split(' ' ) )
return bpe_tokens
def lowerCamelCase_ ( self , snake_case ) -> Tuple:
return self.encoder.get(snake_case , self.encoder.get(self.unk_token ) )
def lowerCamelCase_ ( self , snake_case ) -> Dict:
return self.decoder.get(snake_case )
def lowerCamelCase_ ( self , snake_case ) -> Optional[int]:
_UpperCAmelCase = ''.join(snake_case )
_UpperCAmelCase = bytearray([self.byte_decoder[c] for c in text] ).decode('utf-8' , errors=self.errors )
return text
def lowerCamelCase_ ( self , snake_case , snake_case = None ) -> Tuple[str]:
if not os.path.isdir(snake_case ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
_UpperCAmelCase = os.path.join(
snake_case , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
_UpperCAmelCase = os.path.join(
snake_case , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'] )
with open(snake_case , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=snake_case , ensure_ascii=snake_case ) + '\n' )
_UpperCAmelCase = 0
with open(snake_case , 'w' , encoding='utf-8' ) as writer:
writer.write('#version: 0.2\n' )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda snake_case : kv[1] ):
if index != token_index:
logger.warning(
f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
' Please check that the tokenizer is not corrupted!' )
_UpperCAmelCase = token_index
writer.write(' '.join(snake_case ) + '\n' )
index += 1
return vocab_file, merge_file
def lowerCamelCase_ ( self , snake_case , snake_case = None ) -> List[int]:
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
_UpperCAmelCase = [self.cls_token_id]
_UpperCAmelCase = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def lowerCamelCase_ ( self , snake_case , snake_case = None , snake_case = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=snake_case , token_ids_a=snake_case , already_has_special_tokens=snake_case )
if token_ids_a is None:
return [1] + ([0] * len(snake_case )) + [1]
return [1] + ([0] * len(snake_case )) + [1, 1] + ([0] * len(snake_case )) + [1]
def lowerCamelCase_ ( self , snake_case , snake_case = None ) -> List[int]:
_UpperCAmelCase = [self.sep_token_id]
_UpperCAmelCase = [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 + sep + token_ids_a + sep ) * [0]
def lowerCamelCase_ ( self , snake_case , snake_case=False , **snake_case ) -> List[Any]:
_UpperCAmelCase = kwargs.pop('add_prefix_space' , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(snake_case ) > 0 and not text[0].isspace()):
_UpperCAmelCase = ' ' + text
return (text, kwargs)
def lowerCamelCase_ ( self , snake_case , snake_case = None , snake_case = PaddingStrategy.DO_NOT_PAD , snake_case = None , snake_case = None , ) -> dict:
_UpperCAmelCase = super()._pad(
encoded_inputs=snake_case , max_length=snake_case , padding_strategy=snake_case , pad_to_multiple_of=snake_case , return_attention_mask=snake_case , )
# Load from model defaults
if return_attention_mask is None:
_UpperCAmelCase = 'attention_mask' in self.model_input_names
if return_attention_mask and "global_attention_mask" in encoded_inputs:
_UpperCAmelCase = encoded_inputs[self.model_input_names[0]]
# `global_attention_mask` need to have the same length as other (sequential) inputs.
_UpperCAmelCase = len(encoded_inputs['global_attention_mask'] ) != len(snake_case )
if needs_to_be_padded:
_UpperCAmelCase = len(snake_case ) - len(encoded_inputs['global_attention_mask'] )
if self.padding_side == "right":
# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
_UpperCAmelCase = (
encoded_inputs['global_attention_mask'] + [-1] * difference
)
elif self.padding_side == "left":
_UpperCAmelCase = [-1] * difference + encoded_inputs[
'global_attention_mask'
]
else:
raise ValueError('Invalid padding strategy:' + str(self.padding_side ) )
return encoded_inputs
| 24
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase = {
'''configuration_clipseg''': [
'''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPSegConfig''',
'''CLIPSegTextConfig''',
'''CLIPSegVisionConfig''',
],
'''processing_clipseg''': ['''CLIPSegProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''CLIPSegModel''',
'''CLIPSegPreTrainedModel''',
'''CLIPSegTextModel''',
'''CLIPSegVisionModel''',
'''CLIPSegForImageSegmentation''',
]
if TYPE_CHECKING:
from .configuration_clipseg import (
CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIPSegConfig,
CLIPSegTextConfig,
CLIPSegVisionConfig,
)
from .processing_clipseg import CLIPSegProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clipseg import (
CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPSegForImageSegmentation,
CLIPSegModel,
CLIPSegPreTrainedModel,
CLIPSegTextModel,
CLIPSegVisionModel,
)
else:
import sys
lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 24
| 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 = {
'''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/config.json''',
'''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/config.json''',
'''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/config.json''',
'''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/config.json''',
'''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json''',
'''roberta-large-openai-detector''': '''https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json''',
}
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = '''roberta'''
def __init__( self , snake_case=50265 , snake_case=768 , snake_case=12 , snake_case=12 , snake_case=3072 , snake_case="gelu" , snake_case=0.1 , snake_case=0.1 , snake_case=512 , snake_case=2 , snake_case=0.02 , snake_case=1E-12 , snake_case=1 , snake_case=0 , snake_case=2 , snake_case="absolute" , snake_case=True , snake_case=None , **snake_case , ) -> Tuple:
super().__init__(pad_token_id=snake_case , bos_token_id=snake_case , eos_token_id=snake_case , **snake_case )
_UpperCAmelCase = vocab_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = hidden_act
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = type_vocab_size
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = position_embedding_type
_UpperCAmelCase = use_cache
_UpperCAmelCase = classifier_dropout
class lowercase__ ( A ):
'''simple docstring'''
@property
def lowerCamelCase_ ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_UpperCAmelCase = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
_UpperCAmelCase = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
] )
| 24
|
"""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/swin-tiny-patch4-window7-224''': (
'''https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'''
),
# See all Swin models at https://huggingface.co/models?filter=swin
}
class lowercase__ ( A, A ):
'''simple docstring'''
_UpperCAmelCase = '''swin'''
_UpperCAmelCase = {
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self , snake_case=224 , snake_case=4 , snake_case=3 , snake_case=96 , snake_case=[2, 2, 6, 2] , snake_case=[3, 6, 12, 24] , snake_case=7 , snake_case=4.0 , snake_case=True , snake_case=0.0 , snake_case=0.0 , snake_case=0.1 , snake_case="gelu" , snake_case=False , snake_case=0.02 , snake_case=1E-5 , snake_case=32 , snake_case=None , snake_case=None , **snake_case , ) -> List[Any]:
super().__init__(**snake_case )
_UpperCAmelCase = image_size
_UpperCAmelCase = patch_size
_UpperCAmelCase = num_channels
_UpperCAmelCase = embed_dim
_UpperCAmelCase = depths
_UpperCAmelCase = len(snake_case )
_UpperCAmelCase = num_heads
_UpperCAmelCase = window_size
_UpperCAmelCase = mlp_ratio
_UpperCAmelCase = qkv_bias
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = drop_path_rate
_UpperCAmelCase = hidden_act
_UpperCAmelCase = use_absolute_embeddings
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = initializer_range
_UpperCAmelCase = encoder_stride
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
_UpperCAmelCase = int(embed_dim * 2 ** (len(snake_case ) - 1) )
_UpperCAmelCase = ['stem'] + [f'stage{idx}' for idx in range(1 , len(snake_case ) + 1 )]
_UpperCAmelCase , _UpperCAmelCase = get_aligned_output_features_output_indices(
out_features=snake_case , out_indices=snake_case , stage_names=self.stage_names )
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = version.parse('''1.11''' )
@property
def lowerCamelCase_ ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def lowerCamelCase_ ( self ) -> float:
return 1E-4
| 24
| 1
|
"""simple docstring"""
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from ..utils import add_start_docstrings, logging
lowercase = logging.get_logger(__name__)
lowercase = r'''
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):
Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax
or scores for each vocabulary token after SoftMax.
kwargs (`Dict[str, Any]`, *optional*):
Additional stopping criteria specific kwargs.
Return:
`bool`. `False` indicates we should continue, `True` indicates we should stop.
'''
class lowercase__ ( A ):
'''simple docstring'''
@add_start_docstrings(snake_case )
def __call__( self , snake_case , snake_case , **snake_case ) -> bool:
raise NotImplementedError('StoppingCriteria needs to be subclassed' )
class lowercase__ ( A ):
'''simple docstring'''
def __init__( self , snake_case , snake_case = None ) -> Optional[int]:
_UpperCAmelCase = max_length
_UpperCAmelCase = max_position_embeddings
@add_start_docstrings(snake_case )
def __call__( self , snake_case , snake_case , **snake_case ) -> bool:
_UpperCAmelCase = input_ids.shape[-1]
_UpperCAmelCase = cur_len >= self.max_length
if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
logger.warning_once(
'This is a friendly reminder - the current text generation call will exceed the model\'s predefined '
f'maximum length ({self.max_position_embeddings}). Depending on the model, you may observe '
'exceptions, performance degradation, or nothing at all.' )
return is_done
class lowercase__ ( A ):
'''simple docstring'''
def __init__( self , snake_case , snake_case ) -> Tuple:
warnings.warn(
'The class `MaxNewTokensCriteria` is deprecated. '
f'Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` '
'with `max_length = start_length + max_new_tokens` instead.' , snake_case , )
_UpperCAmelCase = start_length
_UpperCAmelCase = max_new_tokens
_UpperCAmelCase = start_length + max_new_tokens
@add_start_docstrings(snake_case )
def __call__( self , snake_case , snake_case , **snake_case ) -> bool:
return input_ids.shape[-1] >= self.max_length
class lowercase__ ( A ):
'''simple docstring'''
def __init__( self , snake_case , snake_case = None ) -> int:
_UpperCAmelCase = max_time
_UpperCAmelCase = time.time() if initial_timestamp is None else initial_timestamp
@add_start_docstrings(snake_case )
def __call__( self , snake_case , snake_case , **snake_case ) -> bool:
return time.time() - self.initial_timestamp > self.max_time
class lowercase__ ( A ):
'''simple docstring'''
@add_start_docstrings(snake_case )
def __call__( self , snake_case , snake_case , **snake_case ) -> bool:
return any(criteria(snake_case , snake_case ) for criteria in self )
@property
def lowerCamelCase_ ( self ) -> Optional[int]:
for stopping_criterium in self:
if isinstance(snake_case , snake_case ):
return stopping_criterium.max_length
elif isinstance(snake_case , snake_case ):
return stopping_criterium.max_length
return None
def UpperCAmelCase ( A : StoppingCriteriaList , A : int ):
'''simple docstring'''
_UpperCAmelCase = stopping_criteria.max_length
_UpperCAmelCase = deepcopy(A )
if stopping_max_length is not None and stopping_max_length != max_length:
warnings.warn('You set different `max_length` for stopping criteria and `max_length` parameter' , A )
elif stopping_max_length is None:
new_stopping_criteria.append(MaxLengthCriteria(max_length=A ) )
return new_stopping_criteria
| 24
|
"""simple docstring"""
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class lowercase__ ( nn.Module ):
'''simple docstring'''
def __init__( self , snake_case = 16 , snake_case = 88 , snake_case = None , snake_case = 1 , snake_case = 0.0 , snake_case = 32 , snake_case = None , snake_case = False , snake_case = None , snake_case = None , snake_case = "geglu" , snake_case = None , ) -> str:
super().__init__()
_UpperCAmelCase = nn.ModuleList(
[
TransformeraDModel(
num_attention_heads=snake_case , attention_head_dim=snake_case , in_channels=snake_case , num_layers=snake_case , dropout=snake_case , norm_num_groups=snake_case , cross_attention_dim=snake_case , attention_bias=snake_case , sample_size=snake_case , num_vector_embeds=snake_case , activation_fn=snake_case , num_embeds_ada_norm=snake_case , )
for _ in range(2 )
] )
# Variables that can be set by a pipeline:
# The ratio of transformer1 to transformer2's output states to be combined during inference
_UpperCAmelCase = 0.5
# The shape of `encoder_hidden_states` is expected to be
# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
_UpperCAmelCase = [77, 257]
# Which transformer to use to encode which condition.
# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
_UpperCAmelCase = [1, 0]
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case=None , snake_case=None , snake_case=None , snake_case = True , ) -> Any:
_UpperCAmelCase = hidden_states
_UpperCAmelCase = []
_UpperCAmelCase = 0
# attention_mask is not used yet
for i in range(2 ):
# for each of the two transformers, pass the corresponding condition tokens
_UpperCAmelCase = encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
_UpperCAmelCase = self.transformer_index_for_condition[i]
_UpperCAmelCase = self.transformers[transformer_index](
snake_case , encoder_hidden_states=snake_case , timestep=snake_case , cross_attention_kwargs=snake_case , return_dict=snake_case , )[0]
encoded_states.append(encoded_state - input_states )
tokens_start += self.condition_lengths[i]
_UpperCAmelCase = encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
_UpperCAmelCase = output_states + input_states
if not return_dict:
return (output_states,)
return TransformeraDModelOutput(sample=snake_case )
| 24
| 1
|
"""simple docstring"""
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
FlaxCrossAttnDownBlockaD,
FlaxCrossAttnUpBlockaD,
FlaxDownBlockaD,
FlaxUNetMidBlockaDCrossAttn,
FlaxUpBlockaD,
)
@flax.struct.dataclass
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = 42
@flax_register_to_config
class lowercase__ ( nn.Module, A, A ):
'''simple docstring'''
_UpperCAmelCase = 32
_UpperCAmelCase = 4
_UpperCAmelCase = 4
_UpperCAmelCase = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
)
_UpperCAmelCase = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
_UpperCAmelCase = False
_UpperCAmelCase = (3_20, 6_40, 12_80, 12_80)
_UpperCAmelCase = 2
_UpperCAmelCase = 8
_UpperCAmelCase = None
_UpperCAmelCase = 12_80
_UpperCAmelCase = 0.0
_UpperCAmelCase = False
_UpperCAmelCase = jnp.floataa
_UpperCAmelCase = True
_UpperCAmelCase = 0
_UpperCAmelCase = False
def lowerCamelCase_ ( self , snake_case ) -> FrozenDict:
# init input tensors
_UpperCAmelCase = (1, self.in_channels, self.sample_size, self.sample_size)
_UpperCAmelCase = jnp.zeros(snake_case , dtype=jnp.floataa )
_UpperCAmelCase = jnp.ones((1,) , dtype=jnp.intaa )
_UpperCAmelCase = jnp.zeros((1, 1, self.cross_attention_dim) , dtype=jnp.floataa )
_UpperCAmelCase , _UpperCAmelCase = jax.random.split(snake_case )
_UpperCAmelCase = {'params': params_rng, 'dropout': dropout_rng}
return self.init(snake_case , snake_case , snake_case , snake_case )["params"]
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = self.block_out_channels
_UpperCAmelCase = block_out_channels[0] * 4
if self.num_attention_heads is not None:
raise ValueError(
'At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19.' )
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
# which is why we correct for the naming here.
_UpperCAmelCase = self.num_attention_heads or self.attention_head_dim
# input
_UpperCAmelCase = nn.Conv(
block_out_channels[0] , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
# time
_UpperCAmelCase = FlaxTimesteps(
block_out_channels[0] , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.config.freq_shift )
_UpperCAmelCase = FlaxTimestepEmbedding(snake_case , dtype=self.dtype )
_UpperCAmelCase = self.only_cross_attention
if isinstance(snake_case , snake_case ):
_UpperCAmelCase = (only_cross_attention,) * len(self.down_block_types )
if isinstance(snake_case , snake_case ):
_UpperCAmelCase = (num_attention_heads,) * len(self.down_block_types )
# down
_UpperCAmelCase = []
_UpperCAmelCase = block_out_channels[0]
for i, down_block_type in enumerate(self.down_block_types ):
_UpperCAmelCase = output_channel
_UpperCAmelCase = block_out_channels[i]
_UpperCAmelCase = i == len(snake_case ) - 1
if down_block_type == "CrossAttnDownBlock2D":
_UpperCAmelCase = FlaxCrossAttnDownBlockaD(
in_channels=snake_case , out_channels=snake_case , dropout=self.dropout , num_layers=self.layers_per_block , num_attention_heads=num_attention_heads[i] , add_downsample=not is_final_block , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
else:
_UpperCAmelCase = FlaxDownBlockaD(
in_channels=snake_case , out_channels=snake_case , dropout=self.dropout , num_layers=self.layers_per_block , add_downsample=not is_final_block , dtype=self.dtype , )
down_blocks.append(snake_case )
_UpperCAmelCase = down_blocks
# mid
_UpperCAmelCase = FlaxUNetMidBlockaDCrossAttn(
in_channels=block_out_channels[-1] , dropout=self.dropout , num_attention_heads=num_attention_heads[-1] , use_linear_projection=self.use_linear_projection , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
# up
_UpperCAmelCase = []
_UpperCAmelCase = list(reversed(snake_case ) )
_UpperCAmelCase = list(reversed(snake_case ) )
_UpperCAmelCase = list(reversed(snake_case ) )
_UpperCAmelCase = reversed_block_out_channels[0]
for i, up_block_type in enumerate(self.up_block_types ):
_UpperCAmelCase = output_channel
_UpperCAmelCase = reversed_block_out_channels[i]
_UpperCAmelCase = reversed_block_out_channels[min(i + 1 , len(snake_case ) - 1 )]
_UpperCAmelCase = i == len(snake_case ) - 1
if up_block_type == "CrossAttnUpBlock2D":
_UpperCAmelCase = FlaxCrossAttnUpBlockaD(
in_channels=snake_case , out_channels=snake_case , prev_output_channel=snake_case , num_layers=self.layers_per_block + 1 , num_attention_heads=reversed_num_attention_heads[i] , add_upsample=not is_final_block , dropout=self.dropout , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
else:
_UpperCAmelCase = FlaxUpBlockaD(
in_channels=snake_case , out_channels=snake_case , prev_output_channel=snake_case , num_layers=self.layers_per_block + 1 , add_upsample=not is_final_block , dropout=self.dropout , dtype=self.dtype , )
up_blocks.append(snake_case )
_UpperCAmelCase = output_channel
_UpperCAmelCase = up_blocks
# out
_UpperCAmelCase = nn.GroupNorm(num_groups=32 , epsilon=1E-5 )
_UpperCAmelCase = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self , snake_case , snake_case , snake_case , snake_case=None , snake_case=None , snake_case = True , snake_case = False , ) -> Union[FlaxUNetaDConditionOutput, Tuple]:
# 1. time
if not isinstance(snake_case , jnp.ndarray ):
_UpperCAmelCase = jnp.array([timesteps] , dtype=jnp.intaa )
elif isinstance(snake_case , jnp.ndarray ) and len(timesteps.shape ) == 0:
_UpperCAmelCase = timesteps.astype(dtype=jnp.floataa )
_UpperCAmelCase = jnp.expand_dims(snake_case , 0 )
_UpperCAmelCase = self.time_proj(snake_case )
_UpperCAmelCase = self.time_embedding(snake_case )
# 2. pre-process
_UpperCAmelCase = jnp.transpose(snake_case , (0, 2, 3, 1) )
_UpperCAmelCase = self.conv_in(snake_case )
# 3. down
_UpperCAmelCase = (sample,)
for down_block in self.down_blocks:
if isinstance(snake_case , snake_case ):
_UpperCAmelCase , _UpperCAmelCase = down_block(snake_case , snake_case , snake_case , deterministic=not train )
else:
_UpperCAmelCase , _UpperCAmelCase = down_block(snake_case , snake_case , deterministic=not train )
down_block_res_samples += res_samples
if down_block_additional_residuals is not None:
_UpperCAmelCase = ()
for down_block_res_sample, down_block_additional_residual in zip(
snake_case , snake_case ):
down_block_res_sample += down_block_additional_residual
new_down_block_res_samples += (down_block_res_sample,)
_UpperCAmelCase = new_down_block_res_samples
# 4. mid
_UpperCAmelCase = self.mid_block(snake_case , snake_case , snake_case , deterministic=not train )
if mid_block_additional_residual is not None:
sample += mid_block_additional_residual
# 5. up
for up_block in self.up_blocks:
_UpperCAmelCase = down_block_res_samples[-(self.layers_per_block + 1) :]
_UpperCAmelCase = down_block_res_samples[: -(self.layers_per_block + 1)]
if isinstance(snake_case , snake_case ):
_UpperCAmelCase = up_block(
snake_case , temb=snake_case , encoder_hidden_states=snake_case , res_hidden_states_tuple=snake_case , deterministic=not train , )
else:
_UpperCAmelCase = up_block(snake_case , temb=snake_case , res_hidden_states_tuple=snake_case , deterministic=not train )
# 6. post-process
_UpperCAmelCase = self.conv_norm_out(snake_case )
_UpperCAmelCase = nn.silu(snake_case )
_UpperCAmelCase = self.conv_out(snake_case )
_UpperCAmelCase = jnp.transpose(snake_case , (0, 3, 1, 2) )
if not return_dict:
return (sample,)
return FlaxUNetaDConditionOutput(sample=snake_case )
| 24
|
"""simple docstring"""
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import CvtForImageClassification, CvtModel
from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class lowercase__ ( A ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(snake_case , 'embed_dim' ) )
self.parent.assertTrue(hasattr(snake_case , 'num_heads' ) )
class lowercase__ :
'''simple docstring'''
def __init__( self , snake_case , snake_case=13 , snake_case=64 , snake_case=3 , snake_case=[16, 48, 96] , snake_case=[1, 3, 6] , snake_case=[1, 2, 10] , snake_case=[7, 3, 3] , snake_case=[4, 2, 2] , snake_case=[2, 1, 1] , snake_case=[2, 2, 2] , snake_case=[False, False, True] , snake_case=[0.0, 0.0, 0.0] , snake_case=0.02 , snake_case=1E-12 , snake_case=True , snake_case=True , snake_case=2 , ) -> Tuple:
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = image_size
_UpperCAmelCase = patch_sizes
_UpperCAmelCase = patch_stride
_UpperCAmelCase = patch_padding
_UpperCAmelCase = is_training
_UpperCAmelCase = use_labels
_UpperCAmelCase = num_labels
_UpperCAmelCase = num_channels
_UpperCAmelCase = embed_dim
_UpperCAmelCase = num_heads
_UpperCAmelCase = stride_kv
_UpperCAmelCase = depth
_UpperCAmelCase = cls_token
_UpperCAmelCase = attention_drop_rate
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_UpperCAmelCase = None
if self.use_labels:
_UpperCAmelCase = ids_tensor([self.batch_size] , self.num_labels )
_UpperCAmelCase = self.get_config()
return config, pixel_values, labels
def lowerCamelCase_ ( self ) -> List[str]:
return CvtConfig(
image_size=self.image_size , num_labels=self.num_labels , num_channels=self.num_channels , embed_dim=self.embed_dim , num_heads=self.num_heads , patch_sizes=self.patch_sizes , patch_padding=self.patch_padding , patch_stride=self.patch_stride , stride_kv=self.stride_kv , depth=self.depth , cls_token=self.cls_token , attention_drop_rate=self.attention_drop_rate , initializer_range=self.initializer_range , )
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case ) -> Optional[int]:
_UpperCAmelCase = CvtModel(config=snake_case )
model.to(snake_case )
model.eval()
_UpperCAmelCase = model(snake_case )
_UpperCAmelCase = (self.image_size, self.image_size)
_UpperCAmelCase , _UpperCAmelCase = image_size[0], image_size[1]
for i in range(len(self.depth ) ):
_UpperCAmelCase = floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
_UpperCAmelCase = floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dim[-1], height, width) )
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case ) -> Optional[Any]:
_UpperCAmelCase = self.num_labels
_UpperCAmelCase = CvtForImageClassification(snake_case )
model.to(snake_case )
model.eval()
_UpperCAmelCase = model(snake_case , labels=snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = self.prepare_config_and_inputs()
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = config_and_inputs
_UpperCAmelCase = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class lowercase__ ( A, A, unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = (CvtModel, CvtForImageClassification) if is_torch_available() else ()
_UpperCAmelCase = (
{'''feature-extraction''': CvtModel, '''image-classification''': CvtForImageClassification}
if is_torch_available()
else {}
)
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = CvtModelTester(self )
_UpperCAmelCase = ConfigTester(self , config_class=snake_case , has_text_modality=snake_case , hidden_size=37 )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def lowerCamelCase_ ( self ) -> Union[str, Any]:
return
@unittest.skip(reason='Cvt does not output attentions' )
def lowerCamelCase_ ( self ) -> str:
pass
@unittest.skip(reason='Cvt does not use inputs_embeds' )
def lowerCamelCase_ ( self ) -> int:
pass
@unittest.skip(reason='Cvt does not support input and output embeddings' )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
pass
def lowerCamelCase_ ( self ) -> Any:
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(snake_case )
_UpperCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCAmelCase = [*signature.parameters.keys()]
_UpperCAmelCase = ['pixel_values']
self.assertListEqual(arg_names[:1] , snake_case )
def lowerCamelCase_ ( self ) -> Optional[int]:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case )
def lowerCamelCase_ ( self ) -> Optional[int]:
def check_hidden_states_output(snake_case , snake_case , snake_case ):
_UpperCAmelCase = model_class(snake_case )
model.to(snake_case )
model.eval()
with torch.no_grad():
_UpperCAmelCase = model(**self._prepare_for_class(snake_case , snake_case ) )
_UpperCAmelCase = outputs.hidden_states
_UpperCAmelCase = len(self.model_tester.depth )
self.assertEqual(len(snake_case ) , snake_case )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:] ) , [
self.model_tester.embed_dim[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
] , )
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = True
check_hidden_states_output(snake_case , snake_case , snake_case )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_UpperCAmelCase = True
check_hidden_states_output(snake_case , snake_case , snake_case )
def lowerCamelCase_ ( self ) -> Any:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*snake_case )
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def lowerCamelCase_ ( self ) -> Dict:
pass
@slow
def lowerCamelCase_ ( self ) -> Dict:
for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase = CvtModel.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def lowerCamelCase_ ( self ) -> List[Any]:
return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
@slow
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(snake_case )
_UpperCAmelCase = self.default_image_processor
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = image_processor(images=snake_case , return_tensors='pt' ).to(snake_case )
# forward pass
with torch.no_grad():
_UpperCAmelCase = model(**snake_case )
# verify the logits
_UpperCAmelCase = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , snake_case )
_UpperCAmelCase = torch.tensor([0.9285, 0.9015, -0.3150] ).to(snake_case )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , snake_case , atol=1E-4 ) )
| 24
| 1
|
"""simple docstring"""
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def UpperCAmelCase ( A : int = 3 ):
'''simple docstring'''
if isinstance(A , A ):
raise TypeError('number of qubits must be a integer.' )
if number_of_qubits <= 0:
raise ValueError('number of qubits must be > 0.' )
if math.floor(A ) != number_of_qubits:
raise ValueError('number of qubits must be exact integer.' )
if number_of_qubits > 10:
raise ValueError('number of qubits too large to simulate(>10).' )
_UpperCAmelCase = QuantumRegister(A , 'qr' )
_UpperCAmelCase = ClassicalRegister(A , 'cr' )
_UpperCAmelCase = QuantumCircuit(A , A )
_UpperCAmelCase = number_of_qubits
for i in range(A ):
quantum_circuit.h(number_of_qubits - i - 1 )
counter -= 1
for j in range(A ):
quantum_circuit.cp(np.pi / 2 ** (counter - j) , A , A )
for k in range(number_of_qubits // 2 ):
quantum_circuit.swap(A , number_of_qubits - k - 1 )
# measure all the qubits
quantum_circuit.measure(A , A )
# simulate with 10000 shots
_UpperCAmelCase = Aer.get_backend('qasm_simulator' )
_UpperCAmelCase = execute(A , A , shots=1_0000 )
return job.result().get_counts(A )
if __name__ == "__main__":
print(
F'''Total count for quantum fourier transform state is: \
{quantum_fourier_transform(3)}'''
)
| 24
|
"""simple docstring"""
from __future__ import annotations
from cmath import sqrt
def UpperCAmelCase ( A : int , A : int , A : int ):
'''simple docstring'''
if a == 0:
raise ValueError('Coefficient \'a\' must not be zero.' )
_UpperCAmelCase = b * b - 4 * a * c
_UpperCAmelCase = (-b + sqrt(A )) / (2 * a)
_UpperCAmelCase = (-b - sqrt(A )) / (2 * a)
return (
root_a.real if not root_a.imag else root_a,
root_a.real if not root_a.imag else root_a,
)
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = quadratic_roots(a=5 , b=6 , c=1 )
print(f'The solutions are: {solutiona} and {solutiona}' )
if __name__ == "__main__":
main()
| 24
| 1
|
"""simple docstring"""
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
lowercase = '''src/transformers'''
# This is to make sure the transformers module imported is the one in the repo.
lowercase = importlib.util.spec_from_file_location(
'''transformers''',
os.path.join(PATH_TO_TRANSFORMERS, '''__init__.py'''),
submodule_search_locations=[PATH_TO_TRANSFORMERS],
)
lowercase = spec.loader.load_module()
lowercase = transformers.models.auto.configuration_auto.CONFIG_MAPPING
# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
lowercase = re.compile('''\[(.+?)\]\((https://huggingface\.co/.+?)\)''')
lowercase = {
'''CLIPConfigMixin''',
'''DecisionTransformerConfigMixin''',
'''EncoderDecoderConfigMixin''',
'''RagConfigMixin''',
'''SpeechEncoderDecoderConfigMixin''',
'''VisionEncoderDecoderConfigMixin''',
'''VisionTextDualEncoderConfigMixin''',
}
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = []
for config_class in list(CONFIG_MAPPING.values() ):
_UpperCAmelCase = False
# source code of `config_class`
_UpperCAmelCase = inspect.getsource(A )
_UpperCAmelCase = _re_checkpoint.findall(A )
for checkpoint in checkpoints:
# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
_UpperCAmelCase , _UpperCAmelCase = checkpoint
# verify the checkpoint name corresponds to the checkpoint link
_UpperCAmelCase = f'https://huggingface.co/{ckpt_name}'
if ckpt_link == ckpt_link_from_name:
_UpperCAmelCase = True
break
_UpperCAmelCase = config_class.__name__
if not checkpoint_found and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
configs_without_checkpoint.append(A )
if len(A ) > 0:
_UpperCAmelCase = '\n'.join(sorted(A ) )
raise ValueError(f'The following configurations don\'t contain any valid checkpoint:\n{message}' )
if __name__ == "__main__":
check_config_docstrings_have_checkpoints()
| 24
|
"""simple docstring"""
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # see https://github.com/huggingface/transformers/issues/11457
class lowercase__ ( A, unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = BarthezTokenizer
_UpperCAmelCase = BarthezTokenizerFast
_UpperCAmelCase = True
_UpperCAmelCase = True
def lowerCamelCase_ ( self ) -> Optional[int]:
super().setUp()
_UpperCAmelCase = BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez' )
tokenizer.save_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname , legacy_format=snake_case )
_UpperCAmelCase = tokenizer
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = '<pad>'
_UpperCAmelCase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case ) , snake_case )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case ) , snake_case )
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '<s>' )
self.assertEqual(vocab_keys[1] , '<pad>' )
self.assertEqual(vocab_keys[-1] , '<mask>' )
self.assertEqual(len(snake_case ) , 101122 )
def lowerCamelCase_ ( self ) -> List[Any]:
self.assertEqual(self.get_tokenizer().vocab_size , 101122 )
@require_torch
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
_UpperCAmelCase = [0, 57, 3018, 70307, 91, 2]
_UpperCAmelCase = self.tokenizer(
snake_case , max_length=len(snake_case ) , padding=snake_case , truncation=snake_case , return_tensors='pt' )
self.assertIsInstance(snake_case , snake_case )
self.assertEqual((2, 6) , batch.input_ids.shape )
self.assertEqual((2, 6) , batch.attention_mask.shape )
_UpperCAmelCase = batch.input_ids.tolist()[0]
self.assertListEqual(snake_case , snake_case )
def lowerCamelCase_ ( self ) -> Optional[Any]:
if not self.test_rust_tokenizer:
return
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = self.get_rust_tokenizer()
_UpperCAmelCase = 'I was born in 92000, and this is falsé.'
_UpperCAmelCase = tokenizer.tokenize(snake_case )
_UpperCAmelCase = rust_tokenizer.tokenize(snake_case )
self.assertListEqual(snake_case , snake_case )
_UpperCAmelCase = tokenizer.encode(snake_case , add_special_tokens=snake_case )
_UpperCAmelCase = rust_tokenizer.encode(snake_case , add_special_tokens=snake_case )
self.assertListEqual(snake_case , snake_case )
_UpperCAmelCase = self.get_rust_tokenizer()
_UpperCAmelCase = tokenizer.encode(snake_case )
_UpperCAmelCase = rust_tokenizer.encode(snake_case )
self.assertListEqual(snake_case , snake_case )
@slow
def lowerCamelCase_ ( self ) -> Optional[int]:
# fmt: off
_UpperCAmelCase = {'input_ids': [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 2, 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, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 5, 2]], '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, 0, 0, 0, 0, 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, 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]]} # noqa: E501
