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"""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 _UpperCAmelCase( lowerCamelCase ):
lowercase__ = 42
lowercase__ = 42
def __init__( self , __a , __a) -> str:
'''simple docstring'''
super().__init__()
self.register_modules(unet=__a , scheduler=__a)
@torch.no_grad()
def __call__( self , __a = 1 , __a = 20_00 , __a = None , __a = "pil" , __a = True , **__a , ) -> Union[ImagePipelineOutput, Tuple]:
'''simple docstring'''
_UpperCamelCase = self.unet.config.sample_size
_UpperCamelCase = (batch_size, 3, img_size, img_size)
_UpperCamelCase = self.unet
_UpperCamelCase = randn_tensor(__a , generator=__a) * self.scheduler.init_noise_sigma
_UpperCamelCase = sample.to(self.device)
self.scheduler.set_timesteps(__a)
self.scheduler.set_sigmas(__a)
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(__a , __a).sample
_UpperCamelCase = self.scheduler.step_correct(__a , __a , generator=__a).prev_sample
# prediction step
_UpperCamelCase = model(__a , __a).sample
_UpperCamelCase = self.scheduler.step_pred(__a , __a , __a , generator=__a)
_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(__a)
if not return_dict:
return (sample,)
return ImagePipelineOutput(images=__a)
| 19
|
def snake_case (UpperCamelCase : int = 2000000 ):
'''simple docstring'''
lowerCamelCase__ = [0 for i in range(n + 1 )]
lowerCamelCase__ = 1
lowerCamelCase__ = 1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in range(i * i , n + 1 , UpperCamelCase ):
lowerCamelCase__ = 1
lowerCamelCase__ = 0
for i in range(UpperCamelCase ):
if primality_list[i] == 0:
sum_of_primes += i
return sum_of_primes
if __name__ == "__main__":
print(f'''{solution() = }''')
| 165
| 0
|
"""simple docstring"""
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / '''utils'''))
from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402
lowercase__ : Any = get_tests_dir('''fixtures''')
class _UpperCAmelCase ( unittest.TestCase):
def _snake_case ( self : Tuple ):
# A mock response for an HTTP head request to emulate server down
snake_case_ : str = mock.Mock()
snake_case_ : Optional[Any] = 500
snake_case_ : str = {}
snake_case_ : Optional[int] = HTTPError
snake_case_ : Tuple = {}
# Download this model to make sure it's in the cache.
snake_case_ : List[str] = WavaVecaFeatureExtractor.from_pretrained('''hf-internal-testing/tiny-random-wav2vec2''' )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch('''requests.Session.request''' , return_value=lowercase_ ) as mock_head:
snake_case_ : int = WavaVecaFeatureExtractor.from_pretrained('''hf-internal-testing/tiny-random-wav2vec2''' )
# This check we did call the fake head request
mock_head.assert_called()
def _snake_case ( self : Union[str, Any] ):
# This test is for deprecated behavior and can be removed in v5
snake_case_ : str = WavaVecaFeatureExtractor.from_pretrained(
'''https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json''' )
@is_staging_test
class _UpperCAmelCase ( unittest.TestCase):
@classmethod
def _snake_case ( cls : int ):
snake_case_ : Dict = TOKEN
HfFolder.save_token(lowercase_ )
@classmethod
def _snake_case ( cls : str ):
try:
delete_repo(token=cls._token , repo_id='''test-feature-extractor''' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='''valid_org/test-feature-extractor-org''' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='''test-dynamic-feature-extractor''' )
except HTTPError:
pass
def _snake_case ( self : Any ):
snake_case_ : int = WavaVecaFeatureExtractor.from_pretrained(lowercase_ )
feature_extractor.push_to_hub('''test-feature-extractor''' , use_auth_token=self._token )
snake_case_ : Optional[Any] = WavaVecaFeatureExtractor.from_pretrained(f"{USER}/test-feature-extractor" )
for k, v in feature_extractor.__dict__.items():
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_ ) )
# Reset repo
delete_repo(token=self._token , repo_id='''test-feature-extractor''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(
lowercase_ , repo_id='''test-feature-extractor''' , push_to_hub=lowercase_ , use_auth_token=self._token )
snake_case_ : Optional[int] = WavaVecaFeatureExtractor.from_pretrained(f"{USER}/test-feature-extractor" )
for k, v in feature_extractor.__dict__.items():
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_ ) )
def _snake_case ( self : Dict ):
snake_case_ : Union[str, Any] = WavaVecaFeatureExtractor.from_pretrained(lowercase_ )
feature_extractor.push_to_hub('''valid_org/test-feature-extractor''' , use_auth_token=self._token )
snake_case_ : Optional[int] = WavaVecaFeatureExtractor.from_pretrained('''valid_org/test-feature-extractor''' )
for k, v in feature_extractor.__dict__.items():
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_ ) )
# Reset repo
delete_repo(token=self._token , repo_id='''valid_org/test-feature-extractor''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(
lowercase_ , repo_id='''valid_org/test-feature-extractor-org''' , push_to_hub=lowercase_ , use_auth_token=self._token )
snake_case_ : int = WavaVecaFeatureExtractor.from_pretrained('''valid_org/test-feature-extractor-org''' )
for k, v in feature_extractor.__dict__.items():
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_ ) )
def _snake_case ( self : str ):
CustomFeatureExtractor.register_for_auto_class()
snake_case_ : Optional[Any] = CustomFeatureExtractor.from_pretrained(lowercase_ )
feature_extractor.push_to_hub('''test-dynamic-feature-extractor''' , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(
feature_extractor.auto_map , {'''AutoFeatureExtractor''': '''custom_feature_extraction.CustomFeatureExtractor'''} , )
snake_case_ : Any = AutoFeatureExtractor.from_pretrained(
f"{USER}/test-dynamic-feature-extractor" , trust_remote_code=lowercase_ )
# Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
self.assertEqual(new_feature_extractor.__class__.__name__ , '''CustomFeatureExtractor''' )
| 485
|
"""simple docstring"""
lowercase__ : Union[str, Any] = {
'''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''',
}
| 485
| 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 snake_case_ ( a_ ,unittest.TestCase ):
__lowerCAmelCase = DiTPipeline
__lowerCAmelCase = CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
__lowerCAmelCase = PipelineTesterMixin.required_optional_params - {
"latents",
"num_images_per_prompt",
"callback",
"callback_steps",
}
__lowerCAmelCase = CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
__lowerCAmelCase = False
def snake_case_ ( self ):
torch.manual_seed(0 )
a_ : Dict = TransformeraDModel(
sample_size=1_6 , num_layers=2 , patch_size=4 , attention_head_dim=8 , num_attention_heads=2 , in_channels=4 , out_channels=8 , attention_bias=a_ , activation_fn="gelu-approximate" , num_embeds_ada_norm=1_0_0_0 , norm_type="ada_norm_zero" , norm_elementwise_affine=a_ , )
a_ : Any = AutoencoderKL()
a_ : str = DDIMScheduler()
a_ : Tuple = {"transformer": transformer.eval(), "vae": vae.eval(), "scheduler": scheduler}
return components
def snake_case_ ( self , a_ , a_=0 ):
if str(a_ ).startswith("mps" ):
a_ : Any = torch.manual_seed(a_ )
else:
a_ : List[str] = torch.Generator(device=a_ ).manual_seed(a_ )
a_ : Dict = {
"class_labels": [1],
"generator": generator,
"num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
def snake_case_ ( self ):
a_ : str = "cpu"
a_ : str = self.get_dummy_components()
a_ : str = self.pipeline_class(**a_ )
pipe.to(a_ )
pipe.set_progress_bar_config(disable=a_ )
a_ : Union[str, Any] = self.get_dummy_inputs(a_ )
a_ : Any = pipe(**a_ ).images
a_ : Optional[Any] = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 1_6, 1_6, 3) )
a_ : List[str] = np.array([0.2_946, 0.6_601, 0.4_329, 0.3_296, 0.4_144, 0.5_319, 0.7_273, 0.5_013, 0.4_457] )
a_ : Tuple = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(a_ , 1e-3 )
def snake_case_ ( self ):
self._test_inference_batch_single_identical(relax_max_difference=a_ , 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 snake_case_ ( self ):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
@require_torch_gpu
@slow
class snake_case_ ( unittest.TestCase ):
def snake_case_ ( self ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def snake_case_ ( self ):
a_ : Any = torch.manual_seed(0 )
a_ : Dict = DiTPipeline.from_pretrained("facebook/DiT-XL-2-256" )
pipe.to("cuda" )
a_ : List[str] = ["vase", "umbrella", "white shark", "white wolf"]
a_ : Union[str, Any] = pipe.get_label_ids(a_ )
a_ : str = pipe(a_ , generator=a_ , num_inference_steps=4_0 , output_type="np" ).images
for word, image in zip(a_ , a_ ):
a_ : int = 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 snake_case_ ( self ):
a_ : int = DiTPipeline.from_pretrained("facebook/DiT-XL-2-512" )
a_ : List[str] = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.to("cuda" )
a_ : Optional[int] = ["vase", "umbrella"]
a_ : Tuple = pipe.get_label_ids(a_ )
a_ : List[Any] = torch.manual_seed(0 )
a_ : List[Any] = pipe(a_ , generator=a_ , num_inference_steps=2_5 , output_type="np" ).images
for word, image in zip(a_ , a_ ):
a_ : List[str] = 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
| 237
|
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE__ ) -> bytes:
# Make sure the supplied data is a bytes-like object
if not isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ ):
a_ : Union[str, Any] = F"""a bytes-like object is required, not '{data.__class__.__name__}'"""
raise TypeError(SCREAMING_SNAKE_CASE__ )
a_ : Optional[Any] = "".join(bin(SCREAMING_SNAKE_CASE__ )[2:].zfill(8 ) for byte in data )
a_ : Tuple = len(SCREAMING_SNAKE_CASE__ ) % 6 != 0
if padding_needed:
# The padding that will be added later
a_ : List[Any] = B"=" * ((6 - len(SCREAMING_SNAKE_CASE__ ) % 6) // 2)
# Append binary_stream with arbitrary binary digits (0's by default) to make its
# length a multiple of 6.
binary_stream += "0" * (6 - len(SCREAMING_SNAKE_CASE__ ) % 6)
else:
a_ : List[Any] = B""
# Encode every 6 binary digits to their corresponding Base64 character
return (
"".join(
B64_CHARSET[int(binary_stream[index : index + 6], 2 )]
for index in range(0, len(SCREAMING_SNAKE_CASE__ ), 6 ) ).encode()
+ padding
)
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE__ ) -> bytes:
# Make sure encoded_data is either a string or a bytes-like object
if not isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ ) and not isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ ):
a_ : int = (
"argument should be a bytes-like object or ASCII string, "
F"""not '{encoded_data.__class__.__name__}'"""
)
raise TypeError(SCREAMING_SNAKE_CASE__ )
# In case encoded_data is a bytes-like object, make sure it contains only
# ASCII characters so we convert it to a string object
if isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ ):
try:
a_ : List[Any] = encoded_data.decode("utf-8" )
except UnicodeDecodeError:
raise ValueError("base64 encoded data should only contain ASCII characters" )
a_ : Union[str, Any] = encoded_data.count("=" )
# Check if the encoded string contains non base64 characters
if padding:
assert all(
char in B64_CHARSET for char in encoded_data[:-padding] ), "Invalid base64 character(s) found."
else:
assert all(
char in B64_CHARSET for char in encoded_data ), "Invalid base64 character(s) found."
# Check the padding
assert len(SCREAMING_SNAKE_CASE__ ) % 4 == 0 and padding < 3, "Incorrect padding"
if padding:
# Remove padding if there is one
a_ : List[str] = encoded_data[:-padding]
a_ : Optional[int] = "".join(
bin(B64_CHARSET.index(SCREAMING_SNAKE_CASE__ ) )[2:].zfill(6 ) for char in encoded_data )[: -padding * 2]
else:
a_ : Optional[int] = "".join(
bin(B64_CHARSET.index(SCREAMING_SNAKE_CASE__ ) )[2:].zfill(6 ) for char in encoded_data )
a_ : Union[str, Any] = [
int(binary_stream[index : index + 8], 2 )
for index in range(0, len(SCREAMING_SNAKE_CASE__ ), 8 )
]
return bytes(SCREAMING_SNAKE_CASE__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 237
| 1
|
from scipy.stats import spearmanr
import datasets
UpperCamelCase__ ="\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no correlation.\nPositive correlations imply that as data in dataset x increases, so\ndoes data in dataset y. Negative correlations imply that as x increases,\ny decreases. Correlations of -1 or +1 imply an exact monotonic relationship.\n\nUnlike the Pearson correlation, the Spearman correlation does not\nassume that both datasets are normally distributed.\n\nThe p-value roughly indicates the probability of an uncorrelated system\nproducing datasets that have a Spearman correlation at least as extreme\nas the one computed from these datasets. The p-values are not entirely\nreliable but are probably reasonable for datasets larger than 500 or so.\n"
UpperCamelCase__ ="\nArgs:\n predictions (`List[float]`): Predicted labels, as returned by a model.\n references (`List[float]`): Ground truth labels.\n return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns\n only the spearmanr score. Defaults to `False`.\nReturns:\n spearmanr (`float`): Spearman correlation coefficient.\n p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.\nExamples:\n Example 1:\n >>> spearmanr_metric = datasets.load_metric(\"spearmanr\")\n >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])\n >>> print(results)\n {'spearmanr': -0.7}\n\n Example 2:\n >>> spearmanr_metric = datasets.load_metric(\"spearmanr\")\n >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],\n ... predictions=[10, 9, 2.5, 6, 4],\n ... return_pvalue=True)\n >>> print(results['spearmanr'])\n -0.7\n >>> print(round(results['spearmanr_pvalue'], 2))\n 0.19\n"
UpperCamelCase__ =r"\\n@book{kokoska2000crc,\n title={CRC standard probability and statistics tables and formulae},\n author={Kokoska, Stephen and Zwillinger, Daniel},\n year={2000},\n publisher={Crc Press}\n}\n@article{2020SciPy-NMeth,\n author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n Kern, Robert and Larson, Eric and Carey, C J and\n Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and\n {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n Harris, Charles R. and Archibald, Anne M. and\n Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and\n {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\n title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n Computing in Python}},\n journal = {Nature Methods},\n year = {2020},\n volume = {17},\n pages = {261--272},\n adsurl = {https://rdcu.be/b08Wh},\n doi = {10.1038/s41592-019-0686-2},\n}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowerCAmelCase__( datasets.Metric ):
'''simple docstring'''
def UpperCamelCase_ ( self ) -> List[str]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Value("float" ),
"references": datasets.Value("float" ),
} ) , reference_urls=["https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html"] , )
def UpperCamelCase_ ( self , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase=False ) -> Optional[int]:
_SCREAMING_SNAKE_CASE : List[Any] = spearmanr(_UpperCamelCase , _UpperCamelCase )
if return_pvalue:
return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
else:
return {"spearmanr": results[0]}
| 715
|
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class lowerCAmelCase__( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self ) -> Dict:
_SCREAMING_SNAKE_CASE : Dict = "ylacombe/bark-small"
_SCREAMING_SNAKE_CASE : List[str] = tempfile.mkdtemp()
_SCREAMING_SNAKE_CASE : List[str] = "en_speaker_1"
_SCREAMING_SNAKE_CASE : List[Any] = "This is a test string"
_SCREAMING_SNAKE_CASE : Optional[int] = "speaker_embeddings_path.json"
_SCREAMING_SNAKE_CASE : Union[str, Any] = "speaker_embeddings"
def UpperCamelCase_ ( self , **__lowerCamelCase ) -> str:
return AutoTokenizer.from_pretrained(self.checkpoint , **__lowerCamelCase )
def UpperCamelCase_ ( self ) -> Union[str, Any]:
shutil.rmtree(self.tmpdirname )
def UpperCamelCase_ ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE : Any = self.get_tokenizer()
_SCREAMING_SNAKE_CASE : Dict = BarkProcessor(tokenizer=__lowerCamelCase )
processor.save_pretrained(self.tmpdirname )
_SCREAMING_SNAKE_CASE : Any = BarkProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
@slow
def UpperCamelCase_ ( self ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE : Dict = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
processor.save_pretrained(
self.tmpdirname , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , speaker_embeddings_directory=self.speaker_embeddings_directory , )
_SCREAMING_SNAKE_CASE : Dict = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)" )
_SCREAMING_SNAKE_CASE : Optional[Any] = BarkProcessor.from_pretrained(
self.tmpdirname , self.speaker_embeddings_dict_path , bos_token="(BOS)" , eos_token="(EOS)" , )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
def UpperCamelCase_ ( self ) -> str:
_SCREAMING_SNAKE_CASE : Any = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
_SCREAMING_SNAKE_CASE : List[Any] = 3_5
_SCREAMING_SNAKE_CASE : Any = 2
_SCREAMING_SNAKE_CASE : int = 8
_SCREAMING_SNAKE_CASE : Optional[Any] = {
"semantic_prompt": np.ones(__lowerCamelCase ),
"coarse_prompt": np.ones((nb_codebooks_coarse, seq_len) ),
"fine_prompt": np.ones((nb_codebooks_total, seq_len) ),
}
# test providing already loaded voice_preset
_SCREAMING_SNAKE_CASE : Optional[int] = processor(text=self.input_string , voice_preset=__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Optional[int] = inputs["history_prompt"]
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(__lowerCamelCase , np.array([] ) ).tolist() )
# test loading voice preset from npz file
_SCREAMING_SNAKE_CASE : Any = os.path.join(self.tmpdirname , "file.npz" )
np.savez(__lowerCamelCase , **__lowerCamelCase )
_SCREAMING_SNAKE_CASE : List[str] = processor(text=self.input_string , voice_preset=__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Tuple = inputs["history_prompt"]
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(__lowerCamelCase , np.array([] ) ).tolist() )
# test loading voice preset from the hub
_SCREAMING_SNAKE_CASE : Optional[Any] = processor(text=self.input_string , voice_preset=self.voice_preset )
def UpperCamelCase_ ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE : List[Any] = self.get_tokenizer()
_SCREAMING_SNAKE_CASE : List[Any] = BarkProcessor(tokenizer=__lowerCamelCase )
_SCREAMING_SNAKE_CASE : str = processor(text=self.input_string )
_SCREAMING_SNAKE_CASE : Optional[Any] = tokenizer(
self.input_string , padding="max_length" , max_length=2_5_6 , add_special_tokens=__lowerCamelCase , return_attention_mask=__lowerCamelCase , return_token_type_ids=__lowerCamelCase , )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key].squeeze().tolist() )
| 381
| 0
|
"""simple docstring"""
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def __lowerCAmelCase ( lowercase : str , lowercase : complex , lowercase : str = "x" , lowercase : float = 10**-10 , lowercase : int = 1 , ) -> complex:
"""simple docstring"""
snake_case : Any = symbols(lowercase )
snake_case : Any = lambdify(lowercase , lowercase )
snake_case : Tuple = lambdify(lowercase , diff(lowercase , lowercase ) )
snake_case : Optional[Any] = starting_point
while True:
if diff_function(lowercase ) != 0:
snake_case : Dict = prev_guess - multiplicity * func(lowercase ) / diff_function(
lowercase )
else:
raise ZeroDivisionError("Could not find root" ) from None
# Precision is checked by comparing the difference of consecutive guesses
if abs(next_guess - prev_guess ) < precision:
return next_guess
snake_case : int = next_guess
# Let's Execute
if __name__ == "__main__":
# Find root of trigonometric function
# Find value of pi
print(F'''The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}''')
# Find root of polynomial
# Find fourth Root of 5
print(F'''The root of x**4 - 5 = 0 is {newton_raphson("x**4 -5", 0.4 +5j)}''')
# Find value of e
print(
"""The root of log(y) - 1 = 0 is """,
F'''{newton_raphson("log(y) - 1", 2, variable="y")}''',
)
# Exponential Roots
print(
"""The root of exp(x) - 1 = 0 is""",
F'''{newton_raphson("exp(x) - 1", 10, precision=0.0_05)}''',
)
# Find root of cos(x)
print(F'''The root of cos(x) = 0 is {newton_raphson("cos(x)", 0)}''')
| 178
|
"""simple docstring"""
__snake_case = """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""
def __lowerCAmelCase ( ) -> None:
"""simple docstring"""
snake_case : str = input("Enter message: " )
snake_case : Tuple = input("Enter key [alphanumeric]: " )
snake_case : Union[str, Any] = input("Encrypt/Decrypt [e/d]: " )
if mode.lower().startswith("e" ):
snake_case : str = "encrypt"
snake_case : Optional[int] = encrypt_message(lowercase , lowercase )
elif mode.lower().startswith("d" ):
snake_case : List[Any] = "decrypt"
snake_case : Tuple = decrypt_message(lowercase , lowercase )
print(F'\n{mode.title()}ed message:' )
print(lowercase )
def __lowerCAmelCase ( lowercase : str , lowercase : str ) -> str:
"""simple docstring"""
return translate_message(lowercase , lowercase , "encrypt" )
def __lowerCAmelCase ( lowercase : str , lowercase : str ) -> str:
"""simple docstring"""
return translate_message(lowercase , lowercase , "decrypt" )
def __lowerCAmelCase ( lowercase : str , lowercase : str , lowercase : str ) -> str:
"""simple docstring"""
snake_case : List[Any] = []
snake_case : List[str] = 0
snake_case : List[Any] = key.upper()
for symbol in message:
snake_case : Optional[int] = LETTERS.find(symbol.upper() )
if num != -1:
if mode == "encrypt":
num += LETTERS.find(key[key_index] )
elif mode == "decrypt":
num -= LETTERS.find(key[key_index] )
num %= len(lowercase )
if symbol.isupper():
translated.append(LETTERS[num] )
elif symbol.islower():
translated.append(LETTERS[num].lower() )
key_index += 1
if key_index == len(lowercase ):
snake_case : List[str] = 0
else:
translated.append(lowercase )
return "".join(lowercase )
if __name__ == "__main__":
main()
| 178
| 1
|
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ : List[str] = logging.get_logger(__name__)
lowercase_ : int = {
'''microsoft/unispeech-large-1500h-cv''': (
'''https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json'''
),
# See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}
class UpperCamelCase ( __SCREAMING_SNAKE_CASE ):
A__ = """unispeech"""
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__=0.0 , 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__=False , snake_case__=True , snake_case__=0.05 , snake_case__=10 , snake_case__=2 , snake_case__=0.0 , snake_case__=10 , snake_case__=0 , 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__=80 , snake_case__=0 , snake_case__=1 , snake_case__=2 , snake_case__=0.5 , **snake_case__ , ):
"""simple docstring"""
super().__init__(**snake_case__ , pad_token_id=snake_case__ , bos_token_id=snake_case__ , eos_token_id=snake_case__ )
_SCREAMING_SNAKE_CASE : Dict = hidden_size
_SCREAMING_SNAKE_CASE : Union[str, Any] = feat_extract_norm
_SCREAMING_SNAKE_CASE : Tuple = feat_extract_activation
_SCREAMING_SNAKE_CASE : Optional[Any] = list(snake_case__ )
_SCREAMING_SNAKE_CASE : Tuple = list(snake_case__ )
_SCREAMING_SNAKE_CASE : List[str] = list(snake_case__ )
_SCREAMING_SNAKE_CASE : List[Any] = conv_bias
_SCREAMING_SNAKE_CASE : Dict = num_conv_pos_embeddings
_SCREAMING_SNAKE_CASE : List[Any] = num_conv_pos_embedding_groups
_SCREAMING_SNAKE_CASE : Tuple = len(self.conv_dim )
_SCREAMING_SNAKE_CASE : List[Any] = num_hidden_layers
_SCREAMING_SNAKE_CASE : Any = intermediate_size
_SCREAMING_SNAKE_CASE : Dict = hidden_act
_SCREAMING_SNAKE_CASE : int = num_attention_heads
_SCREAMING_SNAKE_CASE : Any = hidden_dropout
_SCREAMING_SNAKE_CASE : List[str] = attention_dropout
_SCREAMING_SNAKE_CASE : int = activation_dropout
_SCREAMING_SNAKE_CASE : Tuple = feat_proj_dropout
_SCREAMING_SNAKE_CASE : List[str] = final_dropout
_SCREAMING_SNAKE_CASE : str = layerdrop
_SCREAMING_SNAKE_CASE : Tuple = layer_norm_eps
_SCREAMING_SNAKE_CASE : Optional[Any] = initializer_range
_SCREAMING_SNAKE_CASE : int = num_ctc_classes
_SCREAMING_SNAKE_CASE : Optional[int] = vocab_size
_SCREAMING_SNAKE_CASE : List[Any] = do_stable_layer_norm
_SCREAMING_SNAKE_CASE : Optional[Any] = use_weighted_layer_sum
_SCREAMING_SNAKE_CASE : Optional[Any] = 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
_SCREAMING_SNAKE_CASE : Any = apply_spec_augment
_SCREAMING_SNAKE_CASE : List[str] = mask_time_prob
_SCREAMING_SNAKE_CASE : Optional[Any] = mask_time_length
_SCREAMING_SNAKE_CASE : Dict = mask_time_min_masks
_SCREAMING_SNAKE_CASE : Any = mask_feature_prob
_SCREAMING_SNAKE_CASE : Optional[Any] = mask_feature_length
_SCREAMING_SNAKE_CASE : int = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
_SCREAMING_SNAKE_CASE : Union[str, Any] = num_codevectors_per_group
_SCREAMING_SNAKE_CASE : int = num_codevector_groups
_SCREAMING_SNAKE_CASE : Optional[Any] = contrastive_logits_temperature
_SCREAMING_SNAKE_CASE : List[Any] = feat_quantizer_dropout
_SCREAMING_SNAKE_CASE : Union[str, Any] = num_negatives
_SCREAMING_SNAKE_CASE : int = codevector_dim
_SCREAMING_SNAKE_CASE : Tuple = proj_codevector_dim
_SCREAMING_SNAKE_CASE : int = diversity_loss_weight
# ctc loss
_SCREAMING_SNAKE_CASE : Tuple = ctc_loss_reduction
_SCREAMING_SNAKE_CASE : Tuple = ctc_zero_infinity
# pretraining loss
_SCREAMING_SNAKE_CASE : str = replace_prob
@property
def __SCREAMING_SNAKE_CASE ( self ):
"""simple docstring"""
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 295
|
"""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 UpperCamelCase ( __SCREAMING_SNAKE_CASE ):
A__ = 42
@flax_register_to_config
class UpperCamelCase ( nn.Module , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
A__ = 32
A__ = 4
A__ = 4
A__ = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
)
A__ = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
A__ = False
A__ = (320, 640, 1280, 1280)
A__ = 2
A__ = 8
A__ = None
A__ = 1280
A__ = 0.0
A__ = False
A__ = jnp.floataa
A__ = True
A__ = 0
A__ = False
def __SCREAMING_SNAKE_CASE ( self , snake_case__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : Union[str, Any] = (1, self.in_channels, self.sample_size, self.sample_size)
_SCREAMING_SNAKE_CASE : int = jnp.zeros(snake_case__ , dtype=jnp.floataa )
_SCREAMING_SNAKE_CASE : str = jnp.ones((1,) , dtype=jnp.intaa )
_SCREAMING_SNAKE_CASE : Any = jnp.zeros((1, 1, self.cross_attention_dim) , dtype=jnp.floataa )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : Tuple = jax.random.split(snake_case__ )
_SCREAMING_SNAKE_CASE : Union[str, Any] = {"params": params_rng, "dropout": dropout_rng}
return self.init(snake_case__ , snake_case__ , snake_case__ , snake_case__ )["params"]
def __SCREAMING_SNAKE_CASE ( self ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : List[str] = self.block_out_channels
_SCREAMING_SNAKE_CASE : List[Any] = 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.