# fmt: on
# moussaKam/mbarthez is a french model. So we also use french texts.
_UpperCAmelCase = [
'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
'telles que la traduction et la synthèse de texte.',
]
self.tokenizer_integration_test_util(
expected_encoding=snake_case , model_name='moussaKam/mbarthez' , revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6' , sequences=snake_case , )
| 24
| 1
|
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class lowercase__ ( A, A, unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = CycleDiffusionPipeline
_UpperCAmelCase = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
'''negative_prompt''',
'''height''',
'''width''',
'''negative_prompt_embeds''',
}
_UpperCAmelCase = PipelineTesterMixin.required_optional_params - {'''latents'''}
_UpperCAmelCase = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''source_prompt'''} )
_UpperCAmelCase = IMAGE_TO_IMAGE_IMAGE_PARAMS
_UpperCAmelCase = IMAGE_TO_IMAGE_IMAGE_PARAMS
def lowerCamelCase_ ( self ) -> int:
torch.manual_seed(0 )
_UpperCAmelCase = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') , cross_attention_dim=32 , )
_UpperCAmelCase = DDIMScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule='scaled_linear' , num_train_timesteps=1000 , clip_sample=snake_case , set_alpha_to_one=snake_case , )
torch.manual_seed(0 )
_UpperCAmelCase = 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 , )
torch.manual_seed(0 )
_UpperCAmelCase = 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 , )
_UpperCAmelCase = CLIPTextModel(snake_case )
_UpperCAmelCase = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
_UpperCAmelCase = {
'unet': unet,
'scheduler': scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'safety_checker': None,
'feature_extractor': None,
}
return components
def lowerCamelCase_ ( self , snake_case , snake_case=0 ) -> Optional[Any]:
_UpperCAmelCase = floats_tensor((1, 3, 32, 32) , rng=random.Random(snake_case ) ).to(snake_case )
_UpperCAmelCase = image / 2 + 0.5
if str(snake_case ).startswith('mps' ):
_UpperCAmelCase = torch.manual_seed(snake_case )
else:
_UpperCAmelCase = torch.Generator(device=snake_case ).manual_seed(snake_case )
_UpperCAmelCase = {
'prompt': 'An astronaut riding an elephant',
'source_prompt': 'An astronaut riding a horse',
'image': image,
'generator': generator,
'num_inference_steps': 2,
'eta': 0.1,
'strength': 0.8,
'guidance_scale': 3,
'source_guidance_scale': 1,
'output_type': 'numpy',
}
return inputs
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = 'cpu' # ensure determinism for the device-dependent torch.Generator
_UpperCAmelCase = self.get_dummy_components()
_UpperCAmelCase = CycleDiffusionPipeline(**snake_case )
_UpperCAmelCase = pipe.to(snake_case )
pipe.set_progress_bar_config(disable=snake_case )
_UpperCAmelCase = self.get_dummy_inputs(snake_case )
_UpperCAmelCase = pipe(**snake_case )
_UpperCAmelCase = output.images
_UpperCAmelCase = images[0, -3:, -3:, -1]
assert images.shape == (1, 32, 32, 3)
_UpperCAmelCase = np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
@unittest.skipIf(torch_device != 'cuda' , 'This test requires a GPU' )
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = self.get_dummy_components()
for name, module in components.items():
if hasattr(snake_case , 'half' ):
_UpperCAmelCase = module.half()
_UpperCAmelCase = CycleDiffusionPipeline(**snake_case )
_UpperCAmelCase = pipe.to(snake_case )
pipe.set_progress_bar_config(disable=snake_case )
_UpperCAmelCase = self.get_dummy_inputs(snake_case )
_UpperCAmelCase = pipe(**snake_case )
_UpperCAmelCase = output.images
_UpperCAmelCase = images[0, -3:, -3:, -1]
assert images.shape == (1, 32, 32, 3)
_UpperCAmelCase = np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
@skip_mps
def lowerCamelCase_ ( self ) -> Any:
return super().test_save_load_local()
@unittest.skip('non-deterministic pipeline' )
def lowerCamelCase_ ( self ) -> str:
return super().test_inference_batch_single_identical()
@skip_mps
def lowerCamelCase_ ( self ) -> Any:
return super().test_dict_tuple_outputs_equivalent()
@skip_mps
def lowerCamelCase_ ( self ) -> Tuple:
return super().test_save_load_optional_components()
@skip_mps
def lowerCamelCase_ ( self ) -> List[Any]:
return super().test_attention_slicing_forward_pass()
@slow
@require_torch_gpu
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> Optional[Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase_ ( self ) -> List[Any]:
_UpperCAmelCase = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/cycle-diffusion/black_colored_car.png' )
_UpperCAmelCase = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy' )
_UpperCAmelCase = init_image.resize((512, 512) )
_UpperCAmelCase = 'CompVis/stable-diffusion-v1-4'
_UpperCAmelCase = DDIMScheduler.from_pretrained(snake_case , subfolder='scheduler' )
_UpperCAmelCase = CycleDiffusionPipeline.from_pretrained(
snake_case , scheduler=snake_case , safety_checker=snake_case , torch_dtype=torch.floataa , revision='fp16' )
pipe.to(snake_case )
pipe.set_progress_bar_config(disable=snake_case )
pipe.enable_attention_slicing()
_UpperCAmelCase = 'A black colored car'
_UpperCAmelCase = 'A blue colored car'
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = pipe(
prompt=snake_case , source_prompt=snake_case , image=snake_case , num_inference_steps=100 , eta=0.1 , strength=0.85 , guidance_scale=3 , source_guidance_scale=1 , generator=snake_case , output_type='np' , )
_UpperCAmelCase = output.images
# the values aren't exactly equal, but the images look the same visually
assert np.abs(image - expected_image ).max() < 5E-1
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/cycle-diffusion/black_colored_car.png' )
_UpperCAmelCase = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy' )
_UpperCAmelCase = init_image.resize((512, 512) )
_UpperCAmelCase = 'CompVis/stable-diffusion-v1-4'
_UpperCAmelCase = DDIMScheduler.from_pretrained(snake_case , subfolder='scheduler' )
_UpperCAmelCase = CycleDiffusionPipeline.from_pretrained(snake_case , scheduler=snake_case , safety_checker=snake_case )
pipe.to(snake_case )
pipe.set_progress_bar_config(disable=snake_case )
pipe.enable_attention_slicing()
_UpperCAmelCase = 'A black colored car'
_UpperCAmelCase = 'A blue colored car'
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = pipe(
prompt=snake_case , source_prompt=snake_case , image=snake_case , num_inference_steps=100 , eta=0.1 , strength=0.85 , guidance_scale=3 , source_guidance_scale=1 , generator=snake_case , output_type='np' , )
_UpperCAmelCase = output.images
assert np.abs(image - expected_image ).max() < 2E-2
| 24
|
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class lowercase__ ( A, unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = DiTPipeline
_UpperCAmelCase = CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
_UpperCAmelCase = PipelineTesterMixin.required_optional_params - {
'''latents''',
'''num_images_per_prompt''',
'''callback''',
'''callback_steps''',
}
_UpperCAmelCase = CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
_UpperCAmelCase = False
def lowerCamelCase_ ( self ) -> str:
torch.manual_seed(0 )
_UpperCAmelCase = TransformeraDModel(
sample_size=16 , num_layers=2 , patch_size=4 , attention_head_dim=8 , num_attention_heads=2 , in_channels=4 , out_channels=8 , attention_bias=snake_case , activation_fn='gelu-approximate' , num_embeds_ada_norm=1000 , norm_type='ada_norm_zero' , norm_elementwise_affine=snake_case , )
_UpperCAmelCase = AutoencoderKL()
_UpperCAmelCase = DDIMScheduler()
_UpperCAmelCase = {'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
return components
def lowerCamelCase_ ( self , snake_case , snake_case=0 ) -> Optional[Any]:
if str(snake_case ).startswith('mps' ):
_UpperCAmelCase = torch.manual_seed(snake_case )
else:
_UpperCAmelCase = torch.Generator(device=snake_case ).manual_seed(snake_case )
_UpperCAmelCase = {
'class_labels': [1],
'generator': generator,
'num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
def lowerCamelCase_ ( self ) -> List[Any]:
_UpperCAmelCase = 'cpu'
_UpperCAmelCase = self.get_dummy_components()
_UpperCAmelCase = self.pipeline_class(**snake_case )
pipe.to(snake_case )
pipe.set_progress_bar_config(disable=snake_case )
_UpperCAmelCase = self.get_dummy_inputs(snake_case )
_UpperCAmelCase = pipe(**snake_case ).images
_UpperCAmelCase = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 16, 16, 3) )
_UpperCAmelCase = np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457] )
_UpperCAmelCase = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(snake_case , 1E-3 )
def lowerCamelCase_ ( self ) -> Any:
self._test_inference_batch_single_identical(relax_max_difference=snake_case , expected_max_diff=1E-3 )
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def lowerCamelCase_ ( self ) -> Optional[int]:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
@require_torch_gpu
@slow
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> int:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = DiTPipeline.from_pretrained('facebook/DiT-XL-2-256' )
pipe.to('cuda' )
_UpperCAmelCase = ['vase', 'umbrella', 'white shark', 'white wolf']
_UpperCAmelCase = pipe.get_label_ids(snake_case )
_UpperCAmelCase = pipe(snake_case , generator=snake_case , num_inference_steps=40 , output_type='np' ).images
for word, image in zip(snake_case , snake_case ):
_UpperCAmelCase = load_numpy(
f'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy' )
assert np.abs((expected_image - image).max() ) < 1E-2
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = DiTPipeline.from_pretrained('facebook/DiT-XL-2-512' )
_UpperCAmelCase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.to('cuda' )
_UpperCAmelCase = ['vase', 'umbrella']
_UpperCAmelCase = pipe.get_label_ids(snake_case )
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = pipe(snake_case , generator=snake_case , num_inference_steps=25 , output_type='np' ).images
for word, image in zip(snake_case , snake_case ):
_UpperCAmelCase = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
f'/dit/{word}_512.npy' )
assert np.abs((expected_image - image).max() ) < 1E-1
| 24
| 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
lowercase = logging.get_logger(__name__)
lowercase = {
'''microsoft/beit-base-patch16-224-pt22k''': (
'''https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'''
),
# See all BEiT models at https://huggingface.co/models?filter=beit
}
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = '''beit'''
def __init__( self , snake_case=8192 , snake_case=768 , snake_case=12 , snake_case=12 , snake_case=3072 , snake_case="gelu" , snake_case=0.0 , snake_case=0.0 , snake_case=0.02 , snake_case=1E-12 , snake_case=224 , snake_case=16 , snake_case=3 , snake_case=False , snake_case=False , snake_case=False , snake_case=False , snake_case=0.1 , snake_case=0.1 , snake_case=True , snake_case=[3, 5, 7, 11] , snake_case=[1, 2, 3, 6] , snake_case=True , snake_case=0.4 , snake_case=256 , snake_case=1 , snake_case=False , snake_case=255 , **snake_case , ) -> str:
super().__init__(**snake_case )
_UpperCAmelCase = vocab_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_act
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = image_size
_UpperCAmelCase = patch_size
_UpperCAmelCase = num_channels
_UpperCAmelCase = use_mask_token
_UpperCAmelCase = use_absolute_position_embeddings
_UpperCAmelCase = use_relative_position_bias
_UpperCAmelCase = use_shared_relative_position_bias
_UpperCAmelCase = layer_scale_init_value
_UpperCAmelCase = drop_path_rate
_UpperCAmelCase = use_mean_pooling
# decode head attributes (semantic segmentation)
_UpperCAmelCase = out_indices
_UpperCAmelCase = pool_scales
# auxiliary head attributes (semantic segmentation)
_UpperCAmelCase = use_auxiliary_head
_UpperCAmelCase = auxiliary_loss_weight
_UpperCAmelCase = auxiliary_channels
_UpperCAmelCase = auxiliary_num_convs
_UpperCAmelCase = auxiliary_concat_input
_UpperCAmelCase = semantic_loss_ignore_index
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = version.parse('''1.11''' )
@property
def lowerCamelCase_ ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def lowerCamelCase_ ( self ) -> float:
return 1E-4
| 24
|
"""simple docstring"""
def UpperCAmelCase ( A : int ):
'''simple docstring'''
_UpperCAmelCase = abs(A )
_UpperCAmelCase = 0
while n > 0:
res += n % 10
n //= 10
return res
def UpperCAmelCase ( A : int ):
'''simple docstring'''
_UpperCAmelCase = abs(A )
return n if n < 10 else n % 10 + sum_of_digits(n // 10 )
def UpperCAmelCase ( A : int ):
'''simple docstring'''
return sum(int(A ) for c in str(abs(A ) ) )
def UpperCAmelCase ( ):
'''simple docstring'''
from collections.abc import Callable
from timeit import timeit
def benchmark_a_function(A : Callable , A : int ) -> None:
_UpperCAmelCase = f'{func.__name__}({value})'
_UpperCAmelCase = timeit(f'__main__.{call}' , setup='import __main__' )
print(f'{call:56} = {func(A )} -- {timing:.4f} seconds' )
for value in (26_2144, 1125_8999_0684_2624, 126_7650_6002_2822_9401_4967_0320_5376):
for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
benchmark_a_function(A , A )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 24
| 1
|
"""simple docstring"""
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
from ..test_modeling_tf_common import floats_tensor
from .test_framework_agnostic import GenerationIntegrationTestsMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
AutoTokenizer,
TFAutoModelForCausalLM,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSpeechSeqaSeq,
TFAutoModelForVisionaSeq,
TFBartForConditionalGeneration,
TFLogitsProcessorList,
TFMinLengthLogitsProcessor,
tf_top_k_top_p_filtering,
)
if is_tensorflow_text_available():
import tensorflow_text as text
@require_tf
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = tf.convert_to_tensor(
[
[
8.2220991, # 3rd highest value; idx. 0
-0.5620044,
5.23229752,
4.0386393,
-6.8798378,
-0.54785802,
-3.2012153,
2.92777176,
1.88171953,
7.35341276, # 5th highest value; idx. 9
8.43207833, # 2nd highest value; idx. 10
-9.85711836,
-5.96209236,
-1.13039161,
-7.1115294,
-0.8369633,
-5.3186408,
7.06427407,
0.81369344,
-0.82023817,
-5.9179796,
0.58813443,
-6.99778438,
4.71551189,
-0.18771637,
7.44020759, # 4th highest value; idx. 25
9.38450987, # 1st highest value; idx. 26
2.12662941,
-9.32562038,
2.35652522,
], # cummulative prob of 5 highest values <= 0.6
[
0.58425518,
4.53139238,
-5.57510464,
-6.28030699,
-7.19529503,
-4.02122551,
1.39337037,
-6.06707057,
1.59480517,
-9.643119,
0.03907799,
0.67231762,
-8.88206726,
6.27115922, # 4th highest value; idx. 13
2.28520723,
4.82767506,
4.30421368,
8.8275313, # 2nd highest value; idx. 17
5.44029958, # 5th highest value; idx. 18
-4.4735794,
7.38579536, # 3rd highest value; idx. 20
-2.91051663,
2.61946077,
-2.5674762,
-9.48959302,
-4.02922645,
-1.35416918,
9.67702323, # 1st highest value; idx. 27
-5.89478553,
1.85370467,
], # cummulative prob of 5 highest values <= 0.6
] , dtype=tf.floataa , )
_UpperCAmelCase = tf.convert_to_tensor(
[[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]] , dtype=tf.intaa , ) # expected non filtered idx as noted above
_UpperCAmelCase = tf.convert_to_tensor(
[8.222099, 7.3534126, 8.432078, 7.4402075, 9.38451, 6.271159, 8.827531, 5.4402995, 7.3857956, 9.677023] , dtype=tf.floataa , ) # expected non filtered values as noted above
_UpperCAmelCase = tf_top_k_top_p_filtering(snake_case , top_k=10 , top_p=0.6 , min_tokens_to_keep=4 )
_UpperCAmelCase = output[output != -float('inf' )]
_UpperCAmelCase = tf.cast(
tf.where(tf.not_equal(snake_case , tf.constant(-float('inf' ) , dtype=tf.floataa ) ) ) , dtype=tf.intaa , )
tf.debugging.assert_near(snake_case , snake_case , rtol=1E-12 )
tf.debugging.assert_equal(snake_case , snake_case )
@require_tf
class lowercase__ ( unittest.TestCase, A ):
'''simple docstring'''
if is_tf_available():
_UpperCAmelCase = {
'''AutoModelForCausalLM''': TFAutoModelForCausalLM,
'''AutoModelForSpeechSeq2Seq''': TFAutoModelForSpeechSeqaSeq,
'''AutoModelForSeq2SeqLM''': TFAutoModelForSeqaSeqLM,
'''AutoModelForVision2Seq''': TFAutoModelForVisionaSeq,
'''LogitsProcessorList''': TFLogitsProcessorList,
'''MinLengthLogitsProcessor''': TFMinLengthLogitsProcessor,
'''create_tensor_fn''': tf.convert_to_tensor,
'''floats_tensor''': floats_tensor,
'''return_tensors''': '''tf''',
}
@slow
def lowerCamelCase_ ( self ) -> List[str]:
# TF-only test: tf.saved_model export
_UpperCAmelCase = TFAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2' )
_UpperCAmelCase = 2
_UpperCAmelCase = 2
class lowercase__ ( tf.Module ):
'''simple docstring'''
def __init__( self , snake_case ) -> Optional[Any]:
super(snake_case , self ).__init__()
_UpperCAmelCase = model
@tf.function(
input_signature=(
tf.TensorSpec((None, input_length) , tf.intaa , name='input_ids' ),
tf.TensorSpec((None, input_length) , tf.intaa , name='attention_mask' ),
) , jit_compile=snake_case , )
def lowerCamelCase_ ( self , snake_case , snake_case ) -> Optional[Any]:
_UpperCAmelCase = self.model.generate(
input_ids=snake_case , attention_mask=snake_case , max_new_tokens=snake_case , return_dict_in_generate=snake_case , )
return {"sequences": outputs["sequences"]}
_UpperCAmelCase = [[2, 0], [102, 103]]
_UpperCAmelCase = [[1, 0], [1, 1]]
_UpperCAmelCase = DummyModel(model=snake_case )
with tempfile.TemporaryDirectory() as tmp_dir:
tf.saved_model.save(snake_case , snake_case , signatures={'serving_default': dummy_model.serving} )
_UpperCAmelCase = tf.saved_model.load(snake_case ).signatures['serving_default']
for batch_size in range(1 , len(snake_case ) + 1 ):
_UpperCAmelCase = {
'input_ids': tf.constant(dummy_input_ids[:batch_size] ),
'attention_mask': tf.constant(dummy_attention_masks[:batch_size] ),
}
_UpperCAmelCase = serving_func(**snake_case )['sequences']
_UpperCAmelCase = test_model.generate(**snake_case , max_new_tokens=snake_case )
tf.debugging.assert_equal(snake_case , snake_case )
@slow
def lowerCamelCase_ ( self ) -> int:
# TF-only test: tf.saved_model export
_UpperCAmelCase = TFAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2' )
_UpperCAmelCase = 1
_UpperCAmelCase = 2
class lowercase__ ( tf.Module ):
'''simple docstring'''
def __init__( self , snake_case ) -> str:
super(snake_case , self ).__init__()
_UpperCAmelCase = model
@tf.function(
input_signature=(
tf.TensorSpec((batch_size, None) , tf.intaa , name='input_ids' ),
tf.TensorSpec((batch_size, None) , tf.intaa , name='attention_mask' ),
) , jit_compile=snake_case , )
def lowerCamelCase_ ( self , snake_case , snake_case ) -> int:
_UpperCAmelCase = self.model.generate(
input_ids=snake_case , attention_mask=snake_case , max_new_tokens=snake_case , return_dict_in_generate=snake_case , )
return {"sequences": outputs["sequences"]}
_UpperCAmelCase = [[2], [102, 103]]
_UpperCAmelCase = [[1], [1, 1]]
_UpperCAmelCase = DummyModel(model=snake_case )
with tempfile.TemporaryDirectory() as tmp_dir:
tf.saved_model.save(snake_case , snake_case , signatures={'serving_default': dummy_model.serving} )
_UpperCAmelCase = tf.saved_model.load(snake_case ).signatures['serving_default']
for input_row in range(len(snake_case ) ):
_UpperCAmelCase = {
'input_ids': tf.constant([dummy_input_ids[input_row]] ),
'attention_mask': tf.constant([dummy_attention_masks[input_row]] ),
}
_UpperCAmelCase = serving_func(**snake_case )['sequences']
_UpperCAmelCase = test_model.generate(**snake_case , max_new_tokens=snake_case )
tf.debugging.assert_equal(snake_case , snake_case )
@slow
@require_tensorflow_text
def lowerCamelCase_ ( self ) -> Any:
# TF-only test: tf.saved_model export
with tempfile.TemporaryDirectory() as tmp_dir:
# file needed to load the TF tokenizer
hf_hub_download(repo_id='google/flan-t5-small' , filename='spiece.model' , local_dir=snake_case )
class lowercase__ ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self ) -> Dict:
super().__init__()
_UpperCAmelCase = text.SentencepieceTokenizer(
model=tf.io.gfile.GFile(os.path.join(snake_case , 'spiece.model' ) , 'rb' ).read() )
_UpperCAmelCase = TFAutoModelForSeqaSeqLM.from_pretrained('hf-internal-testing/tiny-random-t5' )
def lowerCamelCase_ ( self , snake_case , *snake_case , **snake_case ) -> str:
_UpperCAmelCase = self.tokenizer.tokenize(snake_case )
_UpperCAmelCase , _UpperCAmelCase = text.pad_model_inputs(
snake_case , max_seq_length=64 , pad_value=self.model.config.pad_token_id )
_UpperCAmelCase = self.model.generate(input_ids=snake_case , attention_mask=snake_case )
return self.tokenizer.detokenize(snake_case )
_UpperCAmelCase = CompleteSentenceTransformer()
_UpperCAmelCase = tf.keras.layers.Input(shape=(1,) , dtype=tf.string , name='inputs' )
_UpperCAmelCase = complete_model(snake_case )
_UpperCAmelCase = tf.keras.Model(snake_case , snake_case )
keras_model.save(snake_case )
def lowerCamelCase_ ( self ) -> List[Any]:
# Has PT equivalent: this test relies on random sampling
_UpperCAmelCase = {
'do_sample': True,
'num_beams': 1,
'top_p': 0.7,
'top_k': 10,
'temperature': 0.7,
}
_UpperCAmelCase = 14
_UpperCAmelCase = AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2' )
_UpperCAmelCase = 'Hello, my dog is cute and'
_UpperCAmelCase = tokenizer(snake_case , return_tensors='tf' )
_UpperCAmelCase = TFAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2' )
_UpperCAmelCase = 638
# forces the generation to happen on CPU, to avoid GPU-related quirks
with tf.device(':/CPU:0' ):
tf.random.set_seed(0 )
_UpperCAmelCase = model.generate(**snake_case , eos_token_id=snake_case , **snake_case )
self.assertTrue(expectation == len(generated_tokens[0] ) )
_UpperCAmelCase = [638, 198]
with tf.device(':/CPU:0' ):
tf.random.set_seed(0 )
_UpperCAmelCase = model.generate(**snake_case , eos_token_id=snake_case , **snake_case )
self.assertTrue(expectation == len(generated_tokens[0] ) )
def lowerCamelCase_ ( self ) -> Dict:
# Has PT equivalent: ample use of framework-specific code
_UpperCAmelCase = AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-bart' )
_UpperCAmelCase = 'Hugging Face is a technology company based in New York and Paris.'
_UpperCAmelCase = bart_tokenizer(snake_case , return_tensors='tf' ).input_ids
_UpperCAmelCase = TFBartForConditionalGeneration.from_pretrained('hf-internal-testing/tiny-random-bart' )
_UpperCAmelCase = bart_model.generate(snake_case ).numpy()
class lowercase__ ( A ):
'''simple docstring'''
def lowerCamelCase_ ( self , snake_case , snake_case=None , **snake_case ) -> str:
return super().call(snake_case , **snake_case )
_UpperCAmelCase = FakeBart.from_pretrained('hf-internal-testing/tiny-random-bart' )
_UpperCAmelCase = bart_model.generate(snake_case , foo='bar' ).numpy()
self.assertTrue(np.array_equal(snake_case , snake_case ) )
class lowercase__ ( bart_model.model.encoder.__class__ ):
'''simple docstring'''
def lowerCamelCase_ ( self , snake_case , **snake_case ) -> int:
return super().call(snake_case , **snake_case )
_UpperCAmelCase = FakeEncoder(bart_model.config , bart_model.model.shared )
_UpperCAmelCase = fake_encoder
# Normal generation still works (the output will be different because the encoder weights are different)
_UpperCAmelCase = bart_model.generate(snake_case ).numpy()
with self.assertRaises(snake_case ):
# FakeEncoder.call() accepts **kwargs -> no filtering -> value error due to unexpected input "foo"
bart_model.generate(snake_case , foo='bar' )
| 24
|
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase ( A : int , A : int ):
'''simple docstring'''
_UpperCAmelCase = []
create_all_state(1 , A , A , [] , A )
return result
def UpperCAmelCase ( A : int , A : int , A : int , A : list[int] , A : list[list[int]] , ):
'''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 : list[list[int]] ):
'''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)
| 24
| 1
|
"""simple docstring"""
import argparse
from collections import defaultdict
import yaml
lowercase = '''docs/source/en/_toctree.yml'''
def UpperCAmelCase ( A : Optional[int] ):
'''simple docstring'''
_UpperCAmelCase = defaultdict(A )
_UpperCAmelCase = []
_UpperCAmelCase = []
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 )
_UpperCAmelCase = new_doc_list
_UpperCAmelCase = [key for key, value in counts.items() if value > 1]
_UpperCAmelCase = []
for duplicate_key in duplicates:
_UpperCAmelCase = 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] )
_UpperCAmelCase = 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 : int=False ):
'''simple docstring'''
with open(A , encoding='utf-8' ) as f:
_UpperCAmelCase = yaml.safe_load(f.read() )
# Get to the API doc
_UpperCAmelCase = 0
while content[api_idx]["title"] != "API":
api_idx += 1
_UpperCAmelCase = content[api_idx]['sections']
# Then to the model doc
_UpperCAmelCase = 0
while api_doc[scheduler_idx]["title"] != "Schedulers":
scheduler_idx += 1
_UpperCAmelCase = api_doc[scheduler_idx]['sections']
_UpperCAmelCase = clean_doc_toc(A )
_UpperCAmelCase = False
if new_scheduler_doc != scheduler_doc:
_UpperCAmelCase = True
if overwrite:
_UpperCAmelCase = new_scheduler_doc
if diff:
if overwrite:
_UpperCAmelCase = 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 : str=False ):
'''simple docstring'''
with open(A , encoding='utf-8' ) as f:
_UpperCAmelCase = yaml.safe_load(f.read() )
# Get to the API doc
_UpperCAmelCase = 0
while content[api_idx]["title"] != "API":
api_idx += 1
_UpperCAmelCase = content[api_idx]['sections']
# Then to the model doc
_UpperCAmelCase = 0
while api_doc[pipeline_idx]["title"] != "Pipelines":
pipeline_idx += 1
_UpperCAmelCase = False
_UpperCAmelCase = api_doc[pipeline_idx]['sections']
_UpperCAmelCase = []
# sort sub pipeline docs
for pipeline_doc in pipeline_docs:
if "section" in pipeline_doc:
_UpperCAmelCase = pipeline_doc['section']
_UpperCAmelCase = clean_doc_toc(A )
if overwrite:
_UpperCAmelCase = new_sub_pipeline_doc
new_pipeline_docs.append(A )
# sort overall pipeline doc
_UpperCAmelCase = clean_doc_toc(A )
if new_pipeline_docs != pipeline_docs:
_UpperCAmelCase = True
if overwrite:
_UpperCAmelCase = new_pipeline_docs
if diff:
if overwrite:
_UpperCAmelCase = 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)
| 24
|
"""simple docstring"""
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS
logging.basicConfig(level=logging.DEBUG)
lowercase = logging.getLogger()
def UpperCAmelCase ( A : Path , A : list ):
'''simple docstring'''
_UpperCAmelCase = '\n'.join(A )
Path(A ).open('w' ).writelines(A )
lowercase = '''patrickvonplaten/t5-tiny-random'''
lowercase = '''sshleifer/bart-tiny-random'''
lowercase = '''sshleifer/tiny-mbart'''
lowercase = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL) # remove noisy download output from tracebacks
class lowercase__ ( A ):
'''simple docstring'''
def lowerCamelCase_ ( self , snake_case ) -> str:
_UpperCAmelCase = Path(self.get_auto_remove_tmp_dir() ) / 'utest_input.source'
_UpperCAmelCase = input_file_name.parent / 'utest_output.txt'
assert not output_file_name.exists()
_UpperCAmelCase = [' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
_dump_articles(snake_case , snake_case )
_UpperCAmelCase = str(Path(self.get_auto_remove_tmp_dir() ) / 'scores.json' )
_UpperCAmelCase = 'translation_en_to_de' if model == T5_TINY else 'summarization'
_UpperCAmelCase = f'\n run_eval_search.py\n {model}\n {input_file_name}\n {output_file_name}\n --score_path {score_path}\n --task {task}\n --num_beams 2\n --length_penalty 2.0\n '.split()
with patch.object(snake_case , 'argv' , snake_case ):
run_generate()
assert Path(snake_case ).exists()
# os.remove(Path(output_file_name))
def lowerCamelCase_ ( self ) -> str:
self.run_eval_tester(snake_case )
@parameterized.expand([BART_TINY, MBART_TINY] )
@slow
def lowerCamelCase_ ( self , snake_case ) -> List[Any]:
self.run_eval_tester(snake_case )
@parameterized.expand([T5_TINY, MBART_TINY] )
@slow
def lowerCamelCase_ ( self , snake_case ) -> Dict:
_UpperCAmelCase = Path(self.get_auto_remove_tmp_dir() ) / 'utest_input.source'
_UpperCAmelCase = input_file_name.parent / 'utest_output.txt'
assert not output_file_name.exists()
_UpperCAmelCase = {
'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
'de': [
'Maschinelles Lernen ist großartig, oder?',
'Ich esse gerne Bananen',
'Morgen ist wieder ein toller Tag!',
],
}
_UpperCAmelCase = Path(self.get_auto_remove_tmp_dir() )
_UpperCAmelCase = str(tmp_dir / 'scores.json' )
_UpperCAmelCase = str(tmp_dir / 'val.target' )
_dump_articles(snake_case , text['en'] )
_dump_articles(snake_case , text['de'] )
_UpperCAmelCase = 'translation_en_to_de' if model == T5_TINY else 'summarization'
_UpperCAmelCase = f'\n run_eval_search.py\n {model}\n {str(snake_case )}\n {str(snake_case )}\n --score_path {score_path}\n --reference_path {reference_path}\n --task {task}\n '.split()
testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0'] )
with patch.object(snake_case , 'argv' , snake_case ):
with CaptureStdout() as cs:
run_search()
_UpperCAmelCase = [' num_beams | length_penalty', model, 'Best score args']
_UpperCAmelCase = ['Info']
if "translation" in task:
expected_strings.append('bleu' )
else:
expected_strings.extend(snake_case )
for w in expected_strings:
assert w in cs.out
for w in un_expected_strings:
assert w not in cs.out
assert Path(snake_case ).exists()
os.remove(Path(snake_case ) )
| 24
| 1
|
"""simple docstring"""
def UpperCAmelCase ( A : int , A : int ):
'''simple docstring'''
while b:
_UpperCAmelCase , _UpperCAmelCase = b, a % b
return a
def UpperCAmelCase ( A : int , A : int ):
'''simple docstring'''
return a if b == 0 else euclidean_gcd_recursive(A , a % b )
def UpperCAmelCase ( ):
'''simple docstring'''
print(f'euclidean_gcd(3, 5) = {euclidean_gcd(3 , 5 )}' )
print(f'euclidean_gcd(5, 3) = {euclidean_gcd(5 , 3 )}' )
print(f'euclidean_gcd(1, 3) = {euclidean_gcd(1 , 3 )}' )
print(f'euclidean_gcd(3, 6) = {euclidean_gcd(3 , 6 )}' )
print(f'euclidean_gcd(6, 3) = {euclidean_gcd(6 , 3 )}' )
print(f'euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3 , 5 )}' )
print(f'euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5 , 3 )}' )
print(f'euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1 , 3 )}' )
print(f'euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3 , 6 )}' )
print(f'euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6 , 3 )}' )
if __name__ == "__main__":
main()
| 24
|
"""simple docstring"""
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal
lowercase = logging.get_logger(__name__)
lowercase = TypeVar('''DatasetType''', Dataset, IterableDataset)
def UpperCAmelCase ( A : List[DatasetType] , A : Optional[List[float]] = None , A : Optional[int] = None , A : Optional[DatasetInfo] = None , A : Optional[NamedSplit] = None , A : Literal["first_exhausted", "all_exhausted"] = "first_exhausted" , ):
'''simple docstring'''
from .arrow_dataset import Dataset
from .iterable_dataset import IterableDataset
if not datasets:
raise ValueError('Unable to interleave an empty list of datasets.' )
for i, dataset in enumerate(A ):
if not isinstance(A , (Dataset, IterableDataset) ):
if isinstance(A , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
'is an empty dataset dictionary.' )
raise ValueError(
f'Dataset at position {i} has at least one split: {list(A )}\n'
f'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(A ) )}\']' )
raise ValueError(
f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(A ).__name__}.' )
if i == 0:
_UpperCAmelCase , _UpperCAmelCase = (
(Dataset, IterableDataset) if isinstance(A , A ) else (IterableDataset, Dataset)
)
elif not isinstance(A , A ):
raise ValueError(
f'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.' )
if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
raise ValueError(f'{stopping_strategy} is not supported. Please enter a valid stopping_strategy.' )
if dataset_type is Dataset:
return _interleave_map_style_datasets(
A , A , A , info=A , split=A , stopping_strategy=A )
else:
return _interleave_iterable_datasets(
A , A , A , info=A , split=A , stopping_strategy=A )
def UpperCAmelCase ( A : List[DatasetType] , A : Optional[DatasetInfo] = None , A : Optional[NamedSplit] = None , A : int = 0 , ):
'''simple docstring'''
if not dsets:
raise ValueError('Unable to concatenate an empty list of datasets.' )
for i, dataset in enumerate(A ):
if not isinstance(A , (Dataset, IterableDataset) ):
if isinstance(A , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
'is an empty dataset dictionary.' )
raise ValueError(
f'Dataset at position {i} has at least one split: {list(A )}\n'
f'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(A ) )}\']' )
raise ValueError(
f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(A ).__name__}.' )
if i == 0:
_UpperCAmelCase , _UpperCAmelCase = (
(Dataset, IterableDataset) if isinstance(A , A ) else (IterableDataset, Dataset)
)
elif not isinstance(A , A ):
raise ValueError(
f'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.' )
if dataset_type is Dataset:
return _concatenate_map_style_datasets(A , info=A , split=A , axis=A )
else:
return _concatenate_iterable_datasets(A , info=A , split=A , axis=A )
| 24
| 1
|
"""simple docstring"""
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
lowercase = '''\
'''
lowercase = '''
Perplexity (PPL) is one of the most common metrics for evaluating language models.
It is defined as the exponentiated average negative log-likelihood of a sequence.
For more information, see https://huggingface.co/docs/transformers/perplexity
'''
lowercase = '''
Args:
model_id (str): model used for calculating Perplexity
NOTE: Perplexity can only be calculated for causal language models.
This includes models such as gpt2, causal variations of bert,
causal versions of t5, and more (the full list can be found
in the AutoModelForCausalLM documentation here:
https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )
input_texts (list of str): input text, each separate text snippet
is one list entry.
batch_size (int): the batch size to run texts through the model. Defaults to 16.
add_start_token (bool): whether to add the start token to the texts,
so the perplexity can include the probability of the first word. Defaults to True.
device (str): device to run on, defaults to \'cuda\' when available
Returns:
perplexity: dictionary containing the perplexity scores for the texts
in the input list, as well as the mean perplexity. If one of the input texts is
longer than the max input length of the model, then it is truncated to the
max length for the perplexity computation.
Examples:
Example 1:
>>> perplexity = datasets.load_metric("perplexity")
>>> input_texts = ["lorem ipsum", "Happy Birthday!", "Bienvenue"]
>>> results = perplexity.compute(model_id=\'gpt2\',
... add_start_token=False,
... input_texts=input_texts) # doctest:+ELLIPSIS
>>> print(list(results.keys()))
[\'perplexities\', \'mean_perplexity\']
>>> print(round(results["mean_perplexity"], 2))
78.22
>>> print(round(results["perplexities"][0], 2))
11.11
Example 2:
>>> perplexity = datasets.load_metric("perplexity")
>>> input_texts = datasets.load_dataset("wikitext",
... "wikitext-2-raw-v1",
... split="test")["text"][:50] # doctest:+ELLIPSIS
[...]
>>> input_texts = [s for s in input_texts if s!=\'\']
>>> results = perplexity.compute(model_id=\'gpt2\',
... input_texts=input_texts) # doctest:+ELLIPSIS
>>> print(list(results.keys()))
[\'perplexities\', \'mean_perplexity\']
>>> print(round(results["mean_perplexity"], 2))
60.35
>>> print(round(results["perplexities"][0], 2))
81.12
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION )
class lowercase__ ( datasets.Metric ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> Union[str, Any]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'input_texts': datasets.Value('string' ),
} ) , reference_urls=['https://huggingface.co/docs/transformers/perplexity'] , )
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case = 16 , snake_case = True , snake_case=None ) -> Optional[int]:
if device is not None:
assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
if device == "gpu":
_UpperCAmelCase = 'cuda'
else:
_UpperCAmelCase = 'cuda' if torch.cuda.is_available() else 'cpu'
_UpperCAmelCase = AutoModelForCausalLM.from_pretrained(snake_case )
_UpperCAmelCase = model.to(snake_case )
_UpperCAmelCase = AutoTokenizer.from_pretrained(snake_case )
# if batch_size > 1 (which generally leads to padding being required), and
# if there is not an already assigned pad_token, assign an existing
# special token to also be the padding token
if tokenizer.pad_token is None and batch_size > 1:
_UpperCAmelCase = list(tokenizer.special_tokens_map_extended.values() )
# check that the model already has at least one special token defined
assert (
len(snake_case ) > 0
), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
# assign one of the special tokens to also be the pad token
tokenizer.add_special_tokens({'pad_token': existing_special_tokens[0]} )
if add_start_token:
# leave room for <BOS> token to be added:
assert (
tokenizer.bos_token is not None
), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
_UpperCAmelCase = model.config.max_length - 1
else:
_UpperCAmelCase = model.config.max_length
_UpperCAmelCase = tokenizer(
snake_case , add_special_tokens=snake_case , padding=snake_case , truncation=snake_case , max_length=snake_case , return_tensors='pt' , return_attention_mask=snake_case , ).to(snake_case )
_UpperCAmelCase = encodings['input_ids']
_UpperCAmelCase = encodings['attention_mask']
# check that each input is long enough:
if add_start_token:
assert torch.all(torch.ge(attn_masks.sum(1 ) , 1 ) ), "Each input text must be at least one token long."
else:
assert torch.all(
torch.ge(attn_masks.sum(1 ) , 2 ) ), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."
_UpperCAmelCase = []
_UpperCAmelCase = CrossEntropyLoss(reduction='none' )
for start_index in logging.tqdm(range(0 , len(snake_case ) , snake_case ) ):
_UpperCAmelCase = min(start_index + batch_size , len(snake_case ) )
_UpperCAmelCase = encoded_texts[start_index:end_index]
_UpperCAmelCase = attn_masks[start_index:end_index]
if add_start_token:
_UpperCAmelCase = torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0 ) ).to(snake_case )
_UpperCAmelCase = torch.cat([bos_tokens_tensor, encoded_batch] , dim=1 )
_UpperCAmelCase = torch.cat(
[torch.ones(bos_tokens_tensor.size() , dtype=torch.intaa ).to(snake_case ), attn_mask] , dim=1 )
_UpperCAmelCase = encoded_batch
with torch.no_grad():
_UpperCAmelCase = model(snake_case , attention_mask=snake_case ).logits
_UpperCAmelCase = out_logits[..., :-1, :].contiguous()
_UpperCAmelCase = labels[..., 1:].contiguous()
_UpperCAmelCase = attn_mask[..., 1:].contiguous()
_UpperCAmelCase = torch.expa(
(loss_fct(shift_logits.transpose(1 , 2 ) , snake_case ) * shift_attention_mask_batch).sum(1 )
/ shift_attention_mask_batch.sum(1 ) )
ppls += perplexity_batch.tolist()
return {"perplexities": ppls, "mean_perplexity": np.mean(snake_case )}
| 24
|
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available
from .test_pipelines_common import ANY
if is_torch_available():
import torch
@is_pipeline_test
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
_UpperCAmelCase = TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case ) -> Dict:
_UpperCAmelCase = TextaTextGenerationPipeline(model=snake_case , tokenizer=snake_case )
return generator, ["Something to write", "Something else"]
def lowerCamelCase_ ( self , snake_case , snake_case ) -> Dict:
_UpperCAmelCase = generator('Something there' )
self.assertEqual(snake_case , [{'generated_text': ANY(snake_case )}] )
# These are encoder decoder, they don't just append to incoming string
self.assertFalse(outputs[0]['generated_text'].startswith('Something there' ) )
_UpperCAmelCase = generator(['This is great !', 'Something else'] , num_return_sequences=2 , do_sample=snake_case )
self.assertEqual(
snake_case , [
[{'generated_text': ANY(snake_case )}, {'generated_text': ANY(snake_case )}],
[{'generated_text': ANY(snake_case )}, {'generated_text': ANY(snake_case )}],
] , )
_UpperCAmelCase = generator(
['This is great !', 'Something else'] , num_return_sequences=2 , batch_size=2 , do_sample=snake_case )
self.assertEqual(
snake_case , [
[{'generated_text': ANY(snake_case )}, {'generated_text': ANY(snake_case )}],
[{'generated_text': ANY(snake_case )}, {'generated_text': ANY(snake_case )}],
] , )
with self.assertRaises(snake_case ):
generator(4 )
@require_torch
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = pipeline('text2text-generation' , model='patrickvonplaten/t5-tiny-random' , framework='pt' )
# do_sample=False necessary for reproducibility
_UpperCAmelCase = generator('Something there' , do_sample=snake_case )
self.assertEqual(snake_case , [{'generated_text': ''}] )
_UpperCAmelCase = 3
_UpperCAmelCase = generator(
'Something there' , num_return_sequences=snake_case , num_beams=snake_case , )
_UpperCAmelCase = [
{'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide Beide'},
{'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide'},
{'generated_text': ''},
]
self.assertEqual(snake_case , snake_case )
_UpperCAmelCase = generator('This is a test' , do_sample=snake_case , num_return_sequences=2 , return_tensors=snake_case )
self.assertEqual(
snake_case , [
{'generated_token_ids': ANY(torch.Tensor )},
{'generated_token_ids': ANY(torch.Tensor )},
] , )
_UpperCAmelCase = generator.model.config.eos_token_id
_UpperCAmelCase = '<pad>'
_UpperCAmelCase = generator(
['This is a test', 'This is a second test'] , do_sample=snake_case , num_return_sequences=2 , batch_size=2 , return_tensors=snake_case , )
self.assertEqual(
snake_case , [
[
{'generated_token_ids': ANY(torch.Tensor )},
{'generated_token_ids': ANY(torch.Tensor )},
],
[
{'generated_token_ids': ANY(torch.Tensor )},
{'generated_token_ids': ANY(torch.Tensor )},
],
] , )
@require_tf
def lowerCamelCase_ ( self ) -> Any:
_UpperCAmelCase = pipeline('text2text-generation' , model='patrickvonplaten/t5-tiny-random' , framework='tf' )
# do_sample=False necessary for reproducibility
_UpperCAmelCase = generator('Something there' , do_sample=snake_case )
self.assertEqual(snake_case , [{'generated_text': ''}] )
| 24
| 1
|
"""simple docstring"""
from sklearn.metrics import fa_score
import datasets
lowercase = '''
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
'''
lowercase = '''
Args:
predictions (`list` of `int`): Predicted labels.
references (`list` of `int`): Ground truth labels.
labels (`list` of `int`): The set of labels to include when `average` is not set to `\'binary\'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.
pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.
average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `\'binary\'`.
- \'binary\': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.
- \'micro\': Calculate metrics globally by counting the total true positives, false negatives and false positives.
- \'macro\': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
- \'weighted\': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `\'macro\'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.
- \'samples\': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
sample_weight (`list` of `float`): Sample weights Defaults to None.
Returns:
f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.
Examples:
Example 1-A simple binary example
>>> f1_metric = datasets.load_metric("f1")
>>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])
>>> print(results)
{\'f1\': 0.5}
Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.
>>> f1_metric = datasets.load_metric("f1")
>>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)
>>> print(round(results[\'f1\'], 2))
0.67
Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.
>>> f1_metric = datasets.load_metric("f1")
>>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])
>>> print(round(results[\'f1\'], 2))
0.35
Example 4-A multiclass example, with different values for the `average` input.
>>> predictions = [0, 2, 1, 0, 0, 1]
>>> references = [0, 1, 2, 0, 1, 2]
>>> results = f1_metric.compute(predictions=predictions, references=references, average="macro")
>>> print(round(results[\'f1\'], 2))
0.27
>>> results = f1_metric.compute(predictions=predictions, references=references, average="micro")
>>> print(round(results[\'f1\'], 2))
0.33
>>> results = f1_metric.compute(predictions=predictions, references=references, average="weighted")
>>> print(round(results[\'f1\'], 2))
0.27
>>> results = f1_metric.compute(predictions=predictions, references=references, average=None)
>>> print(results)
{\'f1\': array([0.8, 0. , 0. ])}
'''
lowercase = '''
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION )
class lowercase__ ( datasets.Metric ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> str:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Sequence(datasets.Value('int32' ) ),
'references': datasets.Sequence(datasets.Value('int32' ) ),
}
if self.config_name == 'multilabel'
else {
'predictions': datasets.Value('int32' ),
'references': datasets.Value('int32' ),
} ) , reference_urls=['https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html'] , )
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case=None , snake_case=1 , snake_case="binary" , snake_case=None ) -> Optional[Any]:
_UpperCAmelCase = fa_score(
snake_case , snake_case , labels=snake_case , pos_label=snake_case , average=snake_case , sample_weight=snake_case )
return {"f1": float(snake_case ) if score.size == 1 else score}
| 24
|
"""simple docstring"""
def UpperCAmelCase ( A : int ):
'''simple docstring'''
_UpperCAmelCase = [[0 for _ in range(A )] for _ in range(m + 1 )]
for i in range(m + 1 ):
_UpperCAmelCase = 1
for n in range(m + 1 ):
for k in range(1 , A ):
memo[n][k] += memo[n][k - 1]
if n - k > 0:
memo[n][k] += memo[n - k - 1][k]
return memo[m][m - 1]
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
try:
lowercase = int(input('''Enter a number: ''').strip())
print(partition(n))
except ValueError:
print('''Please enter a number.''')
else:
try:
lowercase = int(sys.argv[1])
print(partition(n))
except ValueError:
print('''Please pass a number.''')
| 24
| 1
|
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def UpperCAmelCase ( A : str , A : dict ):
'''simple docstring'''
_UpperCAmelCase = BeautifulSoup(requests.get(A , params=A ).content , 'html.parser' )
_UpperCAmelCase = soup.find('div' , attrs={'class': 'gs_ri'} )
_UpperCAmelCase = div.find('div' , attrs={'class': 'gs_fl'} ).find_all('a' )
return anchors[2].get_text()
if __name__ == "__main__":
lowercase = {
'''title''': (
'''Precisely geometry controlled microsupercapacitors for ultrahigh areal '''
'''capacitance, volumetric capacitance, and energy density'''
),
'''journal''': '''Chem. Mater.''',
'''volume''': 30,
'''pages''': '''3979-3990''',
'''year''': 20_18,
'''hl''': '''en''',
}
print(get_citation('''https://scholar.google.com/scholar_lookup''', params=params))
| 24
|
"""simple docstring"""
import os
lowercase = {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 1_00, '''D''': 5_00, '''M''': 10_00}
def UpperCAmelCase ( A : str ):
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase = 0
while index < len(A ) - 1:
_UpperCAmelCase = SYMBOLS[numerals[index]]
_UpperCAmelCase = 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 : int ):
'''simple docstring'''
_UpperCAmelCase = ''
_UpperCAmelCase = num // 1000
numerals += m_count * "M"
num %= 1000
_UpperCAmelCase = num // 100
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 %= 100
_UpperCAmelCase = num // 10
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 %= 10
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 : str = "/p089_roman.txt" ):
'''simple docstring'''
_UpperCAmelCase = 0
with open(os.path.dirname(A ) + roman_numerals_filename ) as filea:
_UpperCAmelCase = filea.readlines()
for line in lines:
_UpperCAmelCase = line.strip()
_UpperCAmelCase = parse_roman_numerals(A )
_UpperCAmelCase = generate_roman_numerals(A )
savings += len(A ) - len(A )
return savings
if __name__ == "__main__":
print(F'''{solution() = }''')
| 24
| 1
|
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
lowercase = transforms.Compose(
[
transforms.Resize((2_56, 2_56)),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
def UpperCAmelCase ( A : Optional[Any] ):
'''simple docstring'''
if isinstance(A , torch.Tensor ):
return image
elif isinstance(A , PIL.Image.Image ):
_UpperCAmelCase = [image]
_UpperCAmelCase = [trans(img.convert('RGB' ) ) for img in image]
_UpperCAmelCase = torch.stack(A )
return image
class lowercase__ ( A ):
'''simple docstring'''
def __init__( self , snake_case , snake_case ) -> List[Any]:
super().__init__()
# make sure scheduler can always be converted to DDIM
_UpperCAmelCase = DDIMScheduler.from_config(scheduler.config )
self.register_modules(unet=snake_case , scheduler=snake_case )
def lowerCamelCase_ ( self , snake_case ) -> Optional[Any]:
if strength < 0 or strength > 1:
raise ValueError(f'The value of strength should in [0.0, 1.0] but is {strength}' )
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case ) -> str:
# get the original timestep using init_timestep
_UpperCAmelCase = min(int(num_inference_steps * strength ) , snake_case )
_UpperCAmelCase = max(num_inference_steps - init_timestep , 0 )
_UpperCAmelCase = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case=None ) -> List[str]:
if not isinstance(snake_case , (torch.Tensor, PIL.Image.Image, list) ):
raise ValueError(
f'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(snake_case )}' )
_UpperCAmelCase = image.to(device=snake_case , dtype=snake_case )
if isinstance(snake_case , snake_case ) and len(snake_case ) != batch_size:
raise ValueError(
f'You have passed a list of generators of length {len(snake_case )}, but requested an effective batch'
f' size of {batch_size}. Make sure the batch size matches the length of the generators.' )
_UpperCAmelCase = init_latents.shape
_UpperCAmelCase = randn_tensor(snake_case , generator=snake_case , device=snake_case , dtype=snake_case )
# get latents
print('add noise to latents at timestep' , snake_case )
_UpperCAmelCase = self.scheduler.add_noise(snake_case , snake_case , snake_case )
_UpperCAmelCase = init_latents
return latents
@torch.no_grad()
def __call__( self , snake_case = None , snake_case = 0.8 , snake_case = 1 , snake_case = None , snake_case = 0.0 , snake_case = 50 , snake_case = None , snake_case = "pil" , snake_case = True , ) -> Union[ImagePipelineOutput, Tuple]:
self.check_inputs(snake_case )
# 2. Preprocess image
_UpperCAmelCase = preprocess(snake_case )
# 3. set timesteps
self.scheduler.set_timesteps(snake_case , device=self.device )
_UpperCAmelCase , _UpperCAmelCase = self.get_timesteps(snake_case , snake_case , self.device )
_UpperCAmelCase = timesteps[:1].repeat(snake_case )
# 4. Prepare latent variables
_UpperCAmelCase = self.prepare_latents(snake_case , snake_case , snake_case , self.unet.dtype , self.device , snake_case )
_UpperCAmelCase = latents
# 5. Denoising loop
for t in self.progress_bar(snake_case ):
# 1. predict noise model_output
_UpperCAmelCase = self.unet(snake_case , snake_case ).sample
# 2. predict previous mean of image x_t-1 and add variance depending on eta
# eta corresponds to η in paper and should be between [0, 1]
# do x_t -> x_t-1
_UpperCAmelCase = self.scheduler.step(
snake_case , snake_case , snake_case , eta=snake_case , use_clipped_model_output=snake_case , generator=snake_case , ).prev_sample
_UpperCAmelCase = (image / 2 + 0.5).clamp(0 , 1 )
_UpperCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
_UpperCAmelCase = self.numpy_to_pil(snake_case )
if not return_dict:
return (image, latent_timestep.item())
return ImagePipelineOutput(images=snake_case )
| 24
|
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_available():
import jax.numpy as jnp
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = {
'task_specific_params': {
'summarization': {'length_penalty': 1.0, 'max_length': 128, 'min_length': 12, 'num_beams': 4},
'summarization_cnn': {'length_penalty': 2.0, 'max_length': 142, 'min_length': 56, 'num_beams': 4},
'summarization_xsum': {'length_penalty': 1.0, 'max_length': 62, 'min_length': 11, 'num_beams': 6},
}
}
_UpperCAmelCase = {
'task_specific_params.summarization.length_penalty': 1.0,
'task_specific_params.summarization.max_length': 128,
'task_specific_params.summarization.min_length': 12,
'task_specific_params.summarization.num_beams': 4,
'task_specific_params.summarization_cnn.length_penalty': 2.0,
'task_specific_params.summarization_cnn.max_length': 142,
'task_specific_params.summarization_cnn.min_length': 56,
'task_specific_params.summarization_cnn.num_beams': 4,
'task_specific_params.summarization_xsum.length_penalty': 1.0,
'task_specific_params.summarization_xsum.max_length': 62,
'task_specific_params.summarization_xsum.min_length': 11,
'task_specific_params.summarization_xsum.num_beams': 6,
}
self.assertEqual(flatten_dict(snake_case ) , snake_case )
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(transpose(snake_case ) , x.transpose() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(transpose(snake_case , axes=(1, 2, 0) ) , x.transpose((1, 2, 0) ) ) )
@require_torch
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(transpose(snake_case ) , transpose(snake_case ).numpy() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(transpose(snake_case , axes=(1, 2, 0) ) , transpose(snake_case , axes=(1, 2, 0) ).numpy() ) )
@require_tf
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(transpose(snake_case ) , transpose(snake_case ).numpy() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(transpose(snake_case , axes=(1, 2, 0) ) , transpose(snake_case , axes=(1, 2, 0) ).numpy() ) )
@require_flax
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(transpose(snake_case ) , np.asarray(transpose(snake_case ) ) ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(transpose(snake_case , axes=(1, 2, 0) ) , np.asarray(transpose(snake_case , axes=(1, 2, 0) ) ) ) )
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(reshape(snake_case , (4, 3) ) , np.reshape(snake_case , (4, 3) ) ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(reshape(snake_case , (12, 5) ) , np.reshape(snake_case , (12, 5) ) ) )
@require_torch
def lowerCamelCase_ ( self ) -> Optional[Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (4, 3) ) , reshape(snake_case , (4, 3) ).numpy() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (12, 5) ) , reshape(snake_case , (12, 5) ).numpy() ) )
@require_tf
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (4, 3) ) , reshape(snake_case , (4, 3) ).numpy() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (12, 5) ) , reshape(snake_case , (12, 5) ).numpy() ) )
@require_flax
def lowerCamelCase_ ( self ) -> Tuple:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (4, 3) ) , np.asarray(reshape(snake_case , (4, 3) ) ) ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (12, 5) ) , np.asarray(reshape(snake_case , (12, 5) ) ) ) )
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = np.random.randn(1 , 3 , 4 )
self.assertTrue(np.allclose(squeeze(snake_case ) , np.squeeze(snake_case ) ) )
_UpperCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
self.assertTrue(np.allclose(squeeze(snake_case , axis=2 ) , np.squeeze(snake_case , axis=2 ) ) )
@require_torch
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(1 , 3 , 4 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case ) , squeeze(snake_case ).numpy() ) )
_UpperCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case , axis=2 ) , squeeze(snake_case , axis=2 ).numpy() ) )
@require_tf
def lowerCamelCase_ ( self ) -> Optional[int]:
_UpperCAmelCase = np.random.randn(1 , 3 , 4 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case ) , squeeze(snake_case ).numpy() ) )
_UpperCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case , axis=2 ) , squeeze(snake_case , axis=2 ).numpy() ) )
@require_flax
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = np.random.randn(1 , 3 , 4 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case ) , np.asarray(squeeze(snake_case ) ) ) )
_UpperCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case , axis=2 ) , np.asarray(squeeze(snake_case , axis=2 ) ) ) )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(expand_dims(snake_case , axis=1 ) , np.expand_dims(snake_case , axis=1 ) ) )
@require_torch
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(expand_dims(snake_case , axis=1 ) , expand_dims(snake_case , axis=1 ).numpy() ) )
@require_tf
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(expand_dims(snake_case , axis=1 ) , expand_dims(snake_case , axis=1 ).numpy() ) )
@require_flax
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(expand_dims(snake_case , axis=1 ) , np.asarray(expand_dims(snake_case , axis=1 ) ) ) )
| 24
| 1
|
"""simple docstring"""
def UpperCAmelCase ( A : list[int] , A : int ):
'''simple docstring'''
_UpperCAmelCase = len(A )
_UpperCAmelCase = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum of zero(0) can be formed by not taking any element
# hence True/1
for i in range(arr_len + 1 ):
_UpperCAmelCase = True
# sum is not zero and set is empty then false
for i in range(1 , required_sum + 1 ):
_UpperCAmelCase = False
for i in range(1 , arr_len + 1 ):
for j in range(1 , required_sum + 1 ):
if arr[i - 1] > j:
_UpperCAmelCase = subset[i - 1][j]
if arr[i - 1] <= j:
_UpperCAmelCase = subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]
return subset[arr_len][required_sum]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 24
|
"""simple docstring"""
import os
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = os.path.join(os.path.dirname(A ) , 'num.txt' )
with open(A ) as file_hand:
return str(sum(int(A ) for line in file_hand ) )[:10]
if __name__ == "__main__":
print(solution())
| 24
| 1
|
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
lowercase = logging.get_logger(__name__)
class lowercase__ ( A ):
'''simple docstring'''
def __init__( self , *snake_case , **snake_case ) -> None:
warnings.warn(
'The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
' use YolosImageProcessor instead.' , snake_case , )
super().__init__(*snake_case , **snake_case )
| 24
|
"""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_roberta''': ['''ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RobertaConfig''', '''RobertaOnnxConfig'''],
'''tokenization_roberta''': ['''RobertaTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = ['''RobertaTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''RobertaForCausalLM''',
'''RobertaForMaskedLM''',
'''RobertaForMultipleChoice''',
'''RobertaForQuestionAnswering''',
'''RobertaForSequenceClassification''',
'''RobertaForTokenClassification''',
'''RobertaModel''',
'''RobertaPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFRobertaForCausalLM''',
'''TFRobertaForMaskedLM''',
'''TFRobertaForMultipleChoice''',
'''TFRobertaForQuestionAnswering''',
'''TFRobertaForSequenceClassification''',
'''TFRobertaForTokenClassification''',
'''TFRobertaMainLayer''',
'''TFRobertaModel''',
'''TFRobertaPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''FlaxRobertaForCausalLM''',
'''FlaxRobertaForMaskedLM''',
'''FlaxRobertaForMultipleChoice''',
'''FlaxRobertaForQuestionAnswering''',
'''FlaxRobertaForSequenceClassification''',
'''FlaxRobertaForTokenClassification''',
'''FlaxRobertaModel''',
'''FlaxRobertaPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
from .tokenization_roberta import RobertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_roberta_fast import RobertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta import (
ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaForCausalLM,
RobertaForMaskedLM,
RobertaForMultipleChoice,
RobertaForQuestionAnswering,
RobertaForSequenceClassification,
RobertaForTokenClassification,
RobertaModel,
RobertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta import (
TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
TFRobertaForMultipleChoice,
TFRobertaForQuestionAnswering,
TFRobertaForSequenceClassification,
TFRobertaForTokenClassification,
TFRobertaMainLayer,
TFRobertaModel,
TFRobertaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
FlaxRobertaPreTrainedModel,
)
else:
import sys
lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 24
| 1
|
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
lowercase = logging.get_logger(__name__)
lowercase = '''▁'''
lowercase = {'''vocab_file''': '''sentencepiece.bpe.model'''}
lowercase = {
'''vocab_file''': {
'''facebook/mbart-large-en-ro''': (
'''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'''
),
'''facebook/mbart-large-cc25''': (
'''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'''
),
}
}
lowercase = {
'''facebook/mbart-large-en-ro''': 10_24,
'''facebook/mbart-large-cc25''': 10_24,
}
# fmt: off
lowercase = ['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''']
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = VOCAB_FILES_NAMES
_UpperCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_UpperCAmelCase = PRETRAINED_VOCAB_FILES_MAP
_UpperCAmelCase = ['''input_ids''', '''attention_mask''']
_UpperCAmelCase = []
_UpperCAmelCase = []
def __init__( self , snake_case , snake_case="<s>" , snake_case="</s>" , snake_case="</s>" , snake_case="<s>" , snake_case="<unk>" , snake_case="<pad>" , snake_case="<mask>" , snake_case=None , snake_case=None , snake_case=None , snake_case = None , snake_case=None , **snake_case , ) -> Tuple:
# Mask token behave like a normal word, i.e. include the space before it
_UpperCAmelCase = AddedToken(snake_case , lstrip=snake_case , rstrip=snake_case ) if isinstance(snake_case , snake_case ) else mask_token
_UpperCAmelCase = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=snake_case , eos_token=snake_case , unk_token=snake_case , sep_token=snake_case , cls_token=snake_case , pad_token=snake_case , mask_token=snake_case , tokenizer_file=snake_case , src_lang=snake_case , tgt_lang=snake_case , additional_special_tokens=snake_case , sp_model_kwargs=self.sp_model_kwargs , **snake_case , )
_UpperCAmelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(snake_case ) )
_UpperCAmelCase = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# Mimic fairseq token-to-id alignment for the first 4 token
_UpperCAmelCase = {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
_UpperCAmelCase = 1
_UpperCAmelCase = len(self.sp_model )
_UpperCAmelCase = {
code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(snake_case )
}
_UpperCAmelCase = {v: k for k, v in self.lang_code_to_id.items()}
_UpperCAmelCase = len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset
self.fairseq_tokens_to_ids.update(self.lang_code_to_id )
_UpperCAmelCase = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
_UpperCAmelCase = list(self.lang_code_to_id.keys() )
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
self._additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in self._additional_special_tokens] )
_UpperCAmelCase = src_lang if src_lang is not None else 'en_XX'
_UpperCAmelCase = self.lang_code_to_id[self._src_lang]
_UpperCAmelCase = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
def __getstate__( self ) -> Optional[Any]:
_UpperCAmelCase = self.__dict__.copy()
_UpperCAmelCase = None
_UpperCAmelCase = self.sp_model.serialized_model_proto()
return state
def __setstate__( self , snake_case ) -> str:
_UpperCAmelCase = d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs' ):
_UpperCAmelCase = {}
_UpperCAmelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
@property
def lowerCamelCase_ ( self ) -> List[Any]:
return len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset + 1 # Plus 1 for the mask token
@property
def lowerCamelCase_ ( self ) -> str:
return self._src_lang
@src_lang.setter
def lowerCamelCase_ ( self , snake_case ) -> None:
_UpperCAmelCase = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def lowerCamelCase_ ( self , snake_case , snake_case = None , snake_case = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=snake_case , token_ids_a=snake_case , already_has_special_tokens=snake_case )
_UpperCAmelCase = [1] * len(self.prefix_tokens )
_UpperCAmelCase = [1] * len(self.suffix_tokens )
if token_ids_a is None:
return prefix_ones + ([0] * len(snake_case )) + suffix_ones
return prefix_ones + ([0] * len(snake_case )) + ([0] * len(snake_case )) + suffix_ones
def lowerCamelCase_ ( self , snake_case , snake_case = None ) -> List[int]:
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def lowerCamelCase_ ( self , snake_case , snake_case = None ) -> List[int]:
_UpperCAmelCase = [self.sep_token_id]
_UpperCAmelCase = [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 + sep + token_ids_a + sep ) * [0]
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case , snake_case , **snake_case ) -> Dict:
if src_lang is None or tgt_lang is None:
raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model' )
_UpperCAmelCase = src_lang
_UpperCAmelCase = self(snake_case , add_special_tokens=snake_case , return_tensors=snake_case , **snake_case )
_UpperCAmelCase = self.convert_tokens_to_ids(snake_case )
_UpperCAmelCase = tgt_lang_id
return inputs
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = {self.convert_ids_to_tokens(snake_case ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def lowerCamelCase_ ( self , snake_case ) -> List[str]:
return self.sp_model.encode(snake_case , out_type=snake_case )
def lowerCamelCase_ ( self , snake_case ) -> List[str]:
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
_UpperCAmelCase = self.sp_model.PieceToId(snake_case )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def lowerCamelCase_ ( self , snake_case ) -> Any:
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def lowerCamelCase_ ( self , snake_case ) -> Optional[Any]:
_UpperCAmelCase = ''.join(snake_case ).replace(snake_case , ' ' ).strip()
return out_string
def lowerCamelCase_ ( self , snake_case , snake_case = None ) -> Tuple[str]:
if not os.path.isdir(snake_case ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
_UpperCAmelCase = os.path.join(
snake_case , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , snake_case )
elif not os.path.isfile(self.vocab_file ):
with open(snake_case , 'wb' ) as fi:
_UpperCAmelCase = self.sp_model.serialized_model_proto()
fi.write(snake_case )
return (out_vocab_file,)
def lowerCamelCase_ ( self , snake_case , snake_case = "en_XX" , snake_case = None , snake_case = "ro_RO" , **snake_case , ) -> BatchEncoding:
_UpperCAmelCase = src_lang
_UpperCAmelCase = tgt_lang
return super().prepare_seqaseq_batch(snake_case , snake_case , **snake_case )
def lowerCamelCase_ ( self ) -> str:
return self.set_src_lang_special_tokens(self.src_lang )
def lowerCamelCase_ ( self ) -> str:
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def lowerCamelCase_ ( self , snake_case ) -> None:
_UpperCAmelCase = self.lang_code_to_id[src_lang]
_UpperCAmelCase = []
_UpperCAmelCase = [self.eos_token_id, self.cur_lang_code]
def lowerCamelCase_ ( self , snake_case ) -> None:
_UpperCAmelCase = self.lang_code_to_id[lang]
_UpperCAmelCase = []
_UpperCAmelCase = [self.eos_token_id, self.cur_lang_code]
| 24
|
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
lowercase = logging.get_logger(__name__)
class lowercase__ ( A ):
'''simple docstring'''
def __init__( self , *snake_case , **snake_case ) -> None:
warnings.warn(
'The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
' use YolosImageProcessor instead.' , snake_case , )
super().__init__(*snake_case , **snake_case )
| 24
| 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__)
| 24
|
"""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
lowercase = logging.get_logger(__name__)
lowercase = {
'''microsoft/beit-base-patch16-224-pt22k''': (
'''https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'''
),
# See all BEiT models at https://huggingface.co/models?filter=beit
}
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = '''beit'''
def __init__( self , snake_case=8192 , snake_case=768 , snake_case=12 , snake_case=12 , snake_case=3072 , snake_case="gelu" , snake_case=0.0 , snake_case=0.0 , snake_case=0.02 , snake_case=1E-12 , snake_case=224 , snake_case=16 , snake_case=3 , snake_case=False , snake_case=False , snake_case=False , snake_case=False , snake_case=0.1 , snake_case=0.1 , snake_case=True , snake_case=[3, 5, 7, 11] , snake_case=[1, 2, 3, 6] , snake_case=True , snake_case=0.4 , snake_case=256 , snake_case=1 , snake_case=False , snake_case=255 , **snake_case , ) -> str:
super().__init__(**snake_case )
_UpperCAmelCase = vocab_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_act
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = image_size
_UpperCAmelCase = patch_size
_UpperCAmelCase = num_channels
_UpperCAmelCase = use_mask_token
_UpperCAmelCase = use_absolute_position_embeddings
_UpperCAmelCase = use_relative_position_bias
_UpperCAmelCase = use_shared_relative_position_bias
_UpperCAmelCase = layer_scale_init_value
_UpperCAmelCase = drop_path_rate
_UpperCAmelCase = use_mean_pooling
# decode head attributes (semantic segmentation)
_UpperCAmelCase = out_indices
_UpperCAmelCase = pool_scales
# auxiliary head attributes (semantic segmentation)
_UpperCAmelCase = use_auxiliary_head
_UpperCAmelCase = auxiliary_loss_weight
_UpperCAmelCase = auxiliary_channels
_UpperCAmelCase = auxiliary_num_convs
_UpperCAmelCase = auxiliary_concat_input
_UpperCAmelCase = semantic_loss_ignore_index
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = version.parse('''1.11''' )
@property
def lowerCamelCase_ ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def lowerCamelCase_ ( self ) -> float:
return 1E-4
| 24
| 1
|
"""simple docstring"""
lowercase = {
0: '''0''',
1: '''1''',
2: '''2''',
3: '''3''',
4: '''4''',
5: '''5''',
6: '''6''',
7: '''7''',
8: '''8''',
9: '''9''',
10: '''a''',
11: '''b''',
12: '''c''',
13: '''d''',
14: '''e''',
15: '''f''',
}
def UpperCAmelCase ( A : float ):
'''simple docstring'''
assert type(A ) in (int, float) and decimal == int(A )
_UpperCAmelCase = int(A )
_UpperCAmelCase = ''
_UpperCAmelCase = False
if decimal < 0:
_UpperCAmelCase = True
decimal *= -1
while decimal > 0:
_UpperCAmelCase , _UpperCAmelCase = divmod(A , 16 )
_UpperCAmelCase = values[remainder] + hexadecimal
_UpperCAmelCase = '0x' + hexadecimal
if negative:
_UpperCAmelCase = '-' + hexadecimal
return hexadecimal
if __name__ == "__main__":
import doctest
doctest.testmod()
| 24
|
"""simple docstring"""
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
lowercase = logging.getLogger(__name__)
if __name__ == "__main__":
lowercase = argparse.ArgumentParser(
description='''Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'''
)
parser.add_argument(
'''--data_file''', type=str, default='''data/dump.bert-base-uncased.pickle''', help='''The binarized dataset.'''
)
parser.add_argument(
'''--token_counts_dump''', type=str, default='''data/token_counts.bert-base-uncased.pickle''', help='''The dump file.'''
)
parser.add_argument('''--vocab_size''', default=3_05_22, type=int)
lowercase = parser.parse_args()
logger.info(F'''Loading data from {args.data_file}''')
with open(args.data_file, '''rb''') as fp:
lowercase = pickle.load(fp)
logger.info('''Counting occurrences for MLM.''')
lowercase = Counter()
for tk_ids in data:
counter.update(tk_ids)
lowercase = [0] * args.vocab_size
for k, v in counter.items():
lowercase = v
logger.info(F'''Dump to {args.token_counts_dump}''')
with open(args.token_counts_dump, '''wb''') as handle:
pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)
| 24
| 1
|
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class lowercase__ ( A, unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = DiTPipeline
_UpperCAmelCase = CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
_UpperCAmelCase = PipelineTesterMixin.required_optional_params - {
'''latents''',
'''num_images_per_prompt''',
'''callback''',
'''callback_steps''',
}
_UpperCAmelCase = CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
_UpperCAmelCase = False
def lowerCamelCase_ ( self ) -> str:
torch.manual_seed(0 )
_UpperCAmelCase = TransformeraDModel(
sample_size=16 , num_layers=2 , patch_size=4 , attention_head_dim=8 , num_attention_heads=2 , in_channels=4 , out_channels=8 , attention_bias=snake_case , activation_fn='gelu-approximate' , num_embeds_ada_norm=1000 , norm_type='ada_norm_zero' , norm_elementwise_affine=snake_case , )
_UpperCAmelCase = AutoencoderKL()
_UpperCAmelCase = DDIMScheduler()
_UpperCAmelCase = {'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
return components
def lowerCamelCase_ ( self , snake_case , snake_case=0 ) -> Optional[Any]:
if str(snake_case ).startswith('mps' ):
_UpperCAmelCase = torch.manual_seed(snake_case )
else:
_UpperCAmelCase = torch.Generator(device=snake_case ).manual_seed(snake_case )
_UpperCAmelCase = {
'class_labels': [1],
'generator': generator,
'num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
def lowerCamelCase_ ( self ) -> List[Any]:
_UpperCAmelCase = 'cpu'
_UpperCAmelCase = self.get_dummy_components()
_UpperCAmelCase = self.pipeline_class(**snake_case )
pipe.to(snake_case )
pipe.set_progress_bar_config(disable=snake_case )
_UpperCAmelCase = self.get_dummy_inputs(snake_case )
_UpperCAmelCase = pipe(**snake_case ).images
_UpperCAmelCase = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 16, 16, 3) )
_UpperCAmelCase = np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457] )
_UpperCAmelCase = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(snake_case , 1E-3 )
def lowerCamelCase_ ( self ) -> Any:
self._test_inference_batch_single_identical(relax_max_difference=snake_case , expected_max_diff=1E-3 )
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def lowerCamelCase_ ( self ) -> Optional[int]:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
@require_torch_gpu
@slow
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> int:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = DiTPipeline.from_pretrained('facebook/DiT-XL-2-256' )
pipe.to('cuda' )
_UpperCAmelCase = ['vase', 'umbrella', 'white shark', 'white wolf']
_UpperCAmelCase = pipe.get_label_ids(snake_case )
_UpperCAmelCase = pipe(snake_case , generator=snake_case , num_inference_steps=40 , output_type='np' ).images
for word, image in zip(snake_case , snake_case ):
_UpperCAmelCase = load_numpy(
f'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy' )
assert np.abs((expected_image - image).max() ) < 1E-2
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = DiTPipeline.from_pretrained('facebook/DiT-XL-2-512' )
_UpperCAmelCase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.to('cuda' )
_UpperCAmelCase = ['vase', 'umbrella']
_UpperCAmelCase = pipe.get_label_ids(snake_case )
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = pipe(snake_case , generator=snake_case , num_inference_steps=25 , output_type='np' ).images
for word, image in zip(snake_case , snake_case ):
_UpperCAmelCase = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
f'/dit/{word}_512.npy' )
assert np.abs((expected_image - image).max() ) < 1E-1
| 24
|
"""simple docstring"""
from itertools import permutations
def UpperCAmelCase ( A : tuple ):
'''simple docstring'''
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
_UpperCAmelCase = [7, 11, 13, 17]
for i, test in enumerate(A ):
if (num[i + 4] * 100 + num[i + 5] * 10 + num[i + 6]) % test != 0:
return False
return True
def UpperCAmelCase ( A : int = 10 ):
'''simple docstring'''
return sum(
int(''.join(map(A , A ) ) )
for num in permutations(range(A ) )
if is_substring_divisible(A ) )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 24
| 1
|
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=A )
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = field(default='''question-answering-extractive''', metadata={'''include_in_asdict_even_if_is_default''': True} )
_UpperCAmelCase = Features({'''question''': Value('''string''' ), '''context''': Value('''string''' )} )
_UpperCAmelCase = Features(
{
'''answers''': Sequence(
{
'''text''': Value('''string''' ),
'''answer_start''': Value('''int32''' ),
} )
} )
_UpperCAmelCase = "question"
_UpperCAmelCase = "context"
_UpperCAmelCase = "answers"
@property
def lowerCamelCase_ ( self ) -> Dict[str, str]:
return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}
| 24
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase = {
'''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
'''tokenization_mvp''': ['''MvpTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = ['''MvpTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''MVP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MvpForCausalLM''',
'''MvpForConditionalGeneration''',
'''MvpForQuestionAnswering''',
'''MvpForSequenceClassification''',
'''MvpModel''',
'''MvpPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
from .tokenization_mvp import MvpTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mvp_fast import MvpTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mvp import (
MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
MvpForCausalLM,
MvpForConditionalGeneration,
MvpForQuestionAnswering,
MvpForSequenceClassification,
MvpModel,
MvpPreTrainedModel,
)
else:
import sys
lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 24
| 1
|
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class lowercase__ ( A, unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = KandinskyImgaImgPipeline
_UpperCAmelCase = ['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''']
_UpperCAmelCase = [
'''prompt''',
'''negative_prompt''',
'''image_embeds''',
'''negative_image_embeds''',
'''image''',
]
_UpperCAmelCase = [
'''generator''',
'''height''',
'''width''',
'''strength''',
'''guidance_scale''',
'''negative_prompt''',
'''num_inference_steps''',
'''return_dict''',
'''guidance_scale''',
'''num_images_per_prompt''',
'''output_type''',
'''return_dict''',
]
_UpperCAmelCase = False
@property
def lowerCamelCase_ ( self ) -> List[Any]:
return 32
@property
def lowerCamelCase_ ( self ) -> Dict:
return 32
@property
def lowerCamelCase_ ( self ) -> Optional[Any]:
return self.time_input_dim
@property
def lowerCamelCase_ ( self ) -> List[Any]:
return self.time_input_dim * 4
@property
def lowerCamelCase_ ( self ) -> Any:
return 100
@property
def lowerCamelCase_ ( self ) -> List[Any]:
_UpperCAmelCase = XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base' )
return tokenizer
@property
def lowerCamelCase_ ( self ) -> List[Any]:
torch.manual_seed(0 )
_UpperCAmelCase = MCLIPConfig(
numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=1005 , )
_UpperCAmelCase = MultilingualCLIP(snake_case )
_UpperCAmelCase = text_encoder.eval()
return text_encoder
@property
def lowerCamelCase_ ( self ) -> Optional[Any]:
torch.manual_seed(0 )
_UpperCAmelCase = {
'in_channels': 4,
# Out channels is double in channels because predicts mean and variance
'out_channels': 8,
'addition_embed_type': 'text_image',
'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
'layers_per_block': 1,
'encoder_hid_dim': self.text_embedder_hidden_size,
'encoder_hid_dim_type': 'text_image_proj',
'cross_attention_dim': self.cross_attention_dim,
'attention_head_dim': 4,
'resnet_time_scale_shift': 'scale_shift',
'class_embed_type': None,
}
_UpperCAmelCase = UNetaDConditionModel(**snake_case )
return model
@property
def lowerCamelCase_ ( self ) -> int:
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def lowerCamelCase_ ( self ) -> Optional[Any]:
torch.manual_seed(0 )
_UpperCAmelCase = VQModel(**self.dummy_movq_kwargs )
return model
def lowerCamelCase_ ( self ) -> List[Any]:
_UpperCAmelCase = self.dummy_text_encoder
_UpperCAmelCase = self.dummy_tokenizer
_UpperCAmelCase = self.dummy_unet
_UpperCAmelCase = self.dummy_movq
_UpperCAmelCase = {
'num_train_timesteps': 1000,
'beta_schedule': 'linear',
'beta_start': 0.00085,
'beta_end': 0.012,
'clip_sample': False,
'set_alpha_to_one': False,
'steps_offset': 0,
'prediction_type': 'epsilon',
'thresholding': False,
}
_UpperCAmelCase = DDIMScheduler(**snake_case )
_UpperCAmelCase = {
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'unet': unet,
'scheduler': scheduler,
'movq': movq,
}
return components
def lowerCamelCase_ ( self , snake_case , snake_case=0 ) -> List[str]:
_UpperCAmelCase = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(snake_case ) ).to(snake_case )
_UpperCAmelCase = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(snake_case )
# create init_image
_UpperCAmelCase = floats_tensor((1, 3, 64, 64) , rng=random.Random(snake_case ) ).to(snake_case )
_UpperCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 )[0]
_UpperCAmelCase = Image.fromarray(np.uinta(snake_case ) ).convert('RGB' ).resize((256, 256) )
if str(snake_case ).startswith('mps' ):
_UpperCAmelCase = torch.manual_seed(snake_case )
else:
_UpperCAmelCase = torch.Generator(device=snake_case ).manual_seed(snake_case )
_UpperCAmelCase = {
'prompt': 'horse',
'image': init_image,
'image_embeds': image_embeds,
'negative_image_embeds': negative_image_embeds,
'generator': generator,
'height': 64,
'width': 64,
'num_inference_steps': 10,
'guidance_scale': 7.0,
'strength': 0.2,
'output_type': 'np',
}
return inputs
def lowerCamelCase_ ( self ) -> Optional[Any]:
_UpperCAmelCase = 'cpu'
_UpperCAmelCase = self.get_dummy_components()
_UpperCAmelCase = self.pipeline_class(**snake_case )
_UpperCAmelCase = pipe.to(snake_case )
pipe.set_progress_bar_config(disable=snake_case )
_UpperCAmelCase = pipe(**self.get_dummy_inputs(snake_case ) )
_UpperCAmelCase = output.images
_UpperCAmelCase = pipe(
**self.get_dummy_inputs(snake_case ) , return_dict=snake_case , )[0]
_UpperCAmelCase = image[0, -3:, -3:, -1]
_UpperCAmelCase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_UpperCAmelCase = np.array(
[0.61474943, 0.6073539, 0.43308544, 0.5928269, 0.47493595, 0.46755973, 0.4613838, 0.45368797, 0.50119233] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'
@slow
@require_torch_gpu
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> str:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/kandinsky/kandinsky_img2img_frog.npy' )
_UpperCAmelCase = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png' )
_UpperCAmelCase = 'A red cartoon frog, 4k'
_UpperCAmelCase = KandinskyPriorPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-1-prior' , torch_dtype=torch.floataa )
pipe_prior.to(snake_case )
_UpperCAmelCase = KandinskyImgaImgPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-1' , torch_dtype=torch.floataa )
_UpperCAmelCase = pipeline.to(snake_case )
pipeline.set_progress_bar_config(disable=snake_case )
_UpperCAmelCase = torch.Generator(device='cpu' ).manual_seed(0 )
_UpperCAmelCase , _UpperCAmelCase = pipe_prior(
snake_case , generator=snake_case , num_inference_steps=5 , negative_prompt='' , ).to_tuple()
_UpperCAmelCase = pipeline(
snake_case , image=snake_case , image_embeds=snake_case , negative_image_embeds=snake_case , generator=snake_case , num_inference_steps=100 , height=768 , width=768 , strength=0.2 , output_type='np' , )
_UpperCAmelCase = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(snake_case , snake_case )
| 24
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase = {
'''configuration_clipseg''': [
'''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPSegConfig''',
'''CLIPSegTextConfig''',
'''CLIPSegVisionConfig''',
],
'''processing_clipseg''': ['''CLIPSegProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''CLIPSegModel''',
'''CLIPSegPreTrainedModel''',
'''CLIPSegTextModel''',
'''CLIPSegVisionModel''',
'''CLIPSegForImageSegmentation''',
]
if TYPE_CHECKING:
from .configuration_clipseg import (
CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIPSegConfig,
CLIPSegTextConfig,
CLIPSegVisionConfig,
)
from .processing_clipseg import CLIPSegProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clipseg import (
CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPSegForImageSegmentation,
CLIPSegModel,
CLIPSegPreTrainedModel,
CLIPSegTextModel,
CLIPSegVisionModel,
)
else:
import sys
lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 24
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowercase = {
'''configuration_llama''': ['''LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LlamaConfig'''],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = ['''LlamaTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = ['''LlamaTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''LlamaForCausalLM''',
'''LlamaModel''',
'''LlamaPreTrainedModel''',
'''LlamaForSequenceClassification''',
]
if TYPE_CHECKING:
from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_llama import LlamaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_llama_fast import LlamaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel
else:
import sys
lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 24
|
"""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/swin-tiny-patch4-window7-224''': (
'''https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'''
),
# See all Swin models at https://huggingface.co/models?filter=swin
}
class lowercase__ ( A, A ):
'''simple docstring'''
_UpperCAmelCase = '''swin'''
_UpperCAmelCase = {
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self , snake_case=224 , snake_case=4 , snake_case=3 , snake_case=96 , snake_case=[2, 2, 6, 2] , snake_case=[3, 6, 12, 24] , snake_case=7 , snake_case=4.0 , snake_case=True , snake_case=0.0 , snake_case=0.0 , snake_case=0.1 , snake_case="gelu" , snake_case=False , snake_case=0.02 , snake_case=1E-5 , snake_case=32 , snake_case=None , snake_case=None , **snake_case , ) -> List[Any]:
super().__init__(**snake_case )
_UpperCAmelCase = image_size
_UpperCAmelCase = patch_size
_UpperCAmelCase = num_channels
_UpperCAmelCase = embed_dim
_UpperCAmelCase = depths
_UpperCAmelCase = len(snake_case )
_UpperCAmelCase = num_heads
_UpperCAmelCase = window_size
_UpperCAmelCase = mlp_ratio
_UpperCAmelCase = qkv_bias
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = drop_path_rate
_UpperCAmelCase = hidden_act
_UpperCAmelCase = use_absolute_embeddings
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = initializer_range
_UpperCAmelCase = encoder_stride
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
_UpperCAmelCase = int(embed_dim * 2 ** (len(snake_case ) - 1) )
_UpperCAmelCase = ['stem'] + [f'stage{idx}' for idx in range(1 , len(snake_case ) + 1 )]
_UpperCAmelCase , _UpperCAmelCase = get_aligned_output_features_output_indices(
out_features=snake_case , out_indices=snake_case , stage_names=self.stage_names )
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = version.parse('''1.11''' )
@property
def lowerCamelCase_ ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def lowerCamelCase_ ( self ) -> float:
return 1E-4
| 24
| 1
|
"""simple docstring"""
def UpperCAmelCase ( A : list[int] ):
'''simple docstring'''
_UpperCAmelCase = []
if len(A ) == 1:
return [nums.copy()]
for _ in range(len(A ) ):
_UpperCAmelCase = nums.pop(0 )
_UpperCAmelCase = permute(A )
for perm in permutations:
perm.append(A )
result.extend(A )
nums.append(A )
return result
def UpperCAmelCase ( A : List[str] ):
'''simple docstring'''
def backtrack(A : Any ):
if start == len(A ) - 1:
output.append(nums[:] )
else:
for i in range(A , len(A ) ):
_UpperCAmelCase , _UpperCAmelCase = nums[i], nums[start]
backtrack(start + 1 )
_UpperCAmelCase , _UpperCAmelCase = nums[i], nums[start] # backtrack
_UpperCAmelCase = []
backtrack(0 )
return output
if __name__ == "__main__":
import doctest
# use res to print the data in permute2 function
lowercase = permutea([1, 2, 3])
print(res)
doctest.testmod()
| 24
|
"""simple docstring"""
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class lowercase__ ( nn.Module ):
'''simple docstring'''
def __init__( self , snake_case = 16 , snake_case = 88 , snake_case = None , snake_case = 1 , snake_case = 0.0 , snake_case = 32 , snake_case = None , snake_case = False , snake_case = None , snake_case = None , snake_case = "geglu" , snake_case = None , ) -> str:
super().__init__()
_UpperCAmelCase = nn.ModuleList(
[
TransformeraDModel(
num_attention_heads=snake_case , attention_head_dim=snake_case , in_channels=snake_case , num_layers=snake_case , dropout=snake_case , norm_num_groups=snake_case , cross_attention_dim=snake_case , attention_bias=snake_case , sample_size=snake_case , num_vector_embeds=snake_case , activation_fn=snake_case , num_embeds_ada_norm=snake_case , )
for _ in range(2 )
] )
# Variables that can be set by a pipeline:
# The ratio of transformer1 to transformer2's output states to be combined during inference
_UpperCAmelCase = 0.5
# The shape of `encoder_hidden_states` is expected to be
# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
_UpperCAmelCase = [77, 257]