_SCREAMING_SNAKE_CASE : List[str] = self.num_attention_heads or self.attention_head_dim
# input
_SCREAMING_SNAKE_CASE : int = nn.Conv(
block_out_channels[0] , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
# time
_SCREAMING_SNAKE_CASE : Optional[Any] = FlaxTimesteps(
block_out_channels[0] , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.config.freq_shift )
_SCREAMING_SNAKE_CASE : Dict = FlaxTimestepEmbedding(snake_case__ , dtype=self.dtype )
_SCREAMING_SNAKE_CASE : str = self.only_cross_attention
if isinstance(snake_case__ , snake_case__ ):
_SCREAMING_SNAKE_CASE : Optional[Any] = (only_cross_attention,) * len(self.down_block_types )
if isinstance(snake_case__ , snake_case__ ):
_SCREAMING_SNAKE_CASE : int = (num_attention_heads,) * len(self.down_block_types )
# down
_SCREAMING_SNAKE_CASE : Tuple = []
_SCREAMING_SNAKE_CASE : List[Any] = block_out_channels[0]
for i, down_block_type in enumerate(self.down_block_types ):
_SCREAMING_SNAKE_CASE : str = output_channel
_SCREAMING_SNAKE_CASE : List[str] = block_out_channels[i]
_SCREAMING_SNAKE_CASE : Union[str, Any] = i == len(snake_case__ ) - 1
if down_block_type == "CrossAttnDownBlock2D":
_SCREAMING_SNAKE_CASE : Optional[int] = 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:
_SCREAMING_SNAKE_CASE : str = 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__ )
_SCREAMING_SNAKE_CASE : Tuple = down_blocks
# mid
_SCREAMING_SNAKE_CASE : Optional[Any] = 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
_SCREAMING_SNAKE_CASE : int = []
_SCREAMING_SNAKE_CASE : Optional[Any] = list(reversed(snake_case__ ) )
_SCREAMING_SNAKE_CASE : Any = list(reversed(snake_case__ ) )
_SCREAMING_SNAKE_CASE : Any = list(reversed(snake_case__ ) )
_SCREAMING_SNAKE_CASE : Union[str, Any] = reversed_block_out_channels[0]
for i, up_block_type in enumerate(self.up_block_types ):
_SCREAMING_SNAKE_CASE : Union[str, Any] = output_channel
_SCREAMING_SNAKE_CASE : List[str] = reversed_block_out_channels[i]
_SCREAMING_SNAKE_CASE : List[Any] = reversed_block_out_channels[min(i + 1 , len(snake_case__ ) - 1 )]
_SCREAMING_SNAKE_CASE : Optional[Any] = i == len(snake_case__ ) - 1
if up_block_type == "CrossAttnUpBlock2D":
_SCREAMING_SNAKE_CASE : int = 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:
_SCREAMING_SNAKE_CASE : str = 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__ )
_SCREAMING_SNAKE_CASE : Any = output_channel
_SCREAMING_SNAKE_CASE : int = up_blocks
# out
_SCREAMING_SNAKE_CASE : str = nn.GroupNorm(num_groups=32 , epsilon=1E-5 )
_SCREAMING_SNAKE_CASE : Optional[Any] = 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 , ):
"""simple docstring"""
if not isinstance(snake_case__ , jnp.ndarray ):
_SCREAMING_SNAKE_CASE : Any = jnp.array([timesteps] , dtype=jnp.intaa )
elif isinstance(snake_case__ , jnp.ndarray ) and len(timesteps.shape ) == 0:
_SCREAMING_SNAKE_CASE : Tuple = timesteps.astype(dtype=jnp.floataa )
_SCREAMING_SNAKE_CASE : Tuple = jnp.expand_dims(snake_case__ , 0 )
_SCREAMING_SNAKE_CASE : List[str] = self.time_proj(snake_case__ )
_SCREAMING_SNAKE_CASE : Dict = self.time_embedding(snake_case__ )
# 2. pre-process
_SCREAMING_SNAKE_CASE : Dict = jnp.transpose(snake_case__ , (0, 2, 3, 1) )
_SCREAMING_SNAKE_CASE : Optional[Any] = self.conv_in(snake_case__ )
# 3. down
_SCREAMING_SNAKE_CASE : Union[str, Any] = (sample,)
for down_block in self.down_blocks:
if isinstance(snake_case__ , snake_case__ ):
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : str = down_block(snake_case__ , snake_case__ , snake_case__ , deterministic=not train )
else:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : int = down_block(snake_case__ , snake_case__ , deterministic=not train )
down_block_res_samples += res_samples
if down_block_additional_residuals is not None:
_SCREAMING_SNAKE_CASE : str = ()
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,)
_SCREAMING_SNAKE_CASE : Union[str, Any] = new_down_block_res_samples
# 4. mid
_SCREAMING_SNAKE_CASE : Dict = 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:
_SCREAMING_SNAKE_CASE : List[str] = down_block_res_samples[-(self.layers_per_block + 1) :]
_SCREAMING_SNAKE_CASE : int = down_block_res_samples[: -(self.layers_per_block + 1)]
if isinstance(snake_case__ , snake_case__ ):
_SCREAMING_SNAKE_CASE : Union[str, Any] = up_block(
snake_case__ , temb=snake_case__ , encoder_hidden_states=snake_case__ , res_hidden_states_tuple=snake_case__ , deterministic=not train , )
else:
_SCREAMING_SNAKE_CASE : List[str] = up_block(snake_case__ , temb=snake_case__ , res_hidden_states_tuple=snake_case__ , deterministic=not train )
# 6. post-process
_SCREAMING_SNAKE_CASE : Optional[Any] = self.conv_norm_out(snake_case__ )
_SCREAMING_SNAKE_CASE : Optional[int] = nn.silu(snake_case__ )
_SCREAMING_SNAKE_CASE : List[str] = self.conv_out(snake_case__ )
_SCREAMING_SNAKE_CASE : List[Any] = jnp.transpose(snake_case__ , (0, 3, 1, 2) )
if not return_dict:
return (sample,)
return FlaxUNetaDConditionOutput(sample=snake_case__ )
| 295
| 1
|
'''simple docstring'''
import torch
from torch import nn
class lowercase__ ( nn.Module ):
'''simple docstring'''
def __init__( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case=1 , __snake_case=False ):
super().__init__()
_SCREAMING_SNAKE_CASE : Dict = n_token
_SCREAMING_SNAKE_CASE : Optional[int] = d_embed
_SCREAMING_SNAKE_CASE : Optional[int] = d_proj
_SCREAMING_SNAKE_CASE : List[str] = cutoffs + [n_token]
_SCREAMING_SNAKE_CASE : List[Any] = [0] + self.cutoffs
_SCREAMING_SNAKE_CASE : int = div_val
_SCREAMING_SNAKE_CASE : List[Any] = self.cutoffs[0]
_SCREAMING_SNAKE_CASE : Optional[Any] = len(self.cutoffs ) - 1
_SCREAMING_SNAKE_CASE : Any = self.shortlist_size + self.n_clusters
if self.n_clusters > 0:
_SCREAMING_SNAKE_CASE : str = nn.Parameter(torch.zeros(self.n_clusters , self.d_embed ) )
_SCREAMING_SNAKE_CASE : Optional[int] = nn.Parameter(torch.zeros(self.n_clusters ) )
_SCREAMING_SNAKE_CASE : Tuple = nn.ModuleList()
_SCREAMING_SNAKE_CASE : int = nn.ParameterList()
if div_val == 1:
for i in range(len(self.cutoffs ) ):
if d_proj != d_embed:
self.out_projs.append(nn.Parameter(torch.FloatTensor(__snake_case , __snake_case ) ) )
else:
self.out_projs.append(__snake_case )
self.out_layers.append(nn.Linear(__snake_case , __snake_case ) )
else:
for i in range(len(self.cutoffs ) ):
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : List[str] = self.cutoff_ends[i], self.cutoff_ends[i + 1]
_SCREAMING_SNAKE_CASE : str = d_embed // (div_val**i)
self.out_projs.append(nn.Parameter(torch.FloatTensor(__snake_case , __snake_case ) ) )
self.out_layers.append(nn.Linear(__snake_case , r_idx - l_idx ) )
_SCREAMING_SNAKE_CASE : Dict = keep_order
def UpperCAmelCase_ ( self , __snake_case , __snake_case , __snake_case , __snake_case ):
if proj is None:
_SCREAMING_SNAKE_CASE : List[Any] = nn.functional.linear(__snake_case , __snake_case , bias=__snake_case )
else:
# if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
_SCREAMING_SNAKE_CASE : List[str] = nn.functional.linear(__snake_case , proj.t().contiguous() )
_SCREAMING_SNAKE_CASE : Tuple = nn.functional.linear(__snake_case , __snake_case , bias=__snake_case )
# else:
# logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
# if bias is not None:
# logit = logit + bias
return logit
def UpperCAmelCase_ ( self , __snake_case , __snake_case=None , __snake_case=False ):
if labels is not None:
# Shift so that tokens < n predict n
_SCREAMING_SNAKE_CASE : Dict = hidden[..., :-1, :].contiguous()
_SCREAMING_SNAKE_CASE : int = labels[..., 1:].contiguous()
_SCREAMING_SNAKE_CASE : List[str] = hidden.view(-1 , hidden.size(-1 ) )
_SCREAMING_SNAKE_CASE : Any = labels.view(-1 )
if hidden.size(0 ) != labels.size(0 ):
raise RuntimeError("""Input and labels should have the same size in the batch dimension.""" )
else:
_SCREAMING_SNAKE_CASE : Tuple = hidden.view(-1 , hidden.size(-1 ) )
if self.n_clusters == 0:
_SCREAMING_SNAKE_CASE : Union[str, Any] = self._compute_logit(__snake_case , self.out_layers[0].weight , self.out_layers[0].bias , self.out_projs[0] )
if labels is not None:
_SCREAMING_SNAKE_CASE : Optional[int] = labels != -100
_SCREAMING_SNAKE_CASE : Union[str, Any] = torch.zeros_like(__snake_case , dtype=hidden.dtype , device=hidden.device )
_SCREAMING_SNAKE_CASE : List[Any] = (
-nn.functional.log_softmax(__snake_case , dim=-1 )[mask].gather(1 , labels[mask].unsqueeze(1 ) ).squeeze(1 )
)
else:
_SCREAMING_SNAKE_CASE : List[Any] = nn.functional.log_softmax(__snake_case , dim=-1 )
else:
# construct weights and biases
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : Union[str, Any] = [], []
for i in range(len(self.cutoffs ) ):
if self.div_val == 1:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : Union[str, Any] = self.cutoff_ends[i], self.cutoff_ends[i + 1]
_SCREAMING_SNAKE_CASE : List[str] = self.out_layers[0].weight[l_idx:r_idx]
_SCREAMING_SNAKE_CASE : Dict = self.out_layers[0].bias[l_idx:r_idx]
else:
_SCREAMING_SNAKE_CASE : Optional[Any] = self.out_layers[i].weight
_SCREAMING_SNAKE_CASE : Tuple = self.out_layers[i].bias
if i == 0:
_SCREAMING_SNAKE_CASE : Optional[Any] = torch.cat([weight_i, self.cluster_weight] , dim=0 )
_SCREAMING_SNAKE_CASE : Optional[int] = torch.cat([bias_i, self.cluster_bias] , dim=0 )
weights.append(__snake_case )
biases.append(__snake_case )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : Union[str, Any] = weights[0], biases[0], self.out_projs[0]
_SCREAMING_SNAKE_CASE : Tuple = self._compute_logit(__snake_case , __snake_case , __snake_case , __snake_case )
_SCREAMING_SNAKE_CASE : Optional[Any] = nn.functional.log_softmax(__snake_case , dim=1 )
if labels is None:
_SCREAMING_SNAKE_CASE : Any = hidden.new_empty((head_logit.size(0 ), self.n_token) )
else:
_SCREAMING_SNAKE_CASE : int = torch.zeros_like(__snake_case , dtype=hidden.dtype , device=hidden.device )
_SCREAMING_SNAKE_CASE : Tuple = 0
_SCREAMING_SNAKE_CASE : List[str] = [0] + self.cutoffs
for i in range(len(__snake_case ) - 1 ):
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : int = cutoff_values[i], cutoff_values[i + 1]
if labels is not None:
_SCREAMING_SNAKE_CASE : int = (labels >= l_idx) & (labels < r_idx)
_SCREAMING_SNAKE_CASE : Dict = mask_i.nonzero().squeeze()
if indices_i.numel() == 0:
continue
_SCREAMING_SNAKE_CASE : Optional[int] = labels.index_select(0 , __snake_case ) - l_idx
_SCREAMING_SNAKE_CASE : Any = head_logprob.index_select(0 , __snake_case )
_SCREAMING_SNAKE_CASE : Optional[int] = hidden.index_select(0 , __snake_case )
else:
_SCREAMING_SNAKE_CASE : List[str] = hidden
if i == 0:
if labels is not None:
_SCREAMING_SNAKE_CASE : Union[str, Any] = head_logprob_i.gather(1 , target_i[:, None] ).squeeze(1 )
else:
_SCREAMING_SNAKE_CASE : Optional[Any] = head_logprob[:, : self.cutoffs[0]]
else:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : Union[str, Any] = weights[i], biases[i], self.out_projs[i]
_SCREAMING_SNAKE_CASE : Optional[int] = self._compute_logit(__snake_case , __snake_case , __snake_case , __snake_case )
_SCREAMING_SNAKE_CASE : List[Any] = nn.functional.log_softmax(__snake_case , dim=1 )
_SCREAMING_SNAKE_CASE : List[str] = self.cutoffs[0] + i - 1 # No probability for the head cluster
if labels is not None:
_SCREAMING_SNAKE_CASE : List[str] = head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
1 , target_i[:, None] ).squeeze(1 )
else:
_SCREAMING_SNAKE_CASE : str = head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
_SCREAMING_SNAKE_CASE : List[Any] = logprob_i
if labels is not None:
if (hasattr(self , """keep_order""" ) and self.keep_order) or keep_order:
out.index_copy_(0 , __snake_case , -logprob_i )
else:
out[offset : offset + logprob_i.size(0 )].copy_(-logprob_i )
offset += logprob_i.size(0 )
return out
def UpperCAmelCase_ ( self , __snake_case ):
if self.n_clusters == 0:
_SCREAMING_SNAKE_CASE : int = self._compute_logit(__snake_case , self.out_layers[0].weight , self.out_layers[0].bias , self.out_projs[0] )
return nn.functional.log_softmax(__snake_case , dim=-1 )
else:
# construct weights and biases
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : List[Any] = [], []
for i in range(len(self.cutoffs ) ):
if self.div_val == 1:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : List[str] = self.cutoff_ends[i], self.cutoff_ends[i + 1]
_SCREAMING_SNAKE_CASE : int = self.out_layers[0].weight[l_idx:r_idx]
_SCREAMING_SNAKE_CASE : str = self.out_layers[0].bias[l_idx:r_idx]
else:
_SCREAMING_SNAKE_CASE : Dict = self.out_layers[i].weight
_SCREAMING_SNAKE_CASE : int = self.out_layers[i].bias
if i == 0:
_SCREAMING_SNAKE_CASE : List[Any] = torch.cat([weight_i, self.cluster_weight] , dim=0 )
_SCREAMING_SNAKE_CASE : Any = torch.cat([bias_i, self.cluster_bias] , dim=0 )
weights.append(__snake_case )
biases.append(__snake_case )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : Tuple = weights[0], biases[0], self.out_projs[0]
_SCREAMING_SNAKE_CASE : int = self._compute_logit(__snake_case , __snake_case , __snake_case , __snake_case )
_SCREAMING_SNAKE_CASE : int = hidden.new_empty((head_logit.size(0 ), self.n_token) )
_SCREAMING_SNAKE_CASE : List[Any] = nn.functional.log_softmax(__snake_case , dim=1 )
_SCREAMING_SNAKE_CASE : Union[str, Any] = [0] + self.cutoffs
for i in range(len(__snake_case ) - 1 ):
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : Optional[int] = cutoff_values[i], cutoff_values[i + 1]
if i == 0:
_SCREAMING_SNAKE_CASE : Tuple = head_logprob[:, : self.cutoffs[0]]
else:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : Union[str, Any] = weights[i], biases[i], self.out_projs[i]
_SCREAMING_SNAKE_CASE : int = self._compute_logit(__snake_case , __snake_case , __snake_case , __snake_case )
_SCREAMING_SNAKE_CASE : Optional[int] = nn.functional.log_softmax(__snake_case , dim=1 )
_SCREAMING_SNAKE_CASE : Any = head_logprob[:, -i] + tail_logprob_i
_SCREAMING_SNAKE_CASE : Any = logprob_i
return out
| 533
|
'''simple docstring'''
import argparse
import datetime
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : Optional[int] = {
"""0""": """Sunday""",
"""1""": """Monday""",
"""2""": """Tuesday""",
"""3""": """Wednesday""",
"""4""": """Thursday""",
"""5""": """Friday""",
"""6""": """Saturday""",
}
_SCREAMING_SNAKE_CASE : Optional[int] = {0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 0}
# Validate
if not 0 < len(SCREAMING_SNAKE_CASE__ ) < 11:
raise ValueError("""Must be 10 characters long""" )
# Get month
_SCREAMING_SNAKE_CASE : int = int(date_input[0] + date_input[1] )
# Validate
if not 0 < m < 13:
raise ValueError("""Month must be between 1 - 12""" )
_SCREAMING_SNAKE_CASE : str = date_input[2]
# Validate
if sep_a not in ["-", "/"]:
raise ValueError("""Date separator must be '-' or '/'""" )
# Get day
_SCREAMING_SNAKE_CASE : int = int(date_input[3] + date_input[4] )
# Validate
if not 0 < d < 32:
raise ValueError("""Date must be between 1 - 31""" )
# Get second separator
_SCREAMING_SNAKE_CASE : str = date_input[5]
# Validate
if sep_a not in ["-", "/"]:
raise ValueError("""Date separator must be '-' or '/'""" )
# Get year
_SCREAMING_SNAKE_CASE : int = int(date_input[6] + date_input[7] + date_input[8] + date_input[9] )
# Arbitrary year range
if not 45 < y < 8500:
raise ValueError(
"""Year out of range. There has to be some sort of limit...right?""" )
# Get datetime obj for validation
_SCREAMING_SNAKE_CASE : Optional[int] = datetime.date(int(SCREAMING_SNAKE_CASE__ ) , int(SCREAMING_SNAKE_CASE__ ) , int(SCREAMING_SNAKE_CASE__ ) )
# Start math
if m <= 2:
_SCREAMING_SNAKE_CASE : Union[str, Any] = y - 1
_SCREAMING_SNAKE_CASE : Optional[int] = m + 12
# maths var
_SCREAMING_SNAKE_CASE : int = int(str(SCREAMING_SNAKE_CASE__ )[:2] )
_SCREAMING_SNAKE_CASE : int = int(str(SCREAMING_SNAKE_CASE__ )[2:] )
_SCREAMING_SNAKE_CASE : int = int(2.6 * m - 5.3_9 )
_SCREAMING_SNAKE_CASE : int = int(c / 4 )
_SCREAMING_SNAKE_CASE : int = int(k / 4 )
_SCREAMING_SNAKE_CASE : int = int(d + k )
_SCREAMING_SNAKE_CASE : int = int(t + u + v + x )
_SCREAMING_SNAKE_CASE : int = int(z - (2 * c) )
_SCREAMING_SNAKE_CASE : int = round(w % 7 )
# End math
# Validate math
if f != convert_datetime_days[dt_ck.weekday()]:
raise AssertionError("""The date was evaluated incorrectly. Contact developer.""" )
# Response
_SCREAMING_SNAKE_CASE : str = f"""Your date {date_input}, is a {days[str(SCREAMING_SNAKE_CASE__ )]}!"""
return response
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCAmelCase_ : Tuple = argparse.ArgumentParser(
description=(
'Find out what day of the week nearly any date is or was. Enter '
'date as a string in the mm-dd-yyyy or mm/dd/yyyy format'
)
)
parser.add_argument(
'date_input', type=str, help='Date as a string (mm-dd-yyyy or mm/dd/yyyy)'
)
UpperCAmelCase_ : List[Any] = parser.parse_args()
zeller(args.date_input)
| 533
| 1
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : str = logging.get_logger(__name__)
__lowerCAmelCase : int = {
"google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json",
"google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/config.json"
# See all FNet models at https://huggingface.co/models?filter=fnet
}
class __lowerCAmelCase ( lowerCAmelCase_ ):
"""simple docstring"""
A__ : str = '''fnet'''
def __init__( self : List[Any] , _snake_case : int=3_2000 , _snake_case : Any=768 , _snake_case : int=12 , _snake_case : Tuple=3072 , _snake_case : Union[str, Any]="gelu_new" , _snake_case : str=0.1 , _snake_case : List[str]=512 , _snake_case : Union[str, Any]=4 , _snake_case : Union[str, Any]=0.02 , _snake_case : List[str]=1E-1_2 , _snake_case : List[Any]=False , _snake_case : Dict=512 , _snake_case : Optional[Any]=3 , _snake_case : List[str]=1 , _snake_case : Optional[Any]=2 , **_snake_case : Union[str, Any] , ):
super().__init__(pad_token_id=_snake_case , bos_token_id=_snake_case , eos_token_id=_snake_case , **_snake_case )
__lowercase : Optional[int] = vocab_size
__lowercase : Optional[int] = max_position_embeddings
__lowercase : Optional[Any] = hidden_size
__lowercase : Dict = num_hidden_layers
__lowercase : Optional[int] = intermediate_size
__lowercase : List[Any] = hidden_act
__lowercase : int = hidden_dropout_prob
__lowercase : Any = initializer_range
__lowercase : str = type_vocab_size
__lowercase : Optional[int] = layer_norm_eps
__lowercase : Any = use_tpu_fourier_optimizations
__lowercase : Tuple = tpu_short_seq_length
| 284
|
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
| 284
| 1
|
from heapq import heappop, heappush
import numpy as np
def lowerCamelCase__ ( __A :np.ndarray ,__A :tuple[int, int] ,__A :tuple[int, int] ,__A :bool ,):
"""simple docstring"""
__snake_case , __snake_case = grid.shape
__snake_case = [-1, 1, 0, 0]
__snake_case = [0, 0, -1, 1]
if allow_diagonal:
dx += [-1, -1, 1, 1]
dy += [-1, 1, -1, 1]
__snake_case , __snake_case = [(0, source)], set()
__snake_case = np.full((rows, cols) ,np.inf )
__snake_case = 0
__snake_case = np.empty((rows, cols) ,dtype=__A )
__snake_case = None
while queue:
((__snake_case) , (__snake_case)) = heappop(__A )
if (x, y) in visited:
continue
visited.add((x, y) )
if (x, y) == destination:
__snake_case = []
while (x, y) != source:
path.append((x, y) )
__snake_case , __snake_case = predecessors[x, y]
path.append(__A ) # add the source manually
path.reverse()
return matrix[destination], path
for i in range(len(__A ) ):
__snake_case , __snake_case = x + dx[i], y + dy[i]
if 0 <= nx < rows and 0 <= ny < cols:
__snake_case = grid[nx][ny]
if next_node == 1 and matrix[nx, ny] > dist + 1:
heappush(__A ,(dist + 1, (nx, ny)) )
__snake_case = dist + 1
__snake_case = (x, y)
return np.inf, []
if __name__ == "__main__":
import doctest
doctest.testmod()
| 268
|
def lowerCamelCase__ ( __A :int ,__A :float ,__A :float ):
"""simple docstring"""
return round(float(moles / volume ) * nfactor )
def lowerCamelCase__ ( __A :float ,__A :float ,__A :float ):
"""simple docstring"""
return round(float((moles * 0.0_821 * temperature) / (volume) ) )
def lowerCamelCase__ ( __A :float ,__A :float ,__A :float ):
"""simple docstring"""
return round(float((moles * 0.0_821 * temperature) / (pressure) ) )
def lowerCamelCase__ ( __A :float ,__A :float ,__A :float ):
"""simple docstring"""
return round(float((pressure * volume) / (0.0_821 * moles) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 268
| 1
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
lowerCAmelCase__ = {
'''google/tapas-base-finetuned-sqa''': (
'''https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'''
),
'''google/tapas-base-finetuned-wtq''': (
'''https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json'''
),
'''google/tapas-base-finetuned-wikisql-supervised''': (
'''https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json'''
),
'''google/tapas-base-finetuned-tabfact''': (
'''https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json'''
),
}
class SCREAMING_SNAKE_CASE__ ( lowercase ):
"""simple docstring"""
a : Optional[int] ="tapas"
def __init__( self , snake_case__=30_522 , snake_case__=768 , snake_case__=12 , snake_case__=12 , snake_case__=3_072 , snake_case__="gelu" , snake_case__=0.1 , snake_case__=0.1 , snake_case__=1_024 , snake_case__=[3, 256, 256, 2, 256, 256, 10] , snake_case__=0.02 , snake_case__=1e-12 , snake_case__=0 , snake_case__=10.0 , snake_case__=0 , snake_case__=1.0 , snake_case__=None , snake_case__=1.0 , snake_case__=False , snake_case__=None , snake_case__=1.0 , snake_case__=1.0 , snake_case__=False , snake_case__=False , snake_case__="ratio" , snake_case__=None , snake_case__=None , snake_case__=64 , snake_case__=32 , snake_case__=False , snake_case__=True , snake_case__=False , snake_case__=False , snake_case__=True , snake_case__=False , snake_case__=None , snake_case__=None , **snake_case__ , ):
"""simple docstring"""
super().__init__(pad_token_id=snake_case__ , **snake_case__ )
# BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
lowerCAmelCase : Union[str, Any] = vocab_size
lowerCAmelCase : Optional[int] = hidden_size
lowerCAmelCase : Any = num_hidden_layers
lowerCAmelCase : str = num_attention_heads
lowerCAmelCase : Optional[int] = hidden_act
lowerCAmelCase : Dict = intermediate_size
lowerCAmelCase : Tuple = hidden_dropout_prob
lowerCAmelCase : Any = attention_probs_dropout_prob
lowerCAmelCase : Any = max_position_embeddings
lowerCAmelCase : List[Any] = type_vocab_sizes
lowerCAmelCase : Union[str, Any] = initializer_range
lowerCAmelCase : Optional[Any] = layer_norm_eps
# Fine-tuning task hyperparameters
lowerCAmelCase : Union[str, Any] = positive_label_weight
lowerCAmelCase : Tuple = num_aggregation_labels
lowerCAmelCase : Union[str, Any] = aggregation_loss_weight
lowerCAmelCase : Optional[int] = use_answer_as_supervision
lowerCAmelCase : List[str] = answer_loss_importance
lowerCAmelCase : List[Any] = use_normalized_answer_loss
lowerCAmelCase : Optional[int] = huber_loss_delta
lowerCAmelCase : Union[str, Any] = temperature
lowerCAmelCase : Dict = aggregation_temperature
lowerCAmelCase : Optional[Any] = use_gumbel_for_cells
lowerCAmelCase : Union[str, Any] = use_gumbel_for_aggregation
lowerCAmelCase : Optional[Any] = average_approximation_function
lowerCAmelCase : Optional[Any] = cell_selection_preference
lowerCAmelCase : int = answer_loss_cutoff
lowerCAmelCase : int = max_num_rows
lowerCAmelCase : str = max_num_columns
lowerCAmelCase : Union[str, Any] = average_logits_per_cell
lowerCAmelCase : Any = select_one_column
lowerCAmelCase : Optional[Any] = allow_empty_column_selection
lowerCAmelCase : Tuple = init_cell_selection_weights_to_zero
lowerCAmelCase : str = reset_position_index_per_cell
lowerCAmelCase : str = disable_per_token_loss
# Aggregation hyperparameters
lowerCAmelCase : Dict = aggregation_labels
lowerCAmelCase : Optional[Any] = no_aggregation_label_index
if isinstance(self.aggregation_labels , snake_case__ ):
lowerCAmelCase : str = {int(snake_case__ ): v for k, v in aggregation_labels.items()}
| 681
|
"""simple docstring"""
import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
if is_torch_available():
import torch
from transformers import TimmBackbone, TimmBackboneConfig
from ...test_pipeline_mixin import PipelineTesterMixin
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __init__( self , snake_case__ , snake_case__=None , snake_case__=None , snake_case__=None , snake_case__="resnet50" , snake_case__=3 , snake_case__=32 , snake_case__=3 , snake_case__=True , snake_case__=True , ):
"""simple docstring"""
lowerCAmelCase : List[str] = parent
lowerCAmelCase : Union[str, Any] = out_indices if out_indices is not None else [4]
lowerCAmelCase : Tuple = stage_names
lowerCAmelCase : Any = out_features
lowerCAmelCase : Any = backbone
lowerCAmelCase : Union[str, Any] = batch_size
lowerCAmelCase : Optional[int] = image_size
lowerCAmelCase : List[str] = num_channels
lowerCAmelCase : int = use_pretrained_backbone
lowerCAmelCase : Tuple = is_training
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : Dict = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCAmelCase : Optional[int] = self.get_config()
return config, pixel_values
def lowercase__ ( self ):
"""simple docstring"""
return TimmBackboneConfig(
image_size=self.image_size , num_channels=self.num_channels , out_features=self.out_features , out_indices=self.out_indices , stage_names=self.stage_names , use_pretrained_backbone=self.use_pretrained_backbone , backbone=self.backbone , )
def lowercase__ ( self , snake_case__ , snake_case__ ):
"""simple docstring"""
lowerCAmelCase : List[Any] = TimmBackbone(config=snake_case__ )
model.to(snake_case__ )
model.eval()
with torch.no_grad():
lowerCAmelCase : Union[str, Any] = model(snake_case__ )
self.parent.assertEqual(
result.feature_map[-1].shape , (self.batch_size, model.channels[-1], 14, 14) , )
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : Union[str, Any] = self.prepare_config_and_inputs()
lowerCAmelCase , lowerCAmelCase : Tuple = config_and_inputs
lowerCAmelCase : str = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
@require_timm
class SCREAMING_SNAKE_CASE__ ( lowercase , lowercase , lowercase , unittest.TestCase ):
"""simple docstring"""
a : Optional[int] =(TimmBackbone,) if is_torch_available() else ()
a : Union[str, Any] ={"feature-extraction": TimmBackbone} if is_torch_available() else {}
a : Tuple =False
a : List[Any] =False
a : Optional[Any] =False
a : Dict =False
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : Optional[Any] = TimmBackboneModelTester(self )
lowerCAmelCase : List[str] = ConfigTester(self , config_class=snake_case__ , has_text_modality=snake_case__ )
def lowercase__ ( self ):
"""simple docstring"""
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 lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : str = "resnet18"
lowerCAmelCase : str = "microsoft/resnet-18"
lowerCAmelCase : List[Any] = AutoBackbone.from_pretrained(snake_case__ , use_timm_backbone=snake_case__ )
lowerCAmelCase : List[str] = AutoBackbone.from_pretrained(snake_case__ )
self.assertEqual(len(timm_model.out_features ) , len(transformers_model.out_features ) )
self.assertEqual(len(timm_model.stage_names ) , len(transformers_model.stage_names ) )
self.assertEqual(timm_model.channels , transformers_model.channels )
# Out indices are set to the last layer by default. For timm models, we don't know
# the number of layers in advance, so we set it to (-1,), whereas for transformers
# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
self.assertEqual(timm_model.out_indices , (-1,) )
self.assertEqual(transformers_model.out_indices , [len(timm_model.stage_names ) - 1] )
lowerCAmelCase : Union[str, Any] = AutoBackbone.from_pretrained(snake_case__ , use_timm_backbone=snake_case__ , out_indices=[1, 2, 3] )
lowerCAmelCase : List[Any] = AutoBackbone.from_pretrained(snake_case__ , out_indices=[1, 2, 3] )
self.assertEqual(timm_model.out_indices , transformers_model.out_indices )
self.assertEqual(len(timm_model.out_features ) , len(transformers_model.out_features ) )
self.assertEqual(timm_model.channels , transformers_model.channels )
@unittest.skip("TimmBackbone doesn't support feed forward chunking" )
def lowercase__ ( self ):
"""simple docstring"""
pass
@unittest.skip("TimmBackbone doesn't have num_hidden_layers attribute" )
def lowercase__ ( self ):
"""simple docstring"""
pass
@unittest.skip("TimmBackbone initialization is managed on the timm side" )
def lowercase__ ( self ):
"""simple docstring"""
pass
@unittest.skip("TimmBackbone models doesn't have inputs_embeds" )
def lowercase__ ( self ):
"""simple docstring"""
pass
@unittest.skip("TimmBackbone models doesn't have inputs_embeds" )
def lowercase__ ( self ):
"""simple docstring"""
pass
@unittest.skip("TimmBackbone model cannot be created without specifying a backbone checkpoint" )
def lowercase__ ( self ):
"""simple docstring"""
pass
@unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone" )
def lowercase__ ( self ):
"""simple docstring"""
pass
@unittest.skip("model weights aren't tied in TimmBackbone." )
def lowercase__ ( self ):
"""simple docstring"""
pass
@unittest.skip("model weights aren't tied in TimmBackbone." )
def lowercase__ ( self ):
"""simple docstring"""
pass
@unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone" )
def lowercase__ ( self ):
"""simple docstring"""
pass
@unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone" )
def lowercase__ ( self ):
"""simple docstring"""
pass
@unittest.skip("TimmBackbone doesn't have hidden size info in its configuration." )
def lowercase__ ( self ):
"""simple docstring"""
pass
@unittest.skip("TimmBackbone doesn't support output_attentions." )
def lowercase__ ( self ):
"""simple docstring"""
pass
@unittest.skip("Safetensors is not supported by timm." )
def lowercase__ ( self ):
"""simple docstring"""
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def lowercase__ ( self ):
"""simple docstring"""
pass
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase , lowerCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase : Dict = model_class(snake_case__ )
lowerCAmelCase : Any = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase : Optional[int] = [*signature.parameters.keys()]
lowerCAmelCase : Any = ["pixel_values"]
self.assertListEqual(arg_names[:1] , snake_case__ )
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase , lowerCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase : int = True
lowerCAmelCase : str = self.has_attentions
# no need to test all models as different heads yield the same functionality
lowerCAmelCase : Optional[int] = self.all_model_classes[0]
lowerCAmelCase : Union[str, Any] = model_class(snake_case__ )
model.to(snake_case__ )
lowerCAmelCase : Optional[int] = self._prepare_for_class(snake_case__ , snake_case__ )
lowerCAmelCase : Dict = model(**snake_case__ )
lowerCAmelCase : Tuple = outputs[0][-1]
# Encoder-/Decoder-only models
lowerCAmelCase : Optional[int] = outputs.hidden_states[0]
hidden_states.retain_grad()
if self.has_attentions:
lowerCAmelCase : Union[str, Any] = outputs.attentions[0]
attentions.retain_grad()
output.flatten()[0].backward(retain_graph=snake_case__ )
self.assertIsNotNone(hidden_states.grad )
if self.has_attentions:
self.assertIsNotNone(attentions.grad )
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase , lowerCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase : Dict = model_class(snake_case__ )
model.to(snake_case__ )
model.eval()
lowerCAmelCase : List[str] = model(**snake_case__ )
self.assertEqual(len(result.feature_maps ) , len(config.out_indices ) )
self.assertEqual(len(model.channels ) , len(config.out_indices ) )
# Check output of last stage is taken if out_features=None, out_indices=None
lowerCAmelCase : Dict = copy.deepcopy(snake_case__ )
lowerCAmelCase : Optional[int] = None
lowerCAmelCase : Dict = model_class(snake_case__ )
model.to(snake_case__ )
model.eval()
lowerCAmelCase : Optional[int] = model(**snake_case__ )
self.assertEqual(len(result.feature_maps ) , 1 )
self.assertEqual(len(model.channels ) , 1 )
# Check backbone can be initialized with fresh weights
lowerCAmelCase : Optional[int] = copy.deepcopy(snake_case__ )
lowerCAmelCase : List[str] = False
lowerCAmelCase : int = model_class(snake_case__ )
model.to(snake_case__ )
model.eval()
lowerCAmelCase : Optional[Any] = model(**snake_case__ )
| 681
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__a = {'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a = [
'VAN_PRETRAINED_MODEL_ARCHIVE_LIST',
'VanForImageClassification',
'VanModel',
'VanPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_van import (
VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
VanForImageClassification,
VanModel,
VanPreTrainedModel,
)
else:
import sys
__a = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 494
|
'''simple docstring'''
import unittest
from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow
if is_flax_available():
import jax
from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
from transformers.models.bert.modeling_flax_bert import FlaxBertModel
from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel
@require_flax
class lowercase__ ( unittest.TestCase ):
@slow
def UpperCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
for model_name in ["bert-base-cased", "bert-large-uncased"]:
with self.subTest(lowerCamelCase__ ):
_UpperCamelCase : Optional[int] = AutoConfig.from_pretrained(lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__ ,lowerCamelCase__ )
_UpperCamelCase : Dict = FlaxAutoModel.from_pretrained(lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__ ,lowerCamelCase__ )
@slow
def UpperCamelCase_ ( self : List[Any] ):
'''simple docstring'''
for model_name in ["roberta-base", "roberta-large"]:
with self.subTest(lowerCamelCase__ ):
_UpperCamelCase : Any = AutoConfig.from_pretrained(lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__ ,lowerCamelCase__ )
_UpperCamelCase : Any = FlaxAutoModel.from_pretrained(lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__ ,lowerCamelCase__ )
@slow
def UpperCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
for model_name in ["bert-base-cased", "bert-large-uncased"]:
_UpperCamelCase : Optional[int] = AutoTokenizer.from_pretrained(lowerCamelCase__ )
_UpperCamelCase : List[Any] = FlaxBertModel.from_pretrained(lowerCamelCase__ )
_UpperCamelCase : Optional[int] = tokenizer('Do you support jax jitted function?' ,return_tensors=TensorType.JAX )
@jax.jit
def eval(**lowerCamelCase__ : Union[str, Any] ):
return model(**lowerCamelCase__ )
eval(**lowerCamelCase__ ).block_until_ready()
@slow
def UpperCamelCase_ ( self : Dict ):
'''simple docstring'''
for model_name in ["roberta-base", "roberta-large"]:
_UpperCamelCase : Optional[int] = AutoTokenizer.from_pretrained(lowerCamelCase__ )
_UpperCamelCase : Tuple = FlaxRobertaModel.from_pretrained(lowerCamelCase__ )
_UpperCamelCase : Optional[Any] = tokenizer('Do you support jax jitted function?' ,return_tensors=TensorType.JAX )
@jax.jit
def eval(**lowerCamelCase__ : Union[str, Any] ):
return model(**lowerCamelCase__ )
eval(**lowerCamelCase__ ).block_until_ready()
def UpperCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
with self.assertRaisesRegex(
lowerCamelCase__ ,'bert-base is not a local folder and is not a valid model identifier' ):
_UpperCamelCase : int = FlaxAutoModel.from_pretrained('bert-base' )
def UpperCamelCase_ ( self : str ):
'''simple docstring'''
with self.assertRaisesRegex(
lowerCamelCase__ ,R'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)' ):
_UpperCamelCase : Tuple = FlaxAutoModel.from_pretrained(lowerCamelCase__ ,revision='aaaaaa' )
def UpperCamelCase_ ( self : List[str] ):
'''simple docstring'''
with self.assertRaisesRegex(
lowerCamelCase__ ,'hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack' ,):
_UpperCamelCase : List[Any] = FlaxAutoModel.from_pretrained('hf-internal-testing/config-no-model' )
def UpperCamelCase_ ( self : List[str] ):
'''simple docstring'''
with self.assertRaisesRegex(lowerCamelCase__ ,'Use `from_pt=True` to load this model' ):
_UpperCamelCase : Tuple = FlaxAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only' )
| 195
| 0
|
'''simple docstring'''
from datetime import datetime
import requests
def lowercase__( __UpperCamelCase: str ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = 'https://downloadgram.net/wp-json/wppress/video-downloader/video?url='
SCREAMING_SNAKE_CASE : List[Any] = requests.get(base_url + url ).json()[0]['urls'][0]['src']
return requests.get(_A ).content
if __name__ == "__main__":
UpperCamelCase_ = input("Enter Video/IGTV url: ").strip()
UpperCamelCase_ = F"""{datetime.now():%Y-%m-%d_%H:%M:%S}.mp4"""
with open(file_name, "wb") as fp:
fp.write(download_video(url))
print(F"""Done. Video saved to disk as {file_name}.""")
| 709
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
UpperCamelCase_ = {"configuration_speech_encoder_decoder": ["SpeechEncoderDecoderConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = ["SpeechEncoderDecoderModel"]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ = ["FlaxSpeechEncoderDecoderModel"]
if TYPE_CHECKING:
from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel
else:
import sys
UpperCamelCase_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 508
| 0
|
"""simple docstring"""
from __future__ import annotations
from typing import Any
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self , _lowercase ) -> None:
'''simple docstring'''
snake_case_ : List[Any] = num_of_nodes
snake_case_ : list[list[int]] = []
snake_case_ : dict[int, int] = {}
def UpperCAmelCase__ ( self , _lowercase , _lowercase , _lowercase ) -> None:
'''simple docstring'''
self.m_edges.append([u_node, v_node, weight] )
def UpperCAmelCase__ ( self , _lowercase ) -> int:
'''simple docstring'''
if self.m_component[u_node] == u_node:
return u_node
return self.find_component(self.m_component[u_node] )
def UpperCAmelCase__ ( self , _lowercase ) -> None:
'''simple docstring'''
if self.m_component[u_node] != u_node:
for k in self.m_component:
snake_case_ : Dict = self.find_component(_lowercase )
def UpperCAmelCase__ ( self , _lowercase , _lowercase , _lowercase ) -> None:
'''simple docstring'''
if component_size[u_node] <= component_size[v_node]:
snake_case_ : Dict = v_node
component_size[v_node] += component_size[u_node]
self.set_component(_lowercase )
elif component_size[u_node] >= component_size[v_node]:
snake_case_ : str = self.find_component(_lowercase )
component_size[u_node] += component_size[v_node]
self.set_component(_lowercase )
def UpperCAmelCase__ ( self ) -> None:
'''simple docstring'''
snake_case_ : str = []
snake_case_ : Any = 0
snake_case_ : list[Any] = [-1] * self.m_num_of_nodes
# A list of components (initialized to all of the nodes)
for node in range(self.m_num_of_nodes ):
self.m_component.update({node: node} )
component_size.append(1 )
snake_case_ : Tuple = self.m_num_of_nodes
while num_of_components > 1:
for edge in self.m_edges:
snake_case_ , snake_case_ , snake_case_ : Tuple = edge
snake_case_ : List[str] = self.m_component[u]
snake_case_ : str = self.m_component[v]
if u_component != v_component:
for component in (u_component, v_component):
if (
minimum_weight_edge[component] == -1
or minimum_weight_edge[component][2] > w
):
snake_case_ : Optional[int] = [u, v, w]
for edge in minimum_weight_edge:
if isinstance(_lowercase , _lowercase ):
snake_case_ , snake_case_ , snake_case_ : Tuple = edge
snake_case_ : Any = self.m_component[u]
snake_case_ : Union[str, Any] = self.m_component[v]
if u_component != v_component:
mst_weight += w
self.union(_lowercase , _lowercase , _lowercase )
print(f'Added edge [{u} - {v}]\nAdded weight: {w}\n' )
num_of_components -= 1
snake_case_ : Optional[Any] = [-1] * self.m_num_of_nodes
print(f'The total weight of the minimal spanning tree is: {mst_weight}' )
def __lowerCAmelCase ( ):
'''simple docstring'''
if __name__ == "__main__":
import doctest
doctest.testmod()
| 58
|
"""simple docstring"""
from __future__ import annotations
from math import pow, sqrt
def SCREAMING_SNAKE_CASE__ ( snake_case : float , snake_case : float , snake_case : float )-> dict[str, float]:
'''simple docstring'''
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if resistance == 0:
return {"resistance": sqrt(pow(snake_case , 2 ) - pow(snake_case , 2 ) )}
elif reactance == 0:
return {"reactance": sqrt(pow(snake_case , 2 ) - pow(snake_case , 2 ) )}
elif impedance == 0:
return {"impedance": sqrt(pow(snake_case , 2 ) + pow(snake_case , 2 ) )}
else:
raise ValueError("Exactly one argument must be 0" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 438
| 0
|
import warnings
from .generation import TFGenerationMixin
class snake_case__ ( lowerCAmelCase_ ):
warnings.warn(
"""Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will """
"""be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead.""" , lowerCAmelCase_ , )
| 706
|
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a_ :List[Any] = logging.get_logger(__name__)
a_ :Union[str, Any] = {"vocab_file": "spiece.model"}
a_ :Optional[Any] = {
"vocab_file": {
"bert_for_seq_generation": (
"https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"
),
}
}
a_ :str = {"bert_for_seq_generation": 512}
class snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = VOCAB_FILES_NAMES
_SCREAMING_SNAKE_CASE = PRETRAINED_VOCAB_FILES_MAP
_SCREAMING_SNAKE_CASE = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = ["""input_ids""", """attention_mask"""]
def __init__( self : str, _snake_case : str, _snake_case : Optional[Any]="<s>", _snake_case : Tuple="</s>", _snake_case : int="<unk>", _snake_case : List[Any]="<pad>", _snake_case : Dict="<::::>", _snake_case : Optional[Dict[str, Any]] = None, **_snake_case : List[Any], ) ->None:
snake_case__ : List[str] = {} if sp_model_kwargs is None else sp_model_kwargs
# Add extra_ids to the special token list
super().__init__(
bos_token=_snake_case, eos_token=_snake_case, unk_token=_snake_case, pad_token=_snake_case, sep_token=_snake_case, sp_model_kwargs=self.sp_model_kwargs, **_snake_case, )
snake_case__ : Optional[int] = vocab_file
snake_case__ : List[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_snake_case )
@property
def lowercase_ ( self : Any ) ->Any:
return self.sp_model.get_piece_size()
def lowercase_ ( self : List[str] ) ->Any:
snake_case__ : Tuple = {self.convert_ids_to_tokens(_snake_case ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : Optional[int] ) ->str:
snake_case__ : List[str] = self.__dict__.copy()
snake_case__ : Any = None
return state
def __setstate__( self : str, _snake_case : Dict ) ->int:
snake_case__ : Union[str, Any] = d
# for backward compatibility
if not hasattr(self, 'sp_model_kwargs' ):
snake_case__ : Dict = {}
snake_case__ : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def lowercase_ ( self : List[str], _snake_case : str ) ->List[str]:
return self.sp_model.encode(_snake_case, out_type=_snake_case )
def lowercase_ ( self : Optional[int], _snake_case : str ) ->Union[str, Any]:
return self.sp_model.piece_to_id(_snake_case )
def lowercase_ ( self : Union[str, Any], _snake_case : Union[str, Any] ) ->int:
snake_case__ : List[str] = self.sp_model.IdToPiece(_snake_case )
return token
def lowercase_ ( self : List[str], _snake_case : Optional[Any] ) ->Any:
snake_case__ : int = []
snake_case__ : Any = ''
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(_snake_case ) + token
snake_case__ : str = []
else:
current_sub_tokens.append(_snake_case )
out_string += self.sp_model.decode(_snake_case )
return out_string.strip()
def lowercase_ ( self : int, _snake_case : str, _snake_case : Optional[str] = None ) ->Tuple[str]:
if not os.path.isdir(_snake_case ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
snake_case__ : List[str] = 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:
snake_case__ : Tuple = self.sp_model.serialized_model_proto()
fi.write(_snake_case )
return (out_vocab_file,)
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|
"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging
__UpperCAmelCase = logging.get_logger(__name__)
class __lowercase ( __lowerCamelCase ):
snake_case_ = ["""input_features""", """is_longer"""]
def __init__( self : str ,A : Union[str, Any]=64 ,A : Tuple=48_000 ,A : Dict=480 ,A : List[str]=10 ,A : str=1_024 ,A : Any=0.0 ,A : Optional[int]=False ,A : float = 0 ,A : float = 14_000 ,A : int = None ,A : str = "fusion" ,A : str = "repeatpad" ,**A : List[Any] ,):
'''simple docstring'''
super().__init__(
feature_size=A ,sampling_rate=A ,padding_value=A ,return_attention_mask=A ,**A ,)
UpperCAmelCase__ : List[Any] = top_db
UpperCAmelCase__ : Union[str, Any] = truncation
UpperCAmelCase__ : Optional[int] = padding
UpperCAmelCase__ : List[Any] = fft_window_size
UpperCAmelCase__ : Optional[Any] = (fft_window_size >> 1) + 1
UpperCAmelCase__ : Any = hop_length
UpperCAmelCase__ : List[str] = max_length_s
UpperCAmelCase__ : List[Any] = max_length_s * sampling_rate
UpperCAmelCase__ : List[Any] = sampling_rate
UpperCAmelCase__ : Optional[int] = frequency_min
UpperCAmelCase__ : Tuple = frequency_max
UpperCAmelCase__ : List[str] = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins ,num_mel_filters=A ,min_frequency=A ,max_frequency=A ,sampling_rate=A ,norm=A ,mel_scale="""htk""" ,)
UpperCAmelCase__ : str = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins ,num_mel_filters=A ,min_frequency=A ,max_frequency=A ,sampling_rate=A ,norm="""slaney""" ,mel_scale="""slaney""" ,)
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = copy.deepcopy(self.__dict__ )
UpperCAmelCase__ : Tuple = self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
if "mel_filters_slaney" in output:
del output["mel_filters_slaney"]
return output
def __lowercase ( self : List[str] ,A : np.array ,A : Optional[np.array] = None ):
'''simple docstring'''
UpperCAmelCase__ : Dict = spectrogram(
A ,window_function(self.fft_window_size ,"""hann""" ) ,frame_length=self.fft_window_size ,hop_length=self.hop_length ,power=2.0 ,mel_filters=A ,log_mel="""dB""" ,)
return log_mel_spectrogram.T
def __lowercase ( self : Optional[Any] ,A : Union[str, Any] ,A : int ,A : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = np.array_split(list(range(0 ,total_frames - chunk_frames + 1 ) ) ,3 )
if len(ranges[1] ) == 0:
# if the audio is too short, we just use the first chunk
UpperCAmelCase__ : List[str] = [0]
if len(ranges[2] ) == 0:
# if the audio is too short, we just use the first chunk
UpperCAmelCase__ : int = [0]
# randomly choose index for each part
UpperCAmelCase__ : Tuple = np.random.choice(ranges[0] )
UpperCAmelCase__ : Tuple = np.random.choice(ranges[1] )
UpperCAmelCase__ : str = np.random.choice(ranges[2] )
UpperCAmelCase__ : List[str] = mel[idx_front : idx_front + chunk_frames, :]
UpperCAmelCase__ : List[str] = mel[idx_middle : idx_middle + chunk_frames, :]
UpperCAmelCase__ : Dict = mel[idx_back : idx_back + chunk_frames, :]
UpperCAmelCase__ : Optional[Any] = torch.tensor(mel[None, None, :] )
UpperCAmelCase__ : int = torch.nn.functional.interpolate(
A ,size=[chunk_frames, 64] ,mode="""bilinear""" ,align_corners=A )
UpperCAmelCase__ : Dict = mel_shrink[0][0].numpy()
UpperCAmelCase__ : Dict = np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] ,axis=0 )
return mel_fusion
def __lowercase ( self : Any ,A : np.array ,A : Optional[int] ,A : Any ,A : Tuple ):
'''simple docstring'''
if waveform.shape[0] > max_length:
if truncation == "rand_trunc":
UpperCAmelCase__ : int = True
# random crop to max_length (for compatibility) -> this should be handled by self.pad
UpperCAmelCase__ : str = len(A ) - max_length
UpperCAmelCase__ : Optional[Any] = np.random.randint(0 ,overflow + 1 )
UpperCAmelCase__ : Optional[int] = waveform[idx : idx + max_length]
UpperCAmelCase__ : Any = self._np_extract_fbank_features(A ,self.mel_filters_slaney )[None, :]
elif truncation == "fusion":
UpperCAmelCase__ : Tuple = self._np_extract_fbank_features(A ,self.mel_filters )
UpperCAmelCase__ : Optional[int] = max_length // self.hop_length + 1 # the +1 related to how the spectrogram is computed
UpperCAmelCase__ : int = mel.shape[0]
if chunk_frames == total_frames:
# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
# In this case, we just use the whole audio.
UpperCAmelCase__ : List[Any] = np.stack([mel, mel, mel, mel] ,axis=0 )
UpperCAmelCase__ : Any = False
else:
UpperCAmelCase__ : Union[str, Any] = self._random_mel_fusion(A ,A ,A )
UpperCAmelCase__ : List[str] = True
else:
raise NotImplementedError(f"data_truncating {truncation} not implemented" )
else:
UpperCAmelCase__ : Optional[Any] = False
# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
if waveform.shape[0] < max_length:
if padding == "repeat":
UpperCAmelCase__ : str = int(max_length / len(A ) )
UpperCAmelCase__ : int = np.stack(np.tile(A ,n_repeat + 1 ) )[:max_length]
if padding == "repeatpad":
UpperCAmelCase__ : List[Any] = int(max_length / len(A ) )
UpperCAmelCase__ : str = np.stack(np.tile(A ,A ) )
UpperCAmelCase__ : Optional[Any] = np.pad(A ,(0, max_length - waveform.shape[0]) ,mode="""constant""" ,constant_values=0 )
if truncation == "fusion":
UpperCAmelCase__ : int = self._np_extract_fbank_features(A ,self.mel_filters )
UpperCAmelCase__ : List[Any] = np.stack([input_mel, input_mel, input_mel, input_mel] ,axis=0 )
else:
UpperCAmelCase__ : Any = self._np_extract_fbank_features(A ,self.mel_filters_slaney )[None, :]
return input_mel, longer
def __call__( self : str ,A : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,A : str = None ,A : Optional[str] = None ,A : Optional[int] = None ,A : Optional[int] = None ,A : Optional[Union[str, TensorType]] = None ,**A : List[str] ,):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = truncation if truncation is not None else self.truncation
UpperCAmelCase__ : Dict = padding if padding else self.padding
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f"The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"
f" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"
f" was sampled 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__ : Optional[int] = isinstance(A ,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__ : List[str] = is_batched_numpy or (
isinstance(A ,(list, tuple) ) and (isinstance(raw_speech[0] ,(np.ndarray, tuple, list) ))
)
if is_batched:
UpperCAmelCase__ : str = [np.asarray(A ,dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(A ,np.ndarray ):
UpperCAmelCase__ : Any = np.asarray(A ,dtype=np.floataa )
elif isinstance(A ,np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
UpperCAmelCase__ : str = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
UpperCAmelCase__ : Optional[Any] = [np.asarray(A )]
# convert to mel spectrogram, truncate and pad if needed.
UpperCAmelCase__ : Tuple = [
self._get_input_mel(A ,max_length if max_length else self.nb_max_samples ,A ,A )
for waveform in raw_speech
]
UpperCAmelCase__ : Optional[int] = []
UpperCAmelCase__ : Tuple = []
for mel, longer in padded_inputs:
input_mel.append(A )
is_longer.append(A )
if truncation == "fusion" and sum(A ) == 0:
# if no audio is longer than 10s, then randomly select one audio to be longer
UpperCAmelCase__ : List[str] = np.random.randint(0 ,len(A ) )
UpperCAmelCase__ : int = True
if isinstance(input_mel[0] ,A ):
UpperCAmelCase__ : Tuple = [np.asarray(A ,dtype=np.floataa ) for feature in input_mel]
# is_longer is a list of bool
UpperCAmelCase__ : List[str] = [[longer] for longer in is_longer]
UpperCAmelCase__ : List[Any] = {"""input_features""": input_mel, """is_longer""": is_longer}
UpperCAmelCase__ : str = BatchFeature(A )
if return_tensors is not None:
UpperCAmelCase__ : int = input_features.convert_to_tensors(A )
return input_features
| 65
|
"""simple docstring"""
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available
if is_sentencepiece_available():
from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json
from ...test_tokenization_common import TokenizerTesterMixin
__UpperCAmelCase = get_tests_dir('fixtures/test_sentencepiece.model')
__UpperCAmelCase = {'target_lang': 'fi', 'source_lang': 'en'}
__UpperCAmelCase = '>>zh<<'
__UpperCAmelCase = 'Helsinki-NLP/'
if is_torch_available():
__UpperCAmelCase = 'pt'
elif is_tf_available():
__UpperCAmelCase = 'tf'
else:
__UpperCAmelCase = 'jax'
@require_sentencepiece
class __lowercase ( __lowerCamelCase , unittest.TestCase ):
snake_case_ = MarianTokenizer
snake_case_ = False
snake_case_ = True
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
super().setUp()
UpperCAmelCase__ : Optional[Any] = ["""</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est""", """\u0120""", """<pad>"""]
UpperCAmelCase__ : int = dict(zip(A ,range(len(A ) ) ) )
UpperCAmelCase__ : Optional[int] = Path(self.tmpdirname )
save_json(A ,save_dir / VOCAB_FILES_NAMES["""vocab"""] )
save_json(A ,save_dir / VOCAB_FILES_NAMES["""tokenizer_config_file"""] )
if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
copyfile(A ,save_dir / VOCAB_FILES_NAMES["""source_spm"""] )
copyfile(A ,save_dir / VOCAB_FILES_NAMES["""target_spm"""] )
UpperCAmelCase__ : Dict = MarianTokenizer.from_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname )
def __lowercase ( self : List[Any] ,**A : List[Any] ):
'''simple docstring'''
return MarianTokenizer.from_pretrained(self.tmpdirname ,**A )
def __lowercase ( self : Union[str, Any] ,A : Tuple ):
'''simple docstring'''
return (
"This is a test",
"This is a test",
)
def __lowercase ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = """</s>"""
UpperCAmelCase__ : int = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(A ) ,A )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(A ) ,A )
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Dict = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] ,"""</s>""" )
self.assertEqual(vocab_keys[1] ,"""<unk>""" )
self.assertEqual(vocab_keys[-1] ,"""<pad>""" )
self.assertEqual(len(A ) ,9 )
def __lowercase ( self : Dict ):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size ,9 )
def __lowercase ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = MarianTokenizer.from_pretrained(f"{ORG_NAME}opus-mt-en-de" )
UpperCAmelCase__ : List[str] = en_de_tokenizer(["""I am a small frog"""] ,return_tensors=A )
self.assertIsInstance(A ,A )
UpperCAmelCase__ : str = [38, 121, 14, 697, 38_848, 0]
self.assertListEqual(A ,batch.input_ids[0] )
UpperCAmelCase__ : Optional[Any] = tempfile.mkdtemp()
en_de_tokenizer.save_pretrained(A )
UpperCAmelCase__ : Tuple = [x.name for x in Path(A ).glob("""*""" )]
self.assertIn("""source.spm""" ,A )
MarianTokenizer.from_pretrained(A )
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = self.get_tokenizer()
UpperCAmelCase__ : Any = tok(
["""I am a small frog""" * 1_000, """I am a small frog"""] ,padding=A ,truncation=A ,return_tensors=A )
self.assertIsInstance(A ,A )
self.assertEqual(batch.input_ids.shape ,(2, 512) )
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : int = self.get_tokenizer()
UpperCAmelCase__ : Tuple = tok(["""I am a tiny frog""", """I am a small frog"""] ,padding=A ,return_tensors=A )
self.assertIsInstance(A ,A )
self.assertEqual(batch_smaller.input_ids.shape ,(2, 10) )
@slow
def __lowercase ( self : Dict ):
'''simple docstring'''
# fmt: off
UpperCAmelCase__ : Optional[int] = {"""input_ids""": [[43_495, 462, 20, 42_164, 1_369, 52, 464, 132, 1_703, 492, 13, 7_491, 38_999, 6, 8, 464, 132, 1_703, 492, 13, 4_669, 37_867, 13, 7_525, 27, 1_593, 988, 13, 33_972, 7_029, 6, 20, 8_251, 383, 2, 270, 5_866, 3_788, 2, 2_353, 8_251, 12_338, 2, 13_958, 387, 2, 3_629, 6_953, 188, 2_900, 2, 13_958, 8_011, 11_501, 23, 8_460, 4_073, 34_009, 20, 435, 11_439, 27, 8, 8_460, 4_073, 6_004, 20, 9_988, 375, 27, 33, 266, 1_945, 1_076, 1_350, 37_867, 3_288, 5, 577, 1_076, 4_374, 8, 5_082, 5, 26_453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 10_767, 6, 316, 304, 4_239, 3, 0], [148, 15_722, 19, 1_839, 12, 1_350, 13, 22_327, 5_082, 5_418, 47_567, 35_938, 59, 318, 19_552, 108, 2_183, 54, 14_976, 4_835, 32, 547, 1_114, 8, 315, 2_417, 5, 92, 19_088, 3, 0, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100], [36, 6_395, 12_570, 39_147, 11_597, 6, 266, 4, 45_405, 7_296, 3, 0, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100]], """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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=A ,model_name="""Helsinki-NLP/opus-mt-en-de""" ,revision="""1a8c2263da11e68e50938f97e10cd57820bd504c""" ,decode_kwargs={"""use_source_tokenizer""": True} ,)
def __lowercase ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = MarianTokenizer.from_pretrained("""hf-internal-testing/test-marian-two-vocabs""" )
UpperCAmelCase__ : Any = """Tämä on testi"""
UpperCAmelCase__ : int = """This is a test"""
UpperCAmelCase__ : List[str] = [76, 7, 2_047, 2]
UpperCAmelCase__ : Optional[Any] = [69, 12, 11, 940, 2]
UpperCAmelCase__ : List[str] = tokenizer(A ).input_ids
self.assertListEqual(A ,A )
UpperCAmelCase__ : Optional[int] = tokenizer(text_target=A ).input_ids
self.assertListEqual(A ,A )
UpperCAmelCase__ : int = tokenizer.decode(A ,skip_special_tokens=A )
self.assertEqual(A ,A )
| 65
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__A : Union[str, Any] = {'configuration_yolos': ['YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'YolosConfig', 'YolosOnnxConfig']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Optional[int] = ['YolosFeatureExtractor']
__A : Tuple = ['YolosImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : int = [
'YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST',
'YolosForObjectDetection',
'YolosModel',
'YolosPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_yolos import YolosFeatureExtractor
from .image_processing_yolos import YolosImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_yolos import (
YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
YolosForObjectDetection,
YolosModel,
YolosPreTrainedModel,
)
else:
import sys
__A : int = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 267
|
'''simple docstring'''
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def UpperCAmelCase ( lowerCamelCase_ :List[Any] ):
'''simple docstring'''
snake_case_ : Tuple = [
"""encoder.version""",
"""decoder.version""",
"""model.encoder.version""",
"""model.decoder.version""",
"""decoder.output_projection.weight""",
"""_float_tensor""",
"""encoder.embed_positions._float_tensor""",
"""decoder.embed_positions._float_tensor""",
]
for k in ignore_keys:
state_dict.pop(lowerCamelCase_ , lowerCamelCase_ )
def UpperCAmelCase ( lowerCamelCase_ :List[Any] ):
'''simple docstring'''
snake_case_ , snake_case_ : Dict = emb.weight.shape
snake_case_ : str = nn.Linear(lowerCamelCase_ , lowerCamelCase_ , bias=lowerCamelCase_ )
snake_case_ : str = emb.weight.data
return lin_layer
def UpperCAmelCase ( lowerCamelCase_ :List[Any] , lowerCamelCase_ :List[str]=None ):
'''simple docstring'''
snake_case_ : Union[str, Any] = {}
for old_key in state_dict.keys():
snake_case_ : Optional[int] = old_key
if "moe_layer.experts." in key:
if expert_idx is not None:
snake_case_ : Dict = key.replace("""moe_layer.experts.0""" , F'''ffn.experts.expert_{expert_idx}''' )
else:
snake_case_ : str = key.replace("""moe_layer.experts.""" , """ffn.experts.expert_""" )
if "gate" in key:
snake_case_ : List[str] = key.replace(""".moe_layer.gate.wg""" , """.ffn.router.classifier""" )
if "fc2" and "experts" not in key:
snake_case_ : Any = key.replace(""".fc2.""" , """.ffn.fc2.""" )
if "fc1" and "experts" not in key:
snake_case_ : str = key.replace(""".fc1.""" , """.ffn.fc1.""" )
if ".encoder_attn." in key:
snake_case_ : str = key.replace(""".encoder_attn.""" , """.cross_attention.""" )
if "encoder_attn_layer_norm" in key:
snake_case_ : List[str] = key.replace("""encoder_attn_layer_norm""" , """cross_attention_layer_norm""" )
if "final_layer_norm" in key:
snake_case_ : Dict = key.replace("""final_layer_norm""" , """ff_layer_norm""" )
snake_case_ : Dict = state_dict[old_key]
return new_dict
def UpperCAmelCase ( lowerCamelCase_ :Tuple , lowerCamelCase_ :Optional[Any] , lowerCamelCase_ :Union[str, Any] , lowerCamelCase_ :Any , lowerCamelCase_ :str = WEIGHTS_NAME ):
'''simple docstring'''
snake_case_ : Tuple = []
snake_case_ : Dict = 0
os.makedirs(lowerCamelCase_ , exist_ok=lowerCamelCase_ )
for expert in range(lowerCamelCase_ ):
snake_case_ : Optional[Any] = switch_checkpoint_path + F'''-rank-{expert}.pt'''
if os.path.isfile(lowerCamelCase_ ):
snake_case_ : List[Any] = torch.load(lowerCamelCase_ )["""model"""]
remove_ignore_keys_(lowerCamelCase_ )
snake_case_ : List[str] = rename_fairseq_keys(lowerCamelCase_ , lowerCamelCase_ )
snake_case_ : List[str] = os.path.join(
lowerCamelCase_ , weights_name.replace(""".bin""" , F'''-{len(lowerCamelCase_ )+1:05d}-of-???.bin''' ) )
torch.save(lowerCamelCase_ , lowerCamelCase_ )
sharded_state_dicts.append(expert_state.keys() )
total_size += sum([value.numel() for key, value in expert_state.items()] ) * dtype_byte_size(
expert_state[list(lowerCamelCase_ )[0]].dtype )
# Add the last block
snake_case_ : Tuple = os.path.join(lowerCamelCase_ , weights_name.replace(""".bin""" , F'''-{len(lowerCamelCase_ )+1:05d}-of-???.bin''' ) )
snake_case_ : Tuple = torch.load(switch_checkpoint_path + """-shared.pt""" )["""model"""]
remove_ignore_keys_(lowerCamelCase_ )
snake_case_ : Tuple = rename_fairseq_keys(lowerCamelCase_ , lowerCamelCase_ )
snake_case_ : List[str] = shared_weights["""decoder.embed_tokens.weight"""]
sharded_state_dicts.append(shared_weights.keys() )
# If we only have the shared weights (dummy model/experts saved on the same file)
if len(lowerCamelCase_ ) == 1:
snake_case_ : Union[str, Any] = os.path.join(lowerCamelCase_ , lowerCamelCase_ )
torch.save(lowerCamelCase_ , lowerCamelCase_ )
return {weights_name: sharded_state_dicts[0]}, None
else:
torch.save(lowerCamelCase_ , lowerCamelCase_ )
# Otherwise, let's build the index
snake_case_ : str = {}
for idx, shard in enumerate(lowerCamelCase_ ):
snake_case_ : List[str] = weights_name.replace(""".bin""" , F'''-{idx+1:05d}-of-{len(lowerCamelCase_ ):05d}.bin''' )
snake_case_ : Optional[int] = os.path.join(lowerCamelCase_ , weights_name.replace(""".bin""" , F'''-{idx+1:05d}-of-???.bin''' ) )
os.rename(lowerCamelCase_ , os.path.join(lowerCamelCase_ , lowerCamelCase_ ) )
for key in shard:
snake_case_ : Optional[int] = shard_file
# Add the metadata
snake_case_ : Any = {"""total_size""": total_size}
snake_case_ : int = {"""metadata""": metadata, """weight_map""": weight_map}
with open(os.path.join(lowerCamelCase_ , lowerCamelCase_ ) , """w""" , encoding="""utf-8""" ) as f:
snake_case_ : List[str] = json.dumps(lowerCamelCase_ , indent=2 , sort_keys=lowerCamelCase_ ) + """\n"""
f.write(lowerCamelCase_ )
return metadata, index
if __name__ == "__main__":
__A : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--nllb_moe_checkpoint_path',
default='/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000',
type=str,
required=False,
help='Path to a directory containing a folder per layer. Follows the original Google format.',
)
parser.add_argument('--dtype', default='float32', type=str, required=False, help='dtype of the saved model')
parser.add_argument(
'--pytorch_dump_folder_path',
default='/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b',
type=str,
required=False,
help='Path to the output pytorch model.',
)
__A : List[str] = parser.parse_args()
__A, __A : List[Any] = shard_on_the_fly(
args.nllb_moe_checkpoint_path,
args.pytorch_dump_folder_path,
128,
args.dtype,
)
__A : Tuple = NllbMoeConfig.from_pretrained(
'facebook/nllb-200-3.3B', encoder_sparse_step=4, decoder_sparse_step=4, num_experts=128
)
config.save_pretrained(args.pytorch_dump_folder_path)
__A : List[str] = NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
print('Done')
model.save_pretrained(args.pytorch_dump_folder_path)
| 267
| 1
|
import fire
from utils import calculate_rouge, save_json
def _lowerCAmelCase ( UpperCamelCase__: Tuple , UpperCamelCase__: List[Any] , UpperCamelCase__: Optional[int]=None , **UpperCamelCase__: List[str] ) -> Dict:
"""simple docstring"""
A = [x.strip() for x in open(UpperCamelCase__ ).readlines()]
A = [x.strip() for x in open(UpperCamelCase__ ).readlines()][: len(UpperCamelCase__ )]
A = calculate_rouge(UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__ )
if save_path is not None:
save_json(UpperCamelCase__ , UpperCamelCase__ , indent=UpperCamelCase__ )
return metrics # these print nicely
if __name__ == "__main__":
fire.Fire(calculate_rouge_path)
| 641
|
import requests
from bsa import BeautifulSoup
def _lowerCAmelCase ( UpperCamelCase__: str = "https://www.worldometers.info/coronavirus" ) -> dict:
"""simple docstring"""
A = BeautifulSoup(requests.get(UpperCamelCase__ ).text , """html.parser""" )
A = soup.findAll("""h1""" )
A = soup.findAll("""div""" , {"""class""": """maincounter-number"""} )
keys += soup.findAll("""span""" , {"""class""": """panel-title"""} )
values += soup.findAll("""div""" , {"""class""": """number-table-main"""} )
return {key.text.strip(): value.text.strip() for key, value in zip(UpperCamelCase__ , UpperCamelCase__ )}
if __name__ == "__main__":
print("\033[1m" + "COVID-19 Status of the World" + "\033[0m\n")
for key, value in world_covidaa_stats().items():
print(f'''{key}\n{value}\n''')
| 641
| 1
|
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_sentencepiece
class snake_case__ ( unittest.TestCase ):
def UpperCAmelCase__ ( self : List[Any] ):
snake_case__ : Dict = 'laion/clap-htsat-unfused'
snake_case__ : str = tempfile.mkdtemp()
def UpperCAmelCase__ ( self : List[Any] , **_lowerCamelCase : Any ):
return RobertaTokenizer.from_pretrained(self.checkpoint , **A__ )
def UpperCAmelCase__ ( self : int , **_lowerCamelCase : str ):
return ClapFeatureExtractor.from_pretrained(self.checkpoint , **A__ )
def UpperCAmelCase__ ( self : Optional[int] ):
shutil.rmtree(self.tmpdirname )
def UpperCAmelCase__ ( self : Union[str, Any] ):
snake_case__ : Union[str, Any] = self.get_tokenizer()
snake_case__ : int = self.get_feature_extractor()
snake_case__ : Union[str, Any] = ClapProcessor(tokenizer=A__ , feature_extractor=A__ )