# Which transformer to use to encode which condition.
# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
_UpperCAmelCase = [1, 0]
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case=None , snake_case=None , snake_case=None , snake_case = True , ) -> Any:
_UpperCAmelCase = hidden_states
_UpperCAmelCase = []
_UpperCAmelCase = 0
# attention_mask is not used yet
for i in range(2 ):
# for each of the two transformers, pass the corresponding condition tokens
_UpperCAmelCase = encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
_UpperCAmelCase = self.transformer_index_for_condition[i]
_UpperCAmelCase = self.transformers[transformer_index](
snake_case , encoder_hidden_states=snake_case , timestep=snake_case , cross_attention_kwargs=snake_case , return_dict=snake_case , )[0]
encoded_states.append(encoded_state - input_states )
tokens_start += self.condition_lengths[i]
_UpperCAmelCase = encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
_UpperCAmelCase = output_states + input_states
if not return_dict:
return (output_states,)
return TransformeraDModelOutput(sample=snake_case )
| 24
| 1
|
"""simple docstring"""
from collections import deque
def UpperCAmelCase ( A : Optional[int] ):
'''simple docstring'''
_UpperCAmelCase = len(A )
_UpperCAmelCase = deque()
_UpperCAmelCase = [False for _ in range(A )]
_UpperCAmelCase = [-1 for _ in range(A )]
_UpperCAmelCase = index_of[:]
def strong_connect(A : List[Any] , A : List[str] , A : Optional[int] ):
_UpperCAmelCase = index # the number when this node is seen
_UpperCAmelCase = index # lowest rank node reachable from here
index += 1
stack.append(A )
_UpperCAmelCase = True
for w in g[v]:
if index_of[w] == -1:
_UpperCAmelCase = strong_connect(A , A , A )
_UpperCAmelCase = (
lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
)
elif on_stack[w]:
_UpperCAmelCase = (
lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
)
if lowlink_of[v] == index_of[v]:
_UpperCAmelCase = []
_UpperCAmelCase = stack.pop()
_UpperCAmelCase = False
component.append(A )
while w != v:
_UpperCAmelCase = stack.pop()
_UpperCAmelCase = False
component.append(A )
components.append(A )
return index
_UpperCAmelCase = []
for v in range(A ):
if index_of[v] == -1:
strong_connect(A , 0 , A )
return components
def UpperCAmelCase ( A : List[Any] , A : Optional[Any] ):
'''simple docstring'''
_UpperCAmelCase = [[] for _ in range(A )]
for u, v in edges:
g[u].append(A )
return g
if __name__ == "__main__":
# Test
lowercase = 7
lowercase = [0, 0, 1, 2, 3, 3, 4, 4, 6]
lowercase = [1, 3, 2, 0, 1, 4, 5, 6, 5]
lowercase = [(u, v) for u, v in zip(source, target)]
lowercase = create_graph(n_vertices, edges)
assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)
| 24
|
"""simple docstring"""
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import CvtForImageClassification, CvtModel
from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class lowercase__ ( A ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(snake_case , 'embed_dim' ) )
self.parent.assertTrue(hasattr(snake_case , 'num_heads' ) )
class lowercase__ :
'''simple docstring'''
def __init__( self , snake_case , snake_case=13 , snake_case=64 , snake_case=3 , snake_case=[16, 48, 96] , snake_case=[1, 3, 6] , snake_case=[1, 2, 10] , snake_case=[7, 3, 3] , snake_case=[4, 2, 2] , snake_case=[2, 1, 1] , snake_case=[2, 2, 2] , snake_case=[False, False, True] , snake_case=[0.0, 0.0, 0.0] , snake_case=0.02 , snake_case=1E-12 , snake_case=True , snake_case=True , snake_case=2 , ) -> Tuple:
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = image_size
_UpperCAmelCase = patch_sizes
_UpperCAmelCase = patch_stride
_UpperCAmelCase = patch_padding
_UpperCAmelCase = is_training
_UpperCAmelCase = use_labels
_UpperCAmelCase = num_labels
_UpperCAmelCase = num_channels
_UpperCAmelCase = embed_dim
_UpperCAmelCase = num_heads
_UpperCAmelCase = stride_kv
_UpperCAmelCase = depth
_UpperCAmelCase = cls_token
_UpperCAmelCase = attention_drop_rate
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_UpperCAmelCase = None
if self.use_labels:
_UpperCAmelCase = ids_tensor([self.batch_size] , self.num_labels )
_UpperCAmelCase = self.get_config()
return config, pixel_values, labels
def lowerCamelCase_ ( self ) -> List[str]:
return CvtConfig(
image_size=self.image_size , num_labels=self.num_labels , num_channels=self.num_channels , embed_dim=self.embed_dim , num_heads=self.num_heads , patch_sizes=self.patch_sizes , patch_padding=self.patch_padding , patch_stride=self.patch_stride , stride_kv=self.stride_kv , depth=self.depth , cls_token=self.cls_token , attention_drop_rate=self.attention_drop_rate , initializer_range=self.initializer_range , )
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case ) -> Optional[int]:
_UpperCAmelCase = CvtModel(config=snake_case )
model.to(snake_case )
model.eval()
_UpperCAmelCase = model(snake_case )
_UpperCAmelCase = (self.image_size, self.image_size)
_UpperCAmelCase , _UpperCAmelCase = image_size[0], image_size[1]
for i in range(len(self.depth ) ):
_UpperCAmelCase = floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
_UpperCAmelCase = floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dim[-1], height, width) )
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case ) -> Optional[Any]:
_UpperCAmelCase = self.num_labels
_UpperCAmelCase = CvtForImageClassification(snake_case )
model.to(snake_case )
model.eval()
_UpperCAmelCase = model(snake_case , labels=snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = self.prepare_config_and_inputs()
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = config_and_inputs
_UpperCAmelCase = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class lowercase__ ( A, A, unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = (CvtModel, CvtForImageClassification) if is_torch_available() else ()
_UpperCAmelCase = (
{'''feature-extraction''': CvtModel, '''image-classification''': CvtForImageClassification}
if is_torch_available()
else {}
)
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = CvtModelTester(self )
_UpperCAmelCase = ConfigTester(self , config_class=snake_case , has_text_modality=snake_case , hidden_size=37 )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def lowerCamelCase_ ( self ) -> Union[str, Any]:
return
@unittest.skip(reason='Cvt does not output attentions' )
def lowerCamelCase_ ( self ) -> str:
pass
@unittest.skip(reason='Cvt does not use inputs_embeds' )
def lowerCamelCase_ ( self ) -> int:
pass
@unittest.skip(reason='Cvt does not support input and output embeddings' )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
pass
def lowerCamelCase_ ( self ) -> Any:
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(snake_case )
_UpperCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCAmelCase = [*signature.parameters.keys()]
_UpperCAmelCase = ['pixel_values']
self.assertListEqual(arg_names[:1] , snake_case )
def lowerCamelCase_ ( self ) -> Optional[int]:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case )
def lowerCamelCase_ ( self ) -> Optional[int]:
def check_hidden_states_output(snake_case , snake_case , snake_case ):
_UpperCAmelCase = model_class(snake_case )
model.to(snake_case )
model.eval()
with torch.no_grad():
_UpperCAmelCase = model(**self._prepare_for_class(snake_case , snake_case ) )
_UpperCAmelCase = outputs.hidden_states
_UpperCAmelCase = len(self.model_tester.depth )
self.assertEqual(len(snake_case ) , snake_case )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:] ) , [
self.model_tester.embed_dim[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
] , )
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = True
check_hidden_states_output(snake_case , snake_case , snake_case )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_UpperCAmelCase = True
check_hidden_states_output(snake_case , snake_case , snake_case )
def lowerCamelCase_ ( self ) -> Any:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*snake_case )
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def lowerCamelCase_ ( self ) -> Dict:
pass
@slow
def lowerCamelCase_ ( self ) -> Dict:
for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase = CvtModel.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def lowerCamelCase_ ( self ) -> List[Any]:
return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
@slow
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(snake_case )
_UpperCAmelCase = self.default_image_processor
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = image_processor(images=snake_case , return_tensors='pt' ).to(snake_case )
# forward pass
with torch.no_grad():
_UpperCAmelCase = model(**snake_case )
# verify the logits
_UpperCAmelCase = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , snake_case )
_UpperCAmelCase = torch.tensor([0.9285, 0.9015, -0.3150] ).to(snake_case )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , snake_case , atol=1E-4 ) )
| 24
| 1
|
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase = logging.get_logger(__name__)
lowercase = {
'''microsoft/wavlm-base''': '''https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json''',
# See all WavLM models at https://huggingface.co/models?filter=wavlm
}
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = '''wavlm'''
def __init__( self , snake_case=32 , snake_case=768 , snake_case=12 , snake_case=12 , snake_case=3072 , snake_case="gelu" , snake_case=0.1 , snake_case=0.1 , snake_case=0.1 , snake_case=0.0 , snake_case=0.1 , snake_case=0.1 , snake_case=0.02 , snake_case=1E-5 , snake_case="group" , snake_case="gelu" , snake_case=(512, 512, 512, 512, 512, 512, 512) , snake_case=(5, 2, 2, 2, 2, 2, 2) , snake_case=(10, 3, 3, 3, 3, 2, 2) , snake_case=False , snake_case=128 , snake_case=16 , snake_case=320 , snake_case=800 , snake_case=False , snake_case=True , snake_case=0.05 , snake_case=10 , snake_case=2 , snake_case=0.0 , snake_case=10 , snake_case=320 , snake_case=2 , snake_case=0.1 , snake_case=100 , snake_case=256 , snake_case=256 , snake_case=0.1 , snake_case="mean" , snake_case=False , snake_case=False , snake_case=256 , snake_case=(512, 512, 512, 512, 1500) , snake_case=(5, 3, 3, 1, 1) , snake_case=(1, 2, 3, 1, 1) , snake_case=512 , snake_case=80 , snake_case=0 , snake_case=1 , snake_case=2 , snake_case=False , snake_case=3 , snake_case=2 , snake_case=3 , snake_case=None , **snake_case , ) -> List[str]:
super().__init__(**snake_case , pad_token_id=snake_case , bos_token_id=snake_case , eos_token_id=snake_case )
_UpperCAmelCase = hidden_size
_UpperCAmelCase = feat_extract_norm
_UpperCAmelCase = feat_extract_activation
_UpperCAmelCase = list(snake_case )
_UpperCAmelCase = list(snake_case )
_UpperCAmelCase = list(snake_case )
_UpperCAmelCase = conv_bias
_UpperCAmelCase = num_buckets
_UpperCAmelCase = max_bucket_distance
_UpperCAmelCase = num_conv_pos_embeddings
_UpperCAmelCase = num_conv_pos_embedding_groups
_UpperCAmelCase = len(self.conv_dim )
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_act
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = hidden_dropout
_UpperCAmelCase = attention_dropout
_UpperCAmelCase = activation_dropout
_UpperCAmelCase = feat_proj_dropout
_UpperCAmelCase = final_dropout
_UpperCAmelCase = layerdrop
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = initializer_range
_UpperCAmelCase = num_ctc_classes
_UpperCAmelCase = vocab_size
_UpperCAmelCase = do_stable_layer_norm
_UpperCAmelCase = use_weighted_layer_sum
_UpperCAmelCase = classifier_proj_size
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
f' {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,'
f' `len(config.conv_kernel) = {len(self.conv_kernel )}`.' )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
_UpperCAmelCase = apply_spec_augment
_UpperCAmelCase = mask_time_prob
_UpperCAmelCase = mask_time_length
_UpperCAmelCase = mask_time_min_masks
_UpperCAmelCase = mask_feature_prob
_UpperCAmelCase = mask_feature_length
# parameters for pretraining with codevector quantized representations
_UpperCAmelCase = num_codevectors_per_group
_UpperCAmelCase = num_codevector_groups
_UpperCAmelCase = contrastive_logits_temperature
_UpperCAmelCase = num_negatives
_UpperCAmelCase = codevector_dim
_UpperCAmelCase = proj_codevector_dim
_UpperCAmelCase = diversity_loss_weight
# ctc loss
_UpperCAmelCase = ctc_loss_reduction
_UpperCAmelCase = ctc_zero_infinity
# adapter
_UpperCAmelCase = add_adapter
_UpperCAmelCase = adapter_kernel_size
_UpperCAmelCase = adapter_stride
_UpperCAmelCase = num_adapter_layers
_UpperCAmelCase = output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
_UpperCAmelCase = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
_UpperCAmelCase = list(snake_case )
_UpperCAmelCase = list(snake_case )
_UpperCAmelCase = list(snake_case )
_UpperCAmelCase = xvector_output_dim
@property
def lowerCamelCase_ ( self ) -> Tuple:
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 24
|
"""simple docstring"""
from __future__ import annotations
from cmath import sqrt
def UpperCAmelCase ( A : int , A : int , A : int ):
'''simple docstring'''
if a == 0:
raise ValueError('Coefficient \'a\' must not be zero.' )
_UpperCAmelCase = b * b - 4 * a * c
_UpperCAmelCase = (-b + sqrt(A )) / (2 * a)
_UpperCAmelCase = (-b - sqrt(A )) / (2 * a)
return (
root_a.real if not root_a.imag else root_a,
root_a.real if not root_a.imag else root_a,
)
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = quadratic_roots(a=5 , b=6 , c=1 )
print(f'The solutions are: {solutiona} and {solutiona}' )
if __name__ == "__main__":
main()
| 24
| 1
|
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from numpy import array
def UpperCAmelCase ( A : list[list[float]] ):
'''simple docstring'''
_UpperCAmelCase = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementation only works for 2x2 matrices
if len(A ) == 2 and len(matrix[0] ) == 2 and len(matrix[1] ) == 2:
# Calculate the determinant of the matrix
_UpperCAmelCase = float(
d(matrix[0][0] ) * d(matrix[1][1] ) - d(matrix[1][0] ) * d(matrix[0][1] ) )
if determinant == 0:
raise ValueError('This matrix has no inverse.' )
# Creates a copy of the matrix with swapped positions of the elements
_UpperCAmelCase = [[0.0, 0.0], [0.0, 0.0]]
_UpperCAmelCase , _UpperCAmelCase = matrix[1][1], matrix[0][0]
_UpperCAmelCase , _UpperCAmelCase = -matrix[1][0], -matrix[0][1]
# Calculate the inverse of the matrix
return [
[(float(d(A ) ) / determinant) or 0.0 for n in row] for row in swapped_matrix
]
elif (
len(A ) == 3
and len(matrix[0] ) == 3
and len(matrix[1] ) == 3
and len(matrix[2] ) == 3
):
# Calculate the determinant of the matrix using Sarrus rule
_UpperCAmelCase = float(
(
(d(matrix[0][0] ) * d(matrix[1][1] ) * d(matrix[2][2] ))
+ (d(matrix[0][1] ) * d(matrix[1][2] ) * d(matrix[2][0] ))
+ (d(matrix[0][2] ) * d(matrix[1][0] ) * d(matrix[2][1] ))
)
- (
(d(matrix[0][2] ) * d(matrix[1][1] ) * d(matrix[2][0] ))
+ (d(matrix[0][1] ) * d(matrix[1][0] ) * d(matrix[2][2] ))
+ (d(matrix[0][0] ) * d(matrix[1][2] ) * d(matrix[2][1] ))
) )
if determinant == 0:
raise ValueError('This matrix has no inverse.' )
# Creating cofactor matrix
_UpperCAmelCase = [
[d(0.0 ), d(0.0 ), d(0.0 )],
[d(0.0 ), d(0.0 ), d(0.0 )],
[d(0.0 ), d(0.0 ), d(0.0 )],
]
_UpperCAmelCase = (d(matrix[1][1] ) * d(matrix[2][2] )) - (
d(matrix[1][2] ) * d(matrix[2][1] )
)
_UpperCAmelCase = -(
(d(matrix[1][0] ) * d(matrix[2][2] )) - (d(matrix[1][2] ) * d(matrix[2][0] ))
)
_UpperCAmelCase = (d(matrix[1][0] ) * d(matrix[2][1] )) - (
d(matrix[1][1] ) * d(matrix[2][0] )
)
_UpperCAmelCase = -(
(d(matrix[0][1] ) * d(matrix[2][2] )) - (d(matrix[0][2] ) * d(matrix[2][1] ))
)
_UpperCAmelCase = (d(matrix[0][0] ) * d(matrix[2][2] )) - (
d(matrix[0][2] ) * d(matrix[2][0] )
)
_UpperCAmelCase = -(
(d(matrix[0][0] ) * d(matrix[2][1] )) - (d(matrix[0][1] ) * d(matrix[2][0] ))
)
_UpperCAmelCase = (d(matrix[0][1] ) * d(matrix[1][2] )) - (
d(matrix[0][2] ) * d(matrix[1][1] )
)
_UpperCAmelCase = -(
(d(matrix[0][0] ) * d(matrix[1][2] )) - (d(matrix[0][2] ) * d(matrix[1][0] ))
)
_UpperCAmelCase = (d(matrix[0][0] ) * d(matrix[1][1] )) - (
d(matrix[0][1] ) * d(matrix[1][0] )
)
# Transpose the cofactor matrix (Adjoint matrix)
_UpperCAmelCase = array(A )
for i in range(3 ):
for j in range(3 ):
_UpperCAmelCase = cofactor_matrix[j][i]
# Inverse of the matrix using the formula (1/determinant) * adjoint matrix
_UpperCAmelCase = array(A )
for i in range(3 ):
for j in range(3 ):
inverse_matrix[i][j] /= d(A )
# Calculate the inverse of the matrix
return [[float(d(A ) ) or 0.0 for n in row] for row in inverse_matrix]
raise ValueError('Please provide a matrix of size 2x2 or 3x3.' )
| 24
|
"""simple docstring"""
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # see https://github.com/huggingface/transformers/issues/11457
class lowercase__ ( A, unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = BarthezTokenizer
_UpperCAmelCase = BarthezTokenizerFast
_UpperCAmelCase = True
_UpperCAmelCase = True
def lowerCamelCase_ ( self ) -> Optional[int]:
super().setUp()
_UpperCAmelCase = BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez' )
tokenizer.save_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname , legacy_format=snake_case )
_UpperCAmelCase = tokenizer
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = '<pad>'
_UpperCAmelCase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case ) , snake_case )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case ) , snake_case )
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '<s>' )
self.assertEqual(vocab_keys[1] , '<pad>' )
self.assertEqual(vocab_keys[-1] , '<mask>' )
self.assertEqual(len(snake_case ) , 101122 )
def lowerCamelCase_ ( self ) -> List[Any]:
self.assertEqual(self.get_tokenizer().vocab_size , 101122 )
@require_torch
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
_UpperCAmelCase = [0, 57, 3018, 70307, 91, 2]
_UpperCAmelCase = self.tokenizer(
snake_case , max_length=len(snake_case ) , padding=snake_case , truncation=snake_case , return_tensors='pt' )
self.assertIsInstance(snake_case , snake_case )
self.assertEqual((2, 6) , batch.input_ids.shape )
self.assertEqual((2, 6) , batch.attention_mask.shape )
_UpperCAmelCase = batch.input_ids.tolist()[0]
self.assertListEqual(snake_case , snake_case )
def lowerCamelCase_ ( self ) -> Optional[Any]:
if not self.test_rust_tokenizer:
return
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = self.get_rust_tokenizer()
_UpperCAmelCase = 'I was born in 92000, and this is falsé.'
_UpperCAmelCase = tokenizer.tokenize(snake_case )
_UpperCAmelCase = rust_tokenizer.tokenize(snake_case )
self.assertListEqual(snake_case , snake_case )
_UpperCAmelCase = tokenizer.encode(snake_case , add_special_tokens=snake_case )
_UpperCAmelCase = rust_tokenizer.encode(snake_case , add_special_tokens=snake_case )
self.assertListEqual(snake_case , snake_case )
_UpperCAmelCase = self.get_rust_tokenizer()
_UpperCAmelCase = tokenizer.encode(snake_case )
_UpperCAmelCase = rust_tokenizer.encode(snake_case )
self.assertListEqual(snake_case , snake_case )
@slow
def lowerCamelCase_ ( self ) -> Optional[int]:
# fmt: off
_UpperCAmelCase = {'input_ids': [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 2, 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, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 5, 2]], '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, 0, 0, 0, 0, 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, 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]]} # noqa: E501
# fmt: on
# moussaKam/mbarthez is a french model. So we also use french texts.
_UpperCAmelCase = [
'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
'telles que la traduction et la synthèse de texte.',
]
self.tokenizer_integration_test_util(
expected_encoding=snake_case , model_name='moussaKam/mbarthez' , revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6' , sequences=snake_case , )
| 24
| 1
|
"""simple docstring"""
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
lowercase = logging.getLogger(__name__)
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = argparse.ArgumentParser(
description='Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.' )
parser.add_argument(
'--dataset_name' , type=A , default='wikitext' , help='Name of the training. Explore datasets at: hf.co/datasets.' , )
parser.add_argument(
'--dataset_config' , type=A , default='wikitext-103-raw-v1' , help='Configuration name of the dataset.' )
parser.add_argument(
'--tokenizer_name_or_path' , type=A , default='sayakpaul/unigram-tokenizer-wikitext' , help='Tokenizer identifier. Can be a local filepath or a Hub identifier.' , )
parser.add_argument(
'--shard_size' , type=A , default=1000 , help='Number of entries to go in a single shard.' , )
parser.add_argument('--split' , type=A , default='train' , choices=['train', 'test', 'validation'] )
parser.add_argument(
'--limit' , default=A , type=A , help='Limit the number of shards (used for debugging).' , )
parser.add_argument(
'--max_length' , type=A , default=512 , help='Maximum sequence length. For training on TPUs, it helps to have a maximum'
' sequence length that is a multiple of 8.' , )
parser.add_argument(
'--output_dir' , default='tf-tpu' , type=A , help='Output directory where the TFRecord shards will be saved. If the'
' path is appended with `gs://` (\'gs://tf-tpu\', for example) then the TFRecord'
' shards will be directly saved to a Google Cloud Storage bucket.' , )
_UpperCAmelCase = parser.parse_args()
return args
def UpperCAmelCase ( A : Tuple ):
'''simple docstring'''
def fn(A : int ):
return tokenizer(examples['text'] )
return fn
def UpperCAmelCase ( A : int ):
'''simple docstring'''
_UpperCAmelCase = []
for i in range(len(tokenized_data['input_ids'] ) ):
_UpperCAmelCase = {
'input_ids': tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data['input_ids'][i] ) ),
'attention_mask': tf.train.Feature(
intaa_list=tf.train.IntaaList(value=tokenized_data['attention_mask'][i] ) ),
}
_UpperCAmelCase = tf.train.Features(feature=A )
_UpperCAmelCase = tf.train.Example(features=A )
_UpperCAmelCase = example.SerializeToString()
records.append(A )
return records
def UpperCAmelCase ( A : Union[str, Any] ):
'''simple docstring'''
_UpperCAmelCase = datasets.load_dataset(args.dataset_name , args.dataset_config , split=args.split )
if args.limit is not None:
_UpperCAmelCase = min(len(A ) , args.limit )
_UpperCAmelCase = dataset.select(range(A ) )
print(f'Limiting the dataset to {args.limit} entries.' )
_UpperCAmelCase = AutoTokenizer.from_pretrained(args.tokenizer_name_or_path )
# Handle output directory creation.
# For serializing into a Google Cloud Storage Bucket, one needs to first
# create a bucket.
if "gs" not in args.output_dir:
if not os.path.exists(args.output_dir ):
os.makedirs(args.output_dir )
_UpperCAmelCase = os.path.join(args.output_dir , args.split )
if not os.path.exists(A ):
os.makedirs(A )
else:
_UpperCAmelCase = os.path.join(args.output_dir , args.split )
# Tokenize the whole dataset at once.
_UpperCAmelCase = tokenize_function(A )
_UpperCAmelCase = dataset.map(A , batched=A , num_proc=4 , remove_columns=['text'] )
# We need to concatenate all our texts together, and then split the result
# into chunks of a fixed size, which we will call block_size. To do this, we
# will use the map method again, with the option batched=True. When we use batched=True,
# the function we pass to map() will be passed multiple inputs at once, allowing us
# to group them into more or fewer examples than we had in the input.
# This allows us to create our new fixed-length samples. The advantage of this
# method is that we don't lose a whole lot of content from the dataset compared to the
# case where we simply tokenize with a pre-defined max_length.
def group_texts(A : Any ):
# Concatenate all texts.
_UpperCAmelCase = {k: sum(examples[k] , [] ) for k in examples.keys()}
_UpperCAmelCase = len(concatenated_examples[list(examples.keys() )[0]] )
# We drop the small remainder, though you could add padding instead if the model supports it
# In this, as in all things, we advise you to follow your heart 🫀
_UpperCAmelCase = (total_length // args.max_length) * args.max_length
# Split by chunks of max_len.
_UpperCAmelCase = {
k: [t[i : i + args.max_length] for i in range(0 , A , args.max_length )]
for k, t in concatenated_examples.items()
}
return result
_UpperCAmelCase = dataset_tokenized.map(A , batched=A , batch_size=1000 , num_proc=4 )
_UpperCAmelCase = 0
_UpperCAmelCase = 0
for shard in range(0 , len(A ) , args.shard_size ):
_UpperCAmelCase = grouped_dataset[shard : shard + args.shard_size]
_UpperCAmelCase = len(dataset_snapshot['input_ids'] )
_UpperCAmelCase = os.path.join(A , f'dataset-{shard_count}-{records_containing}.tfrecord' )
_UpperCAmelCase = get_serialized_examples(A )
with tf.io.TFRecordWriter(A ) as out_file:
for i in range(len(A ) ):
_UpperCAmelCase = serialized_examples[i]
out_file.write(A )
print('Wrote file {} containing {} records'.format(A , A ) )
shard_count += 1
total_records += records_containing
with open(f'split-{args.split}-records-count.txt' , 'w' ) as f:
print(f'Total {args.split} records: {total_records}' , file=A )
if __name__ == "__main__":
lowercase = parse_args()
main(args)
| 24
|
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class lowercase__ ( A, unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = DiTPipeline
_UpperCAmelCase = CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
_UpperCAmelCase = PipelineTesterMixin.required_optional_params - {
'''latents''',
'''num_images_per_prompt''',
'''callback''',
'''callback_steps''',
}
_UpperCAmelCase = CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
_UpperCAmelCase = False
def lowerCamelCase_ ( self ) -> str:
torch.manual_seed(0 )
_UpperCAmelCase = TransformeraDModel(
sample_size=16 , num_layers=2 , patch_size=4 , attention_head_dim=8 , num_attention_heads=2 , in_channels=4 , out_channels=8 , attention_bias=snake_case , activation_fn='gelu-approximate' , num_embeds_ada_norm=1000 , norm_type='ada_norm_zero' , norm_elementwise_affine=snake_case , )
_UpperCAmelCase = AutoencoderKL()
_UpperCAmelCase = DDIMScheduler()
_UpperCAmelCase = {'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
return components
def lowerCamelCase_ ( self , snake_case , snake_case=0 ) -> Optional[Any]:
if str(snake_case ).startswith('mps' ):
_UpperCAmelCase = torch.manual_seed(snake_case )
else:
_UpperCAmelCase = torch.Generator(device=snake_case ).manual_seed(snake_case )
_UpperCAmelCase = {
'class_labels': [1],
'generator': generator,
'num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
def lowerCamelCase_ ( self ) -> List[Any]:
_UpperCAmelCase = 'cpu'
_UpperCAmelCase = self.get_dummy_components()
_UpperCAmelCase = self.pipeline_class(**snake_case )
pipe.to(snake_case )
pipe.set_progress_bar_config(disable=snake_case )
_UpperCAmelCase = self.get_dummy_inputs(snake_case )
_UpperCAmelCase = pipe(**snake_case ).images
_UpperCAmelCase = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 16, 16, 3) )
_UpperCAmelCase = np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457] )
_UpperCAmelCase = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(snake_case , 1E-3 )
def lowerCamelCase_ ( self ) -> Any:
self._test_inference_batch_single_identical(relax_max_difference=snake_case , expected_max_diff=1E-3 )
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def lowerCamelCase_ ( self ) -> Optional[int]:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
@require_torch_gpu
@slow
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> int:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = DiTPipeline.from_pretrained('facebook/DiT-XL-2-256' )
pipe.to('cuda' )
_UpperCAmelCase = ['vase', 'umbrella', 'white shark', 'white wolf']
_UpperCAmelCase = pipe.get_label_ids(snake_case )
_UpperCAmelCase = pipe(snake_case , generator=snake_case , num_inference_steps=40 , output_type='np' ).images
for word, image in zip(snake_case , snake_case ):
_UpperCAmelCase = load_numpy(
f'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy' )
assert np.abs((expected_image - image).max() ) < 1E-2
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = DiTPipeline.from_pretrained('facebook/DiT-XL-2-512' )
_UpperCAmelCase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.to('cuda' )
_UpperCAmelCase = ['vase', 'umbrella']
_UpperCAmelCase = pipe.get_label_ids(snake_case )
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = pipe(snake_case , generator=snake_case , num_inference_steps=25 , output_type='np' ).images
for word, image in zip(snake_case , snake_case ):
_UpperCAmelCase = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
f'/dit/{word}_512.npy' )
assert np.abs((expected_image - image).max() ) < 1E-1
| 24
| 1
|
"""simple docstring"""
lowercase = {
'''Pillow''': '''Pillow''',
'''accelerate''': '''accelerate>=0.11.0''',
'''compel''': '''compel==0.1.8''',
'''black''': '''black~=23.1''',
'''datasets''': '''datasets''',
'''filelock''': '''filelock''',
'''flax''': '''flax>=0.4.1''',
'''hf-doc-builder''': '''hf-doc-builder>=0.3.0''',
'''huggingface-hub''': '''huggingface-hub>=0.13.2''',
'''requests-mock''': '''requests-mock==1.10.0''',
'''importlib_metadata''': '''importlib_metadata''',
'''invisible-watermark''': '''invisible-watermark''',
'''isort''': '''isort>=5.5.4''',
'''jax''': '''jax>=0.2.8,!=0.3.2''',
'''jaxlib''': '''jaxlib>=0.1.65''',
'''Jinja2''': '''Jinja2''',
'''k-diffusion''': '''k-diffusion>=0.0.12''',
'''torchsde''': '''torchsde''',
'''note_seq''': '''note_seq''',
'''librosa''': '''librosa''',
'''numpy''': '''numpy''',
'''omegaconf''': '''omegaconf''',
'''parameterized''': '''parameterized''',
'''protobuf''': '''protobuf>=3.20.3,<4''',
'''pytest''': '''pytest''',
'''pytest-timeout''': '''pytest-timeout''',
'''pytest-xdist''': '''pytest-xdist''',
'''ruff''': '''ruff>=0.0.241''',
'''safetensors''': '''safetensors''',
'''sentencepiece''': '''sentencepiece>=0.1.91,!=0.1.92''',
'''scipy''': '''scipy''',
'''onnx''': '''onnx''',
'''regex''': '''regex!=2019.12.17''',
'''requests''': '''requests''',
'''tensorboard''': '''tensorboard''',
'''torch''': '''torch>=1.4''',
'''torchvision''': '''torchvision''',
'''transformers''': '''transformers>=4.25.1''',
'''urllib3''': '''urllib3<=2.0.0''',
}
| 24
|
"""simple docstring"""
def UpperCAmelCase ( A : int ):
'''simple docstring'''
_UpperCAmelCase = abs(A )
_UpperCAmelCase = 0
while n > 0:
res += n % 10
n //= 10
return res
def UpperCAmelCase ( A : int ):
'''simple docstring'''
_UpperCAmelCase = abs(A )
return n if n < 10 else n % 10 + sum_of_digits(n // 10 )
def UpperCAmelCase ( A : int ):
'''simple docstring'''
return sum(int(A ) for c in str(abs(A ) ) )
def UpperCAmelCase ( ):
'''simple docstring'''
from collections.abc import Callable
from timeit import timeit
def benchmark_a_function(A : Callable , A : int ) -> None:
_UpperCAmelCase = f'{func.__name__}({value})'
_UpperCAmelCase = timeit(f'__main__.{call}' , setup='import __main__' )
print(f'{call:56} = {func(A )} -- {timing:.4f} seconds' )
for value in (26_2144, 1125_8999_0684_2624, 126_7650_6002_2822_9401_4967_0320_5376):
for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
benchmark_a_function(A , A )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 24
| 1
|
"""simple docstring"""
import argparse
import importlib
from pathlib import Path
# Test all the extensions added in the setup
lowercase = [
'''kernels/rwkv/wkv_cuda.cu''',
'''kernels/rwkv/wkv_op.cpp''',
'''kernels/deformable_detr/ms_deform_attn.h''',
'''kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh''',
'''models/graphormer/algos_graphormer.pyx''',
]
def UpperCAmelCase ( A : Optional[int] ):
'''simple docstring'''
for file in FILES_TO_FIND:
if not (transformers_path / file).exists():
return False
return True
if __name__ == "__main__":
lowercase = argparse.ArgumentParser()
parser.add_argument('''--check_lib''', action='''store_true''', help='''Whether to check the build or the actual package.''')
lowercase = parser.parse_args()
if args.check_lib:
lowercase = importlib.import_module('''transformers''')
lowercase = Path(transformers_module.__file__).parent
else:
lowercase = Path.cwd() / '''build/lib/transformers'''
if not test_custom_files_are_present(transformers_path):
raise ValueError('''The built release does not contain the custom files. Fix this before going further!''')