processor.save_pretrained(self.tmpdirname )
snake_case__ : Union[str, Any] = ClapProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , A__ )
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor , A__ )
def UpperCAmelCase__ ( self : List[Any] ):
snake_case__ : Union[str, Any] = ClapProcessor(tokenizer=self.get_tokenizer() , feature_extractor=self.get_feature_extractor() )
processor.save_pretrained(self.tmpdirname )
snake_case__ : Dict = self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)' )
snake_case__ : Dict = self.get_feature_extractor(do_normalize=A__ , padding_value=1.0 )
snake_case__ : Any = ClapProcessor.from_pretrained(
self.tmpdirname , bos_token='(BOS)' , eos_token='(EOS)' , do_normalize=A__ , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , A__ )
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.feature_extractor , A__ )
def UpperCAmelCase__ ( self : List[Any] ):
snake_case__ : int = self.get_feature_extractor()
snake_case__ : List[str] = self.get_tokenizer()
snake_case__ : Dict = ClapProcessor(tokenizer=A__ , feature_extractor=A__ )
snake_case__ : List[str] = floats_list((3, 1_0_0_0) )
snake_case__ : Optional[Any] = feature_extractor(A__ , return_tensors='np' )
snake_case__ : str = processor(audios=A__ , return_tensors='np' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def UpperCAmelCase__ ( self : List[str] ):
snake_case__ : Optional[int] = self.get_feature_extractor()
snake_case__ : Dict = self.get_tokenizer()
snake_case__ : Any = ClapProcessor(tokenizer=A__ , feature_extractor=A__ )
snake_case__ : str = 'This is a test string'
snake_case__ : Optional[int] = processor(text=A__ )
snake_case__ : int = tokenizer(A__ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def UpperCAmelCase__ ( self : str ):
snake_case__ : List[Any] = self.get_feature_extractor()
snake_case__ : int = self.get_tokenizer()
snake_case__ : List[str] = ClapProcessor(tokenizer=A__ , feature_extractor=A__ )
snake_case__ : Optional[int] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
snake_case__ : str = processor.batch_decode(A__ )
snake_case__ : Optional[Any] = tokenizer.batch_decode(A__ )
self.assertListEqual(A__ , A__ )
def UpperCAmelCase__ ( self : int ):
snake_case__ : Any = self.get_feature_extractor()
snake_case__ : Optional[int] = self.get_tokenizer()
snake_case__ : Optional[int] = ClapProcessor(tokenizer=A__ , feature_extractor=A__ )
self.assertListEqual(
processor.model_input_names[2:] , feature_extractor.model_input_names , msg='`processor` and `feature_extractor` model input names do not match' , )
| 700
|
def lowercase__( A = 1_0_0_0 ):
snake_case__ : Any = 3
snake_case__ : List[str] = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 1_5 == 0:
result -= a
a += 1
return result
if __name__ == "__main__":
print(F"""{solution() = }""")
| 303
| 0
|
"""simple docstring"""
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
class lowerCAmelCase__ ( _lowerCamelCase ):
'''simple docstring'''
def __init__( self : Dict , _SCREAMING_SNAKE_CASE : str = "▁" , _SCREAMING_SNAKE_CASE : bool = True , _SCREAMING_SNAKE_CASE : Union[str, AddedToken] = "<unk>" , _SCREAMING_SNAKE_CASE : Union[str, AddedToken] = "</s>" , _SCREAMING_SNAKE_CASE : Union[str, AddedToken] = "<pad>" , ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = {
'pad': {'id': 0, 'token': pad_token},
'eos': {'id': 1, 'token': eos_token},
'unk': {'id': 2, 'token': unk_token},
}
SCREAMING_SNAKE_CASE : Optional[Any] = [None] * len(self.special_tokens )
for token_dict in self.special_tokens.values():
SCREAMING_SNAKE_CASE : Tuple = token_dict['token']
SCREAMING_SNAKE_CASE : Tuple = Tokenizer(Unigram() )
SCREAMING_SNAKE_CASE : List[str] = normalizers.Sequence(
[
normalizers.Nmt(),
normalizers.NFKC(),
normalizers.Replace(Regex(' {2,}' ) , ' ' ),
normalizers.Lowercase(),
] )
SCREAMING_SNAKE_CASE : List[Any] = pre_tokenizers.Sequence(
[
pre_tokenizers.Metaspace(replacement=_SCREAMING_SNAKE_CASE , add_prefix_space=_SCREAMING_SNAKE_CASE ),
pre_tokenizers.Digits(individual_digits=_SCREAMING_SNAKE_CASE ),
pre_tokenizers.Punctuation(),
] )
SCREAMING_SNAKE_CASE : Tuple = decoders.Metaspace(replacement=_SCREAMING_SNAKE_CASE , add_prefix_space=_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE : Union[str, Any] = TemplateProcessing(
single=f"""$A {self.special_tokens["eos"]["token"]}""" , special_tokens=[(self.special_tokens['eos']['token'], self.special_tokens['eos']['id'])] , )
SCREAMING_SNAKE_CASE : Union[str, Any] = {
'model': 'SentencePieceUnigram',
'replacement': replacement,
'add_prefix_space': add_prefix_space,
}
super().__init__(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def _lowerCAmelCase ( self : str , _SCREAMING_SNAKE_CASE : Union[str, List[str]] , _SCREAMING_SNAKE_CASE : int = 8_000 , _SCREAMING_SNAKE_CASE : bool = True , ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = trainers.UnigramTrainer(
vocab_size=_SCREAMING_SNAKE_CASE , special_tokens=self.special_tokens_list , show_progress=_SCREAMING_SNAKE_CASE , )
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
SCREAMING_SNAKE_CASE : Dict = [files]
self._tokenizer.train(_SCREAMING_SNAKE_CASE , trainer=_SCREAMING_SNAKE_CASE )
self.add_unk_id()
def _lowerCAmelCase ( self : List[str] , _SCREAMING_SNAKE_CASE : Union[Iterator[str], Iterator[Iterator[str]]] , _SCREAMING_SNAKE_CASE : int = 8_000 , _SCREAMING_SNAKE_CASE : bool = True , ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = trainers.UnigramTrainer(
vocab_size=_SCREAMING_SNAKE_CASE , special_tokens=self.special_tokens_list , show_progress=_SCREAMING_SNAKE_CASE , )
self._tokenizer.train_from_iterator(_SCREAMING_SNAKE_CASE , trainer=_SCREAMING_SNAKE_CASE )
self.add_unk_id()
def _lowerCAmelCase ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = json.loads(self._tokenizer.to_str() )
SCREAMING_SNAKE_CASE : str = self.special_tokens['unk']['id']
SCREAMING_SNAKE_CASE : int = Tokenizer.from_str(json.dumps(_SCREAMING_SNAKE_CASE ) )
| 265
|
"""simple docstring"""
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
A_ : Union[str, Any] = False
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
pass
@nightly
@require_torch_gpu
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
def _lowerCAmelCase ( self : Any ) -> List[str]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _lowerCAmelCase ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = VersatileDiffusionTextToImagePipeline.from_pretrained('shi-labs/versatile-diffusion' )
# remove text_unet
pipe.remove_unused_weights()
pipe.to(_SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE : Union[str, Any] = 'A painting of a squirrel eating a burger '
SCREAMING_SNAKE_CASE : List[str] = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : int = pipe(
prompt=_SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE , guidance_scale=7.5 , num_inference_steps=2 , output_type='numpy' ).images
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE : Dict = VersatileDiffusionTextToImagePipeline.from_pretrained(_SCREAMING_SNAKE_CASE )
pipe.to(_SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE : Union[str, Any] = generator.manual_seed(0 )
SCREAMING_SNAKE_CASE : Tuple = pipe(
prompt=_SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE , guidance_scale=7.5 , num_inference_steps=2 , output_type='numpy' ).images
assert np.abs(image - new_image ).sum() < 1E-5, "Models don't have the same forward pass"
def _lowerCAmelCase ( self : Dict ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = VersatileDiffusionTextToImagePipeline.from_pretrained(
'shi-labs/versatile-diffusion' , torch_dtype=torch.floataa )
pipe.to(_SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE : Optional[int] = 'A painting of a squirrel eating a burger '
SCREAMING_SNAKE_CASE : Dict = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Any = pipe(
prompt=_SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE , guidance_scale=7.5 , num_inference_steps=50 , output_type='numpy' ).images
SCREAMING_SNAKE_CASE : Tuple = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : Union[str, Any] = np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 265
| 1
|
def A_ ( snake_case : int ) -> bool:
'''simple docstring'''
if num < 0:
return False
__UpperCamelCase = num
__UpperCamelCase = 0
while num > 0:
__UpperCamelCase = rev_num * 10 + (num % 10)
num //= 10
return num_copy == rev_num
if __name__ == "__main__":
import doctest
doctest.testmod()
| 451
|
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
"""simple docstring"""
_snake_case = FlaxAutoencoderKL
@property
def A__ ( self )-> int:
'''simple docstring'''
__UpperCamelCase = 4
__UpperCamelCase = 3
__UpperCamelCase = (32, 32)
__UpperCamelCase = jax.random.PRNGKey(0 )
__UpperCamelCase = jax.random.uniform(SCREAMING_SNAKE_CASE_ , ((batch_size, num_channels) + sizes) )
return {"sample": image, "prng_key": prng_key}
def A__ ( self )-> List[Any]:
'''simple docstring'''
__UpperCamelCase = {
'''block_out_channels''': [32, 64],
'''in_channels''': 3,
'''out_channels''': 3,
'''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
'''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
'''latent_channels''': 4,
}
__UpperCamelCase = self.dummy_input
return init_dict, inputs_dict
| 451
| 1
|
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
SCREAMING_SNAKE_CASE = logging.getLogger(__name__)
SCREAMING_SNAKE_CASE = 5_0 # max width of layer names
SCREAMING_SNAKE_CASE = 7_0 # max width of quantizer names
def a (lowerCAmelCase__ ):
__a = parser.add_argument_group("""quant_trainer arguments""" )
group.add_argument("""--wprec""" , type=lowerCAmelCase__ , default=8 , help="""weight precision""" )
group.add_argument("""--aprec""" , type=lowerCAmelCase__ , default=8 , help="""activation precision""" )
group.add_argument("""--quant-per-tensor""" , action="""store_true""" , help="""per tensor weight scaling""" )
group.add_argument("""--quant-disable""" , action="""store_true""" , help="""disable all quantizers""" )
group.add_argument("""--quant-disable-embeddings""" , action="""store_true""" , help="""disable all embeddings quantizers""" )
group.add_argument("""--quant-disable-keyword""" , type=lowerCAmelCase__ , nargs="""+""" , help="""disable quantizers by keyword""" )
group.add_argument("""--quant-disable-layer-module""" , type=lowerCAmelCase__ , help="""disable quantizers by keyword under layer.""" )
group.add_argument("""--quant-enable-layer-module""" , type=lowerCAmelCase__ , help="""enable quantizers by keyword under layer""" )
group.add_argument("""--calibrator""" , default="""max""" , help="""which quantization range calibrator to use""" )
group.add_argument("""--percentile""" , default=lowerCAmelCase__ , type=lowerCAmelCase__ , help="""percentile for PercentileCalibrator""" )
group.add_argument("""--fuse-qkv""" , action="""store_true""" , help="""use the same scale factor for qkv""" )
group.add_argument("""--clip-gelu""" , metavar="""N""" , type=lowerCAmelCase__ , help="""clip gelu output maximum value to N""" )
group.add_argument(
"""--recalibrate-weights""" , action="""store_true""" , help=(
"""recalibrate weight amaxes by taking the max of the weights."""
""" amaxes will be computed with the current quantization granularity (axis)."""
) , )
def a (lowerCAmelCase__ ):
if args.calibrator == "max":
__a = """max"""
elif args.calibrator == "percentile":
if args.percentile is None:
raise ValueError("""Specify --percentile when using percentile calibrator""" )
__a = """histogram"""
elif args.calibrator == "mse":
__a = """histogram"""
else:
raise ValueError(f'''Invalid calibrator {args.calibrator}''' )
__a = QuantDescriptor(num_bits=args.aprec , calib_method=lowerCAmelCase__ )
__a = QuantDescriptor(num_bits=args.wprec , axis=(None if args.quant_per_tensor else (0,)) )
quant_nn.QuantLinear.set_default_quant_desc_input(lowerCAmelCase__ )
quant_nn.QuantLinear.set_default_quant_desc_weight(lowerCAmelCase__ )
def a (lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__=False , lowerCAmelCase__=False ):
logger.info("""Configuring Model for Quantization""" )
logger.info(f'''using quantization package {pytorch_quantization.__file__}''' )
if not calib:
if args.quant_disable_embeddings:
set_quantizer_by_name(lowerCAmelCase__ , ["""embeddings"""] , which="""weight""" , _disabled=lowerCAmelCase__ )
if args.quant_disable:
set_quantizer_by_name(lowerCAmelCase__ , [""""""] , _disabled=lowerCAmelCase__ )
if args.quant_disable_keyword:
set_quantizer_by_name(lowerCAmelCase__ , args.quant_disable_keyword , _disabled=lowerCAmelCase__ )
if args.quant_disable_layer_module:
set_quantizer_by_name(lowerCAmelCase__ , [r"""layer.\d+.""" + args.quant_disable_layer_module] , _disabled=lowerCAmelCase__ )
if args.quant_enable_layer_module:
set_quantizer_by_name(lowerCAmelCase__ , [r"""layer.\d+.""" + args.quant_enable_layer_module] , _disabled=lowerCAmelCase__ )
if args.recalibrate_weights:
recalibrate_weights(lowerCAmelCase__ )
if args.fuse_qkv:
fuse_qkv(lowerCAmelCase__ , lowerCAmelCase__ )
if args.clip_gelu:
clip_gelu(lowerCAmelCase__ , args.clip_gelu )
# if args.local_rank in [-1, 0] and not calib:
print_quant_summary(lowerCAmelCase__ )
def a (lowerCAmelCase__ ):
logger.info("""Enabling Calibration""" )
for name, module in model.named_modules():
if name.endswith("""_quantizer""" ):
if module._calibrator is not None:
module.disable_quant()
module.enable_calib()
else:
module.disable()
logger.info(f'''{name:80}: {module}''' )
def a (lowerCAmelCase__ , lowerCAmelCase__ ):
logger.info("""Loading calibrated amax""" )
for name, module in model.named_modules():
if name.endswith("""_quantizer""" ):
if module._calibrator is not None:
if isinstance(module._calibrator , calib.MaxCalibrator ):
module.load_calib_amax()
else:
module.load_calib_amax("""percentile""" , percentile=args.percentile )
module.enable_quant()
module.disable_calib()
else:
module.enable()
model.cuda()
print_quant_summary(lowerCAmelCase__ )
def a (lowerCAmelCase__ , lowerCAmelCase__ ):
def fusea(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
for mod in [qq, qk, qv]:
if not hasattr(lowerCAmelCase__ , """_amax""" ):
print(""" WARNING: NO AMAX BUFFER""" )
return
__a = qq._amax.detach().item()
__a = qk._amax.detach().item()
__a = qv._amax.detach().item()
__a = max(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
qq._amax.fill_(lowerCAmelCase__ )
qk._amax.fill_(lowerCAmelCase__ )
qv._amax.fill_(lowerCAmelCase__ )
logger.info(f''' q={q:5.2f} k={k:5.2f} v={v:5.2f} -> {amax:5.2f}''' )
for name, mod in model.named_modules():
if name.endswith(""".attention.self""" ):
logger.info(f'''FUSE_QKV: {name:{name_width}}''' )
fusea(mod.matmul_q_input_quantizer , mod.matmul_k_input_quantizer , mod.matmul_v_input_quantizer )
if args.quant_per_tensor:
fusea(mod.query._weight_quantizer , mod.key._weight_quantizer , mod.value._weight_quantizer )
def a (lowerCAmelCase__ , lowerCAmelCase__ ):
for name, mod in model.named_modules():
if name.endswith(""".output.dense""" ) and not name.endswith("""attention.output.dense""" ):
__a = mod._input_quantizer._amax.data.detach().item()
mod._input_quantizer._amax.data.detach().clamp_(max=lowerCAmelCase__ )
__a = mod._input_quantizer._amax.data.detach().item()
logger.info(f'''CLIP_GELU: {name:{name_width}} amax: {amax_init:5.2f} -> {amax:5.2f}''' )
def a (lowerCAmelCase__ ):
for name, mod in model.named_modules():
if hasattr(lowerCAmelCase__ , """_weight_quantizer""" ) and mod._weight_quantizer.axis is not None:
__a = mod.weight.shape[0]
__a = mod._weight_quantizer._amax.detach()
__a = torch.ones(lowerCAmelCase__ , dtype=amax.dtype , device=amax.device ) * amax
print(f'''expanding {name} {amax} -> {mod._weight_quantizer._amax}''' )
def a (lowerCAmelCase__ ):
for name, mod in model.named_modules():
if hasattr(lowerCAmelCase__ , """_weight_quantizer""" ):
if not hasattr(mod.weight_quantizer , """_amax""" ):
print("""RECALIB: {name:{name_width}} WARNING: NO AMAX BUFFER""" )
continue
# determine which axes to reduce across
# e.g. a 4D tensor quantized per axis 0 should reduce over (1,2,3)
__a = set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis )
__a = set(range(len(mod.weight.size() ) ) ) - axis_set
__a = pytorch_quantization.utils.reduce_amax(mod.weight , axis=lowerCAmelCase__ , keepdims=lowerCAmelCase__ ).detach()
logger.info(f'''RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}''' )
__a = amax
def a (lowerCAmelCase__ , lowerCAmelCase__=25 , lowerCAmelCase__=180 , lowerCAmelCase__=None ):
if ignore is None:
__a = []
elif not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
__a = [ignore]
__a = 0
for name, mod in model.named_modules():
if not hasattr(lowerCAmelCase__ , """weight""" ):
continue
__a = max(lowerCAmelCase__ , len(lowerCAmelCase__ ) )
for name, mod in model.named_modules():
__a = getattr(lowerCAmelCase__ , """_input_quantizer""" , lowerCAmelCase__ )
__a = getattr(lowerCAmelCase__ , """_weight_quantizer""" , lowerCAmelCase__ )
if not hasattr(lowerCAmelCase__ , """weight""" ):
continue
if type(lowerCAmelCase__ ) in ignore:
continue
if [True for s in ignore if type(lowerCAmelCase__ ) is str and s in name]:
continue
__a = f'''Act:{input_q.extra_repr()}'''
__a = f'''Wgt:{weight_q.extra_repr()}'''
__a = f'''{name:{name_width}} {act_str} {wgt_str}'''
if len(lowerCAmelCase__ ) <= line_width:
logger.info(lowerCAmelCase__ )
else:
logger.info(f'''{name:{name_width}} {act_str}''' )
logger.info(f'''{' ':{name_width}} {wgt_str}''' )
def a (lowerCAmelCase__ ):
__a = 0
for name, mod in model.named_modules():
if isinstance(lowerCAmelCase__ , pytorch_quantization.nn.TensorQuantizer ):
print(f'''{name:80} {mod}''' )
count += 1
print(f'''{count} TensorQuantizers found in model''' )
def a (lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
__a = getattr(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
if quantizer_mod is not None:
assert hasattr(lowerCAmelCase__ , lowerCAmelCase__ )
setattr(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
else:
logger.warning(f'''{name} has no {quantizer}''' )
def a (lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__="both" , **lowerCAmelCase__ ):
__a = f'''Warning: changing {which} quantizers of {name:{qname_width}}'''
for k, v in kwargs.items():
s += f''' {k}={v}'''
if which in ["input", "both"]:
set_quantizer(lowerCAmelCase__ , lowerCAmelCase__ , """_input_quantizer""" , lowerCAmelCase__ , lowerCAmelCase__ )
if which in ["weight", "both"]:
set_quantizer(lowerCAmelCase__ , lowerCAmelCase__ , """_weight_quantizer""" , lowerCAmelCase__ , lowerCAmelCase__ )
logger.info(lowerCAmelCase__ )
def a (lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ ):
for name, mod in model.named_modules():
if hasattr(lowerCAmelCase__ , """_input_quantizer""" ) or hasattr(lowerCAmelCase__ , """_weight_quantizer""" ):
for n in names:
if re.search(lowerCAmelCase__ , lowerCAmelCase__ ):
set_quantizers(lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ )
elif name.endswith("""_quantizer""" ):
for n in names:
if re.search(lowerCAmelCase__ , lowerCAmelCase__ ):
__a = f'''Warning: changing {name:{name_width}}'''
for k, v in kwargs.items():
s += f''' {k}={v}'''
setattr(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
logger.info(lowerCAmelCase__ )
| 99
|
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
_snake_case : List[str] = logging.get_logger(__name__)
class a (_lowerCAmelCase ):
"""simple docstring"""
def __init__( self : List[str] , *lowerCamelCase : str , **lowerCamelCase : Union[str, Any] ) -> None:
warnings.warn(
"The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
" use DeiTImageProcessor instead." , lowerCamelCase , )
super().__init__(*lowerCamelCase , **lowerCamelCase )
| 81
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE = {
'configuration_funnel': ['FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FunnelConfig'],
'convert_funnel_original_tf_checkpoint_to_pytorch': [],
'tokenization_funnel': ['FunnelTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE = ['FunnelTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE = [
'FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST',
'FunnelBaseModel',
'FunnelForMaskedLM',
'FunnelForMultipleChoice',
'FunnelForPreTraining',
'FunnelForQuestionAnswering',
'FunnelForSequenceClassification',
'FunnelForTokenClassification',
'FunnelModel',
'FunnelPreTrainedModel',
'load_tf_weights_in_funnel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE = [
'TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFFunnelBaseModel',
'TFFunnelForMaskedLM',
'TFFunnelForMultipleChoice',
'TFFunnelForPreTraining',
'TFFunnelForQuestionAnswering',
'TFFunnelForSequenceClassification',
'TFFunnelForTokenClassification',
'TFFunnelModel',
'TFFunnelPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
from .tokenization_funnel import FunnelTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_funnel_fast import FunnelTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_funnel import (
FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
FunnelBaseModel,
FunnelForMaskedLM,
FunnelForMultipleChoice,
FunnelForPreTraining,
FunnelForQuestionAnswering,
FunnelForSequenceClassification,
FunnelForTokenClassification,
FunnelModel,
FunnelPreTrainedModel,
load_tf_weights_in_funnel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_funnel import (
TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFFunnelBaseModel,
TFFunnelForMaskedLM,
TFFunnelForMultipleChoice,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForSequenceClassification,
TFFunnelForTokenClassification,
TFFunnelModel,
TFFunnelPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 707
|
"""simple docstring"""
def __lowerCAmelCase( __UpperCAmelCase ):
"""simple docstring"""
_lowercase : str = [0 for i in range(len(__UpperCAmelCase ) )]
# initialize interval's left pointer and right pointer
_lowercase , _lowercase : str = 0, 0
for i in range(1 ,len(__UpperCAmelCase ) ):
# case when current index is inside the interval
if i <= right_pointer:
_lowercase : Union[str, Any] = min(right_pointer - i + 1 ,z_result[i - left_pointer] )
_lowercase : Any = min_edge
while go_next(__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ):
z_result[i] += 1
# if new index's result gives us more right interval,
# we've to update left_pointer and right_pointer
if i + z_result[i] - 1 > right_pointer:
_lowercase , _lowercase : Optional[int] = i, i + z_result[i] - 1
return z_result
def __lowerCAmelCase( __UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ):
"""simple docstring"""
return i + z_result[i] < len(__UpperCAmelCase ) and s[z_result[i]] == s[i + z_result[i]]
def __lowerCAmelCase( __UpperCAmelCase ,__UpperCAmelCase ):
"""simple docstring"""
_lowercase : Union[str, Any] = 0
# concatenate 'pattern' and 'input_str' and call z_function
# with concatenated string
_lowercase : List[str] = z_function(pattern + input_str )
for val in z_result:
# if value is greater then length of the pattern string
# that means this index is starting position of substring
# which is equal to pattern string
if val >= len(__UpperCAmelCase ):
answer += 1
return answer
if __name__ == "__main__":
import doctest
doctest.testmod()
| 283
| 0
|
'''simple docstring'''
def _a (lowercase__ : str ) -> bool:
"""simple docstring"""
__snake_case = 0
for ch in input_str:
__snake_case = ord(lowercase__ )
__snake_case = pow(2 , lowercase__ )
# If we already turned on bit for current character's unicode
if bitmap >> ch_unicode & 1 == 1:
return False
bitmap |= ch_bit_index_on
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
| 56
|
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
from accelerate.utils import ComputeEnvironment
from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment # noqa: F401
from .sagemaker import get_sagemaker_input
_a : str = "Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine"
def _a () -> Dict:
"""simple docstring"""
__snake_case = _ask_options(
'In which compute environment are you running?' , ['This machine', 'AWS (Amazon SageMaker)'] , _convert_compute_environment , )
if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
__snake_case = get_sagemaker_input()
else:
__snake_case = get_cluster_input()
return config
def _a (lowercase__ : Union[str, Any]=None ) -> int:
"""simple docstring"""
if subparsers is not None:
__snake_case = subparsers.add_parser('config' , description=lowercase__ )
else:
__snake_case = argparse.ArgumentParser('Accelerate config command' , description=lowercase__ )
parser.add_argument(
'--config_file' , default=lowercase__ , help=(
'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
'with \'huggingface\'.'
) , )
if subparsers is not None:
parser.set_defaults(func=lowercase__ )
return parser
def _a (lowercase__ : List[str] ) -> Union[str, Any]:
"""simple docstring"""
__snake_case = get_user_input()
if args.config_file is not None:
__snake_case = args.config_file
else:
if not os.path.isdir(lowercase__ ):
os.makedirs(lowercase__ )
__snake_case = default_yaml_config_file
if config_file.endswith('.json' ):
config.to_json_file(lowercase__ )
else:
config.to_yaml_file(lowercase__ )
print(f'accelerate configuration saved at {config_file}' )
def _a () -> int:
"""simple docstring"""
__snake_case = config_command_parser()
__snake_case = parser.parse_args()
config_command(lowercase__ )
if __name__ == "__main__":
main()
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from math import sqrt
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> int:
'''simple docstring'''
__UpperCAmelCase : Dict = 0
for i in range(1 , int(sqrt(lowercase_ ) + 1 ) ):
if n % i == 0 and i != sqrt(lowercase_ ):
total += i + n // i
elif i == sqrt(lowercase_ ):
total += i
return total - n
def __SCREAMING_SNAKE_CASE ( lowercase_ = 10000 ) -> int:
'''simple docstring'''
__UpperCAmelCase : Tuple = sum(
i
for i in range(1 , lowercase_ )
if sum_of_divisors(sum_of_divisors(lowercase_ ) ) == i and sum_of_divisors(lowercase_ ) != i )
return total
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 706
|
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
lowerCAmelCase = """https://www.indeed.co.in/jobs?q=mobile+app+development&l="""
def __SCREAMING_SNAKE_CASE ( lowercase_ = "mumbai" ) -> Generator[tuple[str, str], None, None]:
'''simple docstring'''
__UpperCAmelCase : List[Any] = BeautifulSoup(requests.get(url + location ).content , '''html.parser''' )
# This attribute finds out all the specifics listed in a job
for job in soup.find_all('''div''' , attrs={'''data-tn-component''': '''organicJob'''} ):
__UpperCAmelCase : str = job.find('''a''' , attrs={'''data-tn-element''': '''jobTitle'''} ).text.strip()
__UpperCAmelCase : List[str] = job.find('''span''' , {'''class''': '''company'''} ).text.strip()
yield job_title, company_name
if __name__ == "__main__":
for i, job in enumerate(fetch_jobs("""Bangalore"""), 1):
print(F'Job {i:>2} is {job[0]} at {job[1]}')
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|
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : Optional[Any]) -> Dict:
'''simple docstring'''
if "cls_token" in name:
_lowercase : List[str] = name.replace('cls_token' , 'vit.embeddings.cls_token')
if "mask_token" in name:
_lowercase : Optional[Any] = name.replace('mask_token' , 'decoder.mask_token')
if "decoder_pos_embed" in name:
_lowercase : Tuple = name.replace('decoder_pos_embed' , 'decoder.decoder_pos_embed')
if "pos_embed" in name and "decoder" not in name:
_lowercase : List[Any] = name.replace('pos_embed' , 'vit.embeddings.position_embeddings')
if "patch_embed.proj" in name:
_lowercase : Tuple = name.replace('patch_embed.proj' , 'vit.embeddings.patch_embeddings.projection')
if "patch_embed.norm" in name:
_lowercase : Dict = name.replace('patch_embed.norm' , 'vit.embeddings.norm')
if "decoder_blocks" in name:
_lowercase : List[Any] = name.replace('decoder_blocks' , 'decoder.decoder_layers')
if "blocks" in name:
_lowercase : List[str] = name.replace('blocks' , 'vit.encoder.layer')
if "attn.proj" in name:
_lowercase : Optional[Any] = name.replace('attn.proj' , 'attention.output.dense')
if "attn" in name:
_lowercase : Tuple = name.replace('attn' , 'attention.self')
if "norm1" in name:
_lowercase : Any = name.replace('norm1' , 'layernorm_before')
if "norm2" in name:
_lowercase : str = name.replace('norm2' , 'layernorm_after')
if "mlp.fc1" in name:
_lowercase : int = name.replace('mlp.fc1' , 'intermediate.dense')
if "mlp.fc2" in name:
_lowercase : Optional[Any] = name.replace('mlp.fc2' , 'output.dense')
if "decoder_embed" in name:
_lowercase : List[str] = name.replace('decoder_embed' , 'decoder.decoder_embed')
if "decoder_norm" in name:
_lowercase : List[str] = name.replace('decoder_norm' , 'decoder.decoder_norm')
if "decoder_pred" in name:
_lowercase : List[str] = name.replace('decoder_pred' , 'decoder.decoder_pred')
if "norm.weight" in name and "decoder" not in name:
_lowercase : List[str] = name.replace('norm.weight' , 'vit.layernorm.weight')
if "norm.bias" in name and "decoder" not in name:
_lowercase : Union[str, Any] = name.replace('norm.bias' , 'vit.layernorm.bias')
return name
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : Any) -> List[str]:
'''simple docstring'''
for key in orig_state_dict.copy().keys():
_lowercase : Tuple = orig_state_dict.pop(lowerCAmelCase__)
if "qkv" in key:
_lowercase : Tuple = key.split('.')
_lowercase : Optional[int] = int(key_split[1])
if "decoder_blocks" in key:
_lowercase : List[Any] = config.decoder_hidden_size
_lowercase : Any = 'decoder.decoder_layers.'
if "weight" in key:
_lowercase : Dict = val[:dim, :]
_lowercase : Any = val[dim : dim * 2, :]
_lowercase : Optional[int] = val[-dim:, :]
elif "bias" in key:
_lowercase : Union[str, Any] = val[:dim]
_lowercase : Dict = val[dim : dim * 2]
_lowercase : int = val[-dim:]
else:
_lowercase : Optional[int] = config.hidden_size
_lowercase : str = 'vit.encoder.layer.'
if "weight" in key:
_lowercase : str = val[:dim, :]
_lowercase : Dict = val[dim : dim * 2, :]
_lowercase : Tuple = val[-dim:, :]
elif "bias" in key:
_lowercase : str = val[:dim]
_lowercase : str = val[dim : dim * 2]
_lowercase : Any = val[-dim:]
else:
_lowercase : List[str] = val
return orig_state_dict
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : List[str] , lowerCAmelCase__ : Optional[int]) -> str:
'''simple docstring'''
_lowercase : List[Any] = ViTMAEConfig()
if "large" in checkpoint_url:
_lowercase : str = 10_24
_lowercase : str = 40_96
_lowercase : List[Any] = 24
_lowercase : Optional[Any] = 16
elif "huge" in checkpoint_url:
_lowercase : int = 14
_lowercase : Optional[int] = 12_80
_lowercase : Optional[Any] = 51_20
_lowercase : Optional[Any] = 32
_lowercase : Dict = 16
_lowercase : Dict = ViTMAEForPreTraining(lowerCAmelCase__)
_lowercase : Union[str, Any] = torch.hub.load_state_dict_from_url(lowerCAmelCase__ , map_location='cpu')['model']
_lowercase : Union[str, Any] = ViTMAEImageProcessor(size=config.image_size)
_lowercase : str = convert_state_dict(lowerCAmelCase__ , lowerCAmelCase__)
model.load_state_dict(lowerCAmelCase__)
model.eval()
_lowercase : Optional[Any] = 'https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg'
_lowercase : Tuple = Image.open(requests.get(lowerCAmelCase__ , stream=lowerCAmelCase__).raw)
_lowercase : str = ViTMAEImageProcessor(size=config.image_size)
_lowercase : Optional[int] = image_processor(images=lowerCAmelCase__ , return_tensors='pt')
# forward pass
torch.manual_seed(2)
_lowercase : Optional[int] = model(**lowerCAmelCase__)
_lowercase : Tuple = outputs.logits
if "large" in checkpoint_url:
_lowercase : List[str] = torch.tensor(
[[-0.7_3_0_9, -0.7_1_2_8, -1.0_1_6_9], [-1.0_1_6_1, -0.9_0_5_8, -1.1_8_7_8], [-1.0_4_7_8, -0.9_4_1_1, -1.1_9_1_1]])
elif "huge" in checkpoint_url:
_lowercase : str = torch.tensor(
[[-1.1_5_9_9, -0.9_1_9_9, -1.2_2_2_1], [-1.1_9_5_2, -0.9_2_6_9, -1.2_3_0_7], [-1.2_1_4_3, -0.9_3_3_7, -1.2_2_6_2]])
else:
_lowercase : Tuple = torch.tensor(
[[-0.9_1_9_2, -0.8_4_8_1, -1.1_2_5_9], [-1.1_3_4_9, -1.0_0_3_4, -1.2_5_9_9], [-1.1_7_5_7, -1.0_4_2_9, -1.2_7_2_6]])
# verify logits
assert torch.allclose(logits[0, :3, :3] , lowerCAmelCase__ , atol=1E-4)
print(F'''Saving model to {pytorch_dump_folder_path}''')
model.save_pretrained(lowerCAmelCase__)
print(F'''Saving image processor to {pytorch_dump_folder_path}''')
image_processor.save_pretrained(lowerCAmelCase__)
if __name__ == "__main__":
A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--checkpoint_url''',
default='''https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth''',
type=str,
help='''URL of the checkpoint you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
A = parser.parse_args()
convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
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|
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer
@dataclass
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase__ ):
'''simple docstring'''
lowerCAmelCase__ : torch.FloatTensor
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase__ , lowerCAmelCase__ ):
'''simple docstring'''
@register_to_config
def __init__( self : Optional[Any] ,UpperCamelCase : int = 3 ,UpperCamelCase : int = 3 ,UpperCamelCase : Tuple[str] = ("DownEncoderBlock2D",) ,UpperCamelCase : Tuple[str] = ("UpDecoderBlock2D",) ,UpperCamelCase : Tuple[int] = (64,) ,UpperCamelCase : int = 1 ,UpperCamelCase : str = "silu" ,UpperCamelCase : int = 3 ,UpperCamelCase : int = 32 ,UpperCamelCase : int = 256 ,UpperCamelCase : int = 32 ,UpperCamelCase : Optional[int] = None ,UpperCamelCase : float = 0.1_8_2_1_5 ,UpperCamelCase : str = "group" ,) -> List[Any]:
super().__init__()
# pass init params to Encoder
_lowercase : Any = Encoder(
in_channels=UpperCamelCase ,out_channels=UpperCamelCase ,down_block_types=UpperCamelCase ,block_out_channels=UpperCamelCase ,layers_per_block=UpperCamelCase ,act_fn=UpperCamelCase ,norm_num_groups=UpperCamelCase ,double_z=UpperCamelCase ,)
_lowercase : Tuple = vq_embed_dim if vq_embed_dim is not None else latent_channels
_lowercase : List[str] = nn.Convad(UpperCamelCase ,UpperCamelCase ,1 )
_lowercase : Dict = VectorQuantizer(UpperCamelCase ,UpperCamelCase ,beta=0.2_5 ,remap=UpperCamelCase ,sane_index_shape=UpperCamelCase )
_lowercase : List[str] = nn.Convad(UpperCamelCase ,UpperCamelCase ,1 )
# pass init params to Decoder
_lowercase : Optional[Any] = Decoder(
in_channels=UpperCamelCase ,out_channels=UpperCamelCase ,up_block_types=UpperCamelCase ,block_out_channels=UpperCamelCase ,layers_per_block=UpperCamelCase ,act_fn=UpperCamelCase ,norm_num_groups=UpperCamelCase ,norm_type=UpperCamelCase ,)
@apply_forward_hook
def _lowerCamelCase ( self : Optional[int] ,UpperCamelCase : torch.FloatTensor ,UpperCamelCase : bool = True ) -> VQEncoderOutput:
_lowercase : Optional[int] = self.encoder(UpperCamelCase )
_lowercase : Any = self.quant_conv(UpperCamelCase )
if not return_dict:
return (h,)
return VQEncoderOutput(latents=UpperCamelCase )
@apply_forward_hook
def _lowerCamelCase ( self : Optional[int] ,UpperCamelCase : torch.FloatTensor ,UpperCamelCase : bool = False ,UpperCamelCase : bool = True ) -> Union[DecoderOutput, torch.FloatTensor]:
# also go through quantization layer
if not force_not_quantize:
_lowercase , _lowercase , _lowercase : Optional[int] = self.quantize(UpperCamelCase )
else:
_lowercase : Tuple = h
_lowercase : Optional[int] = self.post_quant_conv(UpperCamelCase )
_lowercase : Dict = self.decoder(UpperCamelCase ,quant if self.config.norm_type == 'spatial' else None )
if not return_dict:
return (dec,)
return DecoderOutput(sample=UpperCamelCase )
def _lowerCamelCase ( self : Optional[Any] ,UpperCamelCase : torch.FloatTensor ,UpperCamelCase : bool = True ) -> Union[DecoderOutput, torch.FloatTensor]:
_lowercase : Dict = sample
_lowercase : Dict = self.encode(UpperCamelCase ).latents
_lowercase : str = self.decode(UpperCamelCase ).sample
if not return_dict:
return (dec,)
return DecoderOutput(sample=UpperCamelCase )
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"""simple docstring"""
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def _A (__a , __a , __a , __a ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = BigBirdConfig.from_json_file(__a )
print(f'Building PyTorch model from configuration: {config}' )
if is_trivia_qa:
SCREAMING_SNAKE_CASE_ : List[Any] = BigBirdForQuestionAnswering(__a )
else:
SCREAMING_SNAKE_CASE_ : Tuple = BigBirdForPreTraining(__a )
# Load weights from tf checkpoint
load_tf_weights_in_big_bird(__a , __a , is_trivia_qa=__a )
# Save pytorch-model
print(f'Save PyTorch model to {pytorch_dump_path}' )
model.save_pretrained(__a )
if __name__ == "__main__":
UpperCAmelCase_ : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
)
parser.add_argument(
"""--big_bird_config_file""",
default=None,
type=str,
required=True,
help=(
"""The config json file corresponding to the pre-trained BERT model. \n"""
"""This specifies the model architecture."""
),
)
parser.add_argument(
"""--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
parser.add_argument(
"""--is_trivia_qa""", action="""store_true""", help="""Whether to convert a model with a trivia_qa head."""
)
UpperCAmelCase_ : Optional[int] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
)
| 176
|
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ : List[Any] = logging.get_logger(__name__)
def _A (__a ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = DPTConfig(embedding_type='''hybrid''' )
if "large" in checkpoint_url:
SCREAMING_SNAKE_CASE_ : int = 10_24
SCREAMING_SNAKE_CASE_ : Dict = 40_96
SCREAMING_SNAKE_CASE_ : Optional[int] = 24
SCREAMING_SNAKE_CASE_ : Any = 16
SCREAMING_SNAKE_CASE_ : int = [5, 11, 17, 23]
SCREAMING_SNAKE_CASE_ : List[str] = [2_56, 5_12, 10_24, 10_24]
SCREAMING_SNAKE_CASE_ : Optional[int] = (1, 3_84, 3_84)
if "nyu" or "midas" in checkpoint_url:
SCREAMING_SNAKE_CASE_ : List[str] = 7_68
SCREAMING_SNAKE_CASE_ : Union[str, Any] = [1, 1, 1, 0.5]
SCREAMING_SNAKE_CASE_ : Optional[Any] = [2_56, 5_12, 7_68, 7_68]
SCREAMING_SNAKE_CASE_ : Union[str, Any] = 1_50
SCREAMING_SNAKE_CASE_ : str = 16
SCREAMING_SNAKE_CASE_ : Optional[int] = (1, 3_84, 3_84)
SCREAMING_SNAKE_CASE_ : int = False
SCREAMING_SNAKE_CASE_ : Tuple = '''project'''
if "ade" in checkpoint_url:
SCREAMING_SNAKE_CASE_ : str = True
SCREAMING_SNAKE_CASE_ : Dict = 7_68
SCREAMING_SNAKE_CASE_ : Tuple = [1, 1, 1, 0.5]
SCREAMING_SNAKE_CASE_ : List[str] = 1_50
SCREAMING_SNAKE_CASE_ : Union[str, Any] = 16
SCREAMING_SNAKE_CASE_ : Tuple = '''huggingface/label-files'''
SCREAMING_SNAKE_CASE_ : List[Any] = '''ade20k-id2label.json'''
SCREAMING_SNAKE_CASE_ : List[str] = json.load(open(cached_download(hf_hub_url(__a , __a , repo_type='''dataset''' ) ) , '''r''' ) )
SCREAMING_SNAKE_CASE_ : Dict = {int(__a ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE_ : Optional[int] = idalabel
SCREAMING_SNAKE_CASE_ : Optional[Any] = {v: k for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE_ : Any = [1, 1_50, 4_80, 4_80]
return config, expected_shape
def _A (__a ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = ['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
for k in ignore_keys:
state_dict.pop(__a , __a )
def _A (__a ) -> Optional[int]:
"""simple docstring"""
if (
"pretrained.model" in name
and "cls_token" not in name
and "pos_embed" not in name
and "patch_embed" not in name
):
SCREAMING_SNAKE_CASE_ : Optional[int] = name.replace('''pretrained.model''' , '''dpt.encoder''' )
if "pretrained.model" in name:
SCREAMING_SNAKE_CASE_ : List[str] = name.replace('''pretrained.model''' , '''dpt.embeddings''' )
if "patch_embed" in name:
SCREAMING_SNAKE_CASE_ : str = name.replace('''patch_embed''' , '''''' )
if "pos_embed" in name:
SCREAMING_SNAKE_CASE_ : Any = name.replace('''pos_embed''' , '''position_embeddings''' )
if "attn.proj" in name:
SCREAMING_SNAKE_CASE_ : Tuple = name.replace('''attn.proj''' , '''attention.output.dense''' )
if "proj" in name and "project" not in name:
SCREAMING_SNAKE_CASE_ : Dict = name.replace('''proj''' , '''projection''' )
if "blocks" in name:
SCREAMING_SNAKE_CASE_ : str = name.replace('''blocks''' , '''layer''' )
if "mlp.fc1" in name:
SCREAMING_SNAKE_CASE_ : List[str] = name.replace('''mlp.fc1''' , '''intermediate.dense''' )
if "mlp.fc2" in name:
SCREAMING_SNAKE_CASE_ : Tuple = name.replace('''mlp.fc2''' , '''output.dense''' )
if "norm1" in name and "backbone" not in name:
SCREAMING_SNAKE_CASE_ : Optional[Any] = name.replace('''norm1''' , '''layernorm_before''' )
if "norm2" in name and "backbone" not in name:
SCREAMING_SNAKE_CASE_ : Tuple = name.replace('''norm2''' , '''layernorm_after''' )
if "scratch.output_conv" in name:
SCREAMING_SNAKE_CASE_ : Tuple = name.replace('''scratch.output_conv''' , '''head''' )
if "scratch" in name:
SCREAMING_SNAKE_CASE_ : List[Any] = name.replace('''scratch''' , '''neck''' )
if "layer1_rn" in name:
SCREAMING_SNAKE_CASE_ : List[str] = name.replace('''layer1_rn''' , '''convs.0''' )
if "layer2_rn" in name:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = name.replace('''layer2_rn''' , '''convs.1''' )
if "layer3_rn" in name:
SCREAMING_SNAKE_CASE_ : int = name.replace('''layer3_rn''' , '''convs.2''' )
if "layer4_rn" in name:
SCREAMING_SNAKE_CASE_ : Optional[int] = name.replace('''layer4_rn''' , '''convs.3''' )
if "refinenet" in name:
SCREAMING_SNAKE_CASE_ : Dict = int(name[len('''neck.refinenet''' ) : len('''neck.refinenet''' ) + 1] )
# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
SCREAMING_SNAKE_CASE_ : int = name.replace(f'refinenet{layer_idx}' , f'fusion_stage.layers.{abs(layer_idx-4 )}' )
if "out_conv" in name:
SCREAMING_SNAKE_CASE_ : List[str] = name.replace('''out_conv''' , '''projection''' )
if "resConfUnit1" in name:
SCREAMING_SNAKE_CASE_ : str = name.replace('''resConfUnit1''' , '''residual_layer1''' )
if "resConfUnit2" in name:
SCREAMING_SNAKE_CASE_ : str = name.replace('''resConfUnit2''' , '''residual_layer2''' )
if "conv1" in name:
SCREAMING_SNAKE_CASE_ : Any = name.replace('''conv1''' , '''convolution1''' )
if "conv2" in name:
SCREAMING_SNAKE_CASE_ : int = name.replace('''conv2''' , '''convolution2''' )
# readout blocks
if "pretrained.act_postprocess1.0.project.0" in name:
SCREAMING_SNAKE_CASE_ : Dict = name.replace('''pretrained.act_postprocess1.0.project.0''' , '''neck.reassemble_stage.readout_projects.0.0''' )
if "pretrained.act_postprocess2.0.project.0" in name:
SCREAMING_SNAKE_CASE_ : List[str] = name.replace('''pretrained.act_postprocess2.0.project.0''' , '''neck.reassemble_stage.readout_projects.1.0''' )
if "pretrained.act_postprocess3.0.project.0" in name:
SCREAMING_SNAKE_CASE_ : List[Any] = name.replace('''pretrained.act_postprocess3.0.project.0''' , '''neck.reassemble_stage.readout_projects.2.0''' )
if "pretrained.act_postprocess4.0.project.0" in name:
SCREAMING_SNAKE_CASE_ : Tuple = name.replace('''pretrained.act_postprocess4.0.project.0''' , '''neck.reassemble_stage.readout_projects.3.0''' )
# resize blocks
if "pretrained.act_postprocess1.3" in name:
SCREAMING_SNAKE_CASE_ : Tuple = name.replace('''pretrained.act_postprocess1.3''' , '''neck.reassemble_stage.layers.0.projection''' )
if "pretrained.act_postprocess1.4" in name:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = name.replace('''pretrained.act_postprocess1.4''' , '''neck.reassemble_stage.layers.0.resize''' )
if "pretrained.act_postprocess2.3" in name:
SCREAMING_SNAKE_CASE_ : Optional[Any] = name.replace('''pretrained.act_postprocess2.3''' , '''neck.reassemble_stage.layers.1.projection''' )
if "pretrained.act_postprocess2.4" in name:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = name.replace('''pretrained.act_postprocess2.4''' , '''neck.reassemble_stage.layers.1.resize''' )
if "pretrained.act_postprocess3.3" in name:
SCREAMING_SNAKE_CASE_ : str = name.replace('''pretrained.act_postprocess3.3''' , '''neck.reassemble_stage.layers.2.projection''' )
if "pretrained.act_postprocess4.3" in name:
SCREAMING_SNAKE_CASE_ : Dict = name.replace('''pretrained.act_postprocess4.3''' , '''neck.reassemble_stage.layers.3.projection''' )
if "pretrained.act_postprocess4.4" in name:
SCREAMING_SNAKE_CASE_ : Optional[int] = name.replace('''pretrained.act_postprocess4.4''' , '''neck.reassemble_stage.layers.3.resize''' )
if "pretrained" in name:
SCREAMING_SNAKE_CASE_ : Any = name.replace('''pretrained''' , '''dpt''' )
if "bn" in name:
SCREAMING_SNAKE_CASE_ : Optional[int] = name.replace('''bn''' , '''batch_norm''' )
if "head" in name:
SCREAMING_SNAKE_CASE_ : int = name.replace('''head''' , '''head.head''' )
if "encoder.norm" in name:
SCREAMING_SNAKE_CASE_ : Any = name.replace('''encoder.norm''' , '''layernorm''' )
if "auxlayer" in name:
SCREAMING_SNAKE_CASE_ : int = name.replace('''auxlayer''' , '''auxiliary_head.head''' )
if "backbone" in name:
SCREAMING_SNAKE_CASE_ : Optional[Any] = name.replace('''backbone''' , '''backbone.bit.encoder''' )
if ".." in name:
SCREAMING_SNAKE_CASE_ : List[str] = name.replace('''..''' , '''.''' )
if "stem.conv" in name:
SCREAMING_SNAKE_CASE_ : List[Any] = name.replace('''stem.conv''' , '''bit.embedder.convolution''' )
if "blocks" in name:
SCREAMING_SNAKE_CASE_ : Optional[int] = name.replace('''blocks''' , '''layers''' )
if "convolution" in name and "backbone" in name:
SCREAMING_SNAKE_CASE_ : List[Any] = name.replace('''convolution''' , '''conv''' )
if "layer" in name and "backbone" in name:
SCREAMING_SNAKE_CASE_ : List[str] = name.replace('''layer''' , '''layers''' )
if "backbone.bit.encoder.bit" in name:
SCREAMING_SNAKE_CASE_ : int = name.replace('''backbone.bit.encoder.bit''' , '''backbone.bit''' )
if "embedder.conv" in name:
SCREAMING_SNAKE_CASE_ : Optional[int] = name.replace('''embedder.conv''' , '''embedder.convolution''' )
if "backbone.bit.encoder.stem.norm" in name:
SCREAMING_SNAKE_CASE_ : Optional[int] = name.replace('''backbone.bit.encoder.stem.norm''' , '''backbone.bit.embedder.norm''' )
return name
def _A (__a , __a ) -> Dict:
"""simple docstring"""
for i in range(config.num_hidden_layers ):
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
SCREAMING_SNAKE_CASE_ : Dict = state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.weight' )
SCREAMING_SNAKE_CASE_ : Optional[int] = state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.bias' )
# next, add query, keys and values (in that order) to the state dict
SCREAMING_SNAKE_CASE_ : Optional[Any] = in_proj_weight[: config.hidden_size, :]
SCREAMING_SNAKE_CASE_ : Optional[Any] = in_proj_bias[: config.hidden_size]
SCREAMING_SNAKE_CASE_ : Optional[int] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
SCREAMING_SNAKE_CASE_ : int = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
SCREAMING_SNAKE_CASE_ : List[str] = in_proj_weight[
-config.hidden_size :, :
]
SCREAMING_SNAKE_CASE_ : Any = in_proj_bias[-config.hidden_size :]
def _A () -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
SCREAMING_SNAKE_CASE_ : Optional[int] = Image.open(requests.get(__a , stream=__a ).raw )
return im
@torch.no_grad()
def _A (__a , __a , __a , __a , __a ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : int = get_dpt_config(__a )
# load original state_dict from URL
# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
SCREAMING_SNAKE_CASE_ : int = torch.load(__a , map_location='''cpu''' )
# remove certain keys
remove_ignore_keys_(__a )
# rename keys
for key in state_dict.copy().keys():
SCREAMING_SNAKE_CASE_ : List[Any] = state_dict.pop(__a )
SCREAMING_SNAKE_CASE_ : Dict = val
# read in qkv matrices
read_in_q_k_v(__a , __a )
# load HuggingFace model
SCREAMING_SNAKE_CASE_ : Union[str, Any] = DPTForSemanticSegmentation(__a ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(__a )
model.load_state_dict(__a )
model.eval()
# Check outputs on an image
SCREAMING_SNAKE_CASE_ : Dict = 4_80 if '''ade''' in checkpoint_url else 3_84
SCREAMING_SNAKE_CASE_ : Optional[Any] = DPTImageProcessor(size=__a )
SCREAMING_SNAKE_CASE_ : Optional[int] = prepare_img()
SCREAMING_SNAKE_CASE_ : Tuple = image_processor(__a , return_tensors='''pt''' )
# forward pass
SCREAMING_SNAKE_CASE_ : Optional[Any] = model(**__a ).logits if '''ade''' in checkpoint_url else model(**__a ).predicted_depth
if show_prediction:
SCREAMING_SNAKE_CASE_ : List[Any] = (
torch.nn.functional.interpolate(
outputs.unsqueeze(1 ) , size=(image.size[1], image.size[0]) , mode='''bicubic''' , align_corners=__a , )
.squeeze()
.cpu()
.numpy()
)
Image.fromarray((prediction / prediction.max()) * 2_55 ).show()
if pytorch_dump_folder_path is not None:
Path(__a ).mkdir(exist_ok=__a )
print(f'Saving model to {pytorch_dump_folder_path}' )
model.save_pretrained(__a )
print(f'Saving image processor to {pytorch_dump_folder_path}' )
image_processor.save_pretrained(__a )
if push_to_hub:
model.push_to_hub('''ybelkada/dpt-hybrid-midas''' )
image_processor.push_to_hub('''ybelkada/dpt-hybrid-midas''' )
if __name__ == "__main__":
UpperCAmelCase_ : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--checkpoint_url""",
default="""https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt""",
type=str,
help="""URL of the original DPT checkpoint you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default=None,
type=str,
required=False,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument(
"""--push_to_hub""",
action="""store_true""",
)
parser.add_argument(
"""--model_name""",
default="""dpt-large""",
type=str,
help="""Name of the model, in case you're pushing to the hub.""",
)
parser.add_argument(
"""--show_prediction""",
action="""store_true""",
)
UpperCAmelCase_ : List[Any] = parser.parse_args()
convert_dpt_checkpoint(
args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
)
| 176
| 1
|
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
if is_tf_available():
import tensorflow as tf
if is_tensorflow_text_available():
from transformers.models.bert import TFBertTokenizer
_snake_case : Union[str, Any] = ["bert-base-uncased", "bert-base-cased"]
_snake_case : Dict = "hf-internal-testing/tiny-bert-tf-only"
if is_tf_available():
class a (tf.keras.Model ):
"""simple docstring"""
def __init__( self : List[Any] , lowerCamelCase : List[Any] ) -> Dict:
super().__init__()
__snake_case : List[Any] = tokenizer
__snake_case : Dict = AutoConfig.from_pretrained(lowerCamelCase )
__snake_case : Dict = TFAutoModel.from_config(lowerCamelCase )
def __snake_case ( self : Optional[int] , lowerCamelCase : Optional[Any] ) -> Optional[int]:
__snake_case : int = self.tokenizer(lowerCamelCase )
__snake_case : Dict = self.bert(**lowerCamelCase )
return out["pooler_output"]
@require_tf
@require_tensorflow_text
class a (unittest.TestCase ):
"""simple docstring"""
def __snake_case ( self : Union[str, Any] ) -> Optional[Any]:
super().setUp()
__snake_case : str = [
BertTokenizer.from_pretrained(lowerCamelCase ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
] # repeat for when fast_bert_tokenizer=false
__snake_case : str = [TFBertTokenizer.from_pretrained(lowerCamelCase ) for checkpoint in TOKENIZER_CHECKPOINTS] + [
TFBertTokenizer.from_pretrained(lowerCamelCase , use_fast_bert_tokenizer=lowerCamelCase )
for checkpoint in TOKENIZER_CHECKPOINTS
]
assert len(self.tokenizers ) == len(self.tf_tokenizers )
__snake_case : Optional[int] = [
"This is a straightforward English test sentence.",
"This one has some weird characters\rto\nsee\r\nif those\u00E9break things.",
"Now we're going to add some Chinese: 一 二 三 一二三",
"And some much more rare Chinese: 齉 堃 齉堃",
"Je vais aussi écrire en français pour tester les accents",
"Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ",
]
__snake_case : Tuple = list(zip(self.test_sentences , self.test_sentences[::-1] ) )
def __snake_case ( self : Tuple ) -> str:
for tokenizer, tf_tokenizer in zip(self.tokenizers , self.tf_tokenizers ):
for test_inputs in (self.test_sentences, self.paired_sentences):
__snake_case : Optional[Any] = tokenizer(lowerCamelCase , return_tensors="tf" , padding="longest" )
__snake_case : int = tf_tokenizer(lowerCamelCase )
for key in python_outputs.keys():
self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape ) )
self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key] , tf.intaa ) == tf_outputs[key] ) )
@slow
def __snake_case ( self : Any ) -> Dict:
for tf_tokenizer in self.tf_tokenizers:
__snake_case : List[Any] = tf_tokenizer(self.paired_sentences )
__snake_case : List[Any] = tf_tokenizer(
text=[sentence[0] for sentence in self.paired_sentences] , text_pair=[sentence[1] for sentence in self.paired_sentences] , )
for key in merged_outputs.keys():
self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key] , tf.intaa ) == separated_outputs[key] ) )
@slow
def __snake_case ( self : Dict ) -> Optional[int]:
for tf_tokenizer in self.tf_tokenizers:
__snake_case : Optional[int] = tf.function(lowerCamelCase )
for test_inputs in (self.test_sentences, self.paired_sentences):
__snake_case : Tuple = tf.constant(lowerCamelCase )
__snake_case : int = compiled_tokenizer(lowerCamelCase )
__snake_case : str = tf_tokenizer(lowerCamelCase )
for key in eager_outputs.keys():
self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key] ) )
@slow
def __snake_case ( self : Union[str, Any] ) -> str:
for tf_tokenizer in self.tf_tokenizers:
__snake_case : Optional[int] = ModelToSave(tokenizer=lowerCamelCase )
__snake_case : Any = tf.convert_to_tensor(self.test_sentences )
__snake_case : Any = model(lowerCamelCase ) # Build model with some sample inputs
with TemporaryDirectory() as tempdir:
__snake_case : Optional[Any] = Path(lowerCamelCase ) / "saved.model"
model.save(lowerCamelCase )
__snake_case : Tuple = tf.keras.models.load_model(lowerCamelCase )
__snake_case : Optional[Any] = loaded_model(lowerCamelCase )
# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output ) ) , 1E-5 )
| 81
|
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( lowerCamelCase__ : list[int] ):
'''simple docstring'''
if not nums: # Makes sure that the list is not empty
raise ValueError("""List is empty""" )
A: Tuple = sum(lowerCamelCase__ ) / len(lowerCamelCase__ ) # Calculate the average
return sum(abs(x - average ) for x in nums ) / len(lowerCamelCase__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 135
| 0
|
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def _lowerCAmelCase ( __lowerCamelCase : Optional[int] , __lowerCamelCase : Dict ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Tuple = torch.load(__lowerCamelCase , map_location="cpu" )
__SCREAMING_SNAKE_CASE : Dict = chkpt["model"]
# We have the base model one level deeper than the original XLM repository
__SCREAMING_SNAKE_CASE : Dict = {}
for k, v in state_dict.items():
if "pred_layer" in k:
__SCREAMING_SNAKE_CASE : Optional[int] = v
else:
__SCREAMING_SNAKE_CASE : Any = v
__SCREAMING_SNAKE_CASE : List[Any] = chkpt["params"]
__SCREAMING_SNAKE_CASE : Optional[int] = {n: v for n, v in config.items() if not isinstance(__lowerCamelCase , (torch.FloatTensor, numpy.ndarray) )}
__SCREAMING_SNAKE_CASE : Dict = chkpt["dico_word2id"]
__SCREAMING_SNAKE_CASE : Dict = {s + "</w>" if s.find("@@" ) == -1 and i > 13 else s.replace("@@" , "" ): i for s, i in vocab.items()}
# Save pytorch-model
__SCREAMING_SNAKE_CASE : Dict = pytorch_dump_folder_path + "/" + WEIGHTS_NAME
__SCREAMING_SNAKE_CASE : Optional[Any] = pytorch_dump_folder_path + "/" + CONFIG_NAME
__SCREAMING_SNAKE_CASE : List[Any] = pytorch_dump_folder_path + "/" + VOCAB_FILES_NAMES["vocab_file"]
print(F"""Save PyTorch model to {pytorch_weights_dump_path}""" )
torch.save(__lowerCamelCase , __lowerCamelCase )
print(F"""Save configuration file to {pytorch_config_dump_path}""" )
with open(__lowerCamelCase , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(__lowerCamelCase , indent=2 ) + "\n" )
print(F"""Save vocab file to {pytorch_config_dump_path}""" )
with open(__lowerCamelCase , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(__lowerCamelCase , indent=2 ) + "\n" )
if __name__ == "__main__":
_lowerCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--xlm_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
_lowerCamelCase = parser.parse_args()
convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)
| 447
|
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
is_tf_available,
is_torch_available,
)
_lowerCamelCase = logging.getLogger(__name__)
@dataclass(frozen=UpperCamelCase )
class _SCREAMING_SNAKE_CASE :
lowerCAmelCase = 42
lowerCAmelCase = 42
lowerCAmelCase = None
lowerCAmelCase = None
lowerCAmelCase = None
@dataclass(frozen=UpperCamelCase )
class _SCREAMING_SNAKE_CASE :
lowerCAmelCase = 42
lowerCAmelCase = None
lowerCAmelCase = None
lowerCAmelCase = None
lowerCAmelCase = None
if is_torch_available():
import torch
from torch.utils.data import Dataset
class _SCREAMING_SNAKE_CASE (UpperCamelCase ):
lowerCAmelCase = 42
def __init__( self : str , UpperCamelCase : str , UpperCamelCase : PreTrainedTokenizer , UpperCamelCase : str , UpperCamelCase : Optional[int] = None , UpperCamelCase : Union[str, Any]=False , UpperCamelCase : bool = False , )->List[str]:
__SCREAMING_SNAKE_CASE : int = hans_processors[task]()
__SCREAMING_SNAKE_CASE : Optional[Any] = os.path.join(
UpperCamelCase , "cached_{}_{}_{}_{}".format(
"dev" if evaluate else "train" , tokenizer.__class__.__name__ , str(UpperCamelCase ) , UpperCamelCase , ) , )
__SCREAMING_SNAKE_CASE : Optional[Any] = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE : Union[str, Any] = label_list[2], label_list[1]
__SCREAMING_SNAKE_CASE : Any = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
__SCREAMING_SNAKE_CASE : str = cached_features_file + ".lock"
with FileLock(UpperCamelCase ):
if os.path.exists(UpperCamelCase ) and not overwrite_cache:
logger.info(F"""Loading features from cached file {cached_features_file}""" )
__SCREAMING_SNAKE_CASE : Optional[Any] = torch.load(UpperCamelCase )
else:
logger.info(F"""Creating features from dataset file at {data_dir}""" )
__SCREAMING_SNAKE_CASE : Union[str, Any] = (
processor.get_dev_examples(UpperCamelCase ) if evaluate else processor.get_train_examples(UpperCamelCase )
)
logger.info("Training examples: %s" , len(UpperCamelCase ) )
__SCREAMING_SNAKE_CASE : Any = hans_convert_examples_to_features(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
logger.info("Saving features into cached file %s" , UpperCamelCase )
torch.save(self.features , UpperCamelCase )
def __len__( self : Tuple )->Any:
return len(self.features )
def __getitem__( self : Union[str, Any] , UpperCamelCase : int )->InputFeatures:
return self.features[i]
def __snake_case ( self : int )->Tuple:
return self.label_list
if is_tf_available():
import tensorflow as tf
class _SCREAMING_SNAKE_CASE :
lowerCAmelCase = 42
def __init__( self : Dict , UpperCamelCase : str , UpperCamelCase : PreTrainedTokenizer , UpperCamelCase : str , UpperCamelCase : Optional[int] = 1_2_8 , UpperCamelCase : Union[str, Any]=False , UpperCamelCase : bool = False , )->str:
__SCREAMING_SNAKE_CASE : str = hans_processors[task]()
__SCREAMING_SNAKE_CASE : Union[str, Any] = processor.get_labels()
if tokenizer.__class__ in (
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaTokenizer,
BartTokenizer,
BartTokenizerFast,
):
# HACK(label indices are swapped in RoBERTa pretrained model)
__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE : List[str] = label_list[2], label_list[1]
__SCREAMING_SNAKE_CASE : Union[str, Any] = label_list
__SCREAMING_SNAKE_CASE : str = processor.get_dev_examples(UpperCamelCase ) if evaluate else processor.get_train_examples(UpperCamelCase )
__SCREAMING_SNAKE_CASE : str = hans_convert_examples_to_features(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
def gen():
for ex_index, ex in tqdm.tqdm(enumerate(self.features ) , desc="convert examples to features" ):
if ex_index % 1_0_0_0_0 == 0:
logger.info("Writing example %d of %d" % (ex_index, len(UpperCamelCase )) )
yield (
{
"example_id": 0,
"input_ids": ex.input_ids,
"attention_mask": ex.attention_mask,
"token_type_ids": ex.token_type_ids,
},
ex.label,
)
__SCREAMING_SNAKE_CASE : List[str] = tf.data.Dataset.from_generator(
UpperCamelCase , (
{
"example_id": tf.intaa,
"input_ids": tf.intaa,
"attention_mask": tf.intaa,
"token_type_ids": tf.intaa,
},
tf.intaa,
) , (
{
"example_id": tf.TensorShape([] ),
"input_ids": tf.TensorShape([None, None] ),
"attention_mask": tf.TensorShape([None, None] ),
"token_type_ids": tf.TensorShape([None, None] ),
},
tf.TensorShape([] ),
) , )
def __snake_case ( self : List[Any] )->str:
return self.dataset
def __len__( self : Tuple )->List[str]:
return len(self.features )
def __getitem__( self : Optional[Any] , UpperCamelCase : Tuple )->InputFeatures:
return self.features[i]
def __snake_case ( self : List[Any] )->Optional[int]:
return self.label_list
class _SCREAMING_SNAKE_CASE (UpperCamelCase ):
def __snake_case ( self : List[Any] , UpperCamelCase : Union[str, Any] )->Tuple:
return self._create_examples(self._read_tsv(os.path.join(UpperCamelCase , "heuristics_train_set.txt" ) ) , "train" )
def __snake_case ( self : List[str] , UpperCamelCase : Optional[int] )->Any:
return self._create_examples(self._read_tsv(os.path.join(UpperCamelCase , "heuristics_evaluation_set.txt" ) ) , "dev" )
def __snake_case ( self : Optional[int] )->Tuple:
return ["contradiction", "entailment", "neutral"]
def __snake_case ( self : List[str] , UpperCamelCase : str , UpperCamelCase : Any )->Tuple:
__SCREAMING_SNAKE_CASE : Optional[int] = []
for i, line in enumerate(UpperCamelCase ):
if i == 0:
continue
__SCREAMING_SNAKE_CASE : str = "%s-%s" % (set_type, line[0])
__SCREAMING_SNAKE_CASE : List[str] = line[5]
__SCREAMING_SNAKE_CASE : List[str] = line[6]
__SCREAMING_SNAKE_CASE : Optional[Any] = line[7][2:] if line[7].startswith("ex" ) else line[7]
__SCREAMING_SNAKE_CASE : Optional[Any] = line[0]
examples.append(InputExample(guid=UpperCamelCase , text_a=UpperCamelCase , text_b=UpperCamelCase , label=UpperCamelCase , pairID=UpperCamelCase ) )
return examples
def _lowerCAmelCase ( __lowerCamelCase : List[InputExample] , __lowerCamelCase : List[str] , __lowerCamelCase : int , __lowerCamelCase : PreTrainedTokenizer , ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : str = {label: i for i, label in enumerate(__lowerCamelCase )}
__SCREAMING_SNAKE_CASE : Any = []
for ex_index, example in tqdm.tqdm(enumerate(__lowerCamelCase ) , desc="convert examples to features" ):
if ex_index % 10000 == 0:
logger.info("Writing example %d" % (ex_index) )
__SCREAMING_SNAKE_CASE : Union[str, Any] = tokenizer(
example.text_a , example.text_b , add_special_tokens=__lowerCamelCase , max_length=__lowerCamelCase , padding="max_length" , truncation=__lowerCamelCase , return_overflowing_tokens=__lowerCamelCase , )
__SCREAMING_SNAKE_CASE : List[str] = label_map[example.label] if example.label in label_map else 0
__SCREAMING_SNAKE_CASE : Union[str, Any] = int(example.pairID )
features.append(InputFeatures(**__lowerCamelCase , label=__lowerCamelCase , pairID=__lowerCamelCase ) )
for i, example in enumerate(examples[:5] ):
logger.info("*** Example ***" )
logger.info(F"""guid: {example}""" )
logger.info(F"""features: {features[i]}""" )
return features
_lowerCamelCase = {
"""hans""": 3,
}
_lowerCamelCase = {
"""hans""": HansProcessor,
}
| 447
| 1
|
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengthCriteria,
MaxNewTokensCriteria,
MaxTimeCriteria,
StoppingCriteriaList,
validate_stopping_criteria,
)
@require_torch
class __UpperCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def snake_case_ ( self , __A ):
__a = 3
__a = 250
__a = ids_tensor((batch_size, length) , __A )
__a = torch.ones((batch_size, length) , device=__A , dtype=torch.float ) / length
return input_ids, scores
def snake_case_ ( self ):
__a , __a = self._get_tensors(5 )
__a = StoppingCriteriaList(
[
MaxLengthCriteria(max_length=10 ),
MaxTimeCriteria(max_time=0.1 ),
] )
self.assertFalse(criteria(__A , __A ) )
__a , __a = self._get_tensors(9 )
self.assertFalse(criteria(__A , __A ) )
__a , __a = self._get_tensors(10 )
self.assertTrue(criteria(__A , __A ) )
def snake_case_ ( self ):
__a = MaxLengthCriteria(max_length=10 )
__a , __a = self._get_tensors(5 )
self.assertFalse(criteria(__A , __A ) )
__a , __a = self._get_tensors(9 )
self.assertFalse(criteria(__A , __A ) )
__a , __a = self._get_tensors(10 )
self.assertTrue(criteria(__A , __A ) )
def snake_case_ ( self ):
__a = MaxNewTokensCriteria(start_length=5 , max_new_tokens=5 )
__a , __a = self._get_tensors(5 )
self.assertFalse(criteria(__A , __A ) )
__a , __a = self._get_tensors(9 )
self.assertFalse(criteria(__A , __A ) )
__a , __a = self._get_tensors(10 )
self.assertTrue(criteria(__A , __A ) )
__a = StoppingCriteriaList([criteria] )
self.assertEqual(criteria_list.max_length , 10 )
def snake_case_ ( self ):
__a , __a = self._get_tensors(5 )
__a = MaxTimeCriteria(max_time=0.1 )
self.assertFalse(criteria(__A , __A ) )
__a = MaxTimeCriteria(max_time=0.1 , initial_timestamp=time.time() - 0.2 )
self.assertTrue(criteria(__A , __A ) )
def snake_case_ ( self ):
validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10 )] ) , 10 )
with self.assertWarns(__A ):
validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10 )] ) , 11 )
__a = validate_stopping_criteria(StoppingCriteriaList() , 11 )
self.assertEqual(len(__A ) , 1 )
| 99
|
"""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, PreTrainedTokenizer
from ...utils import logging
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = """▁"""
_UpperCamelCase = {"""vocab_file""": """sentencepiece.bpe.model""", """monolingual_vocab_file""": """dict.txt"""}
_UpperCamelCase = {
"""vocab_file""": {
"""vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model""",
},
"""monolingual_vocab_file""": {
"""vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt""",
},
}
_UpperCamelCase = {"""vinai/bartpho-syllable""": 1_0_2_4}
class __a ( __magic_name__ ):
"""simple docstring"""
__UpperCamelCase : int = VOCAB_FILES_NAMES
__UpperCamelCase : List[str] = PRETRAINED_VOCAB_FILES_MAP
__UpperCamelCase : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCamelCase : int = ['input_ids', 'attention_mask']
def __init__( self , snake_case , 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 , ):
"""simple docstring"""
lowerCAmelCase__ : Union[str, Any] = AddedToken(snake_case , lstrip=snake_case , rstrip=snake_case ) if isinstance(snake_case , snake_case ) else mask_token
lowerCAmelCase__ : Union[str, Any] = {} 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 , sp_model_kwargs=self.sp_model_kwargs , **snake_case , )
lowerCAmelCase__ : Union[str, Any] = vocab_file
lowerCAmelCase__ : Optional[Any] = monolingual_vocab_file
lowerCAmelCase__ : Optional[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(snake_case ) )
# Load the reduced vocab
# Keep order of special tokens for backward compatibility
lowerCAmelCase__ : Optional[int] = {}
lowerCAmelCase__ : int = 0
for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
if str(snake_case ) not in self.fairseq_tokens_to_ids:
lowerCAmelCase__ : Optional[int] = cnt
cnt += 1
with open(snake_case , "r" , encoding="utf-8" ) as f:
for line in f.readlines():
lowerCAmelCase__ : Union[str, Any] = line.strip().split()[0]
lowerCAmelCase__ : List[str] = len(self.fairseq_tokens_to_ids )
if str(snake_case ) not in self.fairseq_tokens_to_ids:
lowerCAmelCase__ : int = len(self.fairseq_tokens_to_ids )
lowerCAmelCase__ : Union[str, Any] = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__( self ):
"""simple docstring"""
lowerCAmelCase__ : List[Any] = self.__dict__.copy()
lowerCAmelCase__ : str = None
lowerCAmelCase__ : Union[str, Any] = self.sp_model.serialized_model_proto()
return state
def __setstate__( self , snake_case ):
"""simple docstring"""
lowerCAmelCase__ : Any = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
lowerCAmelCase__ : Any = {}
lowerCAmelCase__ : Dict = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
def SCREAMING_SNAKE_CASE_ ( self , snake_case , snake_case = None ):
"""simple docstring"""
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
lowerCAmelCase__ : Any = [self.cls_token_id]
lowerCAmelCase__ : Dict = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def SCREAMING_SNAKE_CASE_ ( self , snake_case , snake_case = None , snake_case = False ):
"""simple docstring"""
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 SCREAMING_SNAKE_CASE_ ( self , snake_case , snake_case = None ):
"""simple docstring"""
lowerCAmelCase__ : Optional[Any] = [self.sep_token_id]
lowerCAmelCase__ : Optional[int] = [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]
@property
def SCREAMING_SNAKE_CASE_ ( self ):
"""simple docstring"""
return len(self.fairseq_ids_to_tokens )
def SCREAMING_SNAKE_CASE_ ( self ):
"""simple docstring"""
lowerCAmelCase__ : List[str] = {self.convert_ids_to_tokens(snake_case ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def SCREAMING_SNAKE_CASE_ ( self , snake_case ):
"""simple docstring"""
return self.sp_model.encode(snake_case , out_type=snake_case )
def SCREAMING_SNAKE_CASE_ ( self , snake_case ):
"""simple docstring"""
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
else:
return self.unk_token_id
def SCREAMING_SNAKE_CASE_ ( self , snake_case ):
"""simple docstring"""
return self.fairseq_ids_to_tokens[index]
def SCREAMING_SNAKE_CASE_ ( self , snake_case ):
"""simple docstring"""
lowerCAmelCase__ : List[str] = "".join(snake_case ).replace(snake_case , " " ).strip()
return out_string
def SCREAMING_SNAKE_CASE_ ( self , snake_case , snake_case = None ):
"""simple docstring"""
if not os.path.isdir(snake_case ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
lowerCAmelCase__ : List[str] = os.path.join(
snake_case , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
lowerCAmelCase__ : Optional[Any] = os.path.join(
snake_case , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["monolingual_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:
lowerCAmelCase__ : str = self.sp_model.serialized_model_proto()
fi.write(snake_case )
if os.path.abspath(self.monolingual_vocab_file ) != os.path.abspath(
snake_case ) and os.path.isfile(self.monolingual_vocab_file ):
copyfile(self.monolingual_vocab_file , snake_case )
elif not os.path.isfile(self.monolingual_vocab_file ):
with open(snake_case , "w" , encoding="utf-8" ) as fp:
for token in self.fairseq_tokens_to_ids:
if token not in self.all_special_tokens:
fp.write(F"""{str(snake_case )} \n""" )
return out_vocab_file, out_monolingual_vocab_file
| 453
| 0
|
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class _UpperCamelCase ( _UpperCAmelCase ):
"""simple docstring"""
def __init__( self , *lowerCAmelCase__ , lowerCAmelCase__=None , lowerCAmelCase__=None , **lowerCAmelCase__ ) -> Optional[int]:
'''simple docstring'''
super().__init__(*lowerCAmelCase__ , **lowerCAmelCase__ )
__lowercase = eval_examples
__lowercase = post_process_function
def _SCREAMING_SNAKE_CASE ( self , lowerCAmelCase__=None , lowerCAmelCase__=None , lowerCAmelCase__=None , lowerCAmelCase__ = "eval" ) -> Optional[Any]:
'''simple docstring'''
__lowercase = self.eval_dataset if eval_dataset is None else eval_dataset
__lowercase = self.get_eval_dataloader(lowerCAmelCase__ )
__lowercase = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
__lowercase = self.compute_metrics
__lowercase = None
__lowercase = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
__lowercase = time.time()
try:
__lowercase = eval_loop(
lowerCAmelCase__ , description='''Evaluation''' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=lowerCAmelCase__ , metric_key_prefix=lowerCAmelCase__ , )
finally:
__lowercase = compute_metrics
__lowercase = self.args.eval_batch_size * self.args.world_size
if F"{metric_key_prefix}_jit_compilation_time" in output.metrics:
start_time += output.metrics[F"{metric_key_prefix}_jit_compilation_time"]
output.metrics.update(
speed_metrics(
lowerCAmelCase__ , lowerCAmelCase__ , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
# Only the main node write the results by default
__lowercase = self.post_process_function(lowerCAmelCase__ , lowerCAmelCase__ , output.predictions )
__lowercase = self.compute_metrics(lowerCAmelCase__ )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F"{metric_key_prefix}_" ):
__lowercase = metrics.pop(lowerCAmelCase__ )
metrics.update(output.metrics )
else:
__lowercase = output.metrics
if self.args.should_log:
# Only the main node log the results by default
self.log(lowerCAmelCase__ )
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
__lowercase = self.callback_handler.on_evaluate(self.args , self.state , self.control , lowerCAmelCase__ )
return metrics
def _SCREAMING_SNAKE_CASE ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__=None , lowerCAmelCase__ = "test" ) -> Any:
'''simple docstring'''
__lowercase = self.get_test_dataloader(lowerCAmelCase__ )
# Temporarily disable metric computation, we will do it in the loop here.
__lowercase = self.compute_metrics
__lowercase = None
__lowercase = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
__lowercase = time.time()
try:
__lowercase = eval_loop(
lowerCAmelCase__ , description='''Prediction''' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=lowerCAmelCase__ , metric_key_prefix=lowerCAmelCase__ , )
finally:
__lowercase = compute_metrics
__lowercase = self.args.eval_batch_size * self.args.world_size
if F"{metric_key_prefix}_jit_compilation_time" in output.metrics:
start_time += output.metrics[F"{metric_key_prefix}_jit_compilation_time"]
output.metrics.update(
speed_metrics(
lowerCAmelCase__ , lowerCAmelCase__ , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is None or self.compute_metrics is None:
return output
__lowercase = self.post_process_function(lowerCAmelCase__ , lowerCAmelCase__ , output.predictions , '''predict''' )
__lowercase = self.compute_metrics(lowerCAmelCase__ )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F"{metric_key_prefix}_" ):
__lowercase = metrics.pop(lowerCAmelCase__ )
metrics.update(output.metrics )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=lowerCAmelCase__ )
| 522
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a : Any = {"""configuration_xlnet""": ["""XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLNetConfig"""]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a : List[str] = ["""XLNetTokenizer"""]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a : Dict = ["""XLNetTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a : Tuple = [
"""XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""XLNetForMultipleChoice""",
"""XLNetForQuestionAnswering""",
"""XLNetForQuestionAnsweringSimple""",
"""XLNetForSequenceClassification""",
"""XLNetForTokenClassification""",
"""XLNetLMHeadModel""",
"""XLNetModel""",
"""XLNetPreTrainedModel""",
"""load_tf_weights_in_xlnet""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a : List[str] = [
"""TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFXLNetForMultipleChoice""",
"""TFXLNetForQuestionAnsweringSimple""",
"""TFXLNetForSequenceClassification""",
"""TFXLNetForTokenClassification""",
"""TFXLNetLMHeadModel""",
"""TFXLNetMainLayer""",
"""TFXLNetModel""",
"""TFXLNetPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlnet import XLNetTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlnet_fast import XLNetTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlnet import (
XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
XLNetForMultipleChoice,
XLNetForQuestionAnswering,
XLNetForQuestionAnsweringSimple,
XLNetForSequenceClassification,
XLNetForTokenClassification,
XLNetLMHeadModel,
XLNetModel,
XLNetPreTrainedModel,
load_tf_weights_in_xlnet,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlnet import (
TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLNetForMultipleChoice,
TFXLNetForQuestionAnsweringSimple,
TFXLNetForSequenceClassification,
TFXLNetForTokenClassification,
TFXLNetLMHeadModel,
TFXLNetMainLayer,
TFXLNetModel,
TFXLNetPreTrainedModel,
)
else:
import sys
__a : str = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 522
| 1
|
from string import ascii_lowercase, ascii_uppercase
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
if not sentence:
return ""
UpperCAmelCase_ : Union[str, Any] = dict(zip(lowerCAmelCase__ , lowerCAmelCase__ ) )
return lower_to_upper.get(sentence[0] , sentence[0] ) + sentence[1:]
if __name__ == "__main__":
from doctest import testmod
testmod()
| 30
|
"""simple docstring"""
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import sqlalchemy
class _A ( lowerCAmelCase ):
def __init__( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = None , __lowerCAmelCase = None , __lowerCAmelCase = False , **__lowerCAmelCase , ):
"""simple docstring"""
super().__init__(features=__lowerCAmelCase , cache_dir=__lowerCAmelCase , keep_in_memory=__lowerCAmelCase , **__lowerCAmelCase )
lowercase = Sql(
cache_dir=__lowerCAmelCase , features=__lowerCAmelCase , sql=__lowerCAmelCase , con=__lowerCAmelCase , **__lowerCAmelCase , )
def A__ ( self ):
"""simple docstring"""
lowercase = None
lowercase = None
lowercase = None
lowercase = None
self.builder.download_and_prepare(
download_config=__lowerCAmelCase , download_mode=__lowerCAmelCase , verification_mode=__lowerCAmelCase , base_path=__lowerCAmelCase , )
# Build dataset for splits
lowercase = self.builder.as_dataset(
split="""train""" , verification_mode=__lowerCAmelCase , in_memory=self.keep_in_memory )
return dataset
class _A :
def __init__( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = None , __lowerCAmelCase = None , **__lowerCAmelCase , ):
"""simple docstring"""
if num_proc is not None and num_proc <= 0:
raise ValueError(f'num_proc {num_proc} must be an integer > 0.' )
lowercase = dataset
lowercase = name
lowercase = con
lowercase = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
lowercase = num_proc
lowercase = to_sql_kwargs
def A__ ( self ):
"""simple docstring"""
lowercase = self.to_sql_kwargs.pop("""sql""" , __lowerCAmelCase )
lowercase = self.to_sql_kwargs.pop("""con""" , __lowerCAmelCase )
lowercase = self.to_sql_kwargs.pop("""index""" , __lowerCAmelCase )
lowercase = self._write(index=__lowerCAmelCase , **self.to_sql_kwargs )
return written
def A__ ( self , __lowerCAmelCase ):
"""simple docstring"""
lowercase , lowercase , lowercase = args
lowercase = {**to_sql_kwargs, """if_exists""": """append"""} if offset > 0 else to_sql_kwargs
lowercase = query_table(
table=self.dataset.data , key=slice(__lowerCAmelCase , offset + self.batch_size ) , indices=self.dataset._indices , )
lowercase = batch.to_pandas()
lowercase = df.to_sql(self.name , self.con , index=__lowerCAmelCase , **__lowerCAmelCase )
return num_rows or len(__lowerCAmelCase )
def A__ ( self , __lowerCAmelCase , **__lowerCAmelCase ):
"""simple docstring"""
lowercase = 0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 , len(self.dataset ) , self.batch_size ) , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += self._batch_sql((offset, index, to_sql_kwargs) )
else:
lowercase , lowercase = len(self.dataset ), self.batch_size
with multiprocessing.Pool(self.num_proc ) as pool:
for num_rows in logging.tqdm(
pool.imap(
self._batch_sql , [(offset, index, to_sql_kwargs) for offset in range(0 , __lowerCAmelCase , __lowerCAmelCase )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += num_rows
return written
| 359
| 0
|
'''simple docstring'''
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
lowerCAmelCase__ = ['text', 'image', 'audio']
def __UpperCAmelCase ( lowerCamelCase_) -> Union[str, Any]:
UpperCamelCase__ : int = []
for input_type in input_types:
if input_type == "text":
inputs.append('Text input')
elif input_type == "image":
inputs.append(
Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO')) / '000000039769.png').resize((512, 512)))
elif input_type == "audio":
inputs.append(torch.ones(3_000))
elif isinstance(lowerCamelCase_ , lowerCamelCase_):
inputs.append(create_inputs(lowerCamelCase_))
else:
raise ValueError(f'Invalid type requested: {input_type}')
return inputs
def __UpperCAmelCase ( lowerCamelCase_) -> Dict:
UpperCamelCase__ : Tuple = []
for output in outputs:
if isinstance(lowerCamelCase_ , (str, AgentText)):
output_types.append('text')
elif isinstance(lowerCamelCase_ , (Image.Image, AgentImage)):
output_types.append('image')
elif isinstance(lowerCamelCase_ , (torch.Tensor, AgentAudio)):
output_types.append('audio')
else:
raise ValueError(f'Invalid output: {output}')
return output_types
@is_tool_test
class __lowercase :
def __UpperCamelCase ( self : List[str]):
self.assertTrue(hasattr(self.tool , 'inputs'))
self.assertTrue(hasattr(self.tool , 'outputs'))
UpperCamelCase__ : int = self.tool.inputs
for _input in inputs:
if isinstance(_input , UpperCAmelCase_):
for __input in _input:
self.assertTrue(__input in authorized_types)
else:
self.assertTrue(_input in authorized_types)
UpperCamelCase__ : Any = self.tool.outputs
for _output in outputs:
self.assertTrue(_output in authorized_types)
def __UpperCamelCase ( self : Tuple):
UpperCamelCase__ : List[str] = create_inputs(self.tool.inputs)
UpperCamelCase__ : Any = self.tool(*UpperCAmelCase_)
# There is a single output
if len(self.tool.outputs) == 1:
UpperCamelCase__ : Optional[Any] = [outputs]
self.assertListEqual(output_types(UpperCAmelCase_) , self.tool.outputs)
def __UpperCamelCase ( self : Optional[int]):
self.assertTrue(hasattr(self.tool , 'description'))
self.assertTrue(hasattr(self.tool , 'default_checkpoint'))
self.assertTrue(self.tool.description.startswith('This is a tool that'))
def __UpperCamelCase ( self : List[str]):
UpperCamelCase__ : Any = create_inputs(self.tool.inputs)
UpperCamelCase__ : Optional[Any] = self.tool(*UpperCAmelCase_)
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_):
UpperCamelCase__ : int = [outputs]
self.assertEqual(len(UpperCAmelCase_) , len(self.tool.outputs))
for output, output_type in zip(UpperCAmelCase_ , self.tool.outputs):
UpperCamelCase__ : int = AGENT_TYPE_MAPPING[output_type]
self.assertTrue(isinstance(UpperCAmelCase_ , UpperCAmelCase_))
def __UpperCamelCase ( self : Union[str, Any]):
UpperCamelCase__ : List[str] = create_inputs(self.tool.inputs)
UpperCamelCase__ : Optional[int] = []
for _input, input_type in zip(UpperCAmelCase_ , self.tool.inputs):
if isinstance(UpperCAmelCase_ , UpperCAmelCase_):
_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input) for _input_type in input_type])
else:
_inputs.append(AGENT_TYPE_MAPPING[input_type](_input))
# Should not raise an error
UpperCamelCase__ : List[Any] = self.tool(*UpperCAmelCase_)
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_):
UpperCamelCase__ : int = [outputs]
self.assertEqual(len(UpperCAmelCase_) , len(self.tool.outputs))
| 707
|
'''simple docstring'''
class __lowercase :
def __init__( self : List[str] , UpperCAmelCase_ : str = "" , UpperCAmelCase_ : bool = False):
# Mapping from the first character of the prefix of the node
UpperCamelCase__ : dict[str, RadixNode] = {}
# A node will be a leaf if the tree contains its word
UpperCamelCase__ : List[Any] = is_leaf
UpperCamelCase__ : Optional[Any] = prefix
def __UpperCamelCase ( self : List[Any] , UpperCAmelCase_ : str):
UpperCamelCase__ : Optional[int] = 0
for q, w in zip(self.prefix , UpperCAmelCase_):
if q != w:
break
x += 1
return self.prefix[:x], self.prefix[x:], word[x:]
def __UpperCamelCase ( self : str , UpperCAmelCase_ : list[str]):
for word in words:
self.insert(UpperCAmelCase_)
def __UpperCamelCase ( self : Optional[int] , UpperCAmelCase_ : str):
# Case 1: If the word is the prefix of the node
# Solution: We set the current node as leaf
if self.prefix == word:
UpperCamelCase__ : Optional[Any] = True
# Case 2: The node has no edges that have a prefix to the word
# Solution: We create an edge from the current node to a new one
# containing the word
elif word[0] not in self.nodes:
UpperCamelCase__ : Optional[Any] = RadixNode(prefix=UpperCAmelCase_ , is_leaf=UpperCAmelCase_)
else:
UpperCamelCase__ : int = self.nodes[word[0]]
UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ : List[Any] = incoming_node.match(
UpperCAmelCase_)
# Case 3: The node prefix is equal to the matching
# Solution: We insert remaining word on the next node
if remaining_prefix == "":
self.nodes[matching_string[0]].insert(UpperCAmelCase_)
# Case 4: The word is greater equal to the matching
# Solution: Create a node in between both nodes, change
# prefixes and add the new node for the remaining word
else:
UpperCamelCase__ : Tuple = remaining_prefix
UpperCamelCase__ : str = self.nodes[matching_string[0]]
UpperCamelCase__ : Optional[Any] = RadixNode(UpperCAmelCase_ , UpperCAmelCase_)
UpperCamelCase__ : str = aux_node
if remaining_word == "":
UpperCamelCase__ : int = True
else:
self.nodes[matching_string[0]].insert(UpperCAmelCase_)
def __UpperCamelCase ( self : Union[str, Any] , UpperCAmelCase_ : str):
UpperCamelCase__ : Optional[Any] = self.nodes.get(word[0] , UpperCAmelCase_)
if not incoming_node:
return False
else:
UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ : int = incoming_node.match(
UpperCAmelCase_)
# If there is remaining prefix, the word can't be on the tree
if remaining_prefix != "":
return False
# This applies when the word and the prefix are equal
elif remaining_word == "":
return incoming_node.is_leaf
# We have word remaining so we check the next node
else:
return incoming_node.find(UpperCAmelCase_)
def __UpperCamelCase ( self : str , UpperCAmelCase_ : str):
UpperCamelCase__ : Optional[int] = self.nodes.get(word[0] , UpperCAmelCase_)
if not incoming_node:
return False
else:
UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ : Union[str, Any] = incoming_node.match(
UpperCAmelCase_)
# If there is remaining prefix, the word can't be on the tree
if remaining_prefix != "":
return False
# We have word remaining so we check the next node
elif remaining_word != "":
return incoming_node.delete(UpperCAmelCase_)
else:
# If it is not a leaf, we don't have to delete
if not incoming_node.is_leaf:
return False
else:
# We delete the nodes if no edges go from it
if len(incoming_node.nodes) == 0:
del self.nodes[word[0]]
# We merge the current node with its only child
if len(self.nodes) == 1 and not self.is_leaf:
UpperCamelCase__ : List[str] = list(self.nodes.values())[0]
UpperCamelCase__ : Tuple = merging_node.is_leaf
self.prefix += merging_node.prefix
UpperCamelCase__ : Tuple = merging_node.nodes
# If there is more than 1 edge, we just mark it as non-leaf
elif len(incoming_node.nodes) > 1:
UpperCamelCase__ : str = False
# If there is 1 edge, we merge it with its child
else:
UpperCamelCase__ : List[Any] = list(incoming_node.nodes.values())[0]
UpperCamelCase__ : Optional[Any] = merging_node.is_leaf
incoming_node.prefix += merging_node.prefix
UpperCamelCase__ : Union[str, Any] = merging_node.nodes
return True
def __UpperCamelCase ( self : str , UpperCAmelCase_ : int = 0):
if self.prefix != "":
print('-' * height , self.prefix , ' (leaf)' if self.is_leaf else '')
for value in self.nodes.values():
value.print_tree(height + 1)
def __UpperCAmelCase ( ) -> bool:
UpperCamelCase__ : Union[str, Any] = 'banana bananas bandana band apple all beast'.split()
UpperCamelCase__ : List[Any] = RadixNode()
root.insert_many(lowerCamelCase_)
assert all(root.find(lowerCamelCase_) for word in words)
assert not root.find('bandanas')
assert not root.find('apps')
root.delete('all')
assert not root.find('all')
root.delete('banana')
assert not root.find('banana')
assert root.find('bananas')
return True
def __UpperCAmelCase ( ) -> None:
assert test_trie()
def __UpperCAmelCase ( ) -> None:
UpperCamelCase__ : List[Any] = RadixNode()
UpperCamelCase__ : List[str] = 'banana bananas bandanas bandana band apple all beast'.split()
root.insert_many(lowerCamelCase_)
print('Words:' , lowerCamelCase_)
print('Tree:')
root.print_tree()
if __name__ == "__main__":
main()
| 6
| 0
|
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OpenAIGPTConfig,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTForSequenceClassification,
OpenAIGPTLMHeadModel,
OpenAIGPTModel,
)
class UpperCAmelCase :
def __init__( self :Tuple , lowercase_ :Optional[Any] , lowercase_ :int=13 , lowercase_ :Any=7 , lowercase_ :str=True , lowercase_ :Tuple=True , lowercase_ :Dict=True , lowercase_ :List[Any]=99 , lowercase_ :Dict=32 , lowercase_ :int=5 , lowercase_ :Union[str, Any]=4 , lowercase_ :List[str]=37 , lowercase_ :Union[str, Any]="gelu" , lowercase_ :List[Any]=0.1 , lowercase_ :List[str]=0.1 , lowercase_ :Any=5_12 , lowercase_ :Optional[Any]=16 , lowercase_ :Optional[Any]=2 , lowercase_ :Optional[int]=0.0_2 , lowercase_ :str=3 , lowercase_ :Tuple=4 , lowercase_ :str=None , )-> List[Any]:
A__ = parent
A__ = batch_size
A__ = seq_length
A__ = is_training
A__ = use_token_type_ids
A__ = use_labels
A__ = vocab_size
A__ = hidden_size
A__ = num_hidden_layers
A__ = num_attention_heads
A__ = intermediate_size
A__ = hidden_act
A__ = hidden_dropout_prob
A__ = attention_probs_dropout_prob
A__ = max_position_embeddings
A__ = type_vocab_size
A__ = type_sequence_label_size
A__ = initializer_range
A__ = num_labels
A__ = num_choices
A__ = scope
A__ = self.vocab_size - 1
def UpperCAmelCase_ ( self :Dict )-> Dict:
A__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A__ = None
if self.use_token_type_ids:
A__ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
A__ = None
A__ = None
A__ = None
if self.use_labels:
A__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
A__ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
A__ = ids_tensor([self.batch_size] , self.num_choices )
A__ = OpenAIGPTConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , pad_token_id=self.pad_token_id , )
A__ = ids_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
head_mask,
token_type_ids,
sequence_labels,
token_labels,
choice_labels,
)
def UpperCAmelCase_ ( self :str , lowercase_ :Dict , lowercase_ :int , lowercase_ :Optional[Any] , lowercase_ :Union[str, Any] , *lowercase_ :Optional[Any] )-> Union[str, Any]:
A__ = OpenAIGPTModel(config=lowercase_ )
model.to(lowercase_ )
model.eval()
A__ = model(lowercase_ , token_type_ids=lowercase_ , head_mask=lowercase_ )
A__ = model(lowercase_ , token_type_ids=lowercase_ )
A__ = model(lowercase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase_ ( self :str , lowercase_ :Dict , lowercase_ :Any , lowercase_ :Tuple , lowercase_ :Optional[int] , *lowercase_ :int )-> Tuple:
A__ = OpenAIGPTLMHeadModel(lowercase_ )
model.to(lowercase_ )
model.eval()
A__ = model(lowercase_ , token_type_ids=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCAmelCase_ ( self :Optional[int] , lowercase_ :str , lowercase_ :Union[str, Any] , lowercase_ :Union[str, Any] , lowercase_ :Dict , *lowercase_ :Union[str, Any] )-> Tuple:
A__ = OpenAIGPTDoubleHeadsModel(lowercase_ )
model.to(lowercase_ )
model.eval()
A__ = model(lowercase_ , token_type_ids=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCAmelCase_ ( self :Dict , lowercase_ :Dict , lowercase_ :List[Any] , lowercase_ :str , lowercase_ :Tuple , *lowercase_ :int )-> List[Any]:
A__ = self.num_labels
A__ = OpenAIGPTForSequenceClassification(lowercase_ )
model.to(lowercase_ )
model.eval()
A__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
A__ = model(lowercase_ , token_type_ids=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCAmelCase_ ( self :Optional[int] )-> List[str]:
A__ = self.prepare_config_and_inputs()
(
A__
) = config_and_inputs
A__ = {
'''input_ids''': input_ids,
'''token_type_ids''': token_type_ids,
'''head_mask''': head_mask,
}
return config, inputs_dict
@require_torch
class UpperCAmelCase ( __snake_case , __snake_case , __snake_case , unittest.TestCase ):
__lowercase = (
(OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
if is_torch_available()
else ()
)
__lowercase = (
(OpenAIGPTLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
__lowercase = (
{
"""feature-extraction""": OpenAIGPTModel,
"""text-classification""": OpenAIGPTForSequenceClassification,
"""text-generation""": OpenAIGPTLMHeadModel,
"""zero-shot""": OpenAIGPTForSequenceClassification,
}
if is_torch_available()
else {}
)
def UpperCAmelCase_ ( self :Any , lowercase_ :Tuple , lowercase_ :List[str] , lowercase_ :List[str] , lowercase_ :Optional[int] , lowercase_ :Tuple )-> Any:
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
# tiny config could not be created.
return True
return False
def UpperCAmelCase_ ( self :str , lowercase_ :int , lowercase_ :Union[str, Any] , lowercase_ :int=False )-> Optional[int]:
A__ = super()._prepare_for_class(lowercase_ , lowercase_ , return_labels=lowercase_ )
if return_labels:
if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
A__ = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length) , dtype=torch.long , device=lowercase_ , )
A__ = inputs_dict['''labels''']
A__ = inputs_dict['''labels''']
A__ = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices) , dtype=torch.long , device=lowercase_ , )
A__ = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowercase_ )
return inputs_dict
def UpperCAmelCase_ ( self :List[Any] )-> List[Any]:
A__ = OpenAIGPTModelTester(self )
A__ = ConfigTester(self , config_class=lowercase_ , n_embd=37 )
def UpperCAmelCase_ ( self :Optional[Any] )-> Dict:
self.config_tester.run_common_tests()
def UpperCAmelCase_ ( self :Dict )-> int:
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_model(*lowercase_ )
def UpperCAmelCase_ ( self :Dict )-> Optional[Any]:
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*lowercase_ )
def UpperCAmelCase_ ( self :List[Any] )-> Dict:
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_double_lm_head_model(*lowercase_ )
def UpperCAmelCase_ ( self :Any )-> int:
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*lowercase_ )
@slow
def UpperCAmelCase_ ( self :int )-> Any:
for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A__ = OpenAIGPTModel.from_pretrained(lowercase_ )
self.assertIsNotNone(lowercase_ )
@require_torch
class UpperCAmelCase ( unittest.TestCase ):
@slow
def UpperCAmelCase_ ( self :List[str] )-> str:
A__ = OpenAIGPTLMHeadModel.from_pretrained("openai-gpt" )
model.to(lowercase_ )
A__ = torch.tensor([[4_81, 47_35, 5_44]] , dtype=torch.long , device=lowercase_ ) # the president is
A__ = [
4_81,
47_35,
5_44,
2_46,
9_63,
8_70,
7_62,
2_39,
2_44,
4_04_77,
2_44,
2_49,
7_19,
8_81,
4_87,
5_44,
2_40,
2_44,
6_03,
4_81,
] # the president is a very good man. " \n " i\'m sure he is, " said the
A__ = model.generate(lowercase_ , do_sample=lowercase_ )
self.assertListEqual(output_ids[0].tolist() , lowercase_ )
| 440
|
'''simple docstring'''
import random
def lowerCAmelCase_ ( snake_case__ , snake_case__ ):
'''simple docstring'''
A, A, A : Any = [], [], []
for element in data:
if element < pivot:
less.append(snake_case__ )
elif element > pivot:
greater.append(snake_case__ )
else:
equal.append(snake_case__ )
return less, equal, greater
def lowerCAmelCase_ ( snake_case__ , snake_case__ ):
'''simple docstring'''
if index >= len(snake_case__ ) or index < 0:
return None
A : List[Any] = items[random.randint(0 , len(snake_case__ ) - 1 )]
A : Optional[int] = 0
A, A, A : Dict = _partition(snake_case__ , snake_case__ )
A : int = len(snake_case__ )
A : List[Any] = len(snake_case__ )
# index is the pivot
if m <= index < m + count:
return pivot
# must be in smaller
elif m > index:
return quick_select(snake_case__ , snake_case__ )
# must be in larger
else:
return quick_select(snake_case__ , index - (m + count) )
| 634
| 0
|
'''simple docstring'''
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
__snake_case : List[Any] = [
'word_embeddings_layernorm.weight',
'word_embeddings_layernorm.bias',
'input_layernorm.weight',
'input_layernorm.bias',
'post_attention_layernorm.weight',
'post_attention_layernorm.bias',
'self_attention.dense.bias',
'mlp.dense_4h_to_h.bias',
'ln_f.weight',
'ln_f.bias',
]
__snake_case : Optional[Any] = [
'mlp.dense_4h_to_h.weight',
'self_attention.dense.weight',
]
def __lowerCamelCase ( __snake_case : int, __snake_case : List[str] ) -> Any:
"""simple docstring"""
A__ : Dict ={
"""word_embeddings.weight""": """word_embeddings.weight""",
"""word_embeddings.norm.weight""": """word_embeddings_layernorm.weight""",
"""word_embeddings.norm.bias""": """word_embeddings_layernorm.bias""",
"""weight""": """ln_f.weight""",
"""bias""": """ln_f.bias""",
}
if key in layer_rename_map:
return layer_rename_map[key]
# Handle transformer blocks
A__ : int =int(re.match(r""".*layer_(\d*).*""", __snake_case )[1] )
layer_number -= 3
return f"h.{layer_number}." + key
def __lowerCamelCase ( __snake_case : Any ) -> Optional[int]:
"""simple docstring"""
if dtype == torch.bool:
return 1 / 8
A__ : List[Any] =re.search(r"""[^\d](\d+)$""", str(__snake_case ) )
if bit_search is None:
raise ValueError(f"`dtype` is not a valid dtype: {dtype}." )
A__ : Tuple =int(bit_search.groups()[0] )
return bit_size // 8
def __lowerCamelCase ( __snake_case : str, __snake_case : Optional[Any], __snake_case : Union[str, Any], __snake_case : int, __snake_case : List[str] ) -> Union[str, Any]:
"""simple docstring"""
if bloom_config_file == "":
A__ : Dict =BloomConfig()
else:
A__ : List[Any] =BloomConfig.from_json_file(__snake_case )
if shard_model:
A__ : Optional[Any] =os.listdir(__snake_case )
A__ : int =sorted(filter(lambda __snake_case : s.startswith("""layer""" ) and "model_00" in s, __snake_case ) )
A__ : str ={"""weight_map""": {}, """metadata""": {}}
A__ : str =0
A__ : Dict =None
A__ : int =BloomConfig()
for j, file in enumerate(__snake_case ):
print("""Processing file: {}""".format(__snake_case ) )
A__ : Dict =None
for i in range(__snake_case ):
# load all TP files
A__ : Any =file.replace("""model_00""", f"model_0{i}" )
A__ : Tuple =torch.load(os.path.join(__snake_case, __snake_case ), map_location="""cpu""" )
# Rename keys in the transformers names
A__ : Optional[int] =list(temp.keys() )
for key in keys:
A__ : Optional[int] =temp.pop(__snake_case )
if tensors is None:
A__ : Dict =temp
else:
for key in tensors.keys():
if any(key.endswith(__snake_case ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
tensors[key] += temp[key]
else:
# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
A__ : Union[str, Any] =1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN ) else 0
# We concatenate these weights accross TP ranks
A__ : Any =torch.cat([tensors[key], temp[key]], dim=__snake_case )
# Divide by the number of TP the weights we want to average
for key in tensors.keys():
if any(key.endswith(__snake_case ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
A__ : Dict =tensors[key] / pretraining_tp
torch.save(
__snake_case, os.path.join(
__snake_case, """pytorch_model_{}-of-{}.bin""".format(str(j + 1 ).zfill(5 ), str(len(__snake_case ) ).zfill(5 ) ), ), )
for key in tensors.keys():
A__ : List[Any] =tensors[key]
total_size += value.numel() * get_dtype_size(value.dtype )
if key not in index_dict["weight_map"]:
A__ : Any ="""pytorch_model_{}-of-{}.bin""".format(
str(j + 1 ).zfill(5 ), str(len(__snake_case ) ).zfill(5 ) )
A__ : List[Any] =BloomConfig()
A__ : Union[str, Any] =pytorch_dump_folder_path + """/""" + CONFIG_NAME
A__ : Tuple =total_size
with open(__snake_case, """w""", encoding="""utf-8""" ) as f:
f.write(config.to_json_string() )
with open(os.path.join(__snake_case, WEIGHTS_NAME + """.index.json""" ), """w""", encoding="""utf-8""" ) as f:
A__ : Dict =json.dumps(__snake_case, indent=2, sort_keys=__snake_case ) + """\n"""
f.write(__snake_case )
else:
A__ : Any =BloomModel(__snake_case )
A__ : Any =os.listdir(__snake_case )
A__ : List[str] =sorted(filter(lambda __snake_case : s.startswith("""layer""" ) and "model_00" in s, __snake_case ) )
A__ : Optional[Any] =None
for i, file in enumerate(__snake_case ):
A__ : Union[str, Any] =None
for i in range(__snake_case ):
# load all TP files
A__ : List[Any] =file.replace("""model_00""", f"model_0{i}" )
A__ : Optional[Any] =torch.load(os.path.join(__snake_case, __snake_case ), map_location="""cpu""" )
# Rename keys in the transformers names
A__ : List[str] =list(temp.keys() )
for key in keys:
A__ : Optional[Any] =temp.pop(__snake_case )
if tensors is None:
A__ : Any =temp
else:
for key in tensors.keys():
# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
if any(key.endswith(__snake_case ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
tensors[key] += temp[key]
else:
# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
A__ : Optional[Any] =1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN ) else 0
# We concatenate these weights accross TP ranks
A__ : Any =torch.cat([tensors[key], temp[key]], dim=__snake_case )
# Divide by the number of TP the weights we want to average
for key in tensors.keys():
if any(key.endswith(__snake_case ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
A__ : List[Any] =tensors[key] / pretraining_tp
A__ : Dict =model.load_state_dict(__snake_case, strict=__snake_case )
assert not other_keys.unexpected_keys, f"The keys {other_keys.unexpected_keys} are unexpected"
if missing_keys is None:
A__ : Optional[Any] =set(other_keys.missing_keys )
else:
A__ : Tuple =missing_keys.intersection(set(other_keys.missing_keys ) )
assert not missing_keys, f"The keys {missing_keys} are missing"
# Save pytorch-model
os.makedirs(__snake_case, exist_ok=__snake_case )
A__ : Optional[int] =pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
A__ : Any =pytorch_dump_folder_path + """/""" + CONFIG_NAME
print(f"Save PyTorch model to {pytorch_weights_dump_path} with dtype {config.torch_dtype}" )
if config.torch_dtype is not None:
A__ : str =model.to(config.torch_dtype )
torch.save(model.state_dict(), __snake_case )
print(f"Save configuration file to {pytorch_config_dump_path}" )
with open(__snake_case, """w""", encoding="""utf-8""" ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
__snake_case : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--bloom_checkpoint_path',
default=None,
type=str,
required=True,
help='Path to the Megatron-LM checkpoint path.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
parser.add_argument(
'--bloom_config_file',
default='',
type=str,
help=(
'An optional config json file corresponding to the pre-trained model. \n'
'This specifies the model architecture.'
),
)
parser.add_argument(
'--shard_model',
action='store_true',
help='An optional setting to shard the output model \nThis enables sharding the converted checkpoint',
)
parser.add_argument(
'--pretraining_tp',
default=4,
type=int,
help='Pretraining TP rank that has been used when training the model in Megatron-LM \n',
)
__snake_case : Dict = parser.parse_args()
convert_bloom_checkpoint_to_pytorch(
args.bloom_checkpoint_path,
args.bloom_config_file,
args.pytorch_dump_folder_path,
args.shard_model,
args.pretraining_tp,
)
| 687
|
'''simple docstring'''
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.testing_utils import (
is_torch_available,
require_accelerate,
require_bitsandbytes,
require_torch,
require_torch_gpu,
require_torch_multi_gpu,
slow,
)
def __lowerCamelCase ( __snake_case : Dict ) -> List[str]:
"""simple docstring"""
if model.config.model_type == "gpt2":
return model.transformer.h[0].mlp.c_fc
return model.transformer.h[0].mlp.dense_ah_to_h
if is_torch_available():
import torch
import torch.nn as nn
class lowerCamelCase ( nn.Module ):
'''simple docstring'''
def __init__( self : Union[str, Any] , lowerCAmelCase_ : nn.Module , lowerCAmelCase_ : int ) -> str:
'''simple docstring'''
super().__init__()
A__ : Union[str, Any] =module
A__ : Union[str, Any] =nn.Sequential(
nn.Linear(module.in_features , lowerCAmelCase_ , bias=lowerCAmelCase_ ) , nn.Linear(lowerCAmelCase_ , module.out_features , bias=lowerCAmelCase_ ) , )
A__ : Tuple =(2.0 / (5 * min(module.in_features , module.out_features ))) ** 0.5
nn.init.normal_(self.adapter[0].weight , std=lowerCAmelCase_ )
nn.init.zeros_(self.adapter[1].weight )
self.adapter.to(module.weight.device )
def lowercase__ ( self : List[str] , lowerCAmelCase_ : Optional[int] , *lowerCAmelCase_ : List[str] , **lowerCAmelCase_ : int ) -> Dict:
'''simple docstring'''
return self.module(lowerCAmelCase_ , *lowerCAmelCase_ , **lowerCAmelCase_ ) + self.adapter(lowerCAmelCase_ )
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
__snake_case = 'bigscience/bloom-1b7'
# Constant values
__snake_case = 2.109659552692574
__snake_case = 'Hello my name is'
__snake_case = set()
EXPECTED_OUTPUTS.add('Hello my name is John and I am a professional photographer. I' )
EXPECTED_OUTPUTS.add('Hello my name is John.\nI am a friend of your father.\n' )
EXPECTED_OUTPUTS.add('Hello my name is John Doe, I am a student at the University' )
__snake_case = 10
def lowercase__ ( self : Optional[int] ) -> Tuple:
'''simple docstring'''
# Models and tokenizer
A__ : List[Any] =AutoTokenizer.from_pretrained(self.model_name )
class lowerCamelCase ( lowercase_ ):
'''simple docstring'''
def lowercase__ ( self : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
super().setUp()
# Models and tokenizer
A__ : Optional[int] =AutoModelForCausalLM.from_pretrained(
self.model_name , torch_dtype=torch.floataa , device_map="""auto""" )
A__ : Union[str, Any] =AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=lowerCAmelCase_ , device_map="""auto""" )
def lowercase__ ( self : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
del self.model_fpaa
del self.model_abit
gc.collect()
torch.cuda.empty_cache()
def lowercase__ ( self : Optional[Any] ) -> List[str]:
'''simple docstring'''
A__ : str =self.model_abit.config
self.assertTrue(hasattr(lowerCAmelCase_ , """quantization_config""" ) )
A__ : Union[str, Any] =config.to_dict()
A__ : Any =config.to_diff_dict()
A__ : Optional[Any] =config.to_json_string()
def lowercase__ ( self : Optional[int] ) -> Optional[int]:
'''simple docstring'''
from bitsandbytes.nn import Paramsabit
A__ : int =self.model_fpaa.get_memory_footprint()
A__ : Optional[Any] =self.model_abit.get_memory_footprint()
self.assertAlmostEqual(mem_fpaa / mem_abit , self.EXPECTED_RELATIVE_DIFFERENCE )
A__ : Tuple =get_some_linear_layer(self.model_abit )
self.assertTrue(linear.weight.__class__ == Paramsabit )
def lowercase__ ( self : Optional[Any] ) -> List[Any]:
'''simple docstring'''
from transformers import TaPreTrainedModel
self.model_fpaa.get_memory_footprint()
self.model_abit.get_memory_footprint()
for name, module in self.model_abit.named_modules():
if isinstance(lowerCAmelCase_ , torch.nn.Linear ):
if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
# 4-bit parameters are packed in uint8 variables
self.assertTrue(module.weight.dtype == torch.uinta )
def lowercase__ ( self : Union[str, Any] ) -> Dict:
'''simple docstring'''
A__ : int =self.tokenizer(self.input_text , return_tensors="""pt""" )
A__ : Union[str, Any] =self.model_abit.generate(input_ids=encoded_input["""input_ids"""].to(0 ) , max_new_tokens=10 )
self.assertIn(self.tokenizer.decode(output_sequences[0] , skip_special_tokens=lowerCAmelCase_ ) , self.EXPECTED_OUTPUTS )
def lowercase__ ( self : Optional[Any] ) -> Tuple:
'''simple docstring'''
A__ : Tuple =BitsAndBytesConfig()
A__ : Tuple =True
A__ : Optional[int] =AutoModelForCausalLM.from_pretrained(
self.model_name , quantization_config=lowerCAmelCase_ , device_map="""auto""" )
A__ : Union[str, Any] =self.tokenizer(self.input_text , return_tensors="""pt""" )
A__ : Optional[Any] =model_abit_from_config.generate(
input_ids=encoded_input["""input_ids"""].to(0 ) , max_new_tokens=10 )
self.assertIn(self.tokenizer.decode(output_sequences[0] , skip_special_tokens=lowerCAmelCase_ ) , self.EXPECTED_OUTPUTS )
def lowercase__ ( self : str ) -> List[str]:
'''simple docstring'''
with self.assertRaises(lowerCAmelCase_ ), tempfile.TemporaryDirectory() as tmpdirname:
self.model_abit.save_pretrained(lowerCAmelCase_ )
def lowercase__ ( self : List[str] ) -> Any:
'''simple docstring'''
A__ : Tuple =BitsAndBytesConfig()
with self.assertRaises(lowerCAmelCase_ ):
A__ : Dict =AutoModelForCausalLM.from_pretrained(
self.model_name , quantization_config=lowerCAmelCase_ , load_in_abit=lowerCAmelCase_ , device_map="""auto""" , bnb_abit_quant_type="""nf4""" , )
def lowercase__ ( self : List[Any] ) -> Optional[int]:
'''simple docstring'''
with self.assertRaises(lowerCAmelCase_ ):
# Tries with `str`
self.model_abit.to("""cpu""" )
with self.assertRaises(lowerCAmelCase_ ):
# Tries with a `dtype``
self.model_abit.to(torch.floataa )
with self.assertRaises(lowerCAmelCase_ ):
# Tries with a `device`
self.model_abit.to(torch.device("""cuda:0""" ) )
with self.assertRaises(lowerCAmelCase_ ):
# Tries with a `device`
self.model_abit.float()
with self.assertRaises(lowerCAmelCase_ ):
# Tries with a `device`
self.model_abit.half()
# Test if we did not break anything
A__ : Dict =self.tokenizer(self.input_text , return_tensors="""pt""" )
A__ : Optional[Any] =self.model_fpaa.to(torch.floataa )
A__ : Dict =self.model_fpaa.generate(input_ids=encoded_input["""input_ids"""].to(0 ) , max_new_tokens=10 )
# Check this does not throw an error
A__ : List[str] =self.model_fpaa.to("""cpu""" )
# Check this does not throw an error
A__ : List[str] =self.model_fpaa.half()
# Check this does not throw an error
A__ : int =self.model_fpaa.float()
def lowercase__ ( self : int ) -> Dict:
'''simple docstring'''
A__ : Dict =AutoModelForSeqaSeqLM.from_pretrained("""t5-small""" , load_in_abit=lowerCAmelCase_ , device_map="""auto""" )
self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa )
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
@classmethod
def lowercase__ ( cls : List[str] ) -> Union[str, Any]:
'''simple docstring'''
A__ : Tuple ="""t5-small"""
A__ : Optional[Any] ="""google/flan-t5-small""" # flan-t5 uses dense-act instead of dense-relu-dense
A__ : Optional[int] =AutoTokenizer.from_pretrained(cls.model_name )
A__ : Optional[int] ="""Translate in German: Hello, my dog is cute"""
def lowercase__ ( self : Optional[int] ) -> Dict:
'''simple docstring'''
gc.collect()
torch.cuda.empty_cache()
def lowercase__ ( self : Dict ) -> Optional[Any]:
'''simple docstring'''
from transformers import TaForConditionalGeneration
A__ : Optional[int] =TaForConditionalGeneration._keep_in_fpaa_modules
A__ : Optional[Any] =None
# test with `t5-small`
A__ : str =TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=lowerCAmelCase_ , device_map="""auto""" )
A__ : List[str] =self.tokenizer(self.input_text , return_tensors="""pt""" ).to(0 )
A__ : Optional[Any] =model.generate(**lowerCAmelCase_ )
# test with `flan-t5-small`
A__ : List[str] =TaForConditionalGeneration.from_pretrained(
self.dense_act_model_name , load_in_abit=lowerCAmelCase_ , device_map="""auto""" )
A__ : Tuple =self.tokenizer(self.input_text , return_tensors="""pt""" ).to(0 )
A__ : Union[str, Any] =model.generate(**lowerCAmelCase_ )
A__ : Dict =modules
def lowercase__ ( self : str ) -> Optional[int]:
'''simple docstring'''
import bitsandbytes as bnb
from transformers import TaForConditionalGeneration
# test with `t5-small`
A__ : Optional[int] =TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=lowerCAmelCase_ , device_map="""auto""" )
# there was a bug with decoders - this test checks that it is fixed
self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q , bnb.nn.Linearabit ) )
A__ : Dict =self.tokenizer(self.input_text , return_tensors="""pt""" ).to(0 )
A__ : Any =model.generate(**lowerCAmelCase_ )
# test with `flan-t5-small`
A__ : Union[str, Any] =TaForConditionalGeneration.from_pretrained(
self.dense_act_model_name , load_in_abit=lowerCAmelCase_ , device_map="""auto""" )
A__ : Optional[int] =self.tokenizer(self.input_text , return_tensors="""pt""" ).to(0 )
A__ : Dict =model.generate(**lowerCAmelCase_ )
class lowerCamelCase ( lowercase_ ):
'''simple docstring'''
def lowercase__ ( self : List[Any] ) -> int:
'''simple docstring'''
super().setUp()
# model_name
A__ : Any ="""bigscience/bloom-560m"""
A__ : List[Any] ="""t5-small"""
# Different types of model
A__ : Dict =AutoModel.from_pretrained(self.model_name , load_in_abit=lowerCAmelCase_ , device_map="""auto""" )
# Sequence classification model
A__ : List[Any] =AutoModelForSequenceClassification.from_pretrained(
self.model_name , load_in_abit=lowerCAmelCase_ , device_map="""auto""" )
# CausalLM model
A__ : Union[str, Any] =AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=lowerCAmelCase_ , device_map="""auto""" )
# Seq2seq model
A__ : List[str] =AutoModelForSeqaSeqLM.from_pretrained(
self.seq_to_seq_name , load_in_abit=lowerCAmelCase_ , device_map="""auto""" )
def lowercase__ ( self : Dict ) -> int:
'''simple docstring'''
del self.base_model
del self.sequence_model
del self.model_abit
del self.seq_to_seq_model
gc.collect()
torch.cuda.empty_cache()
def lowercase__ ( self : str ) -> List[Any]:
'''simple docstring'''
from bitsandbytes.nn import Paramsabit
self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit )
# Other heads should be nn.Parameter
self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter )
self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter )
self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter )
class lowerCamelCase ( lowercase_ ):
'''simple docstring'''
def lowercase__ ( self : Optional[Any] ) -> List[Any]:
'''simple docstring'''
super().setUp()
def lowercase__ ( self : Optional[Any] ) -> int:
'''simple docstring'''
del self.pipe
gc.collect()
torch.cuda.empty_cache()
def lowercase__ ( self : Any ) -> Union[str, Any]:
'''simple docstring'''
A__ : Dict =pipeline(
"""text-generation""" , model=self.model_name , model_kwargs={"""device_map""": """auto""", """load_in_4bit""": True, """torch_dtype""": torch.floataa} , max_new_tokens=self.MAX_NEW_TOKENS , )
# Real second forward pass
A__ : Optional[int] =self.pipe(self.input_text )
self.assertIn(pipeline_output[0]["""generated_text"""] , self.EXPECTED_OUTPUTS )
@require_torch_multi_gpu
class lowerCamelCase ( lowercase_ ):
'''simple docstring'''
def lowercase__ ( self : str ) -> int:
'''simple docstring'''
super().setUp()
def lowercase__ ( self : Tuple ) -> Tuple:
'''simple docstring'''
A__ : int =AutoModelForCausalLM.from_pretrained(
self.model_name , load_in_abit=lowerCAmelCase_ , device_map="""balanced""" )
# Check correct device map
self.assertEqual(set(model_parallel.hf_device_map.values() ) , {0, 1} )
# Check that inference pass works on the model
A__ : str =self.tokenizer(self.input_text , return_tensors="""pt""" )
# Second real batch
A__ : Any =model_parallel.generate(input_ids=encoded_input["""input_ids"""].to(0 ) , max_new_tokens=10 )
self.assertIn(self.tokenizer.decode(output_parallel[0] , skip_special_tokens=lowerCAmelCase_ ) , self.EXPECTED_OUTPUTS )
class lowerCamelCase ( lowercase_ ):
'''simple docstring'''
def lowercase__ ( self : int ) -> Optional[Any]:
'''simple docstring'''
A__ : Union[str, Any] ="""facebook/opt-350m"""
super().setUp()
def lowercase__ ( self : List[str] ) -> Dict:
'''simple docstring'''
if version.parse(importlib.metadata.version("""bitsandbytes""" ) ) < version.parse("""0.37.0""" ):
return
# Step 1: freeze all parameters
A__ : Optional[Any] =AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=lowerCAmelCase_ )
self.assertEqual(set(model.hf_device_map.values() ) , {torch.cuda.current_device()} )
for param in model.parameters():
A__ : int =False # freeze the model - train adapters later
if param.ndim == 1:
# cast the small parameters (e.g. layernorm) to fp32 for stability
A__ : Dict =param.data.to(torch.floataa )
# Step 2: add adapters
for _, module in model.named_modules():
if "OPTAttention" in repr(type(lowerCAmelCase_ ) ):
A__ : int =LoRALayer(module.q_proj , rank=16 )
A__ : Any =LoRALayer(module.k_proj , rank=16 )
A__ : Union[str, Any] =LoRALayer(module.v_proj , rank=16 )
# Step 3: dummy batch
A__ : List[Any] =self.tokenizer("""Test batch """ , return_tensors="""pt""" ).to(0 )
# Step 4: Check if the gradient is not None
with torch.cuda.amp.autocast():
A__ : Any =model.forward(**lowerCAmelCase_ )
out.logits.norm().backward()
for module in model.modules():
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
self.assertTrue(module.adapter[1].weight.grad is not None )
self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0 )
elif isinstance(lowerCAmelCase_ , nn.Embedding ):
self.assertTrue(module.weight.grad is None )
class lowerCamelCase ( lowercase_ ):
'''simple docstring'''
__snake_case = 'gpt2-xl'
__snake_case = 3.3191854854152187
| 687
| 1
|
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
return price * (1 + tax_rate)
if __name__ == "__main__":
print(F'{price_plus_tax(100, 0.25) = }')
print(F'{price_plus_tax(125.50, 0.05) = }')
| 413
|
'''simple docstring'''
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def lowerCamelCase__ ( SCREAMING_SNAKE_CASE : str ):
# encoder.embeddings are double copied in original FLAVA
return sum(param.float().sum() if 'encoder.embeddings' not in key else 0 for key, param in state_dict.items() )
def lowerCamelCase__ ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : Optional[int] ):
UpperCAmelCase = {}
for key, value in state_dict.items():
if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
continue
UpperCAmelCase = key.replace('heads.cmd.mim_head.cls.predictions' , 'mmm_image_head' )
UpperCAmelCase = key.replace('heads.cmd.mlm_head.cls.predictions' , 'mmm_text_head' )
UpperCAmelCase = key.replace('heads.cmd.itm_head.cls' , 'itm_head' )
UpperCAmelCase = key.replace('heads.cmd.itm_head.pooler' , 'itm_head.pooler' )
UpperCAmelCase = key.replace('heads.cmd.clip_head.logit_scale' , 'flava.logit_scale' )
UpperCAmelCase = key.replace('heads.fairseq_mlm.cls.predictions' , 'mlm_head' )
UpperCAmelCase = key.replace('heads.imagenet.mim_head.cls.predictions' , 'mim_head' )
UpperCAmelCase = key.replace('mm_text_projection' , 'flava.text_to_mm_projection' )
UpperCAmelCase = key.replace('mm_image_projection' , 'flava.image_to_mm_projection' )
UpperCAmelCase = key.replace('image_encoder.module' , 'flava.image_model' )
UpperCAmelCase = key.replace('text_encoder.module' , 'flava.text_model' )
UpperCAmelCase = key.replace('mm_encoder.module.encoder.cls_token' , 'flava.multimodal_model.cls_token' )
UpperCAmelCase = key.replace('mm_encoder.module' , 'flava.multimodal_model' )
UpperCAmelCase = key.replace('text_projection' , 'flava.text_projection' )
UpperCAmelCase = key.replace('image_projection' , 'flava.image_projection' )
UpperCAmelCase = value.float()
for key, value in codebook_state_dict.items():
UpperCAmelCase = value
return upgrade
@torch.no_grad()
def lowerCamelCase__ ( SCREAMING_SNAKE_CASE : List[str] , SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : Any=None ):
if config_path is not None:
UpperCAmelCase = FlavaConfig.from_pretrained(SCREAMING_SNAKE_CASE )
else:
UpperCAmelCase = FlavaConfig()
UpperCAmelCase = FlavaForPreTraining(SCREAMING_SNAKE_CASE ).eval()
UpperCAmelCase = convert_dalle_checkpoint(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , save_checkpoint=SCREAMING_SNAKE_CASE )
if os.path.exists(SCREAMING_SNAKE_CASE ):
UpperCAmelCase = torch.load(SCREAMING_SNAKE_CASE , map_location='cpu' )
else:
UpperCAmelCase = torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE , map_location='cpu' )
UpperCAmelCase = upgrade_state_dict(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
hf_model.load_state_dict(SCREAMING_SNAKE_CASE )
UpperCAmelCase = hf_model.state_dict()
UpperCAmelCase = count_parameters(SCREAMING_SNAKE_CASE )
UpperCAmelCase = count_parameters(SCREAMING_SNAKE_CASE ) + count_parameters(SCREAMING_SNAKE_CASE )
assert torch.allclose(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , atol=1E-3 )
hf_model.save_pretrained(SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
_a : str = 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 flava checkpoint')
parser.add_argument('--codebook_path', default=None, type=str, help='Path to flava codebook checkpoint')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
_a : str = parser.parse_args()
convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)
| 447
| 0
|
'''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
lowerCAmelCase : Tuple = logging.get_logger(__name__)
lowerCAmelCase : Dict = '''▁'''
lowerCAmelCase : int = {'''vocab_file''': '''sentencepiece.bpe.model'''}
lowerCAmelCase : List[str] = {
'''vocab_file''': {
'''facebook/mbart-large-50-one-to-many-mmt''': (
'''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'''
),
}
}
lowerCAmelCase : Dict = {
'''facebook/mbart-large-50-one-to-many-mmt''': 1_0_2_4,
}
# fmt: off
lowerCAmelCase : Optional[Any] = ['''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''', '''af_ZA''', '''az_AZ''', '''bn_IN''', '''fa_IR''', '''he_IL''', '''hr_HR''', '''id_ID''', '''ka_GE''', '''km_KH''', '''mk_MK''', '''ml_IN''', '''mn_MN''', '''mr_IN''', '''pl_PL''', '''ps_AF''', '''pt_XX''', '''sv_SE''', '''sw_KE''', '''ta_IN''', '''te_IN''', '''th_TH''', '''tl_XX''', '''uk_UA''', '''ur_PK''', '''xh_ZA''', '''gl_ES''', '''sl_SI''']
class UpperCAmelCase__ ( UpperCamelCase__ ):
a : Union[str, Any] = VOCAB_FILES_NAMES
a : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a : Optional[int] = PRETRAINED_VOCAB_FILES_MAP
a : List[str] = ["""input_ids""", """attention_mask"""]
a : List[int] = []
a : List[int] = []
def __init__( self , UpperCamelCase , UpperCamelCase=None , UpperCamelCase=None , UpperCamelCase="</s>" , UpperCamelCase="</s>" , UpperCamelCase="<s>" , UpperCamelCase="<unk>" , UpperCamelCase="<pad>" , UpperCamelCase="<mask>" , UpperCamelCase = None , **UpperCamelCase , ) -> None:
# Mask token behave like a normal word, i.e. include the space before it
__lowerCAmelCase = AddedToken(UpperCamelCase , lstrip=UpperCamelCase , rstrip=UpperCamelCase ) if isinstance(UpperCamelCase , UpperCamelCase ) else mask_token
__lowerCAmelCase = {} if sp_model_kwargs is None else sp_model_kwargs
__lowerCAmelCase = kwargs.get("additional_special_tokens" , [] )
kwargs["additional_special_tokens"] += [
code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
]
super().__init__(
src_lang=UpperCamelCase , tgt_lang=UpperCamelCase , eos_token=UpperCamelCase , unk_token=UpperCamelCase , sep_token=UpperCamelCase , cls_token=UpperCamelCase , pad_token=UpperCamelCase , mask_token=UpperCamelCase , sp_model_kwargs=self.sp_model_kwargs , **UpperCamelCase , )
__lowerCAmelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(UpperCamelCase ) )
__lowerCAmelCase = 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
__lowerCAmelCase = {"<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
__lowerCAmelCase = 1
__lowerCAmelCase = len(self.sp_model )
__lowerCAmelCase = {
code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(UpperCamelCase )
}
__lowerCAmelCase = {v: k for k, v in self.lang_code_to_id.items()}
__lowerCAmelCase = len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset
self.fairseq_tokens_to_ids.update(self.lang_code_to_id )
__lowerCAmelCase = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
__lowerCAmelCase = src_lang if src_lang is not None else "en_XX"
__lowerCAmelCase = self.lang_code_to_id[self._src_lang]
__lowerCAmelCase = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def UpperCAmelCase_ ( self ) -> int:
return len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset + 1 # Plus 1 for the mask token
@property
def UpperCAmelCase_ ( self ) -> str:
return self._src_lang
@src_lang.setter
def UpperCAmelCase_ ( self , UpperCamelCase ) -> None:
__lowerCAmelCase = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def __getstate__( self ) -> Dict:
__lowerCAmelCase = self.__dict__.copy()
__lowerCAmelCase = None
return state
def __setstate__( self , UpperCamelCase ) -> None:
__lowerCAmelCase = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
__lowerCAmelCase = {}
__lowerCAmelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def UpperCAmelCase_ ( self ) -> Dict:
__lowerCAmelCase = {self.convert_ids_to_tokens(UpperCamelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def UpperCAmelCase_ ( self , UpperCamelCase ) -> List[str]:
return self.sp_model.encode(UpperCamelCase , out_type=UpperCamelCase )
def UpperCAmelCase_ ( self , UpperCamelCase ) -> int:
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
__lowerCAmelCase = self.sp_model.PieceToId(UpperCamelCase )
# 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 UpperCAmelCase_ ( self , UpperCamelCase ) -> str:
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 UpperCAmelCase_ ( self , UpperCamelCase ) -> int:
__lowerCAmelCase = []
__lowerCAmelCase = ""
__lowerCAmelCase = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(UpperCamelCase ) + token
__lowerCAmelCase = True
__lowerCAmelCase = []
else:
current_sub_tokens.append(UpperCamelCase )
__lowerCAmelCase = False
out_string += self.sp_model.decode(UpperCamelCase )
return out_string.strip()
def UpperCAmelCase_ ( self , UpperCamelCase , UpperCamelCase = None ) -> Tuple[str]:
if not os.path.isdir(UpperCamelCase ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
__lowerCAmelCase = os.path.join(
UpperCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCamelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , UpperCamelCase )
elif not os.path.isfile(self.vocab_file ):
with open(UpperCamelCase , "wb" ) as fi:
__lowerCAmelCase = self.sp_model.serialized_model_proto()
fi.write(UpperCamelCase )
return (out_vocab_file,)
def UpperCAmelCase_ ( self , UpperCamelCase , UpperCamelCase = None , UpperCamelCase = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=UpperCamelCase , token_ids_a=UpperCamelCase , already_has_special_tokens=UpperCamelCase )
__lowerCAmelCase = [1] * len(self.prefix_tokens )
__lowerCAmelCase = [1] * len(self.suffix_tokens )
if token_ids_a is None:
return prefix_ones + ([0] * len(UpperCamelCase )) + suffix_ones
return prefix_ones + ([0] * len(UpperCamelCase )) + ([0] * len(UpperCamelCase )) + suffix_ones
def UpperCAmelCase_ ( self , UpperCamelCase , UpperCamelCase = 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 UpperCAmelCase_ ( self , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , **UpperCamelCase ) -> str:
if src_lang is None or tgt_lang is None:
raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model" )
__lowerCAmelCase = src_lang
__lowerCAmelCase = self(UpperCamelCase , add_special_tokens=UpperCamelCase , return_tensors=UpperCamelCase , **UpperCamelCase )
__lowerCAmelCase = self.convert_tokens_to_ids(UpperCamelCase )
__lowerCAmelCase = tgt_lang_id
return inputs
def UpperCAmelCase_ ( self , UpperCamelCase , UpperCamelCase = "en_XX" , UpperCamelCase = None , UpperCamelCase = "ro_RO" , **UpperCamelCase , ) -> BatchEncoding:
__lowerCAmelCase = src_lang
__lowerCAmelCase = tgt_lang
return super().prepare_seqaseq_batch(UpperCamelCase , UpperCamelCase , **UpperCamelCase )
def UpperCAmelCase_ ( self ) -> List[str]:
return self.set_src_lang_special_tokens(self.src_lang )
def UpperCAmelCase_ ( self ) -> Any:
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def UpperCAmelCase_ ( self , UpperCamelCase ) -> None:
__lowerCAmelCase = self.lang_code_to_id[src_lang]
__lowerCAmelCase = [self.cur_lang_code_id]
__lowerCAmelCase = [self.eos_token_id]
def UpperCAmelCase_ ( self , UpperCamelCase ) -> None:
__lowerCAmelCase = self.lang_code_to_id[tgt_lang]
__lowerCAmelCase = [self.cur_lang_code_id]
__lowerCAmelCase = [self.eos_token_id]
| 710
|
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import PaddingStrategy, check_min_version, send_example_telemetry
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')
lowerCAmelCase : Any = logging.getLogger(__name__)
@dataclass
class UpperCAmelCase__ :
a : str = field(
metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
a : Optional[str] = field(
default=UpperCamelCase__ , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
a : Optional[str] = field(
default=UpperCamelCase__ , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
a : Optional[str] = field(
default=UpperCamelCase__ , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
a : bool = field(
default=UpperCamelCase__ , metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""} , )
a : str = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
a : bool = field(
default=UpperCamelCase__ , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
@dataclass
class UpperCAmelCase__ :
a : Optional[str] = field(default=UpperCamelCase__ , metadata={"""help""": """The input training data file (a text file)."""} )
a : Optional[str] = field(
default=UpperCamelCase__ , metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""} , )
a : bool = field(
default=UpperCamelCase__ , metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )
a : Optional[int] = field(
default=UpperCamelCase__ , metadata={"""help""": """The number of processes to use for the preprocessing."""} , )
a : Optional[int] = field(
default=UpperCamelCase__ , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. If passed, sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
a : bool = field(
default=UpperCamelCase__ , metadata={
"""help""": (
"""Whether to pad all samples to the maximum sentence length. """
"""If False, will pad the samples dynamically when batching to the maximum length in the batch. More """
"""efficient on GPU but very bad for TPU."""