| 24
|
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase ( A : int , A : int ):
'''simple docstring'''
_UpperCAmelCase = []
create_all_state(1 , A , A , [] , A )
return result
def UpperCAmelCase ( A : int , A : int , A : int , A : list[int] , A : list[list[int]] , ):
'''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 : list[list[int]] ):
'''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)
| 24
| 1
|
"""simple docstring"""
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
lowercase = logging.get_logger(__name__)
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = ['''audio_values''', '''audio_mask''']
def __init__( self , snake_case=2048 , snake_case=1 , snake_case=[16, 16] , snake_case=128 , snake_case=44100 , snake_case=86 , snake_case=2048 , snake_case=0.0 , **snake_case , ) -> Any:
super().__init__(
feature_size=snake_case , sampling_rate=snake_case , padding_value=snake_case , **snake_case , )
_UpperCAmelCase = spectrogram_length
_UpperCAmelCase = num_channels
_UpperCAmelCase = patch_size
_UpperCAmelCase = feature_size // self.patch_size[1]
_UpperCAmelCase = n_fft
_UpperCAmelCase = sampling_rate // hop_length_to_sampling_rate
_UpperCAmelCase = sampling_rate
_UpperCAmelCase = padding_value
_UpperCAmelCase = mel_filter_bank(
num_frequency_bins=1 + n_fft // 2 , num_mel_filters=snake_case , min_frequency=0.0 , max_frequency=22050.0 , sampling_rate=snake_case , norm='slaney' , mel_scale='slaney' , ).T
def lowerCamelCase_ ( self , snake_case ) -> np.ndarray:
_UpperCAmelCase = spectrogram(
snake_case , window_function(self.n_fft , 'hann' ) , frame_length=self.n_fft , hop_length=self.hop_length , power=2.0 , mel_filters=self.mel_filters.T , log_mel='dB' , db_range=80.0 , )
_UpperCAmelCase = log_spec[:, :-1]
_UpperCAmelCase = log_spec - 20.0
_UpperCAmelCase = np.clip(log_spec / 40.0 , -2.0 , 0.0 ) + 1.0
return log_spec
def __call__( self , snake_case , snake_case = None , snake_case = True , snake_case = None , snake_case = False , snake_case = False , **snake_case , ) -> BatchFeature:
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
'This feature extractor is set to support sampling rate'
f' of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled'
f' with {self.sampling_rate} and not {sampling_rate}.' )
else:
logger.warning(
'It is strongly recommended to pass the `sampling_rate` argument to this function. '
'Failing to do so can result in silent errors that might be hard to debug.' )
_UpperCAmelCase = isinstance(snake_case , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f'Only mono-channel audio is supported for input to {self}' )
_UpperCAmelCase = is_batched_numpy or (
isinstance(snake_case , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
_UpperCAmelCase = [np.asarray([speech] , dtype=np.floataa ).T for speech in raw_speech]
elif not is_batched and not isinstance(snake_case , np.ndarray ):
_UpperCAmelCase = np.asarray(snake_case , dtype=np.floataa )
elif isinstance(snake_case , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
_UpperCAmelCase = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
_UpperCAmelCase = [np.asarray([raw_speech] ).T]
# Convert audio signals to log mel spectrograms, truncate by time axis
_UpperCAmelCase = [
self._np_extract_fbank_features(waveform.squeeze() ).T[: self.spectrogram_length] for waveform in raw_speech
]
if isinstance(audio_features[0] , snake_case ):
_UpperCAmelCase = [np.asarray(snake_case , dtype=np.floataa ) for feature in audio_features]
# Create audio attention mask
_UpperCAmelCase = max(
[ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len for feature in audio_features] ) # The maximum number of audio patches in a batch
if return_attention_mask:
_UpperCAmelCase = [
(ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [1]
+ (max_patch_len - ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [0]
for feature in audio_features
]
_UpperCAmelCase = np.array(snake_case ).astype(np.floataa )
# convert into correct format for padding
_UpperCAmelCase = max_patch_len // self.freq_len * self.patch_size[0] # The maximum audio size in a batch
_UpperCAmelCase = np.ones([len(snake_case ), 1, max_time_len, self.feature_size] ).astype(np.floataa )
_UpperCAmelCase = padded_audio_features * self.padding_value
for i in range(len(snake_case ) ):
_UpperCAmelCase = audio_features[i]
_UpperCAmelCase = feature
# return as BatchFeature
if return_attention_mask:
_UpperCAmelCase = {'audio_values': padded_audio_features, 'audio_mask': audio_mask}
else:
_UpperCAmelCase = {'audio_values': padded_audio_features}
_UpperCAmelCase = BatchFeature(data=snake_case , tensor_type=snake_case )
return encoded_inputs
| 24
|
"""simple docstring"""
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS
logging.basicConfig(level=logging.DEBUG)
lowercase = logging.getLogger()
def UpperCAmelCase ( A : Path , A : list ):
'''simple docstring'''
_UpperCAmelCase = '\n'.join(A )
Path(A ).open('w' ).writelines(A )
lowercase = '''patrickvonplaten/t5-tiny-random'''
lowercase = '''sshleifer/bart-tiny-random'''
lowercase = '''sshleifer/tiny-mbart'''
lowercase = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL) # remove noisy download output from tracebacks
class lowercase__ ( A ):
'''simple docstring'''
def lowerCamelCase_ ( self , snake_case ) -> str:
_UpperCAmelCase = Path(self.get_auto_remove_tmp_dir() ) / 'utest_input.source'
_UpperCAmelCase = input_file_name.parent / 'utest_output.txt'
assert not output_file_name.exists()
_UpperCAmelCase = [' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
_dump_articles(snake_case , snake_case )
_UpperCAmelCase = str(Path(self.get_auto_remove_tmp_dir() ) / 'scores.json' )
_UpperCAmelCase = 'translation_en_to_de' if model == T5_TINY else 'summarization'
_UpperCAmelCase = f'\n run_eval_search.py\n {model}\n {input_file_name}\n {output_file_name}\n --score_path {score_path}\n --task {task}\n --num_beams 2\n --length_penalty 2.0\n '.split()
with patch.object(snake_case , 'argv' , snake_case ):
run_generate()
assert Path(snake_case ).exists()
# os.remove(Path(output_file_name))
def lowerCamelCase_ ( self ) -> str:
self.run_eval_tester(snake_case )
@parameterized.expand([BART_TINY, MBART_TINY] )
@slow
def lowerCamelCase_ ( self , snake_case ) -> List[Any]:
self.run_eval_tester(snake_case )
@parameterized.expand([T5_TINY, MBART_TINY] )
@slow
def lowerCamelCase_ ( self , snake_case ) -> Dict:
_UpperCAmelCase = Path(self.get_auto_remove_tmp_dir() ) / 'utest_input.source'
_UpperCAmelCase = input_file_name.parent / 'utest_output.txt'
assert not output_file_name.exists()
_UpperCAmelCase = {
'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
'de': [
'Maschinelles Lernen ist großartig, oder?',
'Ich esse gerne Bananen',
'Morgen ist wieder ein toller Tag!',
],
}
_UpperCAmelCase = Path(self.get_auto_remove_tmp_dir() )
_UpperCAmelCase = str(tmp_dir / 'scores.json' )
_UpperCAmelCase = str(tmp_dir / 'val.target' )
_dump_articles(snake_case , text['en'] )
_dump_articles(snake_case , text['de'] )
_UpperCAmelCase = 'translation_en_to_de' if model == T5_TINY else 'summarization'
_UpperCAmelCase = f'\n run_eval_search.py\n {model}\n {str(snake_case )}\n {str(snake_case )}\n --score_path {score_path}\n --reference_path {reference_path}\n --task {task}\n '.split()
testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0'] )
with patch.object(snake_case , 'argv' , snake_case ):
with CaptureStdout() as cs:
run_search()
_UpperCAmelCase = [' num_beams | length_penalty', model, 'Best score args']
_UpperCAmelCase = ['Info']
if "translation" in task:
expected_strings.append('bleu' )
else:
expected_strings.extend(snake_case )
for w in expected_strings:
assert w in cs.out
for w in un_expected_strings:
assert w not in cs.out
assert Path(snake_case ).exists()
os.remove(Path(snake_case ) )
| 24
| 1
|
"""simple docstring"""
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roformer.modeling_flax_roformer import (
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
)
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def __init__( self , snake_case , snake_case=13 , snake_case=7 , snake_case=True , snake_case=True , snake_case=True , snake_case=True , snake_case=99 , snake_case=32 , snake_case=5 , snake_case=4 , snake_case=37 , snake_case="gelu" , snake_case=0.1 , snake_case=0.1 , snake_case=512 , snake_case=16 , snake_case=2 , snake_case=0.02 , snake_case=4 , ) -> Tuple:
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = seq_length
_UpperCAmelCase = is_training
_UpperCAmelCase = use_attention_mask
_UpperCAmelCase = use_token_type_ids
_UpperCAmelCase = use_labels
_UpperCAmelCase = vocab_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_act
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = type_vocab_size
_UpperCAmelCase = type_sequence_label_size
_UpperCAmelCase = initializer_range
_UpperCAmelCase = num_choices
def lowerCamelCase_ ( self ) -> List[Any]:
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCAmelCase = None
if self.use_attention_mask:
_UpperCAmelCase = random_attention_mask([self.batch_size, self.seq_length] )
_UpperCAmelCase = None
if self.use_token_type_ids:
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_UpperCAmelCase = RoFormerConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=snake_case , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = self.prepare_config_and_inputs()
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = config_and_inputs
_UpperCAmelCase = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
return config, inputs_dict
@require_flax
class lowercase__ ( A, unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = True
_UpperCAmelCase = (
(
FlaxRoFormerModel,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
)
if is_flax_available()
else ()
)
def lowerCamelCase_ ( self ) -> Optional[Any]:
_UpperCAmelCase = FlaxRoFormerModelTester(self )
@slow
def lowerCamelCase_ ( self ) -> Tuple:
for model_class_name in self.all_model_classes:
_UpperCAmelCase = model_class_name.from_pretrained('junnyu/roformer_chinese_small' , from_pt=snake_case )
_UpperCAmelCase = model(np.ones((1, 1) ) )
self.assertIsNotNone(snake_case )
@require_flax
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
@slow
def lowerCamelCase_ ( self ) -> List[Any]:
_UpperCAmelCase = FlaxRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base' )
_UpperCAmelCase = jnp.array([[0, 1, 2, 3, 4, 5]] )
_UpperCAmelCase = model(snake_case )[0]
_UpperCAmelCase = 50000
_UpperCAmelCase = (1, 6, vocab_size)
self.assertEqual(output.shape , snake_case )
_UpperCAmelCase = jnp.array(
[[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]] )
self.assertTrue(jnp.allclose(output[:, :3, :3] , snake_case , atol=1E-4 ) )
| 24
|
"""simple docstring"""
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal
lowercase = logging.get_logger(__name__)
lowercase = TypeVar('''DatasetType''', Dataset, IterableDataset)
def UpperCAmelCase ( A : List[DatasetType] , A : Optional[List[float]] = None , A : Optional[int] = None , A : Optional[DatasetInfo] = None , A : Optional[NamedSplit] = None , A : Literal["first_exhausted", "all_exhausted"] = "first_exhausted" , ):
'''simple docstring'''
from .arrow_dataset import Dataset
from .iterable_dataset import IterableDataset
if not datasets:
raise ValueError('Unable to interleave an empty list of datasets.' )
for i, dataset in enumerate(A ):
if not isinstance(A , (Dataset, IterableDataset) ):
if isinstance(A , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
'is an empty dataset dictionary.' )
raise ValueError(
f'Dataset at position {i} has at least one split: {list(A )}\n'
f'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(A ) )}\']' )
raise ValueError(
f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(A ).__name__}.' )
if i == 0:
_UpperCAmelCase , _UpperCAmelCase = (
(Dataset, IterableDataset) if isinstance(A , A ) else (IterableDataset, Dataset)
)
elif not isinstance(A , A ):
raise ValueError(
f'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.' )
if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
raise ValueError(f'{stopping_strategy} is not supported. Please enter a valid stopping_strategy.' )
if dataset_type is Dataset:
return _interleave_map_style_datasets(
A , A , A , info=A , split=A , stopping_strategy=A )
else:
return _interleave_iterable_datasets(
A , A , A , info=A , split=A , stopping_strategy=A )
def UpperCAmelCase ( A : List[DatasetType] , A : Optional[DatasetInfo] = None , A : Optional[NamedSplit] = None , A : int = 0 , ):
'''simple docstring'''
if not dsets:
raise ValueError('Unable to concatenate an empty list of datasets.' )
for i, dataset in enumerate(A ):
if not isinstance(A , (Dataset, IterableDataset) ):
if isinstance(A , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
'is an empty dataset dictionary.' )
raise ValueError(
f'Dataset at position {i} has at least one split: {list(A )}\n'
f'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(A ) )}\']' )
raise ValueError(
f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(A ).__name__}.' )
if i == 0:
_UpperCAmelCase , _UpperCAmelCase = (
(Dataset, IterableDataset) if isinstance(A , A ) else (IterableDataset, Dataset)
)
elif not isinstance(A , A ):
raise ValueError(
f'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.' )
if dataset_type is Dataset:
return _concatenate_map_style_datasets(A , info=A , split=A , axis=A )
else:
return _concatenate_iterable_datasets(A , info=A , split=A , axis=A )
| 24
| 1
|
"""simple docstring"""
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def UpperCAmelCase ( A : Optional[int] , A : Union[str, Any] ):
'''simple docstring'''
assert isinstance(A , A )
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('keep_in_memory' , [False, True] )
def UpperCAmelCase ( A : Any , A : Optional[int] , A : Optional[int] ):
'''simple docstring'''
_UpperCAmelCase = tmp_path / 'cache'
_UpperCAmelCase = {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
_UpperCAmelCase = JsonDatasetReader(A , cache_dir=A , keep_in_memory=A ).read()
_check_json_dataset(A , A )
@pytest.mark.parametrize(
'features' , [
None,
{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
{'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
{'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
{'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
] , )
def UpperCAmelCase ( A : Dict , A : str , A : List[str] ):
'''simple docstring'''
_UpperCAmelCase = tmp_path / 'cache'
_UpperCAmelCase = {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
_UpperCAmelCase = features.copy() if features else default_expected_features
_UpperCAmelCase = (
Features({feature: Value(A ) for feature, dtype in features.items()} ) if features is not None else None
)
_UpperCAmelCase = JsonDatasetReader(A , features=A , cache_dir=A ).read()
_check_json_dataset(A , A )
@pytest.mark.parametrize(
'features' , [
None,
{'col_3': 'float64', 'col_1': 'string', 'col_2': 'int64'},
] , )
def UpperCAmelCase ( A : Union[str, Any] , A : str , A : List[Any] ):
'''simple docstring'''
_UpperCAmelCase = tmp_path / 'cache'
_UpperCAmelCase = {'col_3': 'float64', 'col_1': 'string', 'col_2': 'int64'}
_UpperCAmelCase = features.copy() if features else default_expected_features
_UpperCAmelCase = (
Features({feature: Value(A ) for feature, dtype in features.items()} ) if features is not None else None
)
_UpperCAmelCase = JsonDatasetReader(A , features=A , cache_dir=A ).read()
assert isinstance(A , A )
assert dataset.num_rows == 2
assert dataset.num_columns == 3
assert dataset.column_names == ["col_3", "col_1", "col_2"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
def UpperCAmelCase ( A : Union[str, Any] , A : Optional[int] ):
'''simple docstring'''
_UpperCAmelCase = {'col_2': 'int64', 'col_3': 'float64', 'col_1': 'string'}
_UpperCAmelCase = features.copy()
_UpperCAmelCase = (
Features({feature: Value(A ) for feature, dtype in features.items()} ) if features is not None else None
)
_UpperCAmelCase = tmp_path / 'cache'
_UpperCAmelCase = JsonDatasetReader(A , features=A , cache_dir=A ).read()
assert isinstance(A , A )
assert dataset.num_rows == 2
assert dataset.num_columns == 3
assert dataset.column_names == ["col_2", "col_3", "col_1"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('split' , [None, NamedSplit('train' ), 'train', 'test'] )
def UpperCAmelCase ( A : Union[str, Any] , A : Any , A : Optional[Any] ):
'''simple docstring'''
_UpperCAmelCase = tmp_path / 'cache'
_UpperCAmelCase = {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
_UpperCAmelCase = JsonDatasetReader(A , cache_dir=A , split=A ).read()
_check_json_dataset(A , A )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize('path_type' , [str, list] )
def UpperCAmelCase ( A : int , A : int , A : Dict ):
'''simple docstring'''
if issubclass(A , A ):
_UpperCAmelCase = jsonl_path
elif issubclass(A , A ):
_UpperCAmelCase = [jsonl_path]
_UpperCAmelCase = tmp_path / 'cache'
_UpperCAmelCase = {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
_UpperCAmelCase = JsonDatasetReader(A , cache_dir=A ).read()
_check_json_dataset(A , A )
def UpperCAmelCase ( A : Tuple , A : Optional[Any] , A : Optional[int]=("train",) ):
'''simple docstring'''
assert isinstance(A , A )
for split in splits:
_UpperCAmelCase = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize('keep_in_memory' , [False, True] )
def UpperCAmelCase ( A : Optional[Any] , A : Dict , A : Union[str, Any] ):
'''simple docstring'''
_UpperCAmelCase = tmp_path / 'cache'
_UpperCAmelCase = {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
_UpperCAmelCase = JsonDatasetReader({'train': jsonl_path} , cache_dir=A , keep_in_memory=A ).read()
_check_json_datasetdict(A , A )
@pytest.mark.parametrize(
'features' , [
None,
{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
{'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
{'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
{'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
] , )
def UpperCAmelCase ( A : Dict , A : List[Any] , A : Union[str, Any] ):
'''simple docstring'''
_UpperCAmelCase = tmp_path / 'cache'
_UpperCAmelCase = {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
_UpperCAmelCase = features.copy() if features else default_expected_features
_UpperCAmelCase = (
Features({feature: Value(A ) for feature, dtype in features.items()} ) if features is not None else None
)
_UpperCAmelCase = JsonDatasetReader({'train': jsonl_path} , features=A , cache_dir=A ).read()
_check_json_datasetdict(A , A )
@pytest.mark.parametrize('split' , [None, NamedSplit('train' ), 'train', 'test'] )
def UpperCAmelCase ( A : List[Any] , A : str , A : int ):
'''simple docstring'''
if split:
_UpperCAmelCase = {split: jsonl_path}
else:
_UpperCAmelCase = 'train'
_UpperCAmelCase = {'train': jsonl_path, 'test': jsonl_path}
_UpperCAmelCase = tmp_path / 'cache'
_UpperCAmelCase = {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
_UpperCAmelCase = JsonDatasetReader(A , cache_dir=A ).read()
_check_json_datasetdict(A , A , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
def UpperCAmelCase ( A : Dict ):
'''simple docstring'''
return json.load(A )
def UpperCAmelCase ( A : Optional[Any] ):
'''simple docstring'''
return [json.loads(A ) for line in buffer]
class lowercase__ :
'''simple docstring'''
@pytest.mark.parametrize('lines, load_json_function' , [(True, load_json_lines), (False, load_json)] )
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case ) -> Optional[Any]:
with io.BytesIO() as buffer:
JsonDatasetWriter(snake_case , snake_case , lines=snake_case ).write()
buffer.seek(0 )
_UpperCAmelCase = load_json_function(snake_case )
assert isinstance(snake_case , snake_case )
assert isinstance(exported_content[0] , snake_case )
assert len(snake_case ) == 10
@pytest.mark.parametrize(
'orient, container, keys, len_at' , [
('records', list, {'tokens', 'labels', 'answers', 'id'}, None),
('split', dict, {'columns', 'data'}, 'data'),
('index', dict, set('0123456789' ), None),
('columns', dict, {'tokens', 'labels', 'answers', 'id'}, 'tokens'),
('values', list, None, None),
('table', dict, {'schema', 'data'}, 'data'),
] , )
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case , snake_case , snake_case ) -> str:
with io.BytesIO() as buffer:
JsonDatasetWriter(snake_case , snake_case , lines=snake_case , orient=snake_case ).write()
buffer.seek(0 )
_UpperCAmelCase = load_json(snake_case )
assert isinstance(snake_case , snake_case )
if keys:
if container is dict:
assert exported_content.keys() == keys
else:
assert exported_content[0].keys() == keys
else:
assert not hasattr(snake_case , 'keys' ) and not hasattr(exported_content[0] , 'keys' )
if len_at:
assert len(exported_content[len_at] ) == 10
else:
assert len(snake_case ) == 10
@pytest.mark.parametrize('lines, load_json_function' , [(True, load_json_lines), (False, load_json)] )
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case ) -> str:
with io.BytesIO() as buffer:
JsonDatasetWriter(snake_case , snake_case , lines=snake_case , num_proc=2 ).write()
buffer.seek(0 )
_UpperCAmelCase = load_json_function(snake_case )
assert isinstance(snake_case , snake_case )
assert isinstance(exported_content[0] , snake_case )
assert len(snake_case ) == 10
@pytest.mark.parametrize(
'orient, container, keys, len_at' , [
('records', list, {'tokens', 'labels', 'answers', 'id'}, None),
('split', dict, {'columns', 'data'}, 'data'),
('index', dict, set('0123456789' ), None),
('columns', dict, {'tokens', 'labels', 'answers', 'id'}, 'tokens'),
('values', list, None, None),
('table', dict, {'schema', 'data'}, 'data'),
] , )
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case , snake_case , snake_case ) -> Tuple:
with io.BytesIO() as buffer:
JsonDatasetWriter(snake_case , snake_case , lines=snake_case , orient=snake_case , num_proc=2 ).write()
buffer.seek(0 )
_UpperCAmelCase = load_json(snake_case )
assert isinstance(snake_case , snake_case )
if keys:
if container is dict:
assert exported_content.keys() == keys
else:
assert exported_content[0].keys() == keys
else:
assert not hasattr(snake_case , 'keys' ) and not hasattr(exported_content[0] , 'keys' )
if len_at:
assert len(exported_content[len_at] ) == 10
else:
assert len(snake_case ) == 10
def lowerCamelCase_ ( self , snake_case ) -> int:
with pytest.raises(snake_case ):
with io.BytesIO() as buffer:
JsonDatasetWriter(snake_case , snake_case , num_proc=0 )
@pytest.mark.parametrize('compression, extension' , [('gzip', 'gz'), ('bz2', 'bz2'), ('xz', 'xz')] )
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case , snake_case , snake_case ) -> Tuple:
_UpperCAmelCase = tmp_path_factory.mktemp('data' ) / f'test.json.{extension}'
_UpperCAmelCase = str(shared_datadir / f'test_file.json.{extension}' )
JsonDatasetWriter(snake_case , snake_case , compression=snake_case ).write()
with fsspec.open(snake_case , 'rb' , compression='infer' ) as f:
_UpperCAmelCase = f.read()
with fsspec.open(snake_case , 'rb' , compression='infer' ) as f:
_UpperCAmelCase = f.read()
assert exported_content == original_content
| 24
|
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available
from .test_pipelines_common import ANY
if is_torch_available():
import torch
@is_pipeline_test
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
_UpperCAmelCase = TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case ) -> Dict:
_UpperCAmelCase = TextaTextGenerationPipeline(model=snake_case , tokenizer=snake_case )
return generator, ["Something to write", "Something else"]
def lowerCamelCase_ ( self , snake_case , snake_case ) -> Dict:
_UpperCAmelCase = generator('Something there' )
self.assertEqual(snake_case , [{'generated_text': ANY(snake_case )}] )
# These are encoder decoder, they don't just append to incoming string
self.assertFalse(outputs[0]['generated_text'].startswith('Something there' ) )
_UpperCAmelCase = generator(['This is great !', 'Something else'] , num_return_sequences=2 , do_sample=snake_case )
self.assertEqual(
snake_case , [
[{'generated_text': ANY(snake_case )}, {'generated_text': ANY(snake_case )}],
[{'generated_text': ANY(snake_case )}, {'generated_text': ANY(snake_case )}],
] , )
_UpperCAmelCase = generator(
['This is great !', 'Something else'] , num_return_sequences=2 , batch_size=2 , do_sample=snake_case )
self.assertEqual(
snake_case , [
[{'generated_text': ANY(snake_case )}, {'generated_text': ANY(snake_case )}],
[{'generated_text': ANY(snake_case )}, {'generated_text': ANY(snake_case )}],
] , )
with self.assertRaises(snake_case ):
generator(4 )
@require_torch
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = pipeline('text2text-generation' , model='patrickvonplaten/t5-tiny-random' , framework='pt' )
# do_sample=False necessary for reproducibility
_UpperCAmelCase = generator('Something there' , do_sample=snake_case )
self.assertEqual(snake_case , [{'generated_text': ''}] )
_UpperCAmelCase = 3
_UpperCAmelCase = generator(
'Something there' , num_return_sequences=snake_case , num_beams=snake_case , )
_UpperCAmelCase = [
{'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide Beide'},
{'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide'},
{'generated_text': ''},
]
self.assertEqual(snake_case , snake_case )
_UpperCAmelCase = generator('This is a test' , do_sample=snake_case , num_return_sequences=2 , return_tensors=snake_case )
self.assertEqual(
snake_case , [
{'generated_token_ids': ANY(torch.Tensor )},
{'generated_token_ids': ANY(torch.Tensor )},
] , )
_UpperCAmelCase = generator.model.config.eos_token_id
_UpperCAmelCase = '<pad>'
_UpperCAmelCase = generator(
['This is a test', 'This is a second test'] , do_sample=snake_case , num_return_sequences=2 , batch_size=2 , return_tensors=snake_case , )
self.assertEqual(
snake_case , [
[
{'generated_token_ids': ANY(torch.Tensor )},
{'generated_token_ids': ANY(torch.Tensor )},
],
[
{'generated_token_ids': ANY(torch.Tensor )},
{'generated_token_ids': ANY(torch.Tensor )},
],
] , )
@require_tf
def lowerCamelCase_ ( self ) -> Any:
_UpperCAmelCase = pipeline('text2text-generation' , model='patrickvonplaten/t5-tiny-random' , framework='tf' )
# do_sample=False necessary for reproducibility
_UpperCAmelCase = generator('Something there' , do_sample=snake_case )
self.assertEqual(snake_case , [{'generated_text': ''}] )
| 24
| 1
|
"""simple docstring"""
from __future__ import annotations
from cmath import sqrt
def UpperCAmelCase ( A : int , A : int , A : int ):
'''simple docstring'''
if a == 0:
raise ValueError('Coefficient \'a\' must not be zero.' )
_UpperCAmelCase = b * b - 4 * a * c
_UpperCAmelCase = (-b + sqrt(A )) / (2 * a)
_UpperCAmelCase = (-b - sqrt(A )) / (2 * a)
return (
root_a.real if not root_a.imag else root_a,
root_a.real if not root_a.imag else root_a,
)
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = quadratic_roots(a=5 , b=6 , c=1 )
print(f'The solutions are: {solutiona} and {solutiona}' )
if __name__ == "__main__":
main()
| 24
|
"""simple docstring"""
def UpperCAmelCase ( A : int ):
'''simple docstring'''
_UpperCAmelCase = [[0 for _ in range(A )] for _ in range(m + 1 )]
for i in range(m + 1 ):
_UpperCAmelCase = 1
for n in range(m + 1 ):
for k in range(1 , A ):
memo[n][k] += memo[n][k - 1]
if n - k > 0:
memo[n][k] += memo[n - k - 1][k]
return memo[m][m - 1]
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
try:
lowercase = int(input('''Enter a number: ''').strip())
print(partition(n))
except ValueError:
print('''Please enter a number.''')
else:
try:
lowercase = int(sys.argv[1])
print(partition(n))
except ValueError:
print('''Please pass a number.''')
| 24
| 1
|
"""simple docstring"""
from __future__ import annotations
lowercase = [True] * 1_00_00_01
lowercase = 2
while i * i <= 1_00_00_00:
if seive[i]:
for j in range(i * i, 1_00_00_01, i):
lowercase = False
i += 1
def UpperCAmelCase ( A : int ):
'''simple docstring'''
return seive[n]
def UpperCAmelCase ( A : int ):
'''simple docstring'''
return any(digit in '02468' for digit in str(A ) )
def UpperCAmelCase ( A : int = 100_0000 ):
'''simple docstring'''
_UpperCAmelCase = [2] # result already includes the number 2.
for num in range(3 , limit + 1 , 2 ):
if is_prime(A ) and not contains_an_even_digit(A ):
_UpperCAmelCase = str(A )
_UpperCAmelCase = [int(str_num[j:] + str_num[:j] ) for j in range(len(A ) )]
if all(is_prime(A ) for i in list_nums ):
result.append(A )
return result
def UpperCAmelCase ( ):
'''simple docstring'''
return len(find_circular_primes() )
if __name__ == "__main__":
print(F'''{len(find_circular_primes()) = }''')
| 24
|
"""simple docstring"""
import os
lowercase = {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 1_00, '''D''': 5_00, '''M''': 10_00}
def UpperCAmelCase ( A : str ):
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase = 0
while index < len(A ) - 1:
_UpperCAmelCase = SYMBOLS[numerals[index]]
_UpperCAmelCase = 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 : int ):
'''simple docstring'''
_UpperCAmelCase = ''
_UpperCAmelCase = num // 1000
numerals += m_count * "M"
num %= 1000
_UpperCAmelCase = num // 100
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 %= 100
_UpperCAmelCase = num // 10
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 %= 10
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 : str = "/p089_roman.txt" ):
'''simple docstring'''
_UpperCAmelCase = 0
with open(os.path.dirname(A ) + roman_numerals_filename ) as filea:
_UpperCAmelCase = filea.readlines()
for line in lines:
_UpperCAmelCase = line.strip()
_UpperCAmelCase = parse_roman_numerals(A )
_UpperCAmelCase = generate_roman_numerals(A )
savings += len(A ) - len(A )
return savings
if __name__ == "__main__":
print(F'''{solution() = }''')
| 24
| 1
|
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging
logging.set_verbosity_info()
lowercase = logging.get_logger(__name__)
def UpperCAmelCase ( A : List[str] ):
'''simple docstring'''
_UpperCAmelCase = SwinConfig(
embed_dim=192 , depths=(2, 2, 18, 2) , num_heads=(6, 12, 24, 48) , window_size=12 , out_features=['stage2', 'stage3', 'stage4'] , )
_UpperCAmelCase = DetaConfig(
backbone_config=A , num_queries=900 , encoder_ffn_dim=2048 , decoder_ffn_dim=2048 , num_feature_levels=5 , assign_first_stage=A , with_box_refine=A , two_stage=A , )
# set labels
_UpperCAmelCase = 'huggingface/label-files'
if "o365" in model_name:
_UpperCAmelCase = 366
_UpperCAmelCase = 'object365-id2label.json'
else:
_UpperCAmelCase = 91
_UpperCAmelCase = 'coco-detection-id2label.json'
_UpperCAmelCase = num_labels
_UpperCAmelCase = json.load(open(cached_download(hf_hub_url(A , A , repo_type='dataset' ) ) , 'r' ) )
_UpperCAmelCase = {int(A ): v for k, v in idalabel.items()}
_UpperCAmelCase = idalabel
_UpperCAmelCase = {v: k for k, v in idalabel.items()}
return config
def UpperCAmelCase ( A : Optional[Any] ):
'''simple docstring'''
_UpperCAmelCase = []
# stem
# fmt: off
rename_keys.append(('backbone.0.body.patch_embed.proj.weight', 'model.backbone.model.embeddings.patch_embeddings.projection.weight') )
rename_keys.append(('backbone.0.body.patch_embed.proj.bias', 'model.backbone.model.embeddings.patch_embeddings.projection.bias') )
rename_keys.append(('backbone.0.body.patch_embed.norm.weight', 'model.backbone.model.embeddings.norm.weight') )
rename_keys.append(('backbone.0.body.patch_embed.norm.bias', 'model.backbone.model.embeddings.norm.bias') )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.norm1.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight') )
rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.norm1.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias') )
rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table') )
rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index') )
rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight') )
rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias') )
rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.norm2.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight') )
rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.norm2.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias') )
rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight') )
rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias') )
rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight') )
rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias') )
if i < 3:
rename_keys.append((f'backbone.0.body.layers.{i}.downsample.reduction.weight', f'model.backbone.model.encoder.layers.{i}.downsample.reduction.weight') )
rename_keys.append((f'backbone.0.body.layers.{i}.downsample.norm.weight', f'model.backbone.model.encoder.layers.{i}.downsample.norm.weight') )
rename_keys.append((f'backbone.0.body.layers.{i}.downsample.norm.bias', f'model.backbone.model.encoder.layers.{i}.downsample.norm.bias') )
rename_keys.append(('backbone.0.body.norm1.weight', 'model.backbone.model.hidden_states_norms.stage2.weight') )
rename_keys.append(('backbone.0.body.norm1.bias', 'model.backbone.model.hidden_states_norms.stage2.bias') )
rename_keys.append(('backbone.0.body.norm2.weight', 'model.backbone.model.hidden_states_norms.stage3.weight') )
rename_keys.append(('backbone.0.body.norm2.bias', 'model.backbone.model.hidden_states_norms.stage3.bias') )
rename_keys.append(('backbone.0.body.norm3.weight', 'model.backbone.model.hidden_states_norms.stage4.weight') )
rename_keys.append(('backbone.0.body.norm3.bias', 'model.backbone.model.hidden_states_norms.stage4.bias') )
# transformer encoder
for i in range(config.encoder_layers ):
rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight', f'model.encoder.layers.{i}.self_attn.sampling_offsets.weight') )
rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias', f'model.encoder.layers.{i}.self_attn.sampling_offsets.bias') )
rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.attention_weights.weight', f'model.encoder.layers.{i}.self_attn.attention_weights.weight') )
rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.attention_weights.bias', f'model.encoder.layers.{i}.self_attn.attention_weights.bias') )
rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.value_proj.weight', f'model.encoder.layers.{i}.self_attn.value_proj.weight') )
rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.value_proj.bias', f'model.encoder.layers.{i}.self_attn.value_proj.bias') )
rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.output_proj.weight', f'model.encoder.layers.{i}.self_attn.output_proj.weight') )
rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.output_proj.bias', f'model.encoder.layers.{i}.self_attn.output_proj.bias') )
rename_keys.append((f'transformer.encoder.layers.{i}.norm1.weight', f'model.encoder.layers.{i}.self_attn_layer_norm.weight') )
rename_keys.append((f'transformer.encoder.layers.{i}.norm1.bias', f'model.encoder.layers.{i}.self_attn_layer_norm.bias') )
rename_keys.append((f'transformer.encoder.layers.{i}.linear1.weight', f'model.encoder.layers.{i}.fc1.weight') )
rename_keys.append((f'transformer.encoder.layers.{i}.linear1.bias', f'model.encoder.layers.{i}.fc1.bias') )
rename_keys.append((f'transformer.encoder.layers.{i}.linear2.weight', f'model.encoder.layers.{i}.fc2.weight') )
rename_keys.append((f'transformer.encoder.layers.{i}.linear2.bias', f'model.encoder.layers.{i}.fc2.bias') )
rename_keys.append((f'transformer.encoder.layers.{i}.norm2.weight', f'model.encoder.layers.{i}.final_layer_norm.weight') )
rename_keys.append((f'transformer.encoder.layers.{i}.norm2.bias', f'model.encoder.layers.{i}.final_layer_norm.bias') )
# transformer decoder
for i in range(config.decoder_layers ):
rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight', f'model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight') )
rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias', f'model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias') )
rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.attention_weights.weight', f'model.decoder.layers.{i}.encoder_attn.attention_weights.weight') )
rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.attention_weights.bias', f'model.decoder.layers.{i}.encoder_attn.attention_weights.bias') )
rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.value_proj.weight', f'model.decoder.layers.{i}.encoder_attn.value_proj.weight') )
rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.value_proj.bias', f'model.decoder.layers.{i}.encoder_attn.value_proj.bias') )
rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.output_proj.weight', f'model.decoder.layers.{i}.encoder_attn.output_proj.weight') )
rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.output_proj.bias', f'model.decoder.layers.{i}.encoder_attn.output_proj.bias') )
rename_keys.append((f'transformer.decoder.layers.{i}.norm1.weight', f'model.decoder.layers.{i}.encoder_attn_layer_norm.weight') )
rename_keys.append((f'transformer.decoder.layers.{i}.norm1.bias', f'model.decoder.layers.{i}.encoder_attn_layer_norm.bias') )
rename_keys.append((f'transformer.decoder.layers.{i}.self_attn.out_proj.weight', f'model.decoder.layers.{i}.self_attn.out_proj.weight') )
rename_keys.append((f'transformer.decoder.layers.{i}.self_attn.out_proj.bias', f'model.decoder.layers.{i}.self_attn.out_proj.bias') )
rename_keys.append((f'transformer.decoder.layers.{i}.norm2.weight', f'model.decoder.layers.{i}.self_attn_layer_norm.weight') )
rename_keys.append((f'transformer.decoder.layers.{i}.norm2.bias', f'model.decoder.layers.{i}.self_attn_layer_norm.bias') )
rename_keys.append((f'transformer.decoder.layers.{i}.linear1.weight', f'model.decoder.layers.{i}.fc1.weight') )
rename_keys.append((f'transformer.decoder.layers.{i}.linear1.bias', f'model.decoder.layers.{i}.fc1.bias') )
rename_keys.append((f'transformer.decoder.layers.{i}.linear2.weight', f'model.decoder.layers.{i}.fc2.weight') )
rename_keys.append((f'transformer.decoder.layers.{i}.linear2.bias', f'model.decoder.layers.{i}.fc2.bias') )
rename_keys.append((f'transformer.decoder.layers.{i}.norm3.weight', f'model.decoder.layers.{i}.final_layer_norm.weight') )
rename_keys.append((f'transformer.decoder.layers.{i}.norm3.bias', f'model.decoder.layers.{i}.final_layer_norm.bias') )
# fmt: on
return rename_keys
def UpperCAmelCase ( A : Any , A : List[Any] , A : List[Any] ):
'''simple docstring'''
_UpperCAmelCase = dct.pop(A )
_UpperCAmelCase = val
def UpperCAmelCase ( A : Any , A : List[str] ):
'''simple docstring'''
_UpperCAmelCase = [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
for i in range(len(backbone_config.depths ) ):
_UpperCAmelCase = num_features[i]
for j in range(backbone_config.depths[i] ):
# fmt: off
# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
_UpperCAmelCase = state_dict.pop(f'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight' )
_UpperCAmelCase = state_dict.pop(f'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias' )
# next, add query, keys and values (in that order) to the state dict
_UpperCAmelCase = in_proj_weight[:dim, :]
_UpperCAmelCase = in_proj_bias[: dim]
_UpperCAmelCase = in_proj_weight[
dim : dim * 2, :
]
_UpperCAmelCase = in_proj_bias[
dim : dim * 2
]
_UpperCAmelCase = in_proj_weight[
-dim :, :
]
_UpperCAmelCase = in_proj_bias[-dim :]
# fmt: on
def UpperCAmelCase ( A : Optional[int] , A : int ):
'''simple docstring'''
_UpperCAmelCase = config.d_model
for i in range(config.decoder_layers ):
# read in weights + bias of input projection layer of self-attention
_UpperCAmelCase = state_dict.pop(f'transformer.decoder.layers.{i}.self_attn.in_proj_weight' )
_UpperCAmelCase = state_dict.pop(f'transformer.decoder.layers.{i}.self_attn.in_proj_bias' )
# next, add query, keys and values (in that order) to the state dict
_UpperCAmelCase = in_proj_weight[:hidden_size, :]
_UpperCAmelCase = in_proj_bias[:hidden_size]
_UpperCAmelCase = in_proj_weight[
hidden_size : hidden_size * 2, :
]
_UpperCAmelCase = in_proj_bias[hidden_size : hidden_size * 2]
_UpperCAmelCase = in_proj_weight[-hidden_size:, :]
_UpperCAmelCase = in_proj_bias[-hidden_size:]
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = 'http://images.cocodataset.org/val2017/000000039769.jpg'
_UpperCAmelCase = Image.open(requests.get(A , stream=A ).raw )
return im
@torch.no_grad()
def UpperCAmelCase ( A : Dict , A : List[str] , A : List[str] ):
'''simple docstring'''
_UpperCAmelCase = get_deta_config(A )
# load original state dict
if model_name == "deta-swin-large":
_UpperCAmelCase = hf_hub_download(repo_id='nielsr/deta-checkpoints' , filename='adet_swin_ft.pth' )
elif model_name == "deta-swin-large-o365":
_UpperCAmelCase = hf_hub_download(repo_id='jozhang97/deta-swin-l-o365' , filename='deta_swin_pt_o365.pth' )
else:
raise ValueError(f'Model name {model_name} not supported' )
_UpperCAmelCase = torch.load(A , map_location='cpu' )['model']
# original state dict
for name, param in state_dict.items():
print(A , param.shape )
# rename keys
_UpperCAmelCase = create_rename_keys(A )
for src, dest in rename_keys:
rename_key(A , A , A )
read_in_swin_q_k_v(A , config.backbone_config )
read_in_decoder_q_k_v(A , A )
# fix some prefixes
for key in state_dict.copy().keys():
if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
_UpperCAmelCase = state_dict.pop(A )
_UpperCAmelCase = val
if "input_proj" in key:
_UpperCAmelCase = state_dict.pop(A )
_UpperCAmelCase = val
if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
_UpperCAmelCase = state_dict.pop(A )
_UpperCAmelCase = val
# finally, create HuggingFace model and load state dict
_UpperCAmelCase = DetaForObjectDetection(A )
model.load_state_dict(A )
model.eval()
_UpperCAmelCase = 'cuda' if torch.cuda.is_available() else 'cpu'
model.to(A )
# load image processor
_UpperCAmelCase = DetaImageProcessor(format='coco_detection' )
# verify our conversion on image
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = processor(images=A , return_tensors='pt' )
_UpperCAmelCase = encoding['pixel_values']
_UpperCAmelCase = model(pixel_values.to(A ) )
# verify logits
print('Logits:' , outputs.logits[0, :3, :3] )
print('Boxes:' , outputs.pred_boxes[0, :3, :3] )
if model_name == "deta-swin-large":
_UpperCAmelCase = torch.tensor(
[[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]] )
_UpperCAmelCase = torch.tensor([[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]] )
elif model_name == "deta-swin-large-o365":
_UpperCAmelCase = torch.tensor(
[[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]] )
_UpperCAmelCase = torch.tensor([[0.2523, 0.5549, 0.4881], [0.7715, 0.4149, 0.4601], [0.5503, 0.2753, 0.0575]] )
assert torch.allclose(outputs.logits[0, :3, :3] , expected_logits.to(A ) , atol=1e-4 )
assert torch.allclose(outputs.pred_boxes[0, :3, :3] , expected_boxes.to(A ) , atol=1e-4 )
print('Everything ok!' )
if pytorch_dump_folder_path:
# Save model and processor
logger.info(f'Saving PyTorch model and processor to {pytorch_dump_folder_path}...' )
Path(A ).mkdir(exist_ok=A )
model.save_pretrained(A )
processor.save_pretrained(A )
# Push to hub
if push_to_hub:
print('Pushing model and processor to hub...' )
model.push_to_hub(f'jozhang97/{model_name}' )
processor.push_to_hub(f'jozhang97/{model_name}' )
if __name__ == "__main__":
lowercase = argparse.ArgumentParser()
parser.add_argument(
'''--model_name''',
type=str,
default='''deta-swin-large''',
choices=['''deta-swin-large''', '''deta-swin-large-o365'''],
help='''Name of the model you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default=None,
type=str,
help='''Path to the folder to output PyTorch model.''',
)
parser.add_argument(
'''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
)
lowercase = parser.parse_args()
convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 24
|
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_available():
import jax.numpy as jnp
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = {
'task_specific_params': {
'summarization': {'length_penalty': 1.0, 'max_length': 128, 'min_length': 12, 'num_beams': 4},
'summarization_cnn': {'length_penalty': 2.0, 'max_length': 142, 'min_length': 56, 'num_beams': 4},
'summarization_xsum': {'length_penalty': 1.0, 'max_length': 62, 'min_length': 11, 'num_beams': 6},
}
}
_UpperCAmelCase = {
'task_specific_params.summarization.length_penalty': 1.0,
'task_specific_params.summarization.max_length': 128,
'task_specific_params.summarization.min_length': 12,
'task_specific_params.summarization.num_beams': 4,
'task_specific_params.summarization_cnn.length_penalty': 2.0,
'task_specific_params.summarization_cnn.max_length': 142,
'task_specific_params.summarization_cnn.min_length': 56,
'task_specific_params.summarization_cnn.num_beams': 4,
'task_specific_params.summarization_xsum.length_penalty': 1.0,
'task_specific_params.summarization_xsum.max_length': 62,
'task_specific_params.summarization_xsum.min_length': 11,
'task_specific_params.summarization_xsum.num_beams': 6,
}
self.assertEqual(flatten_dict(snake_case ) , snake_case )
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(transpose(snake_case ) , x.transpose() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(transpose(snake_case , axes=(1, 2, 0) ) , x.transpose((1, 2, 0) ) ) )
@require_torch
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(transpose(snake_case ) , transpose(snake_case ).numpy() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(transpose(snake_case , axes=(1, 2, 0) ) , transpose(snake_case , axes=(1, 2, 0) ).numpy() ) )
@require_tf
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(transpose(snake_case ) , transpose(snake_case ).numpy() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(transpose(snake_case , axes=(1, 2, 0) ) , transpose(snake_case , axes=(1, 2, 0) ).numpy() ) )
@require_flax
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(transpose(snake_case ) , np.asarray(transpose(snake_case ) ) ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(transpose(snake_case , axes=(1, 2, 0) ) , np.asarray(transpose(snake_case , axes=(1, 2, 0) ) ) ) )
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(reshape(snake_case , (4, 3) ) , np.reshape(snake_case , (4, 3) ) ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(reshape(snake_case , (12, 5) ) , np.reshape(snake_case , (12, 5) ) ) )
@require_torch
def lowerCamelCase_ ( self ) -> Optional[Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (4, 3) ) , reshape(snake_case , (4, 3) ).numpy() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (12, 5) ) , reshape(snake_case , (12, 5) ).numpy() ) )
@require_tf
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (4, 3) ) , reshape(snake_case , (4, 3) ).numpy() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (12, 5) ) , reshape(snake_case , (12, 5) ).numpy() ) )
@require_flax
def lowerCamelCase_ ( self ) -> Tuple:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (4, 3) ) , np.asarray(reshape(snake_case , (4, 3) ) ) ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (12, 5) ) , np.asarray(reshape(snake_case , (12, 5) ) ) ) )
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = np.random.randn(1 , 3 , 4 )
self.assertTrue(np.allclose(squeeze(snake_case ) , np.squeeze(snake_case ) ) )
_UpperCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
self.assertTrue(np.allclose(squeeze(snake_case , axis=2 ) , np.squeeze(snake_case , axis=2 ) ) )
@require_torch
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(1 , 3 , 4 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case ) , squeeze(snake_case ).numpy() ) )
_UpperCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case , axis=2 ) , squeeze(snake_case , axis=2 ).numpy() ) )
@require_tf
def lowerCamelCase_ ( self ) -> Optional[int]:
_UpperCAmelCase = np.random.randn(1 , 3 , 4 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case ) , squeeze(snake_case ).numpy() ) )
_UpperCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case , axis=2 ) , squeeze(snake_case , axis=2 ).numpy() ) )
@require_flax
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = np.random.randn(1 , 3 , 4 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case ) , np.asarray(squeeze(snake_case ) ) ) )
_UpperCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case , axis=2 ) , np.asarray(squeeze(snake_case , axis=2 ) ) ) )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(expand_dims(snake_case , axis=1 ) , np.expand_dims(snake_case , axis=1 ) ) )
@require_torch
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(expand_dims(snake_case , axis=1 ) , expand_dims(snake_case , axis=1 ).numpy() ) )
@require_tf
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(expand_dims(snake_case , axis=1 ) , expand_dims(snake_case , axis=1 ).numpy() ) )
@require_flax
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(expand_dims(snake_case , axis=1 ) , np.asarray(expand_dims(snake_case , axis=1 ) ) ) )
| 24
| 1
|
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
lowercase = logging.get_logger(__name__)
class lowercase__ ( A ):
'''simple docstring'''
def __init__( self , *snake_case , **snake_case ) -> None:
warnings.warn(
'The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
' Please use DeformableDetrImageProcessor instead.' , snake_case , )
super().__init__(*snake_case , **snake_case )
| 24
|
"""simple docstring"""
import os
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = os.path.join(os.path.dirname(A ) , 'num.txt' )
with open(A ) as file_hand:
return str(sum(int(A ) for line in file_hand ) )[:10]
if __name__ == "__main__":
print(solution())
| 24
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase = {
'''configuration_m2m_100''': ['''M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''M2M100Config''', '''M2M100OnnxConfig'''],
'''tokenization_m2m_100''': ['''M2M100Tokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''M2M100ForConditionalGeneration''',
'''M2M100Model''',
'''M2M100PreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
from .tokenization_mam_aaa import MaMaaaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mam_aaa import (
M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
MaMaaaForConditionalGeneration,
MaMaaaModel,
MaMaaaPreTrainedModel,
)
else:
import sys
lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 24
|
"""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_roberta''': ['''ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RobertaConfig''', '''RobertaOnnxConfig'''],
'''tokenization_roberta''': ['''RobertaTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = ['''RobertaTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''RobertaForCausalLM''',
'''RobertaForMaskedLM''',
'''RobertaForMultipleChoice''',
'''RobertaForQuestionAnswering''',
'''RobertaForSequenceClassification''',
'''RobertaForTokenClassification''',
'''RobertaModel''',
'''RobertaPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFRobertaForCausalLM''',
'''TFRobertaForMaskedLM''',
'''TFRobertaForMultipleChoice''',
'''TFRobertaForQuestionAnswering''',
'''TFRobertaForSequenceClassification''',
'''TFRobertaForTokenClassification''',
'''TFRobertaMainLayer''',
'''TFRobertaModel''',
'''TFRobertaPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''FlaxRobertaForCausalLM''',
'''FlaxRobertaForMaskedLM''',
'''FlaxRobertaForMultipleChoice''',
'''FlaxRobertaForQuestionAnswering''',
'''FlaxRobertaForSequenceClassification''',
'''FlaxRobertaForTokenClassification''',
'''FlaxRobertaModel''',
'''FlaxRobertaPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
from .tokenization_roberta import RobertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_roberta_fast import RobertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta import (
ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaForCausalLM,
RobertaForMaskedLM,
RobertaForMultipleChoice,
RobertaForQuestionAnswering,
RobertaForSequenceClassification,
RobertaForTokenClassification,
RobertaModel,
RobertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta import (
TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
TFRobertaForMultipleChoice,
TFRobertaForQuestionAnswering,
TFRobertaForSequenceClassification,
TFRobertaForTokenClassification,
TFRobertaMainLayer,
TFRobertaModel,
TFRobertaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
FlaxRobertaPreTrainedModel,
)
else:
import sys
lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 24
| 1
|
"""simple docstring"""
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
def UpperCAmelCase ( A : Optional[int] ):
'''simple docstring'''
return 1.0 / (1.0 + np.exp(-_outputs ))
def UpperCAmelCase ( A : Union[str, Any] ):
'''simple docstring'''
_UpperCAmelCase = np.max(_outputs , axis=-1 , keepdims=A )
_UpperCAmelCase = np.exp(_outputs - maxes )
return shifted_exp / shifted_exp.sum(axis=-1 , keepdims=A )
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = '''sigmoid'''
_UpperCAmelCase = '''softmax'''
_UpperCAmelCase = '''none'''
@add_end_docstrings(
A, R'''
return_all_scores (`bool`, *optional*, defaults to `False`):
Whether to return all prediction scores or just the one of the predicted class.
function_to_apply (`str`, *optional*, defaults to `"default"`):
The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:
- `"default"`: if the model has a single label, will apply the sigmoid function on the output. If the model
has several labels, will apply the softmax function on the output.
- `"sigmoid"`: Applies the sigmoid function on the output.
- `"softmax"`: Applies the softmax function on the output.
- `"none"`: Does not apply any function on the output.
''', )
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = False
_UpperCAmelCase = ClassificationFunction.NONE
def __init__( self , **snake_case ) -> List[str]:
super().__init__(**snake_case )
self.check_model_type(
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if self.framework == 'tf'
else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING )
def lowerCamelCase_ ( self , snake_case=None , snake_case=None , snake_case="" , **snake_case ) -> Optional[Any]:
# Using "" as default argument because we're going to use `top_k=None` in user code to declare
# "No top_k"
_UpperCAmelCase = tokenizer_kwargs
_UpperCAmelCase = {}
if hasattr(self.model.config , 'return_all_scores' ) and return_all_scores is None:
_UpperCAmelCase = self.model.config.return_all_scores
if isinstance(snake_case , snake_case ) or top_k is None:
_UpperCAmelCase = top_k
_UpperCAmelCase = False
elif return_all_scores is not None:
warnings.warn(
'`return_all_scores` is now deprecated, if want a similar functionality use `top_k=None` instead of'
' `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.' , snake_case , )
if return_all_scores:
_UpperCAmelCase = None
else:
_UpperCAmelCase = 1
if isinstance(snake_case , snake_case ):
_UpperCAmelCase = ClassificationFunction[function_to_apply.upper()]
if function_to_apply is not None:
_UpperCAmelCase = function_to_apply
return preprocess_params, {}, postprocess_params
def __call__( self , *snake_case , **snake_case ) -> Dict:
_UpperCAmelCase = super().__call__(*snake_case , **snake_case )
# TODO try and retrieve it in a nicer way from _sanitize_parameters.
_UpperCAmelCase = 'top_k' not in kwargs
if isinstance(args[0] , snake_case ) and _legacy:
# This pipeline is odd, and return a list when single item is run
return [result]
else:
return result
def lowerCamelCase_ ( self , snake_case , **snake_case ) -> Dict[str, GenericTensor]:
_UpperCAmelCase = self.framework
if isinstance(snake_case , snake_case ):
return self.tokenizer(**snake_case , return_tensors=snake_case , **snake_case )
elif isinstance(snake_case , snake_case ) and len(snake_case ) == 1 and isinstance(inputs[0] , snake_case ) and len(inputs[0] ) == 2:
# It used to be valid to use a list of list of list for text pairs, keeping this path for BC
return self.tokenizer(
text=inputs[0][0] , text_pair=inputs[0][1] , return_tensors=snake_case , **snake_case )
elif isinstance(snake_case , snake_case ):
# This is likely an invalid usage of the pipeline attempting to pass text pairs.
raise ValueError(
'The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a'
' dictionary `{"text": "My text", "text_pair": "My pair"}` in order to send a text pair.' )
return self.tokenizer(snake_case , return_tensors=snake_case , **snake_case )
def lowerCamelCase_ ( self , snake_case ) -> List[Any]:
return self.model(**snake_case )
def lowerCamelCase_ ( self , snake_case , snake_case=None , snake_case=1 , snake_case=True ) -> List[Any]:
# `_legacy` is used to determine if we're running the naked pipeline and in backward
# compatibility mode, or if running the pipeline with `pipeline(..., top_k=1)` we're running
# the more natural result containing the list.
# Default value before `set_parameters`
if function_to_apply is None:
if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
_UpperCAmelCase = ClassificationFunction.SIGMOID
elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
_UpperCAmelCase = ClassificationFunction.SOFTMAX
elif hasattr(self.model.config , 'function_to_apply' ) and function_to_apply is None:
_UpperCAmelCase = self.model.config.function_to_apply
else:
_UpperCAmelCase = ClassificationFunction.NONE
_UpperCAmelCase = model_outputs['logits'][0]
_UpperCAmelCase = outputs.numpy()
if function_to_apply == ClassificationFunction.SIGMOID:
_UpperCAmelCase = sigmoid(snake_case )
elif function_to_apply == ClassificationFunction.SOFTMAX:
_UpperCAmelCase = softmax(snake_case )
elif function_to_apply == ClassificationFunction.NONE:
_UpperCAmelCase = outputs
else:
raise ValueError(f'Unrecognized `function_to_apply` argument: {function_to_apply}' )
if top_k == 1 and _legacy:
return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}
_UpperCAmelCase = [
{'label': self.model.config.idalabel[i], 'score': score.item()} for i, score in enumerate(snake_case )
]
if not _legacy:
dict_scores.sort(key=lambda snake_case : x["score"] , reverse=snake_case )
if top_k is not None:
_UpperCAmelCase = dict_scores[:top_k]
return dict_scores
| 24
|
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
lowercase = logging.get_logger(__name__)
class lowercase__ ( A ):
'''simple docstring'''
def __init__( self , *snake_case , **snake_case ) -> None:
warnings.warn(
'The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
' use YolosImageProcessor instead.' , snake_case , )
super().__init__(*snake_case , **snake_case )
| 24
| 1
|
"""simple docstring"""
from typing import Dict
from .base import GenericTensor, Pipeline
class lowercase__ ( _lowerCamelCase ):
'''simple docstring'''
def lowerCamelCase_ ( self , snake_case=None , snake_case=None , snake_case=None , **snake_case ) -> Optional[int]:
if tokenize_kwargs is None:
_UpperCAmelCase = {}
if truncation is not None:
if "truncation" in tokenize_kwargs:
raise ValueError(
'truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)' )
_UpperCAmelCase = truncation
_UpperCAmelCase = tokenize_kwargs
_UpperCAmelCase = {}
if return_tensors is not None:
_UpperCAmelCase = return_tensors
return preprocess_params, {}, postprocess_params
def lowerCamelCase_ ( self , snake_case , **snake_case ) -> Dict[str, GenericTensor]:
_UpperCAmelCase = self.framework
_UpperCAmelCase = self.tokenizer(A__ , return_tensors=A__ , **A__ )
return model_inputs
def lowerCamelCase_ ( self , snake_case ) -> Optional[int]:
_UpperCAmelCase = self.model(**A__ )
return model_outputs
def lowerCamelCase_ ( self , snake_case , snake_case=False ) -> Optional[Any]:
# [0] is the first available tensor, logits or last_hidden_state.
if return_tensors:
return model_outputs[0]
if self.framework == "pt":
return model_outputs[0].tolist()
elif self.framework == "tf":
return model_outputs[0].numpy().tolist()
def __call__( self , *snake_case , **snake_case ) -> List[str]:
return super().__call__(*A__ , **A__ )
| 700
|
"""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
lowercase = logging.get_logger(__name__)
lowercase = {
'''microsoft/beit-base-patch16-224-pt22k''': (
'''https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'''
),
# See all BEiT models at https://huggingface.co/models?filter=beit
}
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = '''beit'''
def __init__( self , snake_case=8192 , snake_case=768 , snake_case=12 , snake_case=12 , snake_case=3072 , snake_case="gelu" , snake_case=0.0 , snake_case=0.0 , snake_case=0.02 , snake_case=1E-12 , snake_case=224 , snake_case=16 , snake_case=3 , snake_case=False , snake_case=False , snake_case=False , snake_case=False , snake_case=0.1 , snake_case=0.1 , snake_case=True , snake_case=[3, 5, 7, 11] , snake_case=[1, 2, 3, 6] , snake_case=True , snake_case=0.4 , snake_case=256 , snake_case=1 , snake_case=False , snake_case=255 , **snake_case , ) -> str:
super().__init__(**snake_case )
_UpperCAmelCase = vocab_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_act
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = image_size
_UpperCAmelCase = patch_size
_UpperCAmelCase = num_channels
_UpperCAmelCase = use_mask_token
_UpperCAmelCase = use_absolute_position_embeddings
_UpperCAmelCase = use_relative_position_bias
_UpperCAmelCase = use_shared_relative_position_bias
_UpperCAmelCase = layer_scale_init_value
_UpperCAmelCase = drop_path_rate
_UpperCAmelCase = use_mean_pooling
# decode head attributes (semantic segmentation)
_UpperCAmelCase = out_indices
_UpperCAmelCase = pool_scales
# auxiliary head attributes (semantic segmentation)
_UpperCAmelCase = use_auxiliary_head
_UpperCAmelCase = auxiliary_loss_weight
_UpperCAmelCase = auxiliary_channels
_UpperCAmelCase = auxiliary_num_convs
_UpperCAmelCase = auxiliary_concat_input
_UpperCAmelCase = semantic_loss_ignore_index
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = version.parse('''1.11''' )
@property
def lowerCamelCase_ ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def lowerCamelCase_ ( self ) -> float:
return 1E-4
| 24
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
lowercase = {
'''configuration_ernie''': ['''ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieConfig''', '''ErnieOnnxConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ErnieForCausalLM''',
'''ErnieForMaskedLM''',
'''ErnieForMultipleChoice''',
'''ErnieForNextSentencePrediction''',
'''ErnieForPreTraining''',
'''ErnieForQuestionAnswering''',
'''ErnieForSequenceClassification''',
'''ErnieForTokenClassification''',
'''ErnieModel''',
'''ErniePreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ernie import (
ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
ErnieForCausalLM,
ErnieForMaskedLM,
ErnieForMultipleChoice,
ErnieForNextSentencePrediction,
ErnieForPreTraining,
ErnieForQuestionAnswering,
ErnieForSequenceClassification,
ErnieForTokenClassification,
ErnieModel,
ErniePreTrainedModel,
)
else:
import sys
lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 701
|
"""simple docstring"""
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
lowercase = logging.getLogger(__name__)
if __name__ == "__main__":
lowercase = argparse.ArgumentParser(
description='''Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'''
)
parser.add_argument(
'''--data_file''', type=str, default='''data/dump.bert-base-uncased.pickle''', help='''The binarized dataset.'''
)
parser.add_argument(
'''--token_counts_dump''', type=str, default='''data/token_counts.bert-base-uncased.pickle''', help='''The dump file.'''
)
parser.add_argument('''--vocab_size''', default=3_05_22, type=int)
lowercase = parser.parse_args()
logger.info(F'''Loading data from {args.data_file}''')
with open(args.data_file, '''rb''') as fp:
lowercase = pickle.load(fp)
logger.info('''Counting occurrences for MLM.''')
lowercase = Counter()
for tk_ids in data:
counter.update(tk_ids)
lowercase = [0] * args.vocab_size
for k, v in counter.items():
lowercase = v
logger.info(F'''Dump to {args.token_counts_dump}''')
with open(args.token_counts_dump, '''wb''') as handle:
pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)
| 24
| 0
|
"""simple docstring"""
import unittest
import numpy as np
from datasets import load_dataset
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 BeitImageProcessor
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def __init__( self , snake_case , snake_case=7 , snake_case=3 , snake_case=18 , snake_case=30 , snake_case=400 , snake_case=True , snake_case=None , snake_case=True , snake_case=None , snake_case=True , snake_case=[0.5, 0.5, 0.5] , snake_case=[0.5, 0.5, 0.5] , snake_case=False , ) -> str:
_UpperCAmelCase = size if size is not None else {'height': 20, 'width': 20}
_UpperCAmelCase = crop_size if crop_size is not None else {'height': 18, 'width': 18}
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = num_channels
_UpperCAmelCase = image_size
_UpperCAmelCase = min_resolution
_UpperCAmelCase = max_resolution
_UpperCAmelCase = do_resize
_UpperCAmelCase = size
_UpperCAmelCase = do_center_crop
_UpperCAmelCase = crop_size
_UpperCAmelCase = do_normalize
_UpperCAmelCase = image_mean
_UpperCAmelCase = image_std
_UpperCAmelCase = do_reduce_labels
def lowerCamelCase_ ( self ) -> Union[str, Any]:
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_reduce_labels": self.do_reduce_labels,
}
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = load_dataset('hf-internal-testing/fixtures_ade20k' , split='test' )
_UpperCAmelCase = Image.open(dataset[0]['file'] )
_UpperCAmelCase = Image.open(dataset[1]['file'] )
return image, map
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = load_dataset('hf-internal-testing/fixtures_ade20k' , split='test' )
_UpperCAmelCase = Image.open(ds[0]['file'] )
_UpperCAmelCase = Image.open(ds[1]['file'] )
_UpperCAmelCase = Image.open(ds[2]['file'] )
_UpperCAmelCase = Image.open(ds[3]['file'] )
return [imagea, imagea], [mapa, mapa]
@require_torch
@require_vision
class lowercase__ ( snake_case__, unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = BeitImageProcessor if is_vision_available() else None
def lowerCamelCase_ ( self ) -> Optional[Any]:
_UpperCAmelCase = BeitImageProcessingTester(self )
@property
def lowerCamelCase_ ( self ) -> Union[str, Any]:
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(UpperCAmelCase_ , 'do_resize' ) )
self.assertTrue(hasattr(UpperCAmelCase_ , 'size' ) )
self.assertTrue(hasattr(UpperCAmelCase_ , 'do_center_crop' ) )
self.assertTrue(hasattr(UpperCAmelCase_ , 'center_crop' ) )
self.assertTrue(hasattr(UpperCAmelCase_ , 'do_normalize' ) )
self.assertTrue(hasattr(UpperCAmelCase_ , 'image_mean' ) )
self.assertTrue(hasattr(UpperCAmelCase_ , 'image_std' ) )
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'height': 20, 'width': 20} )
self.assertEqual(image_processor.crop_size , {'height': 18, 'width': 18} )
self.assertEqual(image_processor.do_reduce_labels , UpperCAmelCase_ )
_UpperCAmelCase = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , crop_size=84 , reduce_labels=UpperCAmelCase_ )
self.assertEqual(image_processor.size , {'height': 42, 'width': 42} )
self.assertEqual(image_processor.crop_size , {'height': 84, 'width': 84} )
self.assertEqual(image_processor.do_reduce_labels , UpperCAmelCase_ )
def lowerCamelCase_ ( self ) -> Dict:
pass
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_UpperCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCAmelCase_ )
for image in image_inputs:
self.assertIsInstance(UpperCAmelCase_ , Image.Image )
# Test not batched input
_UpperCAmelCase = 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
_UpperCAmelCase = image_processing(UpperCAmelCase_ , 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 lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_UpperCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCAmelCase_ , numpify=UpperCAmelCase_ )
for image in image_inputs:
self.assertIsInstance(UpperCAmelCase_ , np.ndarray )
# Test not batched input
_UpperCAmelCase = 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
_UpperCAmelCase = image_processing(UpperCAmelCase_ , 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 lowerCamelCase_ ( self ) -> Tuple:
_UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_UpperCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCAmelCase_ , torchify=UpperCAmelCase_ )
for image in image_inputs:
self.assertIsInstance(UpperCAmelCase_ , torch.Tensor )
# Test not batched input
_UpperCAmelCase = 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
_UpperCAmelCase = image_processing(UpperCAmelCase_ , 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 lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_UpperCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCAmelCase_ , torchify=UpperCAmelCase_ )
_UpperCAmelCase = []
for image in image_inputs:
self.assertIsInstance(UpperCAmelCase_ , torch.Tensor )
maps.append(torch.zeros(image.shape[-2:] ).long() )
# Test not batched input
_UpperCAmelCase = image_processing(image_inputs[0] , maps[0] , return_tensors='pt' )
self.assertEqual(
encoding['pixel_values'].shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
self.assertEqual(
encoding['labels'].shape , (
1,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
self.assertEqual(encoding['labels'].dtype , torch.long )
self.assertTrue(encoding['labels'].min().item() >= 0 )
self.assertTrue(encoding['labels'].max().item() <= 255 )
# Test batched
_UpperCAmelCase = image_processing(UpperCAmelCase_ , UpperCAmelCase_ , return_tensors='pt' )
self.assertEqual(
encoding['pixel_values'].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'],
) , )
self.assertEqual(
encoding['labels'].shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
self.assertEqual(encoding['labels'].dtype , torch.long )
self.assertTrue(encoding['labels'].min().item() >= 0 )
self.assertTrue(encoding['labels'].max().item() <= 255 )
# Test not batched input (PIL images)
_UpperCAmelCase , _UpperCAmelCase = prepare_semantic_single_inputs()
_UpperCAmelCase = image_processing(UpperCAmelCase_ , UpperCAmelCase_ , return_tensors='pt' )
self.assertEqual(
encoding['pixel_values'].shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
self.assertEqual(
encoding['labels'].shape , (
1,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
self.assertEqual(encoding['labels'].dtype , torch.long )
self.assertTrue(encoding['labels'].min().item() >= 0 )
self.assertTrue(encoding['labels'].max().item() <= 255 )
# Test batched input (PIL images)
_UpperCAmelCase , _UpperCAmelCase = prepare_semantic_batch_inputs()
_UpperCAmelCase = image_processing(UpperCAmelCase_ , UpperCAmelCase_ , return_tensors='pt' )
self.assertEqual(
encoding['pixel_values'].shape , (
2,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
self.assertEqual(
encoding['labels'].shape , (
2,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
self.assertEqual(encoding['labels'].dtype , torch.long )
self.assertTrue(encoding['labels'].min().item() >= 0 )
self.assertTrue(encoding['labels'].max().item() <= 255 )
def lowerCamelCase_ ( self ) -> List[Any]:
_UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
_UpperCAmelCase , _UpperCAmelCase = prepare_semantic_single_inputs()
_UpperCAmelCase = image_processing(UpperCAmelCase_ , UpperCAmelCase_ , return_tensors='pt' )
self.assertTrue(encoding['labels'].min().item() >= 0 )
self.assertTrue(encoding['labels'].max().item() <= 150 )
_UpperCAmelCase = True
_UpperCAmelCase = image_processing(UpperCAmelCase_ , UpperCAmelCase_ , return_tensors='pt' )
self.assertTrue(encoding['labels'].min().item() >= 0 )
self.assertTrue(encoding['labels'].max().item() <= 255 )
| 702
|
"""simple docstring"""
from itertools import permutations
def UpperCAmelCase ( A : tuple ):
'''simple docstring'''
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
_UpperCAmelCase = [7, 11, 13, 17]
for i, test in enumerate(A ):
if (num[i + 4] * 100 + num[i + 5] * 10 + num[i + 6]) % test != 0:
return False
return True
def UpperCAmelCase ( A : int = 10 ):
'''simple docstring'''
return sum(
int(''.join(map(A , A ) ) )
for num in permutations(range(A ) )
if is_substring_divisible(A ) )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 24
| 0
|
"""simple docstring"""
from typing import List
import numpy as np
def UpperCAmelCase ( A : Dict ):
'''simple docstring'''
_UpperCAmelCase = {key: len(A ) for key, value in gen_kwargs.items() if isinstance(A , A )}
if len(set(lists_lengths.values() ) ) > 1:
raise RuntimeError(
(
'Sharding is ambiguous for this dataset: '
+ 'we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n'
+ '\n'.join(f'\t- key {key} has length {length}' for key, length in lists_lengths.items() )
+ '\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, '
+ 'and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.'
) )
_UpperCAmelCase = max(lists_lengths.values() , default=0 )
return max(1 , A )
def UpperCAmelCase ( A : str , A : Optional[int] ):
'''simple docstring'''
_UpperCAmelCase = []
for group_idx in range(A ):
_UpperCAmelCase = num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
if num_shards_to_add == 0:
break
_UpperCAmelCase = shards_indices_per_group[-1].stop if shards_indices_per_group else 0
_UpperCAmelCase = range(A , start + num_shards_to_add )
shards_indices_per_group.append(A )
return shards_indices_per_group
def UpperCAmelCase ( A : Tuple , A : List[str] ):
'''simple docstring'''
_UpperCAmelCase = _number_of_shards_in_gen_kwargs(A )
if num_shards == 1:
return [dict(A )]
else:
_UpperCAmelCase = _distribute_shards(num_shards=A , max_num_jobs=A )
return [
{
key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
if isinstance(A , A )
else value
for key, value in gen_kwargs.items()
}
for group_idx in range(len(A ) )
]
def UpperCAmelCase ( A : Tuple ):
'''simple docstring'''
return {
key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
if isinstance(gen_kwargs_list[0][key] , A )
else gen_kwargs_list[0][key]
for key in gen_kwargs_list[0]
}
def UpperCAmelCase ( A : Dict , A : List[str] ):
'''simple docstring'''
_UpperCAmelCase = {len(A ) for value in gen_kwargs.values() if isinstance(A , A )}
_UpperCAmelCase = {}
for size in list_sizes:
_UpperCAmelCase = list(range(A ) )
rng.shuffle(indices_per_size[size] )
# Now let's copy the gen_kwargs and shuffle the lists based on their sizes
_UpperCAmelCase = dict(A )
for key, value in shuffled_kwargs.items():
if isinstance(A , A ):
_UpperCAmelCase = [value[i] for i in indices_per_size[len(A )]]
return shuffled_kwargs
| 703
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase = {
'''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
'''tokenization_mvp''': ['''MvpTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = ['''MvpTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''MVP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MvpForCausalLM''',
'''MvpForConditionalGeneration''',
'''MvpForQuestionAnswering''',
'''MvpForSequenceClassification''',
'''MvpModel''',
'''MvpPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
from .tokenization_mvp import MvpTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mvp_fast import MvpTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mvp import (
MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
MvpForCausalLM,
MvpForConditionalGeneration,
MvpForQuestionAnswering,
MvpForSequenceClassification,
MvpModel,
MvpPreTrainedModel,
)
else:
import sys
lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 24
| 0
|
"""simple docstring"""
import os
# Precomputes a list of the 100 first triangular numbers
lowercase = [int(0.5 * n * (n + 1)) for n in range(1, 1_01)]
def UpperCAmelCase ( ) -> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase = os.path.dirname(os.path.realpath(_lowercase ) )
_UpperCAmelCase = os.path.join(_lowercase , 'words.txt' )
_UpperCAmelCase = ''
with open(_lowercase ) as f:
_UpperCAmelCase = f.readline()
_UpperCAmelCase = [word.strip('"' ) for word in words.strip('\r\n' ).split(',' )]
_UpperCAmelCase = [
word
for word in [sum(ord(_lowercase ) - 64 for x in word ) for word in words]
if word in TRIANGULAR_NUMBERS
]
return len(_lowercase )
if __name__ == "__main__":
print(solution())
| 704
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase = {
'''configuration_clipseg''': [
'''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPSegConfig''',
'''CLIPSegTextConfig''',
'''CLIPSegVisionConfig''',
],
'''processing_clipseg''': ['''CLIPSegProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''CLIPSegModel''',
'''CLIPSegPreTrainedModel''',
'''CLIPSegTextModel''',
'''CLIPSegVisionModel''',
'''CLIPSegForImageSegmentation''',
]
if TYPE_CHECKING:
from .configuration_clipseg import (
CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIPSegConfig,
CLIPSegTextConfig,
CLIPSegVisionConfig,
)
from .processing_clipseg import CLIPSegProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clipseg import (
CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPSegForImageSegmentation,
CLIPSegModel,
CLIPSegPreTrainedModel,
CLIPSegTextModel,
CLIPSegVisionModel,
)
else:
import sys
lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 24
| 0
|
"""simple docstring"""
import os
def UpperCAmelCase ( A : Tuple ):
'''simple docstring'''
_UpperCAmelCase = len(grid[0] )
_UpperCAmelCase = len(_A )
_UpperCAmelCase = 0
_UpperCAmelCase = 0
_UpperCAmelCase = 0
# Check vertically, horizontally, diagonally at the same time (only works
# for nxn grid)
for i in range(_A ):
for j in range(n_rows - 3 ):
_UpperCAmelCase = grid[j][i] * grid[j + 1][i] * grid[j + 2][i] * grid[j + 3][i]
_UpperCAmelCase = grid[i][j] * grid[i][j + 1] * grid[i][j + 2] * grid[i][j + 3]
# Left-to-right diagonal (\) product
if i < n_columns - 3:
_UpperCAmelCase = (
grid[i][j]
* grid[i + 1][j + 1]
* grid[i + 2][j + 2]
* grid[i + 3][j + 3]
)
# Right-to-left diagonal(/) product
if i > 2:
_UpperCAmelCase = (
grid[i][j]
* grid[i - 1][j + 1]
* grid[i - 2][j + 2]
* grid[i - 3][j + 3]
)
_UpperCAmelCase = max(
_A , _A , _A , _A )
if max_product > largest:
_UpperCAmelCase = max_product
return largest
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = []
with open(os.path.dirname(_A ) + '/grid.txt' ) as file:
for line in file:
grid.append(line.strip('\n' ).split(' ' ) )
_UpperCAmelCase = [[int(_A ) for i in grid[j]] for j in range(len(_A ) )]
return largest_product(_A )
if __name__ == "__main__":
print(solution())
| 705
|
"""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/swin-tiny-patch4-window7-224''': (
'''https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'''
),
# See all Swin models at https://huggingface.co/models?filter=swin
}
class lowercase__ ( A, A ):
'''simple docstring'''
_UpperCAmelCase = '''swin'''
_UpperCAmelCase = {
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self , snake_case=224 , snake_case=4 , snake_case=3 , snake_case=96 , snake_case=[2, 2, 6, 2] , snake_case=[3, 6, 12, 24] , snake_case=7 , snake_case=4.0 , snake_case=True , snake_case=0.0 , snake_case=0.0 , snake_case=0.1 , snake_case="gelu" , snake_case=False , snake_case=0.02 , snake_case=1E-5 , snake_case=32 , snake_case=None , snake_case=None , **snake_case , ) -> List[Any]:
super().__init__(**snake_case )
_UpperCAmelCase = image_size
_UpperCAmelCase = patch_size
_UpperCAmelCase = num_channels
_UpperCAmelCase = embed_dim
_UpperCAmelCase = depths
_UpperCAmelCase = len(snake_case )
_UpperCAmelCase = num_heads
_UpperCAmelCase = window_size
_UpperCAmelCase = mlp_ratio
_UpperCAmelCase = qkv_bias
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = drop_path_rate
_UpperCAmelCase = hidden_act
_UpperCAmelCase = use_absolute_embeddings
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = initializer_range
_UpperCAmelCase = encoder_stride
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
_UpperCAmelCase = int(embed_dim * 2 ** (len(snake_case ) - 1) )
_UpperCAmelCase = ['stem'] + [f'stage{idx}' for idx in range(1 , len(snake_case ) + 1 )]
_UpperCAmelCase , _UpperCAmelCase = get_aligned_output_features_output_indices(
out_features=snake_case , out_indices=snake_case , stage_names=self.stage_names )
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = version.parse('''1.11''' )
@property
def lowerCamelCase_ ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def lowerCamelCase_ ( self ) -> float:
return 1E-4
| 24
| 0
|
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class lowercase__ ( metaclass=__lowerCamelCase ):
'''simple docstring'''
_UpperCAmelCase = ['''flax''']
def __init__( self , *snake_case , **snake_case ) -> Optional[Any]:
requires_backends(self , ['flax'] )
@classmethod
def lowerCamelCase_ ( cls , *snake_case , **snake_case ) -> Tuple:
requires_backends(cls , ['flax'] )
@classmethod
def lowerCamelCase_ ( cls , *snake_case , **snake_case ) -> List[Any]:
requires_backends(cls , ['flax'] )
class lowercase__ ( metaclass=__lowerCamelCase ):
'''simple docstring'''
_UpperCAmelCase = ['''flax''']
def __init__( self , *snake_case , **snake_case ) -> str:
requires_backends(self , ['flax'] )
@classmethod
def lowerCamelCase_ ( cls , *snake_case , **snake_case ) -> Dict:
requires_backends(cls , ['flax'] )
@classmethod
def lowerCamelCase_ ( cls , *snake_case , **snake_case ) -> Dict:
requires_backends(cls , ['flax'] )
class lowercase__ ( metaclass=__lowerCamelCase ):
'''simple docstring'''
_UpperCAmelCase = ['''flax''']
def __init__( self , *snake_case , **snake_case ) -> List[Any]:
requires_backends(self , ['flax'] )
@classmethod
def lowerCamelCase_ ( cls , *snake_case , **snake_case ) -> Dict:
requires_backends(cls , ['flax'] )
@classmethod
def lowerCamelCase_ ( cls , *snake_case , **snake_case ) -> Tuple:
requires_backends(cls , ['flax'] )
class lowercase__ ( metaclass=__lowerCamelCase ):
'''simple docstring'''
_UpperCAmelCase = ['''flax''']
def __init__( self , *snake_case , **snake_case ) -> List[Any]:
requires_backends(self , ['flax'] )
@classmethod
def lowerCamelCase_ ( cls , *snake_case , **snake_case ) -> List[str]:
requires_backends(cls , ['flax'] )
@classmethod
def lowerCamelCase_ ( cls , *snake_case , **snake_case ) -> Optional[Any]:
requires_backends(cls , ['flax'] )
class lowercase__ ( metaclass=__lowerCamelCase ):
'''simple docstring'''
_UpperCAmelCase = ['''flax''']
def __init__( self , *snake_case , **snake_case ) -> Optional[int]:
requires_backends(self , ['flax'] )
@classmethod
def lowerCamelCase_ ( cls , *snake_case , **snake_case ) -> Optional[Any]:
requires_backends(cls , ['flax'] )
@classmethod
def lowerCamelCase_ ( cls , *snake_case , **snake_case ) -> str:
requires_backends(cls , ['flax'] )
class lowercase__ ( metaclass=__lowerCamelCase ):
'''simple docstring'''
_UpperCAmelCase = ['''flax''']
def __init__( self , *snake_case , **snake_case ) -> Optional[int]:
requires_backends(self , ['flax'] )
@classmethod
def lowerCamelCase_ ( cls , *snake_case , **snake_case ) -> int:
requires_backends(cls , ['flax'] )
@classmethod
def lowerCamelCase_ ( cls , *snake_case , **snake_case ) -> Tuple:
requires_backends(cls , ['flax'] )
class lowercase__ ( metaclass=__lowerCamelCase ):
'''simple docstring'''
_UpperCAmelCase = ['''flax''']
def __init__( self , *snake_case , **snake_case ) -> Optional[int]:
requires_backends(self , ['flax'] )
@classmethod
def lowerCamelCase_ ( cls , *snake_case , **snake_case ) -> Optional[int]:
requires_backends(cls , ['flax'] )
@classmethod
def lowerCamelCase_ ( cls , *snake_case , **snake_case ) -> int:
requires_backends(cls , ['flax'] )
class lowercase__ ( metaclass=__lowerCamelCase ):
'''simple docstring'''
_UpperCAmelCase = ['''flax''']
def __init__( self , *snake_case , **snake_case ) -> Optional[int]:
requires_backends(self , ['flax'] )
@classmethod
def lowerCamelCase_ ( cls , *snake_case , **snake_case ) -> Optional[int]:
requires_backends(cls , ['flax'] )
@classmethod
def lowerCamelCase_ ( cls , *snake_case , **snake_case ) -> Union[str, Any]:
requires_backends(cls , ['flax'] )
class lowercase__ ( metaclass=__lowerCamelCase ):
'''simple docstring'''
_UpperCAmelCase = ['''flax''']
def __init__( self , *snake_case , **snake_case ) -> List[str]:
requires_backends(self , ['flax'] )
@classmethod
def lowerCamelCase_ ( cls , *snake_case , **snake_case ) -> Dict:
requires_backends(cls , ['flax'] )
@classmethod
def lowerCamelCase_ ( cls , *snake_case , **snake_case ) -> Tuple:
requires_backends(cls , ['flax'] )
class lowercase__ ( metaclass=__lowerCamelCase ):
'''simple docstring'''
_UpperCAmelCase = ['''flax''']
def __init__( self , *snake_case , **snake_case ) -> List[str]:
requires_backends(self , ['flax'] )
@classmethod
def lowerCamelCase_ ( cls , *snake_case , **snake_case ) -> List[str]:
requires_backends(cls , ['flax'] )
@classmethod
def lowerCamelCase_ ( cls , *snake_case , **snake_case ) -> Dict:
requires_backends(cls , ['flax'] )
class lowercase__ ( metaclass=__lowerCamelCase ):
'''simple docstring'''
_UpperCAmelCase = ['''flax''']
def __init__( self , *snake_case , **snake_case ) -> Dict:
requires_backends(self , ['flax'] )
@classmethod
def lowerCamelCase_ ( cls , *snake_case , **snake_case ) -> List[str]:
requires_backends(cls , ['flax'] )
@classmethod
def lowerCamelCase_ ( cls , *snake_case , **snake_case ) -> Optional[int]:
requires_backends(cls , ['flax'] )
class lowercase__ ( metaclass=__lowerCamelCase ):
'''simple docstring'''
_UpperCAmelCase = ['''flax''']
def __init__( self , *snake_case , **snake_case ) -> str:
requires_backends(self , ['flax'] )
@classmethod
def lowerCamelCase_ ( cls , *snake_case , **snake_case ) -> Optional[Any]:
requires_backends(cls , ['flax'] )
@classmethod
def lowerCamelCase_ ( cls , *snake_case , **snake_case ) -> Tuple:
requires_backends(cls , ['flax'] )
class lowercase__ ( metaclass=__lowerCamelCase ):
'''simple docstring'''
_UpperCAmelCase = ['''flax''']
def __init__( self , *snake_case , **snake_case ) -> List[Any]:
requires_backends(self , ['flax'] )
@classmethod
def lowerCamelCase_ ( cls , *snake_case , **snake_case ) -> str:
requires_backends(cls , ['flax'] )
@classmethod
def lowerCamelCase_ ( cls , *snake_case , **snake_case ) -> List[Any]:
requires_backends(cls , ['flax'] )
| 706
|
"""simple docstring"""
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class lowercase__ ( nn.Module ):
'''simple docstring'''
def __init__( self , snake_case = 16 , snake_case = 88 , snake_case = None , snake_case = 1 , snake_case = 0.0 , snake_case = 32 , snake_case = None , snake_case = False , snake_case = None , snake_case = None , snake_case = "geglu" , snake_case = None , ) -> str:
super().__init__()
_UpperCAmelCase = nn.ModuleList(
[
TransformeraDModel(
num_attention_heads=snake_case , attention_head_dim=snake_case , in_channels=snake_case , num_layers=snake_case , dropout=snake_case , norm_num_groups=snake_case , cross_attention_dim=snake_case , attention_bias=snake_case , sample_size=snake_case , num_vector_embeds=snake_case , activation_fn=snake_case , num_embeds_ada_norm=snake_case , )
for _ in range(2 )
] )
# Variables that can be set by a pipeline:
# The ratio of transformer1 to transformer2's output states to be combined during inference
_UpperCAmelCase = 0.5
# The shape of `encoder_hidden_states` is expected to be
# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
_UpperCAmelCase = [77, 257]
# Which transformer to use to encode which condition.
# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
_UpperCAmelCase = [1, 0]
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case=None , snake_case=None , snake_case=None , snake_case = True , ) -> Any:
_UpperCAmelCase = hidden_states
_UpperCAmelCase = []
_UpperCAmelCase = 0
# attention_mask is not used yet
for i in range(2 ):
# for each of the two transformers, pass the corresponding condition tokens
_UpperCAmelCase = encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
_UpperCAmelCase = self.transformer_index_for_condition[i]
_UpperCAmelCase = self.transformers[transformer_index](
snake_case , encoder_hidden_states=snake_case , timestep=snake_case , cross_attention_kwargs=snake_case , return_dict=snake_case , )[0]
encoded_states.append(encoded_state - input_states )
tokens_start += self.condition_lengths[i]
_UpperCAmelCase = encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
_UpperCAmelCase = output_states + input_states
if not return_dict:
return (output_states,)
return TransformeraDModelOutput(sample=snake_case )
| 24
| 0
|
"""simple docstring"""
import math
def UpperCAmelCase ( A : float , A : float ):
'''simple docstring'''
if (
not isinstance(A , (int, float) )
or power_factor < -1
or power_factor > 1
):
raise ValueError('power_factor must be a valid float value between -1 and 1.' )
return apparent_power * power_factor
def UpperCAmelCase ( A : float , A : float ):
'''simple docstring'''
if (
not isinstance(A , (int, float) )
or power_factor < -1
or power_factor > 1
):
raise ValueError('power_factor must be a valid float value between -1 and 1.' )
return apparent_power * math.sqrt(1 - power_factor**2 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 707
|
"""simple docstring"""
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import CvtForImageClassification, CvtModel
from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class lowercase__ ( A ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(snake_case , 'embed_dim' ) )
self.parent.assertTrue(hasattr(snake_case , 'num_heads' ) )
class lowercase__ :
'''simple docstring'''
def __init__( self , snake_case , snake_case=13 , snake_case=64 , snake_case=3 , snake_case=[16, 48, 96] , snake_case=[1, 3, 6] , snake_case=[1, 2, 10] , snake_case=[7, 3, 3] , snake_case=[4, 2, 2] , snake_case=[2, 1, 1] , snake_case=[2, 2, 2] , snake_case=[False, False, True] , snake_case=[0.0, 0.0, 0.0] , snake_case=0.02 , snake_case=1E-12 , snake_case=True , snake_case=True , snake_case=2 , ) -> Tuple:
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = image_size
_UpperCAmelCase = patch_sizes
_UpperCAmelCase = patch_stride
_UpperCAmelCase = patch_padding
_UpperCAmelCase = is_training
_UpperCAmelCase = use_labels
_UpperCAmelCase = num_labels
_UpperCAmelCase = num_channels
_UpperCAmelCase = embed_dim
_UpperCAmelCase = num_heads
_UpperCAmelCase = stride_kv
_UpperCAmelCase = depth
_UpperCAmelCase = cls_token
_UpperCAmelCase = attention_drop_rate
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_UpperCAmelCase = None
if self.use_labels:
_UpperCAmelCase = ids_tensor([self.batch_size] , self.num_labels )
_UpperCAmelCase = self.get_config()
return config, pixel_values, labels
def lowerCamelCase_ ( self ) -> List[str]:
return CvtConfig(
image_size=self.image_size , num_labels=self.num_labels , num_channels=self.num_channels , embed_dim=self.embed_dim , num_heads=self.num_heads , patch_sizes=self.patch_sizes , patch_padding=self.patch_padding , patch_stride=self.patch_stride , stride_kv=self.stride_kv , depth=self.depth , cls_token=self.cls_token , attention_drop_rate=self.attention_drop_rate , initializer_range=self.initializer_range , )
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case ) -> Optional[int]:
_UpperCAmelCase = CvtModel(config=snake_case )
model.to(snake_case )
model.eval()
_UpperCAmelCase = model(snake_case )
_UpperCAmelCase = (self.image_size, self.image_size)
_UpperCAmelCase , _UpperCAmelCase = image_size[0], image_size[1]
for i in range(len(self.depth ) ):
_UpperCAmelCase = floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
_UpperCAmelCase = floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dim[-1], height, width) )
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case ) -> Optional[Any]:
_UpperCAmelCase = self.num_labels
_UpperCAmelCase = CvtForImageClassification(snake_case )
model.to(snake_case )
model.eval()
_UpperCAmelCase = model(snake_case , labels=snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = self.prepare_config_and_inputs()
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = config_and_inputs
_UpperCAmelCase = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class lowercase__ ( A, A, unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = (CvtModel, CvtForImageClassification) if is_torch_available() else ()
_UpperCAmelCase = (
{'''feature-extraction''': CvtModel, '''image-classification''': CvtForImageClassification}
if is_torch_available()
else {}
)
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = CvtModelTester(self )
_UpperCAmelCase = ConfigTester(self , config_class=snake_case , has_text_modality=snake_case , hidden_size=37 )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def lowerCamelCase_ ( self ) -> Union[str, Any]:
return
@unittest.skip(reason='Cvt does not output attentions' )
def lowerCamelCase_ ( self ) -> str:
pass
@unittest.skip(reason='Cvt does not use inputs_embeds' )
def lowerCamelCase_ ( self ) -> int:
pass
@unittest.skip(reason='Cvt does not support input and output embeddings' )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
pass
def lowerCamelCase_ ( self ) -> Any:
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(snake_case )
_UpperCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCAmelCase = [*signature.parameters.keys()]
_UpperCAmelCase = ['pixel_values']
self.assertListEqual(arg_names[:1] , snake_case )
def lowerCamelCase_ ( self ) -> Optional[int]:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case )
def lowerCamelCase_ ( self ) -> Optional[int]:
def check_hidden_states_output(snake_case , snake_case , snake_case ):
_UpperCAmelCase = model_class(snake_case )
model.to(snake_case )
model.eval()
with torch.no_grad():
_UpperCAmelCase = model(**self._prepare_for_class(snake_case , snake_case ) )
_UpperCAmelCase = outputs.hidden_states
_UpperCAmelCase = len(self.model_tester.depth )
self.assertEqual(len(snake_case ) , snake_case )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:] ) , [
self.model_tester.embed_dim[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
] , )
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = True
check_hidden_states_output(snake_case , snake_case , snake_case )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_UpperCAmelCase = True
check_hidden_states_output(snake_case , snake_case , snake_case )
def lowerCamelCase_ ( self ) -> Any:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*snake_case )
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def lowerCamelCase_ ( self ) -> Dict:
pass
@slow
def lowerCamelCase_ ( self ) -> Dict:
for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase = CvtModel.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def lowerCamelCase_ ( self ) -> List[Any]:
return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
@slow
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(snake_case )
_UpperCAmelCase = self.default_image_processor
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = image_processor(images=snake_case , return_tensors='pt' ).to(snake_case )
# forward pass
with torch.no_grad():
_UpperCAmelCase = model(**snake_case )
# verify the logits
_UpperCAmelCase = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , snake_case )
_UpperCAmelCase = torch.tensor([0.9285, 0.9015, -0.3150] ).to(snake_case )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , snake_case , atol=1E-4 ) )
| 24
| 0
|
"""simple docstring"""
class lowercase__ :
'''simple docstring'''
def __init__( self , snake_case ) -> None:
_UpperCAmelCase = set_counts
_UpperCAmelCase = max(snake_case )
_UpperCAmelCase = len(snake_case )
_UpperCAmelCase = [1] * num_sets
_UpperCAmelCase = list(range(snake_case ) )
def lowerCamelCase_ ( self , snake_case , snake_case ) -> bool:
_UpperCAmelCase = self.get_parent(snake_case )
_UpperCAmelCase = self.get_parent(snake_case )
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]
_UpperCAmelCase = 0
_UpperCAmelCase = dst_parent
if self.ranks[dst_parent] == self.ranks[src_parent]:
self.ranks[dst_parent] += 1
_UpperCAmelCase = self.set_counts[dst_parent]
else:
self.set_counts[src_parent] += self.set_counts[dst_parent]
_UpperCAmelCase = 0
_UpperCAmelCase = src_parent
_UpperCAmelCase = self.set_counts[src_parent]
_UpperCAmelCase = max(self.max_set , snake_case )
return True
def lowerCamelCase_ ( self , snake_case ) -> int:
if self.parents[disj_set] == disj_set:
return disj_set
_UpperCAmelCase = self.get_parent(self.parents[disj_set] )
return self.parents[disj_set]
| 708
|
"""simple docstring"""
from __future__ import annotations
from cmath import sqrt
def UpperCAmelCase ( A : int , A : int , A : int ):
'''simple docstring'''
if a == 0:
raise ValueError('Coefficient \'a\' must not be zero.' )
_UpperCAmelCase = b * b - 4 * a * c
_UpperCAmelCase = (-b + sqrt(A )) / (2 * a)
_UpperCAmelCase = (-b - sqrt(A )) / (2 * a)
return (
root_a.real if not root_a.imag else root_a,
root_a.real if not root_a.imag else root_a,
)
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = quadratic_roots(a=5 , b=6 , c=1 )
print(f'The solutions are: {solutiona} and {solutiona}' )
if __name__ == "__main__":
main()
| 24
| 0
|
"""simple docstring"""
import heapq as hq
import math
from collections.abc import Iterator
class lowercase__ :
'''simple docstring'''
def __init__( self , snake_case ) -> Optional[int]:
_UpperCAmelCase = str(id_ )
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = []
_UpperCAmelCase = {} # {vertex:distance}
def __lt__( self , snake_case ) -> Dict:
return self.key < other.key
def __repr__( self ) -> Any:
return self.id
def lowerCamelCase_ ( self , snake_case ) -> Any:
self.neighbors.append(_lowerCAmelCase )
def lowerCamelCase_ ( self , snake_case , snake_case ) -> Union[str, Any]:
_UpperCAmelCase = weight
def UpperCAmelCase ( A : Optional[int] , A : str , A : Optional[int] , A : Dict ):
'''simple docstring'''
graph[a - 1].add_neighbor(graph[b - 1] )
graph[b - 1].add_neighbor(graph[a - 1] )
# add the edges:
graph[a - 1].add_edge(graph[b - 1] , __lowerCAmelCase )
graph[b - 1].add_edge(graph[a - 1] , __lowerCAmelCase )
def UpperCAmelCase ( A : list , A : Vertex ):
'''simple docstring'''
_UpperCAmelCase = []
for u in graph:
_UpperCAmelCase = math.inf
_UpperCAmelCase = None
_UpperCAmelCase = 0
_UpperCAmelCase = graph[:]
while q:
_UpperCAmelCase = min(__lowerCAmelCase )
q.remove(__lowerCAmelCase )
for v in u.neighbors:
if (v in q) and (u.edges[v.id] < v.key):
_UpperCAmelCase = u
_UpperCAmelCase = u.edges[v.id]
for i in range(1 , len(__lowerCAmelCase ) ):
a.append((int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1) )
return a
def UpperCAmelCase ( A : list , A : Vertex ):
'''simple docstring'''
for u in graph:
_UpperCAmelCase = math.inf
_UpperCAmelCase = None
_UpperCAmelCase = 0
_UpperCAmelCase = list(__lowerCAmelCase )
hq.heapify(__lowerCAmelCase )
while h:
_UpperCAmelCase = hq.heappop(__lowerCAmelCase )
for v in u.neighbors:
if (v in h) and (u.edges[v.id] < v.key):
_UpperCAmelCase = u
_UpperCAmelCase = u.edges[v.id]
hq.heapify(__lowerCAmelCase )
for i in range(1 , len(__lowerCAmelCase ) ):
yield (int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1)
def UpperCAmelCase ( ):
'''simple docstring'''
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 709
|
"""simple docstring"""
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # see https://github.com/huggingface/transformers/issues/11457
class lowercase__ ( A, unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = BarthezTokenizer
_UpperCAmelCase = BarthezTokenizerFast
_UpperCAmelCase = True
_UpperCAmelCase = True
def lowerCamelCase_ ( self ) -> Optional[int]:
super().setUp()
_UpperCAmelCase = BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez' )
tokenizer.save_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname , legacy_format=snake_case )
_UpperCAmelCase = tokenizer
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = '<pad>'
_UpperCAmelCase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case ) , snake_case )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case ) , snake_case )
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '<s>' )
self.assertEqual(vocab_keys[1] , '<pad>' )
self.assertEqual(vocab_keys[-1] , '<mask>' )
self.assertEqual(len(snake_case ) , 101122 )
def lowerCamelCase_ ( self ) -> List[Any]:
self.assertEqual(self.get_tokenizer().vocab_size , 101122 )
@require_torch
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
_UpperCAmelCase = [0, 57, 3018, 70307, 91, 2]
_UpperCAmelCase = self.tokenizer(
snake_case , max_length=len(snake_case ) , padding=snake_case , truncation=snake_case , return_tensors='pt' )
self.assertIsInstance(snake_case , snake_case )
self.assertEqual((2, 6) , batch.input_ids.shape )
self.assertEqual((2, 6) , batch.attention_mask.shape )
_UpperCAmelCase = batch.input_ids.tolist()[0]
self.assertListEqual(snake_case , snake_case )
def lowerCamelCase_ ( self ) -> Optional[Any]:
if not self.test_rust_tokenizer:
return
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = self.get_rust_tokenizer()
_UpperCAmelCase = 'I was born in 92000, and this is falsé.'
_UpperCAmelCase = tokenizer.tokenize(snake_case )
_UpperCAmelCase = rust_tokenizer.tokenize(snake_case )
self.assertListEqual(snake_case , snake_case )
_UpperCAmelCase = tokenizer.encode(snake_case , add_special_tokens=snake_case )
_UpperCAmelCase = rust_tokenizer.encode(snake_case , add_special_tokens=snake_case )
self.assertListEqual(snake_case , snake_case )
_UpperCAmelCase = self.get_rust_tokenizer()
_UpperCAmelCase = tokenizer.encode(snake_case )
_UpperCAmelCase = rust_tokenizer.encode(snake_case )
self.assertListEqual(snake_case , snake_case )
@slow
def lowerCamelCase_ ( self ) -> Optional[int]:
# fmt: off
_UpperCAmelCase = {'input_ids': [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 2, 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, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 5, 2]], '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, 0, 0, 0, 0, 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, 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]]} # noqa: E501
# fmt: on
# moussaKam/mbarthez is a french model. So we also use french texts.
_UpperCAmelCase = [
'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
'telles que la traduction et la synthèse de texte.',
]
self.tokenizer_integration_test_util(
expected_encoding=snake_case , model_name='moussaKam/mbarthez' , revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6' , sequences=snake_case , )
| 24
| 0
|
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
lowercase = logging.get_logger(__name__)
# TODO: upload to AWS
lowercase = {
"yjernite/retribert-base-uncased": (
"https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json"
),
}
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = "retribert"
def __init__( self , snake_case=30522 , snake_case=768 , snake_case=8 , snake_case=12 , snake_case=3072 , snake_case="gelu" , snake_case=0.1 , snake_case=0.1 , snake_case=512 , snake_case=2 , snake_case=0.02 , snake_case=1E-12 , snake_case=True , snake_case=128 , snake_case=0 , **snake_case , ) -> List[Any]:
super().__init__(pad_token_id=UpperCamelCase_ , **UpperCamelCase_ )
_UpperCAmelCase = vocab_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = hidden_act
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = type_vocab_size
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = share_encoders
_UpperCAmelCase = projection_dim
| 710
|
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class lowercase__ ( A, unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = DiTPipeline
_UpperCAmelCase = CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
_UpperCAmelCase = PipelineTesterMixin.required_optional_params - {
'''latents''',
'''num_images_per_prompt''',
'''callback''',
'''callback_steps''',
}
_UpperCAmelCase = CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
_UpperCAmelCase = False
def lowerCamelCase_ ( self ) -> str:
torch.manual_seed(0 )
_UpperCAmelCase = TransformeraDModel(
sample_size=16 , num_layers=2 , patch_size=4 , attention_head_dim=8 , num_attention_heads=2 , in_channels=4 , out_channels=8 , attention_bias=snake_case , activation_fn='gelu-approximate' , num_embeds_ada_norm=1000 , norm_type='ada_norm_zero' , norm_elementwise_affine=snake_case , )
_UpperCAmelCase = AutoencoderKL()
_UpperCAmelCase = DDIMScheduler()
_UpperCAmelCase = {'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
return components
def lowerCamelCase_ ( self , snake_case , snake_case=0 ) -> Optional[Any]:
if str(snake_case ).startswith('mps' ):
_UpperCAmelCase = torch.manual_seed(snake_case )
else:
_UpperCAmelCase = torch.Generator(device=snake_case ).manual_seed(snake_case )
_UpperCAmelCase = {
'class_labels': [1],
'generator': generator,
'num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
def lowerCamelCase_ ( self ) -> List[Any]:
_UpperCAmelCase = 'cpu'
_UpperCAmelCase = self.get_dummy_components()
_UpperCAmelCase = self.pipeline_class(**snake_case )
pipe.to(snake_case )
pipe.set_progress_bar_config(disable=snake_case )
_UpperCAmelCase = self.get_dummy_inputs(snake_case )
_UpperCAmelCase = pipe(**snake_case ).images
_UpperCAmelCase = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 16, 16, 3) )
_UpperCAmelCase = np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457] )
_UpperCAmelCase = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(snake_case , 1E-3 )
def lowerCamelCase_ ( self ) -> Any:
self._test_inference_batch_single_identical(relax_max_difference=snake_case , expected_max_diff=1E-3 )
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def lowerCamelCase_ ( self ) -> Optional[int]:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
@require_torch_gpu
@slow
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> int:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = DiTPipeline.from_pretrained('facebook/DiT-XL-2-256' )
pipe.to('cuda' )
_UpperCAmelCase = ['vase', 'umbrella', 'white shark', 'white wolf']
_UpperCAmelCase = pipe.get_label_ids(snake_case )
_UpperCAmelCase = pipe(snake_case , generator=snake_case , num_inference_steps=40 , output_type='np' ).images
for word, image in zip(snake_case , snake_case ):
_UpperCAmelCase = load_numpy(
f'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy' )
assert np.abs((expected_image - image).max() ) < 1E-2
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = DiTPipeline.from_pretrained('facebook/DiT-XL-2-512' )
_UpperCAmelCase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.to('cuda' )
_UpperCAmelCase = ['vase', 'umbrella']
_UpperCAmelCase = pipe.get_label_ids(snake_case )
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = pipe(snake_case , generator=snake_case , num_inference_steps=25 , output_type='np' ).images
for word, image in zip(snake_case , snake_case ):
_UpperCAmelCase = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
f'/dit/{word}_512.npy' )
assert np.abs((expected_image - image).max() ) < 1E-1
| 24
| 0
|
"""simple docstring"""
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import jax
import jaxlib
lowercase = get_logger()
lowercase = None
class lowercase__ ( TensorFormatter[Mapping, '''jax.Array''', Mapping] ):
'''simple docstring'''
def __init__( self , snake_case=None , snake_case=None , **snake_case ) -> str:
super().__init__(features=__A )
import jax
from jaxlib.xla_client import Device
if isinstance(__A , __A ):
raise ValueError(
f'Expected {device} to be a `str` not {type(__A )}, as `jaxlib.xla_extension.Device` '
'is not serializable neither with `pickle` nor with `dill`. Instead you can surround '
'the device with `str()` to get its string identifier that will be internally mapped '
'to the actual `jaxlib.xla_extension.Device`.' )
_UpperCAmelCase = device if isinstance(__A , __A ) else str(jax.devices()[0] )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
_UpperCAmelCase = self._map_devices_to_str()
if self.device not in list(DEVICE_MAPPING.keys() ):
logger.warning(
f'Device with string identifier {self.device} not listed among the available '
f'devices: {list(DEVICE_MAPPING.keys() )}, so falling back to the default '
f'device: {str(jax.devices()[0] )}.' )
_UpperCAmelCase = str(jax.devices()[0] )
_UpperCAmelCase = jnp_array_kwargs
@staticmethod
def lowerCamelCase_ ( ) -> str:
import jax
return {str(__A ): device for device in jax.devices()}
def lowerCamelCase_ ( self , snake_case ) -> Optional[int]:
import jax
import jax.numpy as jnp
if isinstance(__A , __A ) and column:
if all(
isinstance(__A , jax.Array ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column ):
return jnp.stack(__A , axis=0 )
return column
def lowerCamelCase_ ( self , snake_case ) -> List[str]:
import jax
import jax.numpy as jnp
if isinstance(__A , (str, bytes, type(__A )) ):
return value
elif isinstance(__A , (np.character, np.ndarray) ) and np.issubdtype(value.dtype , np.character ):
return value.tolist()
_UpperCAmelCase = {}
if isinstance(__A , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ):
# the default int precision depends on the jax config
# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
if jax.config.jax_enable_xaa:
_UpperCAmelCase = {"dtype": jnp.intaa}
else:
_UpperCAmelCase = {"dtype": jnp.intaa}
elif isinstance(__A , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
_UpperCAmelCase = {"dtype": jnp.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(__A , PIL.Image.Image ):
_UpperCAmelCase = np.asarray(__A )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
_UpperCAmelCase = self._map_devices_to_str()
with jax.default_device(DEVICE_MAPPING[self.device] ):
# calling jnp.array on a np.ndarray does copy the data
# see https://github.com/google/jax/issues/4486
return jnp.array(__A , **{**default_dtype, **self.jnp_array_kwargs} )
def lowerCamelCase_ ( self , snake_case ) -> List[Any]:
import jax
# support for torch, tf, jax etc.
if config.TORCH_AVAILABLE and "torch" in sys.modules:
import torch
if isinstance(__A , torch.Tensor ):
return self._tensorize(data_struct.detach().cpu().numpy()[()] )
if hasattr(__A , '__array__' ) and not isinstance(__A , jax.Array ):
_UpperCAmelCase = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(__A , np.ndarray ):
if data_struct.dtype == object: # jax arrays cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(__A ) for substruct in data_struct] )
elif isinstance(__A , (list, tuple) ):
return self._consolidate([self.recursive_tensorize(__A ) for substruct in data_struct] )
return self._tensorize(__A )
def lowerCamelCase_ ( self , snake_case ) -> List[Any]:
return map_nested(self._recursive_tensorize , __A , map_list=__A )
def lowerCamelCase_ ( self , snake_case ) -> Any:
_UpperCAmelCase = self.numpy_arrow_extractor().extract_row(__A )
_UpperCAmelCase = self.python_features_decoder.decode_row(__A )
return self.recursive_tensorize(__A )
def lowerCamelCase_ ( self , snake_case ) -> str:
_UpperCAmelCase = self.numpy_arrow_extractor().extract_column(__A )
_UpperCAmelCase = self.python_features_decoder.decode_column(__A , pa_table.column_names[0] )
_UpperCAmelCase = self.recursive_tensorize(__A )
_UpperCAmelCase = self._consolidate(__A )
return column
def lowerCamelCase_ ( self , snake_case ) -> Dict:
_UpperCAmelCase = self.numpy_arrow_extractor().extract_batch(__A )
_UpperCAmelCase = self.python_features_decoder.decode_batch(__A )
_UpperCAmelCase = self.recursive_tensorize(__A )
for column_name in batch:
_UpperCAmelCase = self._consolidate(batch[column_name] )
return batch
| 711
|
"""simple docstring"""
def UpperCAmelCase ( A : int ):
'''simple docstring'''
_UpperCAmelCase = abs(A )
_UpperCAmelCase = 0
while n > 0:
res += n % 10
n //= 10
return res
def UpperCAmelCase ( A : int ):
'''simple docstring'''
_UpperCAmelCase = abs(A )
return n if n < 10 else n % 10 + sum_of_digits(n // 10 )
def UpperCAmelCase ( A : int ):
'''simple docstring'''
return sum(int(A ) for c in str(abs(A ) ) )
def UpperCAmelCase ( ):
'''simple docstring'''
from collections.abc import Callable
from timeit import timeit
def benchmark_a_function(A : Callable , A : int ) -> None:
_UpperCAmelCase = f'{func.__name__}({value})'
_UpperCAmelCase = timeit(f'__main__.{call}' , setup='import __main__' )
print(f'{call:56} = {func(A )} -- {timing:.4f} seconds' )
for value in (26_2144, 1125_8999_0684_2624, 126_7650_6002_2822_9401_4967_0320_5376):
for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
benchmark_a_function(A , A )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 24
| 0
|
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@require_tf
@require_sentencepiece
@require_tokenizers
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
@slow
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = TFCamembertModel.from_pretrained('jplu/tf-camembert-base' )
_UpperCAmelCase = tf.convert_to_tensor(
[[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]] , dtype=tf.intaa , ) # J'aime le camembert !"
_UpperCAmelCase = model(_a )["""last_hidden_state"""]
_UpperCAmelCase = tf.TensorShape((1, 10, 768) )
self.assertEqual(output.shape , _a )
# compare the actual values for a slice.
_UpperCAmelCase = tf.convert_to_tensor(
[[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]] , dtype=tf.floataa , )
# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
# camembert.eval()
# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-4 ) )
| 712
|
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase ( A : int , A : int ):
'''simple docstring'''
_UpperCAmelCase = []
create_all_state(1 , A , A , [] , A )
return result
def UpperCAmelCase ( A : int , A : int , A : int , A : list[int] , A : list[list[int]] , ):
'''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 : list[list[int]] ):
'''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)
| 24
| 0
|
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class lowercase__ ( _snake_case, unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = KandinskyVaaInpaintPipeline
_UpperCAmelCase = ['image_embeds', 'negative_image_embeds', 'image', 'mask_image']
_UpperCAmelCase = [
'image_embeds',
'negative_image_embeds',
'image',
'mask_image',
]
_UpperCAmelCase = [
'generator',
'height',
'width',
'latents',
'guidance_scale',
'num_inference_steps',
'return_dict',
'guidance_scale',
'num_images_per_prompt',
'output_type',
'return_dict',
]
_UpperCAmelCase = False
@property
def lowerCamelCase_ ( self ) -> Dict:
return 32
@property
def lowerCamelCase_ ( self ) -> Any:
return 32
@property
def lowerCamelCase_ ( self ) -> Union[str, Any]:
return self.time_input_dim
@property
def lowerCamelCase_ ( self ) -> List[str]:
return self.time_input_dim * 4
@property
def lowerCamelCase_ ( self ) -> List[Any]:
return 100
@property
def lowerCamelCase_ ( self ) -> Any:
torch.manual_seed(0 )
_UpperCAmelCase = {
'in_channels': 9,
# Out channels is double in channels because predicts mean and variance
'out_channels': 8,
'addition_embed_type': 'image',
'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
'layers_per_block': 1,
'encoder_hid_dim': self.text_embedder_hidden_size,
'encoder_hid_dim_type': 'image_proj',
'cross_attention_dim': self.cross_attention_dim,
'attention_head_dim': 4,
'resnet_time_scale_shift': 'scale_shift',
'class_embed_type': None,
}
_UpperCAmelCase = UNetaDConditionModel(**snake_case )
return model
@property
def lowerCamelCase_ ( self ) -> int:
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def lowerCamelCase_ ( self ) -> Tuple:
torch.manual_seed(0 )
_UpperCAmelCase = VQModel(**self.dummy_movq_kwargs )
return model
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = self.dummy_unet
_UpperCAmelCase = self.dummy_movq
_UpperCAmelCase = DDIMScheduler(
num_train_timesteps=1000 , beta_schedule='linear' , beta_start=0.00085 , beta_end=0.012 , clip_sample=snake_case , set_alpha_to_one=snake_case , steps_offset=1 , prediction_type='epsilon' , thresholding=snake_case , )
_UpperCAmelCase = {
'unet': unet,
'scheduler': scheduler,
'movq': movq,
}
return components
def lowerCamelCase_ ( self , snake_case , snake_case=0 ) -> Any:
_UpperCAmelCase = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(snake_case ) ).to(snake_case )
_UpperCAmelCase = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
snake_case )
# create init_image
_UpperCAmelCase = floats_tensor((1, 3, 64, 64) , rng=random.Random(snake_case ) ).to(snake_case )
_UpperCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 )[0]
_UpperCAmelCase = Image.fromarray(np.uinta(snake_case ) ).convert('RGB' ).resize((256, 256) )
# create mask
_UpperCAmelCase = np.ones((64, 64) , dtype=np.floataa )
_UpperCAmelCase = 0
if str(snake_case ).startswith('mps' ):
_UpperCAmelCase = torch.manual_seed(snake_case )
else:
_UpperCAmelCase = torch.Generator(device=snake_case ).manual_seed(snake_case )
_UpperCAmelCase = {
'image': init_image,
'mask_image': mask,
'image_embeds': image_embeds,
'negative_image_embeds': negative_image_embeds,
'generator': generator,
'height': 64,
'width': 64,
'num_inference_steps': 2,
'guidance_scale': 4.0,
'output_type': 'np',
}
return inputs
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = 'cpu'
_UpperCAmelCase = self.get_dummy_components()
_UpperCAmelCase = self.pipeline_class(**snake_case )
_UpperCAmelCase = pipe.to(snake_case )
pipe.set_progress_bar_config(disable=snake_case )
_UpperCAmelCase = pipe(**self.get_dummy_inputs(snake_case ) )
_UpperCAmelCase = output.images
_UpperCAmelCase = pipe(
**self.get_dummy_inputs(snake_case ) , return_dict=snake_case , )[0]
_UpperCAmelCase = image[0, -3:, -3:, -1]
_UpperCAmelCase = image_from_tuple[0, -3:, -3:, -1]
print(f'image.shape {image.shape}' )
assert image.shape == (1, 64, 64, 3)
_UpperCAmelCase = np.array(
[0.50775903, 0.49527195, 0.48824543, 0.50192237, 0.48644906, 0.49373814, 0.4780598, 0.47234827, 0.48327848] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'
def lowerCamelCase_ ( self ) -> Tuple:
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> List[Any]:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase_ ( self ) -> Any:
_UpperCAmelCase = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy' )
_UpperCAmelCase = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png' )
_UpperCAmelCase = np.ones((768, 768) , dtype=np.floataa )
_UpperCAmelCase = 0
_UpperCAmelCase = 'a hat'
_UpperCAmelCase = KandinskyVaaPriorPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-2-prior' , torch_dtype=torch.floataa )
pipe_prior.to(snake_case )
_UpperCAmelCase = KandinskyVaaInpaintPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-2-decoder-inpaint' , torch_dtype=torch.floataa )
_UpperCAmelCase = pipeline.to(snake_case )
pipeline.set_progress_bar_config(disable=snake_case )
_UpperCAmelCase = torch.Generator(device='cpu' ).manual_seed(0 )
_UpperCAmelCase , _UpperCAmelCase = pipe_prior(
snake_case , generator=snake_case , num_inference_steps=5 , negative_prompt='' , ).to_tuple()
_UpperCAmelCase = pipeline(
image=snake_case , mask_image=snake_case , image_embeds=snake_case , negative_image_embeds=snake_case , generator=snake_case , num_inference_steps=100 , height=768 , width=768 , output_type='np' , )
_UpperCAmelCase = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(snake_case , snake_case )
| 713
|
"""simple docstring"""
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS
logging.basicConfig(level=logging.DEBUG)
lowercase = logging.getLogger()
def UpperCAmelCase ( A : Path , A : list ):
'''simple docstring'''
_UpperCAmelCase = '\n'.join(A )
Path(A ).open('w' ).writelines(A )
lowercase = '''patrickvonplaten/t5-tiny-random'''
lowercase = '''sshleifer/bart-tiny-random'''
lowercase = '''sshleifer/tiny-mbart'''
lowercase = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL) # remove noisy download output from tracebacks
class lowercase__ ( A ):
'''simple docstring'''
def lowerCamelCase_ ( self , snake_case ) -> str:
_UpperCAmelCase = Path(self.get_auto_remove_tmp_dir() ) / 'utest_input.source'
_UpperCAmelCase = input_file_name.parent / 'utest_output.txt'
assert not output_file_name.exists()
_UpperCAmelCase = [' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
_dump_articles(snake_case , snake_case )
_UpperCAmelCase = str(Path(self.get_auto_remove_tmp_dir() ) / 'scores.json' )
_UpperCAmelCase = 'translation_en_to_de' if model == T5_TINY else 'summarization'
_UpperCAmelCase = f'\n run_eval_search.py\n {model}\n {input_file_name}\n {output_file_name}\n --score_path {score_path}\n --task {task}\n --num_beams 2\n --length_penalty 2.0\n '.split()
with patch.object(snake_case , 'argv' , snake_case ):
run_generate()
assert Path(snake_case ).exists()
# os.remove(Path(output_file_name))
def lowerCamelCase_ ( self ) -> str:
self.run_eval_tester(snake_case )
@parameterized.expand([BART_TINY, MBART_TINY] )
@slow
def lowerCamelCase_ ( self , snake_case ) -> List[Any]:
self.run_eval_tester(snake_case )
@parameterized.expand([T5_TINY, MBART_TINY] )
@slow
def lowerCamelCase_ ( self , snake_case ) -> Dict:
_UpperCAmelCase = Path(self.get_auto_remove_tmp_dir() ) / 'utest_input.source'
_UpperCAmelCase = input_file_name.parent / 'utest_output.txt'
assert not output_file_name.exists()
_UpperCAmelCase = {
'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
'de': [
'Maschinelles Lernen ist großartig, oder?',
'Ich esse gerne Bananen',
'Morgen ist wieder ein toller Tag!',
],
}
_UpperCAmelCase = Path(self.get_auto_remove_tmp_dir() )
_UpperCAmelCase = str(tmp_dir / 'scores.json' )
_UpperCAmelCase = str(tmp_dir / 'val.target' )
_dump_articles(snake_case , text['en'] )
_dump_articles(snake_case , text['de'] )
_UpperCAmelCase = 'translation_en_to_de' if model == T5_TINY else 'summarization'
_UpperCAmelCase = f'\n run_eval_search.py\n {model}\n {str(snake_case )}\n {str(snake_case )}\n --score_path {score_path}\n --reference_path {reference_path}\n --task {task}\n '.split()
testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0'] )
with patch.object(snake_case , 'argv' , snake_case ):
with CaptureStdout() as cs:
run_search()
_UpperCAmelCase = [' num_beams | length_penalty', model, 'Best score args']
_UpperCAmelCase = ['Info']
if "translation" in task:
expected_strings.append('bleu' )
else:
expected_strings.extend(snake_case )
for w in expected_strings:
assert w in cs.out
for w in un_expected_strings:
assert w not in cs.out
assert Path(snake_case ).exists()
os.remove(Path(snake_case ) )
| 24
| 0
|
"""simple docstring"""
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
lowercase = input('''Enter image url: ''').strip()
print(F'''Downloading image from {url} ...''')
lowercase = BeautifulSoup(requests.get(url).content, '''html.parser''')
# The image URL is in the content field of the first meta tag with property og:image
lowercase = soup.find('''meta''', {'''property''': '''og:image'''})['''content''']
lowercase = requests.get(image_url).content
lowercase = F'''{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg'''
with open(file_name, '''wb''') as fp:
fp.write(image_data)
print(F'''Done. Image saved to disk as {file_name}.''')
| 714
|
"""simple docstring"""
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal
lowercase = logging.get_logger(__name__)
lowercase = TypeVar('''DatasetType''', Dataset, IterableDataset)
def UpperCAmelCase ( A : List[DatasetType] , A : Optional[List[float]] = None , A : Optional[int] = None , A : Optional[DatasetInfo] = None , A : Optional[NamedSplit] = None , A : Literal["first_exhausted", "all_exhausted"] = "first_exhausted" , ):
'''simple docstring'''
from .arrow_dataset import Dataset
from .iterable_dataset import IterableDataset
if not datasets:
raise ValueError('Unable to interleave an empty list of datasets.' )
for i, dataset in enumerate(A ):
if not isinstance(A , (Dataset, IterableDataset) ):
if isinstance(A , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
'is an empty dataset dictionary.' )
raise ValueError(
f'Dataset at position {i} has at least one split: {list(A )}\n'
f'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(A ) )}\']' )
raise ValueError(
f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(A ).__name__}.' )
if i == 0:
_UpperCAmelCase , _UpperCAmelCase = (
(Dataset, IterableDataset) if isinstance(A , A ) else (IterableDataset, Dataset)
)
elif not isinstance(A , A ):
raise ValueError(
f'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.' )
if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
raise ValueError(f'{stopping_strategy} is not supported. Please enter a valid stopping_strategy.' )
if dataset_type is Dataset:
return _interleave_map_style_datasets(
A , A , A , info=A , split=A , stopping_strategy=A )
else:
return _interleave_iterable_datasets(
A , A , A , info=A , split=A , stopping_strategy=A )
def UpperCAmelCase ( A : List[DatasetType] , A : Optional[DatasetInfo] = None , A : Optional[NamedSplit] = None , A : int = 0 , ):
'''simple docstring'''
if not dsets:
raise ValueError('Unable to concatenate an empty list of datasets.' )
for i, dataset in enumerate(A ):
if not isinstance(A , (Dataset, IterableDataset) ):
if isinstance(A , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
'is an empty dataset dictionary.' )
raise ValueError(
f'Dataset at position {i} has at least one split: {list(A )}\n'
f'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(A ) )}\']' )
raise ValueError(
f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(A ).__name__}.' )
if i == 0:
_UpperCAmelCase , _UpperCAmelCase = (
(Dataset, IterableDataset) if isinstance(A , A ) else (IterableDataset, Dataset)
)
elif not isinstance(A , A ):
raise ValueError(
f'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.' )
if dataset_type is Dataset:
return _concatenate_map_style_datasets(A , info=A , split=A , axis=A )
else:
return _concatenate_iterable_datasets(A , info=A , split=A , axis=A )
| 24
| 0
|
"""simple docstring"""
import os
# Precomputes a list of the 100 first triangular numbers
lowercase = [int(0.5 * n * (n + 1)) for n in range(1, 1_01)]
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = os.path.dirname(os.path.realpath(__A ) )
_UpperCAmelCase = os.path.join(__A , 'words.txt' )
_UpperCAmelCase = """"""
with open(__A ) as f:
_UpperCAmelCase = f.readline()
_UpperCAmelCase = [word.strip('\"' ) for word in words.strip('\r\n' ).split(',' )]
_UpperCAmelCase = [
word
for word in [sum(ord(__A ) - 64 for x in word ) for word in words]
if word in TRIANGULAR_NUMBERS
]
return len(__A )
if __name__ == "__main__":
print(solution())
| 715
|
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available
from .test_pipelines_common import ANY
if is_torch_available():
import torch
@is_pipeline_test
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
_UpperCAmelCase = TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case ) -> Dict:
_UpperCAmelCase = TextaTextGenerationPipeline(model=snake_case , tokenizer=snake_case )
return generator, ["Something to write", "Something else"]
def lowerCamelCase_ ( self , snake_case , snake_case ) -> Dict:
_UpperCAmelCase = generator('Something there' )
self.assertEqual(snake_case , [{'generated_text': ANY(snake_case )}] )
# These are encoder decoder, they don't just append to incoming string
self.assertFalse(outputs[0]['generated_text'].startswith('Something there' ) )
_UpperCAmelCase = generator(['This is great !', 'Something else'] , num_return_sequences=2 , do_sample=snake_case )
self.assertEqual(
snake_case , [
[{'generated_text': ANY(snake_case )}, {'generated_text': ANY(snake_case )}],
[{'generated_text': ANY(snake_case )}, {'generated_text': ANY(snake_case )}],
] , )
_UpperCAmelCase = generator(
['This is great !', 'Something else'] , num_return_sequences=2 , batch_size=2 , do_sample=snake_case )
self.assertEqual(
snake_case , [
[{'generated_text': ANY(snake_case )}, {'generated_text': ANY(snake_case )}],
[{'generated_text': ANY(snake_case )}, {'generated_text': ANY(snake_case )}],
] , )
with self.assertRaises(snake_case ):
generator(4 )
@require_torch
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = pipeline('text2text-generation' , model='patrickvonplaten/t5-tiny-random' , framework='pt' )
# do_sample=False necessary for reproducibility
_UpperCAmelCase = generator('Something there' , do_sample=snake_case )
self.assertEqual(snake_case , [{'generated_text': ''}] )
_UpperCAmelCase = 3
_UpperCAmelCase = generator(
'Something there' , num_return_sequences=snake_case , num_beams=snake_case , )
_UpperCAmelCase = [
{'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide Beide'},
{'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide'},
{'generated_text': ''},
]
self.assertEqual(snake_case , snake_case )
_UpperCAmelCase = generator('This is a test' , do_sample=snake_case , num_return_sequences=2 , return_tensors=snake_case )
self.assertEqual(
snake_case , [
{'generated_token_ids': ANY(torch.Tensor )},
{'generated_token_ids': ANY(torch.Tensor )},
] , )
_UpperCAmelCase = generator.model.config.eos_token_id
_UpperCAmelCase = '<pad>'
_UpperCAmelCase = generator(
['This is a test', 'This is a second test'] , do_sample=snake_case , num_return_sequences=2 , batch_size=2 , return_tensors=snake_case , )
self.assertEqual(
snake_case , [
[
{'generated_token_ids': ANY(torch.Tensor )},
{'generated_token_ids': ANY(torch.Tensor )},
],
[
{'generated_token_ids': ANY(torch.Tensor )},
{'generated_token_ids': ANY(torch.Tensor )},
],
] , )
@require_tf
def lowerCamelCase_ ( self ) -> Any:
_UpperCAmelCase = pipeline('text2text-generation' , model='patrickvonplaten/t5-tiny-random' , framework='tf' )
# do_sample=False necessary for reproducibility
_UpperCAmelCase = generator('Something there' , do_sample=snake_case )
self.assertEqual(snake_case , [{'generated_text': ''}] )
| 24
| 0
|
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase ( A : Optional[int] ):
'''simple docstring'''
create_state_space_tree(_lowerCamelCase , [] , 0 , [0 for i in range(len(_lowerCamelCase ) )] )
def UpperCAmelCase ( A : List[str] , A : Optional[int] , A : Optional[int] , A : str , ):
'''simple docstring'''
if index == len(_lowerCamelCase ):
print(_lowerCamelCase )
return
for i in range(len(_lowerCamelCase ) ):
if not index_used[i]:
current_sequence.append(sequence[i] )
_UpperCAmelCase = True
create_state_space_tree(_lowerCamelCase , _lowerCamelCase , index + 1 , _lowerCamelCase )
current_sequence.pop()
_UpperCAmelCase = False
lowercase = [3, 1, 2, 4]
generate_all_permutations(sequence)
lowercase = ['''A''', '''B''', '''C''']
generate_all_permutations(sequence_a)
| 716
|
"""simple docstring"""
def UpperCAmelCase ( A : int ):
'''simple docstring'''
_UpperCAmelCase = [[0 for _ in range(A )] for _ in range(m + 1 )]
for i in range(m + 1 ):
_UpperCAmelCase = 1
for n in range(m + 1 ):
for k in range(1 , A ):
memo[n][k] += memo[n][k - 1]
if n - k > 0:
memo[n][k] += memo[n - k - 1][k]
return memo[m][m - 1]
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
try:
lowercase = int(input('''Enter a number: ''').strip())
print(partition(n))
except ValueError:
print('''Please enter a number.''')
else:
try:
lowercase = int(sys.argv[1])
print(partition(n))
except ValueError:
print('''Please pass a number.''')
| 24
| 0
|
"""simple docstring"""
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, TrainingArguments
from transformers.hf_argparser import make_choice_type_function, string_to_bool
# Since Python 3.10, we can use the builtin `|` operator for Union types
# See PEP 604: https://peps.python.org/pep-0604
lowercase = sys.version_info >= (3, 10)
def UpperCAmelCase ( A : Optional[Any]=None , A : Optional[Any]=None ):
'''simple docstring'''
return field(default_factory=lambda: default , metadata=_lowerCamelCase )
@dataclass
class lowercase__ :
'''simple docstring'''
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
_UpperCAmelCase = 42
@dataclass
class lowercase__ :
'''simple docstring'''
_UpperCAmelCase = 42
_UpperCAmelCase = field(default='''toto''', metadata={'''help''': '''help message'''} )
@dataclass
class lowercase__ :
'''simple docstring'''
_UpperCAmelCase = False
_UpperCAmelCase = True
_UpperCAmelCase = None
class lowercase__ ( _A ):
'''simple docstring'''
_UpperCAmelCase = '''titi'''
_UpperCAmelCase = '''toto'''
class lowercase__ ( _A ):
'''simple docstring'''
_UpperCAmelCase = '''titi'''
_UpperCAmelCase = '''toto'''
_UpperCAmelCase = 42
@dataclass
class lowercase__ :
'''simple docstring'''
_UpperCAmelCase = '''toto'''
def lowerCamelCase_ ( self ) -> Any:
_UpperCAmelCase = BasicEnum(self.foo )
@dataclass
class lowercase__ :
'''simple docstring'''
_UpperCAmelCase = '''toto'''
def lowerCamelCase_ ( self ) -> List[Any]:
_UpperCAmelCase = MixedTypeEnum(self.foo )
@dataclass
class lowercase__ :
'''simple docstring'''
_UpperCAmelCase = None
_UpperCAmelCase = field(default=_A, metadata={'''help''': '''help message'''} )
_UpperCAmelCase = None
_UpperCAmelCase = list_field(default=[] )
_UpperCAmelCase = list_field(default=[] )
@dataclass
class lowercase__ :
'''simple docstring'''
_UpperCAmelCase = list_field(default=[] )
_UpperCAmelCase = list_field(default=[1, 2, 3] )
_UpperCAmelCase = list_field(default=['''Hallo''', '''Bonjour''', '''Hello'''] )
_UpperCAmelCase = list_field(default=[0.1, 0.2, 0.3] )
@dataclass
class lowercase__ :
'''simple docstring'''
_UpperCAmelCase = field()
_UpperCAmelCase = field()
_UpperCAmelCase = field()
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = BasicEnum(self.required_enum )
@dataclass
class lowercase__ :
'''simple docstring'''
_UpperCAmelCase = 42
_UpperCAmelCase = field()
_UpperCAmelCase = None
_UpperCAmelCase = field(default='''toto''', metadata={'''help''': '''help message'''} )
_UpperCAmelCase = list_field(default=['''Hallo''', '''Bonjour''', '''Hello'''] )
if is_python_no_less_than_3_10:
@dataclass
class lowercase__ :
'''simple docstring'''
_UpperCAmelCase = False
_UpperCAmelCase = True
_UpperCAmelCase = None
@dataclass
class lowercase__ :
'''simple docstring'''
_UpperCAmelCase = None
_UpperCAmelCase = field(default=_A, metadata={'''help''': '''help message'''} )
_UpperCAmelCase = None
_UpperCAmelCase = list_field(default=[] )
_UpperCAmelCase = list_field(default=[] )
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase_ ( self , snake_case , snake_case ) -> Optional[Any]:
self.assertEqual(len(a._actions ) , len(b._actions ) )
for x, y in zip(a._actions , b._actions ):
_UpperCAmelCase = {k: v for k, v in vars(__lowerCamelCase ).items() if k != "container"}
_UpperCAmelCase = {k: v for k, v in vars(__lowerCamelCase ).items() if k != "container"}
# Choices with mixed type have custom function as "type"
# So we need to compare results directly for equality
if xx.get('choices' , __lowerCamelCase ) and yy.get('choices' , __lowerCamelCase ):
for expected_choice in yy["choices"] + xx["choices"]:
self.assertEqual(xx['type'](__lowerCamelCase ) , yy['type'](__lowerCamelCase ) )
del xx["type"], yy["type"]
self.assertEqual(__lowerCamelCase , __lowerCamelCase )
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = HfArgumentParser(__lowerCamelCase )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('--foo' , type=__lowerCamelCase , required=__lowerCamelCase )
expected.add_argument('--bar' , type=__lowerCamelCase , required=__lowerCamelCase )
expected.add_argument('--baz' , type=__lowerCamelCase , required=__lowerCamelCase )
expected.add_argument('--flag' , type=__lowerCamelCase , default=__lowerCamelCase , const=__lowerCamelCase , nargs='?' )
self.argparsersEqual(__lowerCamelCase , __lowerCamelCase )
_UpperCAmelCase = ["--foo", "1", "--baz", "quux", "--bar", "0.5"]
(_UpperCAmelCase ) = parser.parse_args_into_dataclasses(__lowerCamelCase , look_for_args_file=__lowerCamelCase )
self.assertFalse(example.flag )
def lowerCamelCase_ ( self ) -> Tuple:
_UpperCAmelCase = HfArgumentParser(__lowerCamelCase )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('--foo' , default=42 , type=__lowerCamelCase )
expected.add_argument('--baz' , default='toto' , type=__lowerCamelCase , help='help message' )
self.argparsersEqual(__lowerCamelCase , __lowerCamelCase )
def lowerCamelCase_ ( self ) -> Tuple:
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('--foo' , type=__lowerCamelCase , default=__lowerCamelCase , const=__lowerCamelCase , nargs='?' )
expected.add_argument('--baz' , type=__lowerCamelCase , default=__lowerCamelCase , const=__lowerCamelCase , nargs='?' )
# A boolean no_* argument always has to come after its "default: True" regular counter-part
# and its default must be set to False
expected.add_argument('--no_baz' , action='store_false' , default=__lowerCamelCase , dest='baz' )
expected.add_argument('--opt' , type=__lowerCamelCase , default=__lowerCamelCase )
_UpperCAmelCase = [WithDefaultBoolExample]
if is_python_no_less_than_3_10:
dataclass_types.append(__lowerCamelCase )
for dataclass_type in dataclass_types:
_UpperCAmelCase = HfArgumentParser(__lowerCamelCase )
self.argparsersEqual(__lowerCamelCase , __lowerCamelCase )
_UpperCAmelCase = parser.parse_args([] )
self.assertEqual(__lowerCamelCase , Namespace(foo=__lowerCamelCase , baz=__lowerCamelCase , opt=__lowerCamelCase ) )
_UpperCAmelCase = parser.parse_args(['--foo', '--no_baz'] )
self.assertEqual(__lowerCamelCase , Namespace(foo=__lowerCamelCase , baz=__lowerCamelCase , opt=__lowerCamelCase ) )
_UpperCAmelCase = parser.parse_args(['--foo', '--baz'] )
self.assertEqual(__lowerCamelCase , Namespace(foo=__lowerCamelCase , baz=__lowerCamelCase , opt=__lowerCamelCase ) )
_UpperCAmelCase = parser.parse_args(['--foo', 'True', '--baz', 'True', '--opt', 'True'] )
self.assertEqual(__lowerCamelCase , Namespace(foo=__lowerCamelCase , baz=__lowerCamelCase , opt=__lowerCamelCase ) )
_UpperCAmelCase = parser.parse_args(['--foo', 'False', '--baz', 'False', '--opt', 'False'] )
self.assertEqual(__lowerCamelCase , Namespace(foo=__lowerCamelCase , baz=__lowerCamelCase , opt=__lowerCamelCase ) )
def lowerCamelCase_ ( self ) -> List[Any]:
_UpperCAmelCase = HfArgumentParser(__lowerCamelCase )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument(
'--foo' , default='toto' , choices=['titi', 'toto', 42] , type=make_choice_type_function(['titi', 'toto', 42] ) , )
self.argparsersEqual(__lowerCamelCase , __lowerCamelCase )
_UpperCAmelCase = parser.parse_args([] )
self.assertEqual(args.foo , 'toto' )
_UpperCAmelCase = parser.parse_args_into_dataclasses([] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.toto )
_UpperCAmelCase = parser.parse_args(['--foo', 'titi'] )
self.assertEqual(args.foo , 'titi' )
_UpperCAmelCase = parser.parse_args_into_dataclasses(['--foo', 'titi'] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.titi )
_UpperCAmelCase = parser.parse_args(['--foo', '42'] )
self.assertEqual(args.foo , 42 )
_UpperCAmelCase = parser.parse_args_into_dataclasses(['--foo', '42'] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.fourtytwo )
def lowerCamelCase_ ( self ) -> Tuple:
@dataclass
class lowercase__ :
'''simple docstring'''
_UpperCAmelCase = '''toto'''
_UpperCAmelCase = HfArgumentParser(__lowerCamelCase )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument(
'--foo' , default='toto' , choices=('titi', 'toto', 42) , type=make_choice_type_function(['titi', 'toto', 42] ) , )
self.argparsersEqual(__lowerCamelCase , __lowerCamelCase )
_UpperCAmelCase = parser.parse_args([] )
self.assertEqual(args.foo , 'toto' )
_UpperCAmelCase = parser.parse_args(['--foo', 'titi'] )
self.assertEqual(args.foo , 'titi' )
_UpperCAmelCase = parser.parse_args(['--foo', '42'] )
self.assertEqual(args.foo , 42 )
def lowerCamelCase_ ( self ) -> Tuple:
_UpperCAmelCase = HfArgumentParser(__lowerCamelCase )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('--foo_int' , nargs='+' , default=[] , type=__lowerCamelCase )
expected.add_argument('--bar_int' , nargs='+' , default=[1, 2, 3] , type=__lowerCamelCase )
expected.add_argument('--foo_str' , nargs='+' , default=['Hallo', 'Bonjour', 'Hello'] , type=__lowerCamelCase )
expected.add_argument('--foo_float' , nargs='+' , default=[0.1, 0.2, 0.3] , type=__lowerCamelCase )
self.argparsersEqual(__lowerCamelCase , __lowerCamelCase )
_UpperCAmelCase = parser.parse_args([] )
self.assertEqual(
__lowerCamelCase , Namespace(foo_int=[] , bar_int=[1, 2, 3] , foo_str=['Hallo', 'Bonjour', 'Hello'] , foo_float=[0.1, 0.2, 0.3] ) , )
_UpperCAmelCase = parser.parse_args('--foo_int 1 --bar_int 2 3 --foo_str a b c --foo_float 0.1 0.7'.split() )
self.assertEqual(__lowerCamelCase , Namespace(foo_int=[1] , bar_int=[2, 3] , foo_str=['a', 'b', 'c'] , foo_float=[0.1, 0.7] ) )
def lowerCamelCase_ ( self ) -> Optional[int]:
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('--foo' , default=__lowerCamelCase , type=__lowerCamelCase )
expected.add_argument('--bar' , default=__lowerCamelCase , type=__lowerCamelCase , help='help message' )
expected.add_argument('--baz' , default=__lowerCamelCase , type=__lowerCamelCase )
expected.add_argument('--ces' , nargs='+' , default=[] , type=__lowerCamelCase )
expected.add_argument('--des' , nargs='+' , default=[] , type=__lowerCamelCase )
_UpperCAmelCase = [OptionalExample]
if is_python_no_less_than_3_10:
dataclass_types.append(__lowerCamelCase )
for dataclass_type in dataclass_types:
_UpperCAmelCase = HfArgumentParser(__lowerCamelCase )
self.argparsersEqual(__lowerCamelCase , __lowerCamelCase )
_UpperCAmelCase = parser.parse_args([] )
self.assertEqual(__lowerCamelCase , Namespace(foo=__lowerCamelCase , bar=__lowerCamelCase , baz=__lowerCamelCase , ces=[] , des=[] ) )
_UpperCAmelCase = parser.parse_args('--foo 12 --bar 3.14 --baz 42 --ces a b c --des 1 2 3'.split() )
self.assertEqual(__lowerCamelCase , Namespace(foo=12 , bar=3.14 , baz='42' , ces=['a', 'b', 'c'] , des=[1, 2, 3] ) )
def lowerCamelCase_ ( self ) -> Optional[int]:
_UpperCAmelCase = HfArgumentParser(__lowerCamelCase )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('--required_list' , nargs='+' , type=__lowerCamelCase , required=__lowerCamelCase )
expected.add_argument('--required_str' , type=__lowerCamelCase , required=__lowerCamelCase )
expected.add_argument(
'--required_enum' , type=make_choice_type_function(['titi', 'toto'] ) , choices=['titi', 'toto'] , required=__lowerCamelCase , )
self.argparsersEqual(__lowerCamelCase , __lowerCamelCase )
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = HfArgumentParser(__lowerCamelCase )
_UpperCAmelCase = argparse.ArgumentParser()
expected.add_argument('--foo' , type=__lowerCamelCase , required=__lowerCamelCase )
expected.add_argument(
'--required_enum' , type=make_choice_type_function(['titi', 'toto'] ) , choices=['titi', 'toto'] , required=__lowerCamelCase , )
expected.add_argument('--opt' , type=__lowerCamelCase , default=__lowerCamelCase )
expected.add_argument('--baz' , default='toto' , type=__lowerCamelCase , help='help message' )
expected.add_argument('--foo_str' , nargs='+' , default=['Hallo', 'Bonjour', 'Hello'] , type=__lowerCamelCase )
self.argparsersEqual(__lowerCamelCase , __lowerCamelCase )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = HfArgumentParser(__lowerCamelCase )
_UpperCAmelCase = {
"foo": 12,
"bar": 3.14,
"baz": "42",
"flag": True,
}
_UpperCAmelCase = parser.parse_dict(__lowerCamelCase )[0]
_UpperCAmelCase = BasicExample(**__lowerCamelCase )
self.assertEqual(__lowerCamelCase , __lowerCamelCase )
def lowerCamelCase_ ( self ) -> Optional[Any]:
_UpperCAmelCase = HfArgumentParser(__lowerCamelCase )
_UpperCAmelCase = {
"foo": 12,
"bar": 3.14,
"baz": "42",
"flag": True,
"extra": 42,
}
self.assertRaises(__lowerCamelCase , parser.parse_dict , __lowerCamelCase , allow_extra_keys=__lowerCamelCase )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = HfArgumentParser(__lowerCamelCase )
_UpperCAmelCase = {
"foo": 12,
"bar": 3.14,
"baz": "42",
"flag": True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
_UpperCAmelCase = os.path.join(__lowerCamelCase , 'temp_json' )
os.mkdir(__lowerCamelCase )
with open(temp_local_path + '.json' , 'w+' ) as f:
json.dump(__lowerCamelCase , __lowerCamelCase )
_UpperCAmelCase = parser.parse_yaml_file(Path(temp_local_path + '.json' ) )[0]
_UpperCAmelCase = BasicExample(**__lowerCamelCase )
self.assertEqual(__lowerCamelCase , __lowerCamelCase )
def lowerCamelCase_ ( self ) -> Tuple:
_UpperCAmelCase = HfArgumentParser(__lowerCamelCase )
_UpperCAmelCase = {
"foo": 12,
"bar": 3.14,
"baz": "42",
"flag": True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
_UpperCAmelCase = os.path.join(__lowerCamelCase , 'temp_yaml' )
os.mkdir(__lowerCamelCase )
with open(temp_local_path + '.yaml' , 'w+' ) as f:
yaml.dump(__lowerCamelCase , __lowerCamelCase )
_UpperCAmelCase = parser.parse_yaml_file(Path(temp_local_path + '.yaml' ) )[0]
_UpperCAmelCase = BasicExample(**__lowerCamelCase )
self.assertEqual(__lowerCamelCase , __lowerCamelCase )
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = HfArgumentParser(__lowerCamelCase )
self.assertIsNotNone(__lowerCamelCase )
| 717
|
"""simple docstring"""
import os
lowercase = {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 1_00, '''D''': 5_00, '''M''': 10_00}
def UpperCAmelCase ( A : str ):
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase = 0
while index < len(A ) - 1:
_UpperCAmelCase = SYMBOLS[numerals[index]]
_UpperCAmelCase = 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 : int ):
'''simple docstring'''
_UpperCAmelCase = ''
_UpperCAmelCase = num // 1000
numerals += m_count * "M"
num %= 1000
_UpperCAmelCase = num // 100
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 %= 100
_UpperCAmelCase = num // 10
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 %= 10
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 : str = "/p089_roman.txt" ):
'''simple docstring'''
_UpperCAmelCase = 0
with open(os.path.dirname(A ) + roman_numerals_filename ) as filea:
_UpperCAmelCase = filea.readlines()
for line in lines:
_UpperCAmelCase = line.strip()
_UpperCAmelCase = parse_roman_numerals(A )
_UpperCAmelCase = generate_roman_numerals(A )
savings += len(A ) - len(A )
return savings
if __name__ == "__main__":
print(F'''{solution() = }''')
| 24
| 0
|
"""simple docstring"""
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
get_from_cache,
http_get,
http_head,
)
lowercase = "\\n Text data.\n Second line of data."
lowercase = "file"
@pytest.fixture(scope='session' )
def UpperCAmelCase ( A : str ):
'''simple docstring'''
_UpperCAmelCase = tmp_path_factory.mktemp('data' ) / (FILE_PATH + '''.zstd''')
_UpperCAmelCase = bytes(a_ , 'utf-8' )
with zstd.open(a_ , 'wb' ) as f:
f.write(a_ )
return path
@pytest.fixture
def UpperCAmelCase ( A : Optional[int] ):
'''simple docstring'''
with open(os.path.join(tmpfs.local_root_dir , a_ ) , 'w' ) as f:
f.write(a_ )
return FILE_PATH
@pytest.mark.parametrize('compression_format' , ['gzip', 'xz', 'zstd'] )
def UpperCAmelCase ( A : str , A : Tuple , A : Dict , A : Optional[Any] , A : Tuple , A : Union[str, Any] ):
'''simple docstring'''
_UpperCAmelCase = {'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_path}
_UpperCAmelCase = input_paths[compression_format]
_UpperCAmelCase = tmp_path / '''cache'''
_UpperCAmelCase = DownloadConfig(cache_dir=a_ , extract_compressed_file=a_ )
_UpperCAmelCase = cached_path(a_ , download_config=a_ )
with open(a_ ) as f:
_UpperCAmelCase = f.read()
with open(a_ ) as f:
_UpperCAmelCase = f.read()
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize('default_extracted' , [True, False] )
@pytest.mark.parametrize('default_cache_dir' , [True, False] )
def UpperCAmelCase ( A : List[Any] , A : Optional[int] , A : List[Any] , A : Optional[int] , A : Tuple ):
'''simple docstring'''
_UpperCAmelCase = '''custom_cache'''
_UpperCAmelCase = '''custom_extracted_dir'''
_UpperCAmelCase = tmp_path / '''custom_extracted_path'''
if default_extracted:
_UpperCAmelCase = ('''downloads''' if default_cache_dir else custom_cache_dir, '''extracted''')
else:
monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_DIR' , a_ )
monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_PATH' , str(a_ ) )
_UpperCAmelCase = custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)
_UpperCAmelCase = xz_file
_UpperCAmelCase = (
DownloadConfig(extract_compressed_file=a_ )
if default_cache_dir
else DownloadConfig(cache_dir=tmp_path / custom_cache_dir , extract_compressed_file=a_ )
)
_UpperCAmelCase = cached_path(a_ , download_config=a_ )
assert Path(a_ ).parent.parts[-2:] == expected
def UpperCAmelCase ( A : Any ):
'''simple docstring'''
_UpperCAmelCase = str(Path(a_ ).resolve() )
assert cached_path(a_ ) == text_file
# relative path
_UpperCAmelCase = str(Path(a_ ).resolve().relative_to(Path(os.getcwd() ) ) )
assert cached_path(a_ ) == text_file
def UpperCAmelCase ( A : Optional[int] ):
'''simple docstring'''
_UpperCAmelCase = str(tmp_path.resolve() / '__missing_file__.txt' )
with pytest.raises(a_ ):
cached_path(a_ )
# relative path
_UpperCAmelCase = '''./__missing_file__.txt'''
with pytest.raises(a_ ):
cached_path(a_ )
def UpperCAmelCase ( A : Optional[int] ):
'''simple docstring'''
_UpperCAmelCase = get_from_cache(f'tmp://{tmpfs_file}' )
with open(a_ ) as f:
_UpperCAmelCase = f.read()
assert output_file_content == FILE_CONTENT
@patch('datasets.config.HF_DATASETS_OFFLINE' , a_ )
def UpperCAmelCase ( ):
'''simple docstring'''
with pytest.raises(a_ ):
cached_path('https://huggingface.co' )
@patch('datasets.config.HF_DATASETS_OFFLINE' , a_ )
def UpperCAmelCase ( A : Dict ):
'''simple docstring'''
_UpperCAmelCase = tmp_path_factory.mktemp('data' ) / '''file.html'''
with pytest.raises(a_ ):
http_get('https://huggingface.co' , temp_file=a_ )
with pytest.raises(a_ ):
http_head('https://huggingface.co' )
@patch('datasets.config.HF_DATASETS_OFFLINE' , a_ )
def UpperCAmelCase ( A : List[str] ):
'''simple docstring'''
_UpperCAmelCase = tmp_path_factory.mktemp('data' ) / '''file.html'''
with pytest.raises(a_ ):
ftp_get('ftp://huggingface.co' , temp_file=a_ )
with pytest.raises(a_ ):
ftp_head('ftp://huggingface.co' )
@patch('datasets.config.HF_DATASETS_OFFLINE' , a_ )
def UpperCAmelCase ( A : str ):
'''simple docstring'''
_UpperCAmelCase = tmp_path_factory.mktemp('data' ) / '''file.html'''
with pytest.raises(a_ ):
fsspec_get('s3://huggingface.co' , temp_file=a_ )
with pytest.raises(a_ ):
fsspec_head('s3://huggingface.co' )
| 718
|
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_available():
import jax.numpy as jnp
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = {
'task_specific_params': {
'summarization': {'length_penalty': 1.0, 'max_length': 128, 'min_length': 12, 'num_beams': 4},
'summarization_cnn': {'length_penalty': 2.0, 'max_length': 142, 'min_length': 56, 'num_beams': 4},
'summarization_xsum': {'length_penalty': 1.0, 'max_length': 62, 'min_length': 11, 'num_beams': 6},
}
}
_UpperCAmelCase = {
'task_specific_params.summarization.length_penalty': 1.0,
'task_specific_params.summarization.max_length': 128,
'task_specific_params.summarization.min_length': 12,
'task_specific_params.summarization.num_beams': 4,
'task_specific_params.summarization_cnn.length_penalty': 2.0,
'task_specific_params.summarization_cnn.max_length': 142,
'task_specific_params.summarization_cnn.min_length': 56,
'task_specific_params.summarization_cnn.num_beams': 4,
'task_specific_params.summarization_xsum.length_penalty': 1.0,
'task_specific_params.summarization_xsum.max_length': 62,
'task_specific_params.summarization_xsum.min_length': 11,
'task_specific_params.summarization_xsum.num_beams': 6,
}
self.assertEqual(flatten_dict(snake_case ) , snake_case )
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(transpose(snake_case ) , x.transpose() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(transpose(snake_case , axes=(1, 2, 0) ) , x.transpose((1, 2, 0) ) ) )
@require_torch
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(transpose(snake_case ) , transpose(snake_case ).numpy() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(transpose(snake_case , axes=(1, 2, 0) ) , transpose(snake_case , axes=(1, 2, 0) ).numpy() ) )
@require_tf
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(transpose(snake_case ) , transpose(snake_case ).numpy() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(transpose(snake_case , axes=(1, 2, 0) ) , transpose(snake_case , axes=(1, 2, 0) ).numpy() ) )
@require_flax
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(transpose(snake_case ) , np.asarray(transpose(snake_case ) ) ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(transpose(snake_case , axes=(1, 2, 0) ) , np.asarray(transpose(snake_case , axes=(1, 2, 0) ) ) ) )
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(reshape(snake_case , (4, 3) ) , np.reshape(snake_case , (4, 3) ) ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(reshape(snake_case , (12, 5) ) , np.reshape(snake_case , (12, 5) ) ) )
@require_torch
def lowerCamelCase_ ( self ) -> Optional[Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (4, 3) ) , reshape(snake_case , (4, 3) ).numpy() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (12, 5) ) , reshape(snake_case , (12, 5) ).numpy() ) )
@require_tf
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (4, 3) ) , reshape(snake_case , (4, 3) ).numpy() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (12, 5) ) , reshape(snake_case , (12, 5) ).numpy() ) )
@require_flax
def lowerCamelCase_ ( self ) -> Tuple:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (4, 3) ) , np.asarray(reshape(snake_case , (4, 3) ) ) ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (12, 5) ) , np.asarray(reshape(snake_case , (12, 5) ) ) ) )
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = np.random.randn(1 , 3 , 4 )
self.assertTrue(np.allclose(squeeze(snake_case ) , np.squeeze(snake_case ) ) )
_UpperCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
self.assertTrue(np.allclose(squeeze(snake_case , axis=2 ) , np.squeeze(snake_case , axis=2 ) ) )
@require_torch
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(1 , 3 , 4 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case ) , squeeze(snake_case ).numpy() ) )
_UpperCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case , axis=2 ) , squeeze(snake_case , axis=2 ).numpy() ) )
@require_tf
def lowerCamelCase_ ( self ) -> Optional[int]:
_UpperCAmelCase = np.random.randn(1 , 3 , 4 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case ) , squeeze(snake_case ).numpy() ) )
_UpperCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case , axis=2 ) , squeeze(snake_case , axis=2 ).numpy() ) )
@require_flax
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = np.random.randn(1 , 3 , 4 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case ) , np.asarray(squeeze(snake_case ) ) ) )
_UpperCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case , axis=2 ) , np.asarray(squeeze(snake_case , axis=2 ) ) ) )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(expand_dims(snake_case , axis=1 ) , np.expand_dims(snake_case , axis=1 ) ) )
@require_torch
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(expand_dims(snake_case , axis=1 ) , expand_dims(snake_case , axis=1 ).numpy() ) )
@require_tf
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(expand_dims(snake_case , axis=1 ) , expand_dims(snake_case , axis=1 ).numpy() ) )
@require_flax
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(expand_dims(snake_case , axis=1 ) , np.asarray(expand_dims(snake_case , axis=1 ) ) ) )
| 24
| 0
|
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
if is_vision_available():
from PIL import Image
else:
class lowercase__ :
'''simple docstring'''
@staticmethod
def lowerCamelCase_ ( *snake_case , **snake_case ) -> str:
pass
def UpperCAmelCase ( A : List[Any] ):
'''simple docstring'''
_UpperCAmelCase = hashlib.mda(image.tobytes() )
return m.hexdigest()[:10]
def UpperCAmelCase ( A : str ):
'''simple docstring'''
_UpperCAmelCase = np.array(_A )
_UpperCAmelCase = npimg.shape
return {"hash": hashimage(_A ), "shape": shape}
@is_pipeline_test
@require_vision
@require_torch
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = dict(
(list(MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if MODEL_FOR_MASK_GENERATION_MAPPING else []) )
_UpperCAmelCase = dict(
(list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else []) )
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case ) -> Optional[Any]:
_UpperCAmelCase = MaskGenerationPipeline(model=__UpperCamelCase , image_processor=__UpperCamelCase )
return image_segmenter, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def lowerCamelCase_ ( self , snake_case , snake_case ) -> Dict:
pass
@require_tf
@unittest.skip('Image segmentation not implemented in TF' )
def lowerCamelCase_ ( self ) -> List[str]:
pass
@slow
@require_torch
def lowerCamelCase_ ( self ) -> Optional[int]:
_UpperCAmelCase = pipeline('mask-generation' , model='facebook/sam-vit-huge' )
_UpperCAmelCase = image_segmenter('http://images.cocodataset.org/val2017/000000039769.jpg' , points_per_batch=256 )
# Shortening by hashing
_UpperCAmelCase = []
for i, o in enumerate(outputs['masks'] ):
new_outupt += [{"mask": mask_to_test_readable(__UpperCamelCase ), "scores": outputs["scores"][i]}]
# fmt: off
self.assertEqual(
nested_simplify(__UpperCamelCase , decimals=4 ) , [
{'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.0444},
{'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.021},
{'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0167},
{'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0132},
{'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0053},
{'mask': {'hash': 'e2d0b7a0b7', 'shape': (480, 640)}, 'scores': 0.9967},
{'mask': {'hash': '453c7844bd', 'shape': (480, 640)}, 'scores': 0.993},
{'mask': {'hash': '3d44f2926d', 'shape': (480, 640)}, 'scores': 0.9909},
{'mask': {'hash': '64033ddc3f', 'shape': (480, 640)}, 'scores': 0.9879},
{'mask': {'hash': '801064ff79', 'shape': (480, 640)}, 'scores': 0.9834},
{'mask': {'hash': '6172f276ef', 'shape': (480, 640)}, 'scores': 0.9716},
{'mask': {'hash': 'b49e60e084', 'shape': (480, 640)}, 'scores': 0.9612},
{'mask': {'hash': 'a811e775fd', 'shape': (480, 640)}, 'scores': 0.9599},
{'mask': {'hash': 'a6a8ebcf4b', 'shape': (480, 640)}, 'scores': 0.9552},
{'mask': {'hash': '9d8257e080', 'shape': (480, 640)}, 'scores': 0.9532},
{'mask': {'hash': '32de6454a8', 'shape': (480, 640)}, 'scores': 0.9516},
{'mask': {'hash': 'af3d4af2c8', 'shape': (480, 640)}, 'scores': 0.9499},
{'mask': {'hash': '3c6db475fb', 'shape': (480, 640)}, 'scores': 0.9483},
{'mask': {'hash': 'c290813fb9', 'shape': (480, 640)}, 'scores': 0.9464},
{'mask': {'hash': 'b6f0b8f606', 'shape': (480, 640)}, 'scores': 0.943},
{'mask': {'hash': '92ce16bfdf', 'shape': (480, 640)}, 'scores': 0.943},
{'mask': {'hash': 'c749b25868', 'shape': (480, 640)}, 'scores': 0.9408},
{'mask': {'hash': 'efb6cab859', 'shape': (480, 640)}, 'scores': 0.9335},
{'mask': {'hash': '1ff2eafb30', 'shape': (480, 640)}, 'scores': 0.9326},
{'mask': {'hash': '788b798e24', 'shape': (480, 640)}, 'scores': 0.9262},
{'mask': {'hash': 'abea804f0e', 'shape': (480, 640)}, 'scores': 0.8999},
{'mask': {'hash': '7b9e8ddb73', 'shape': (480, 640)}, 'scores': 0.8986},
{'mask': {'hash': 'cd24047c8a', 'shape': (480, 640)}, 'scores': 0.8984},
{'mask': {'hash': '6943e6bcbd', 'shape': (480, 640)}, 'scores': 0.8873},
{'mask': {'hash': 'b5f47c9191', 'shape': (480, 640)}, 'scores': 0.8871}
] , )
# fmt: on
@require_torch
@slow
def lowerCamelCase_ ( self ) -> Any:
_UpperCAmelCase = 'facebook/sam-vit-huge'
_UpperCAmelCase = pipeline('mask-generation' , model=__UpperCamelCase )
_UpperCAmelCase = image_segmenter(
'http://images.cocodataset.org/val2017/000000039769.jpg' , pred_iou_thresh=1 , points_per_batch=256 )
# Shortening by hashing
_UpperCAmelCase = []
for i, o in enumerate(outputs['masks'] ):
new_outupt += [{"mask": mask_to_test_readable(__UpperCamelCase ), "scores": outputs["scores"][i]}]
self.assertEqual(
nested_simplify(__UpperCamelCase , decimals=4 ) , [
{'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.0444},
{'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.0210},
{'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.0167},
{'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.0132},
{'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.0053},
] , )
| 719
|
"""simple docstring"""
import os
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = os.path.join(os.path.dirname(A ) , 'num.txt' )
with open(A ) as file_hand:
return str(sum(int(A ) for line in file_hand ) )[:10]
if __name__ == "__main__":
print(solution())
| 24
| 0
|
"""simple docstring"""
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
ComputeEnvironment,
CustomDtype,
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
DynamoBackend,
FPaRecipeKwargs,
FullyShardedDataParallelPlugin,
GradientAccumulationPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
KwargsHandler,
LoggerType,
MegatronLMPlugin,
PrecisionType,
ProjectConfiguration,
RNGType,
SageMakerDistributedType,
TensorInformation,
TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
get_ccl_version,
is_abit_bnb_available,
is_abit_bnb_available,
is_aim_available,
is_bfaa_available,
is_bnb_available,
is_botoa_available,
is_ccl_available,
is_comet_ml_available,
is_datasets_available,
is_deepspeed_available,
is_fpa_available,
is_ipex_available,
is_megatron_lm_available,
is_mlflow_available,
is_mps_available,
is_npu_available,
is_rich_available,
is_safetensors_available,
is_sagemaker_available,
is_tensorboard_available,
is_tpu_available,
is_transformers_available,
is_wandb_available,
is_xpu_available,
)
from .modeling import (
check_device_map,
check_tied_parameters_in_config,
check_tied_parameters_on_same_device,
compute_module_sizes,
convert_file_size_to_int,
dtype_byte_size,
find_tied_parameters,
get_balanced_memory,
get_max_layer_size,
get_max_memory,
get_mixed_precision_context_manager,
id_tensor_storage,
infer_auto_device_map,
load_checkpoint_in_model,
load_offloaded_weights,
load_state_dict,
named_module_tensors,
retie_parameters,
set_module_tensor_to_device,
shard_checkpoint,
)
from .offload import (
OffloadedWeightsLoader,
PrefixedDataset,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
save_offload_index,
)
from .operations import (
broadcast,
broadcast_object_list,
concatenate,
convert_outputs_to_fpaa,
convert_to_fpaa,
find_batch_size,
find_device,
gather,
gather_object,
get_data_structure,
honor_type,
initialize_tensors,
is_namedtuple,
is_tensor_information,
is_torch_tensor,
listify,
pad_across_processes,
recursively_apply,
reduce,
send_to_device,
slice_tensors,
)
from .versions import compare_versions, is_torch_version
if is_deepspeed_available():
from .deepspeed import (
DeepSpeedEngineWrapper,
DeepSpeedOptimizerWrapper,
DeepSpeedSchedulerWrapper,
DummyOptim,
DummyScheduler,
HfDeepSpeedConfig,
)
from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
PrepareForLaunch,
_filter_args,
prepare_deepspeed_cmd_env,
prepare_multi_gpu_env,
prepare_sagemager_args_inputs,
prepare_simple_launcher_cmd_env,
prepare_tpu,
)
from .megatron_lm import (
AbstractTrainStep,
BertTrainStep,
GPTTrainStep,
MegatronEngine,
MegatronLMDummyDataLoader,
MegatronLMDummyScheduler,
MegatronLMOptimizerWrapper,
MegatronLMSchedulerWrapper,
TaTrainStep,
avg_losses_across_data_parallel_group,
gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
extract_model_from_parallel,
get_pretty_name,
is_port_in_use,
merge_dicts,
patch_environment,
save,
wait_for_everyone,
write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers
| 720
|
"""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_roberta''': ['''ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RobertaConfig''', '''RobertaOnnxConfig'''],
'''tokenization_roberta''': ['''RobertaTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = ['''RobertaTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''RobertaForCausalLM''',
'''RobertaForMaskedLM''',
'''RobertaForMultipleChoice''',
'''RobertaForQuestionAnswering''',
'''RobertaForSequenceClassification''',
'''RobertaForTokenClassification''',
'''RobertaModel''',
'''RobertaPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFRobertaForCausalLM''',
'''TFRobertaForMaskedLM''',
'''TFRobertaForMultipleChoice''',
'''TFRobertaForQuestionAnswering''',
'''TFRobertaForSequenceClassification''',
'''TFRobertaForTokenClassification''',
'''TFRobertaMainLayer''',
'''TFRobertaModel''',
'''TFRobertaPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''FlaxRobertaForCausalLM''',
'''FlaxRobertaForMaskedLM''',
'''FlaxRobertaForMultipleChoice''',
'''FlaxRobertaForQuestionAnswering''',
'''FlaxRobertaForSequenceClassification''',
'''FlaxRobertaForTokenClassification''',
'''FlaxRobertaModel''',
'''FlaxRobertaPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
from .tokenization_roberta import RobertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_roberta_fast import RobertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta import (
ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaForCausalLM,
RobertaForMaskedLM,
RobertaForMultipleChoice,
RobertaForQuestionAnswering,
RobertaForSequenceClassification,
RobertaForTokenClassification,
RobertaModel,
RobertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta import (
TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
TFRobertaForMultipleChoice,
TFRobertaForQuestionAnswering,
TFRobertaForSequenceClassification,
TFRobertaForTokenClassification,
TFRobertaMainLayer,
TFRobertaModel,
TFRobertaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
FlaxRobertaPreTrainedModel,
)
else:
import sys
lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 24
| 0
|
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