)
} , )
a : Optional[int] = field(
default=UpperCamelCase__ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
a : Optional[int] = field(
default=UpperCamelCase__ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
def UpperCAmelCase_ ( self ) -> Tuple:
if self.train_file is not None:
__lowerCAmelCase = self.train_file.split("." )[-1]
assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
if self.validation_file is not None:
__lowerCAmelCase = self.validation_file.split("." )[-1]
assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
@dataclass
class UpperCAmelCase__ :
a : PreTrainedTokenizerBase
a : Union[bool, str, PaddingStrategy] = True
a : Optional[int] = None
a : Optional[int] = None
def __call__( self , UpperCamelCase ) -> Optional[int]:
__lowerCAmelCase = "label" if "label" in features[0].keys() else "labels"
__lowerCAmelCase = [feature.pop(UpperCamelCase ) for feature in features]
__lowerCAmelCase = len(UpperCamelCase )
__lowerCAmelCase = len(features[0]["input_ids"] )
__lowerCAmelCase = [
[{k: v[i] for k, v in feature.items()} for i in range(UpperCamelCase )] for feature in features
]
__lowerCAmelCase = list(chain(*UpperCamelCase ) )
__lowerCAmelCase = self.tokenizer.pad(
UpperCamelCase , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors="pt" , )
# Un-flatten
__lowerCAmelCase = {k: v.view(UpperCamelCase , UpperCamelCase , -1 ) for k, v in batch.items()}
# Add back labels
__lowerCAmelCase = torch.tensor(UpperCamelCase , dtype=torch.intaa )
return batch
def __lowerCAmelCase ( ):
'''simple docstring'''
__lowerCAmelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(".json" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("run_swag" , lowerCamelCase , lowerCamelCase )
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
__lowerCAmelCase = training_args.get_process_log_level()
logger.setLevel(lowerCamelCase )
datasets.utils.logging.set_verbosity(lowerCamelCase )
transformers.utils.logging.set_verbosity(lowerCamelCase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
+ f'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' )
logger.info(f'''Training/evaluation parameters {training_args}''' )
# Detecting last checkpoint.
__lowerCAmelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
__lowerCAmelCase = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
"Use --overwrite_output_dir to overcome." )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch." )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.train_file is not None or data_args.validation_file is not None:
__lowerCAmelCase = {}
if data_args.train_file is not None:
__lowerCAmelCase = data_args.train_file
if data_args.validation_file is not None:
__lowerCAmelCase = data_args.validation_file
__lowerCAmelCase = data_args.train_file.split("." )[-1]
__lowerCAmelCase = load_dataset(
lowerCamelCase , data_files=lowerCamelCase , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
else:
# Downloading and loading the swag dataset from the hub.
__lowerCAmelCase = load_dataset(
"swag" , "regular" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Load pretrained model and tokenizer
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
__lowerCAmelCase = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
__lowerCAmelCase = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
__lowerCAmelCase = AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path , from_tf=bool(".ckpt" in model_args.model_name_or_path ) , config=lowerCamelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# When using your own dataset or a different dataset from swag, you will probably need to change this.
__lowerCAmelCase = [f'''ending{i}''' for i in range(4 )]
__lowerCAmelCase = "sent1"
__lowerCAmelCase = "sent2"
if data_args.max_seq_length is None:
__lowerCAmelCase = tokenizer.model_max_length
if max_seq_length > 10_24:
logger.warning(
"The chosen tokenizer supports a `model_max_length` that is longer than the default `block_size` value"
" of 1024. If you would like to use a longer `block_size` up to `tokenizer.model_max_length` you can"
" override this default with `--block_size xxx`." )
__lowerCAmelCase = 10_24
else:
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f'''The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the'''
f'''model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.''' )
__lowerCAmelCase = min(data_args.max_seq_length , tokenizer.model_max_length )
# Preprocessing the datasets.
def preprocess_function(lowerCamelCase : Tuple ):
__lowerCAmelCase = [[context] * 4 for context in examples[context_name]]
__lowerCAmelCase = examples[question_header_name]
__lowerCAmelCase = [
[f'''{header} {examples[end][i]}''' for end in ending_names] for i, header in enumerate(lowerCamelCase )
]
# Flatten out
__lowerCAmelCase = list(chain(*lowerCamelCase ) )
__lowerCAmelCase = list(chain(*lowerCamelCase ) )
# Tokenize
__lowerCAmelCase = tokenizer(
lowerCamelCase , lowerCamelCase , truncation=lowerCamelCase , max_length=lowerCamelCase , padding="max_length" if data_args.pad_to_max_length else False , )
# Un-flatten
return {k: [v[i : i + 4] for i in range(0 , len(lowerCamelCase ) , 4 )] for k, v in tokenized_examples.items()}
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError("--do_train requires a train dataset" )
__lowerCAmelCase = raw_datasets["train"]
if data_args.max_train_samples is not None:
__lowerCAmelCase = min(len(lowerCamelCase ) , data_args.max_train_samples )
__lowerCAmelCase = train_dataset.select(range(lowerCamelCase ) )
with training_args.main_process_first(desc="train dataset map pre-processing" ):
__lowerCAmelCase = train_dataset.map(
lowerCamelCase , batched=lowerCamelCase , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , )
if training_args.do_eval:
if "validation" not in raw_datasets:
raise ValueError("--do_eval requires a validation dataset" )
__lowerCAmelCase = raw_datasets["validation"]
if data_args.max_eval_samples is not None:
__lowerCAmelCase = min(len(lowerCamelCase ) , data_args.max_eval_samples )
__lowerCAmelCase = eval_dataset.select(range(lowerCamelCase ) )
with training_args.main_process_first(desc="validation dataset map pre-processing" ):
__lowerCAmelCase = eval_dataset.map(
lowerCamelCase , batched=lowerCamelCase , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , )
# Data collator
__lowerCAmelCase = (
default_data_collator
if data_args.pad_to_max_length
else DataCollatorForMultipleChoice(tokenizer=lowerCamelCase , pad_to_multiple_of=8 if training_args.fpaa else None )
)
# Metric
def compute_metrics(lowerCamelCase : Dict ):
__lowerCAmelCase , __lowerCAmelCase = eval_predictions
__lowerCAmelCase = np.argmax(lowerCamelCase , axis=1 )
return {"accuracy": (preds == label_ids).astype(np.floataa ).mean().item()}
# Initialize our Trainer
__lowerCAmelCase = Trainer(
model=lowerCamelCase , args=lowerCamelCase , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , tokenizer=lowerCamelCase , data_collator=lowerCamelCase , compute_metrics=lowerCamelCase , )
# Training
if training_args.do_train:
__lowerCAmelCase = None
if training_args.resume_from_checkpoint is not None:
__lowerCAmelCase = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
__lowerCAmelCase = last_checkpoint
__lowerCAmelCase = trainer.train(resume_from_checkpoint=lowerCamelCase )
trainer.save_model() # Saves the tokenizer too for easy upload
__lowerCAmelCase = train_result.metrics
__lowerCAmelCase = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(lowerCamelCase )
)
__lowerCAmelCase = min(lowerCamelCase , len(lowerCamelCase ) )
trainer.log_metrics("train" , lowerCamelCase )
trainer.save_metrics("train" , lowerCamelCase )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("*** Evaluate ***" )
__lowerCAmelCase = trainer.evaluate()
__lowerCAmelCase = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowerCamelCase )
__lowerCAmelCase = min(lowerCamelCase , len(lowerCamelCase ) )
trainer.log_metrics("eval" , lowerCamelCase )
trainer.save_metrics("eval" , lowerCamelCase )
__lowerCAmelCase = {
"finetuned_from": model_args.model_name_or_path,
"tasks": "multiple-choice",
"dataset_tags": "swag",
"dataset_args": "regular",
"dataset": "SWAG",
"language": "en",
}
if training_args.push_to_hub:
trainer.push_to_hub(**lowerCamelCase )
else:
trainer.create_model_card(**lowerCamelCase )
def __lowerCAmelCase ( lowerCamelCase : Tuple ):
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 39
| 0
|
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('''ignore''', category=UserWarning, module='''torch.optim.lr_scheduler''')
class __lowerCamelCase :
def __init__( self: Dict,A_: Optional[int],A_: Dict,A_: bool = True,A_: bool = False ):
'''simple docstring'''
__UpperCamelCase = scheduler
__UpperCamelCase = optimizers if isinstance(A_,(list, tuple) ) else [optimizers]
__UpperCamelCase = split_batches
__UpperCamelCase = step_with_optimizer
__UpperCamelCase = GradientState()
def snake_case_ ( self: Optional[int],*A_: int,**A_: str ):
'''simple docstring'''
if not self.step_with_optimizer:
# No link between scheduler and optimizer -> just step
self.scheduler.step(*A_,**A_ )
return
# Otherwise, first make sure the optimizer was stepped.
if not self.gradient_state.sync_gradients:
if self.gradient_state.adjust_scheduler:
self.scheduler._step_count += 1
return
for opt in self.optimizers:
if opt.step_was_skipped:
return
if self.split_batches:
# Split batches -> the training dataloader batch size is not changed so one step per training step
self.scheduler.step(*A_,**A_ )
else:
# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
# num_processes steps per training step
__UpperCamelCase = AcceleratorState().num_processes
for _ in range(A_ ):
# Special case when using OneCycle and `drop_last` was not used
if hasattr(self.scheduler,'total_steps' ):
if self.scheduler._step_count <= self.scheduler.total_steps:
self.scheduler.step(*A_,**A_ )
else:
self.scheduler.step(*A_,**A_ )
def snake_case_ ( self: Optional[Any] ):
'''simple docstring'''
return self.scheduler.get_last_lr()
def snake_case_ ( self: Optional[int] ):
'''simple docstring'''
return self.scheduler.state_dict()
def snake_case_ ( self: Optional[int],A_: Tuple ):
'''simple docstring'''
self.scheduler.load_state_dict(A_ )
def snake_case_ ( self: int ):
'''simple docstring'''
return self.scheduler.get_lr()
def snake_case_ ( self: Optional[int],*A_: Union[str, Any],**A_: List[Any] ):
'''simple docstring'''
return self.scheduler.print_lr(*A_,**A_ )
| 1
|
'''simple docstring'''
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
def UpperCamelCase__ ( _lowercase : str ) -> List[Any]:
__UpperCAmelCase: List[str] = [False] * len(_lowercase )
__UpperCAmelCase: str = [-1] * len(_lowercase )
def dfs(_lowercase : Dict , _lowercase : Optional[int] ):
__UpperCAmelCase: Optional[int] = True
__UpperCAmelCase: Optional[int] = c
for u in graph[v]:
if not visited[u]:
dfs(_lowercase , 1 - c )
for i in range(len(_lowercase ) ):
if not visited[i]:
dfs(_lowercase , 0 )
for i in range(len(_lowercase ) ):
for j in graph[i]:
if color[i] == color[j]:
return False
return True
# Adjacency list of graph
SCREAMING_SNAKE_CASE_ = {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}
print(check_bipartite_dfs(graph))
| 523
| 0
|
"""simple docstring"""
def __UpperCamelCase ( snake_case__ , snake_case__ ):
assert x is not None
assert y is not None
A_ : Optional[Any] = len(SCREAMING_SNAKE_CASE_ )
A_ : Union[str, Any] = len(SCREAMING_SNAKE_CASE_ )
# declaring the array for storing the dp values
A_ : int = [[0] * (n + 1) for _ in range(m + 1 )] # noqa: E741
for i in range(1 , m + 1 ):
for j in range(1 , n + 1 ):
A_ : Any = 1 if x[i - 1] == y[j - 1] else 0
A_ : Optional[Any] = max(l[i - 1][j] , l[i][j - 1] , l[i - 1][j - 1] + match )
A_ : Optional[int] = ""
A_ : int = m, n
while i > 0 and j > 0:
A_ : Any = 1 if x[i - 1] == y[j - 1] else 0
if l[i][j] == l[i - 1][j - 1] + match:
if match == 1:
A_ : List[str] = x[i - 1] + seq
i -= 1
j -= 1
elif l[i][j] == l[i - 1][j]:
i -= 1
else:
j -= 1
return l[m][n], seq
if __name__ == "__main__":
_lowerCAmelCase = "AGGTAB"
_lowerCAmelCase = "GXTXAYB"
_lowerCAmelCase = 4
_lowerCAmelCase = "GTAB"
_lowerCAmelCase , _lowerCAmelCase = longest_common_subsequence(a, b)
print("len =", ln, ", sub-sequence =", subseq)
import doctest
doctest.testmod()
| 702
|
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
_A : Optional[int] = ["""image_processor""", """tokenizer"""]
_A : List[Any] = """ViTImageProcessor"""
_A : Optional[Any] = ("""CLIPTokenizer""", """CLIPTokenizerFast""")
def __init__(self , lowerCAmelCase_=None , lowerCAmelCase_=None , **lowerCAmelCase_ ):
A_ : Union[str, Any] = None
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" , lowerCAmelCase_ , )
A_ : Union[str, Any] = kwargs.pop("""feature_extractor""" )
A_ : Optional[int] = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("""You need to specify an `image_processor`.""" )
if tokenizer is None:
raise ValueError("""You need to specify a `tokenizer`.""" )
super().__init__(lowerCAmelCase_ , lowerCAmelCase_ )
def __call__(self , lowerCAmelCase_=None , lowerCAmelCase_=None , lowerCAmelCase_=None , lowerCAmelCase_=None , **lowerCAmelCase_ ):
if text is None and visual_prompt is None and images is None:
raise ValueError("""You have to specify either text, visual prompt or images.""" )
if text is not None and visual_prompt is not None:
raise ValueError("""You have to specify exactly one type of prompt. Either text or visual prompt.""" )
if text is not None:
A_ : Tuple = self.tokenizer(lowerCAmelCase_ , return_tensors=lowerCAmelCase_ , **lowerCAmelCase_ )
if visual_prompt is not None:
A_ : Union[str, Any] = self.image_processor(lowerCAmelCase_ , return_tensors=lowerCAmelCase_ , **lowerCAmelCase_ )
if images is not None:
A_ : Any = self.image_processor(lowerCAmelCase_ , return_tensors=lowerCAmelCase_ , **lowerCAmelCase_ )
if visual_prompt is not None and images is not None:
A_ : List[str] = {
"""pixel_values""": image_features.pixel_values,
"""conditional_pixel_values""": prompt_features.pixel_values,
}
return encoding
elif text is not None and images is not None:
A_ : str = image_features.pixel_values
return encoding
elif text is not None:
return encoding
elif visual_prompt is not None:
A_ : Any = {
"""conditional_pixel_values""": prompt_features.pixel_values,
}
return encoding
else:
return BatchEncoding(data=dict(**lowerCAmelCase_ ) , tensor_type=lowerCAmelCase_ )
def lowerCamelCase(self , *lowerCAmelCase_ , **lowerCAmelCase_ ):
return self.tokenizer.batch_decode(*lowerCAmelCase_ , **lowerCAmelCase_ )
def lowerCamelCase(self , *lowerCAmelCase_ , **lowerCAmelCase_ ):
return self.tokenizer.decode(*lowerCAmelCase_ , **lowerCAmelCase_ )
@property
def lowerCamelCase(self ):
warnings.warn(
"""`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" , lowerCAmelCase_ , )
return self.image_processor_class
@property
def lowerCamelCase(self ):
warnings.warn(
"""`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" , lowerCAmelCase_ , )
return self.image_processor
| 480
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|
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {
"""bert-base-uncased""": """https://huggingface.co/bert-base-uncased/resolve/main/config.json""",
"""bert-large-uncased""": """https://huggingface.co/bert-large-uncased/resolve/main/config.json""",
"""bert-base-cased""": """https://huggingface.co/bert-base-cased/resolve/main/config.json""",
"""bert-large-cased""": """https://huggingface.co/bert-large-cased/resolve/main/config.json""",
"""bert-base-multilingual-uncased""": """https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json""",
"""bert-base-multilingual-cased""": """https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json""",
"""bert-base-chinese""": """https://huggingface.co/bert-base-chinese/resolve/main/config.json""",
"""bert-base-german-cased""": """https://huggingface.co/bert-base-german-cased/resolve/main/config.json""",
"""bert-large-uncased-whole-word-masking""": (
"""https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json"""
),
"""bert-large-cased-whole-word-masking""": (
"""https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json"""
),
"""bert-large-uncased-whole-word-masking-finetuned-squad""": (
"""https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json"""
),
"""bert-large-cased-whole-word-masking-finetuned-squad""": (
"""https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json"""
),
"""bert-base-cased-finetuned-mrpc""": """https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json""",
"""bert-base-german-dbmdz-cased""": """https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json""",
"""bert-base-german-dbmdz-uncased""": """https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json""",
"""cl-tohoku/bert-base-japanese""": """https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json""",
"""cl-tohoku/bert-base-japanese-whole-word-masking""": (
"""https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json"""
),
"""cl-tohoku/bert-base-japanese-char""": (
"""https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json"""
),
"""cl-tohoku/bert-base-japanese-char-whole-word-masking""": (
"""https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json"""
),
"""TurkuNLP/bert-base-finnish-cased-v1""": (
"""https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json"""
),
"""TurkuNLP/bert-base-finnish-uncased-v1""": (
"""https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json"""
),
"""wietsedv/bert-base-dutch-cased""": """https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json""",
# See all BERT models at https://huggingface.co/models?filter=bert
}
class lowercase__ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
UpperCamelCase = '''bert'''
def __init__( self : Optional[Any] , _UpperCAmelCase : Optional[int]=30522 , _UpperCAmelCase : Tuple=768 , _UpperCAmelCase : Union[str, Any]=12 , _UpperCAmelCase : int=12 , _UpperCAmelCase : Optional[Any]=3072 , _UpperCAmelCase : Union[str, Any]="gelu" , _UpperCAmelCase : str=0.1 , _UpperCAmelCase : List[str]=0.1 , _UpperCAmelCase : int=512 , _UpperCAmelCase : Optional[Any]=2 , _UpperCAmelCase : Optional[Any]=0.02 , _UpperCAmelCase : int=1e-12 , _UpperCAmelCase : Union[str, Any]=0 , _UpperCAmelCase : List[str]="absolute" , _UpperCAmelCase : List[str]=True , _UpperCAmelCase : Dict=None , **_UpperCAmelCase : str , ) -> int:
'''simple docstring'''
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_ = position_embedding_type
UpperCAmelCase_ = use_cache
UpperCAmelCase_ = classifier_dropout
class lowercase__ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
@property
def lowercase__ ( self : Tuple ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
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),
("token_type_ids", dynamic_axis),
] )
| 82
|
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
"""asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
# See all SEW-D models at https://huggingface.co/models?filter=sew-d
}
class _lowerCAmelCase ( _lowercase ):
A__ = 'sew-d'
def __init__( self , __UpperCAmelCase=32 , __UpperCAmelCase=768 , __UpperCAmelCase=12 , __UpperCAmelCase=12 , __UpperCAmelCase=3072 , __UpperCAmelCase=2 , __UpperCAmelCase=512 , __UpperCAmelCase=256 , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=("p2c", "c2p") , __UpperCAmelCase="layer_norm" , __UpperCAmelCase="gelu_python" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.0 , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.02 , __UpperCAmelCase=1e-7 , __UpperCAmelCase=1e-5 , __UpperCAmelCase="group" , __UpperCAmelCase="gelu" , __UpperCAmelCase=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) , __UpperCAmelCase=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) , __UpperCAmelCase=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) , __UpperCAmelCase=False , __UpperCAmelCase=128 , __UpperCAmelCase=16 , __UpperCAmelCase=True , __UpperCAmelCase=0.05 , __UpperCAmelCase=10 , __UpperCAmelCase=2 , __UpperCAmelCase=0.0 , __UpperCAmelCase=10 , __UpperCAmelCase=0 , __UpperCAmelCase="mean" , __UpperCAmelCase=False , __UpperCAmelCase=False , __UpperCAmelCase=256 , __UpperCAmelCase=0 , __UpperCAmelCase=1 , __UpperCAmelCase=2 , **__UpperCAmelCase , ):
super().__init__(**__UpperCAmelCase , pad_token_id=__UpperCAmelCase , bos_token_id=__UpperCAmelCase , eos_token_id=__UpperCAmelCase )
lowerCAmelCase__ : Optional[int] = hidden_size
lowerCAmelCase__ : Optional[int] = feat_extract_norm
lowerCAmelCase__ : str = feat_extract_activation
lowerCAmelCase__ : int = list(__UpperCAmelCase )
lowerCAmelCase__ : int = list(__UpperCAmelCase )
lowerCAmelCase__ : Any = list(__UpperCAmelCase )
lowerCAmelCase__ : int = conv_bias
lowerCAmelCase__ : List[Any] = num_conv_pos_embeddings
lowerCAmelCase__ : Optional[int] = num_conv_pos_embedding_groups
lowerCAmelCase__ : int = len(self.conv_dim )
lowerCAmelCase__ : Union[str, Any] = num_hidden_layers
lowerCAmelCase__ : Any = intermediate_size
lowerCAmelCase__ : int = squeeze_factor
lowerCAmelCase__ : int = max_position_embeddings
lowerCAmelCase__ : Any = position_buckets
lowerCAmelCase__ : Optional[int] = share_att_key
lowerCAmelCase__ : Tuple = relative_attention
lowerCAmelCase__ : Optional[int] = norm_rel_ebd
lowerCAmelCase__ : Tuple = list(__UpperCAmelCase )
lowerCAmelCase__ : List[Any] = hidden_act
lowerCAmelCase__ : Any = num_attention_heads
lowerCAmelCase__ : Optional[int] = hidden_dropout
lowerCAmelCase__ : Union[str, Any] = attention_dropout
lowerCAmelCase__ : str = activation_dropout
lowerCAmelCase__ : List[Any] = feat_proj_dropout
lowerCAmelCase__ : Any = final_dropout
lowerCAmelCase__ : Optional[int] = layer_norm_eps
lowerCAmelCase__ : List[str] = feature_layer_norm_eps
lowerCAmelCase__ : Tuple = initializer_range
lowerCAmelCase__ : Tuple = vocab_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)`,'''
f"""but is `len(config.conv_dim) = {len(self.conv_dim )}`, `len(config.conv_stride)"""
f"""= {len(self.conv_stride )}`, `len(config.conv_kernel) = {len(self.conv_kernel )}`.""" )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
lowerCAmelCase__ : Tuple = apply_spec_augment
lowerCAmelCase__ : List[str] = mask_time_prob
lowerCAmelCase__ : int = mask_time_length
lowerCAmelCase__ : int = mask_time_min_masks
lowerCAmelCase__ : Optional[int] = mask_feature_prob
lowerCAmelCase__ : int = mask_feature_length
lowerCAmelCase__ : int = mask_feature_min_masks
# ctc loss
lowerCAmelCase__ : Optional[Any] = ctc_loss_reduction
lowerCAmelCase__ : Any = ctc_zero_infinity
# sequence classification
lowerCAmelCase__ : Tuple = use_weighted_layer_sum
lowerCAmelCase__ : Dict = classifier_proj_size
@property
def __magic_name__( self ):
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 678
| 0
|
"""simple docstring"""
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
lowercase__ = get_logger(__name__)
class lowerCAmelCase__ :
'''simple docstring'''
lowerCamelCase__ = """dummy_data"""
lowerCamelCase__ = """datasets"""
lowerCamelCase__ = False
def __init__( self , lowercase , lowercase , lowercase , lowercase = None , lowercase = False , lowercase = True , lowercase = None , ):
_lowerCamelCase : Any = 0
_lowerCamelCase : Union[str, Any] = dataset_name
_lowerCamelCase : Any = cache_dir
_lowerCamelCase : Dict = use_local_dummy_data
_lowerCamelCase : Dict = config
# download_callbacks take a single url as input
_lowerCamelCase : List[Callable] = download_callbacks or []
# if False, it doesn't load existing files and it returns the paths of the dummy files relative
# to the dummy_data zip file root
_lowerCamelCase : int = load_existing_dummy_data
# TODO(PVP, QL) might need to make this more general
_lowerCamelCase : Union[str, Any] = str(lowercase )
# to be downloaded
_lowerCamelCase : Any = None
_lowerCamelCase : List[str] = None
@property
def A_ ( self ):
if self._dummy_file is None:
_lowerCamelCase : int = self.download_dummy_data()
return self._dummy_file
@property
def A_ ( self ):
if self.config is not None:
# structure is dummy / config_name / version_name
return os.path.join('dummy' , self.config.name , self.version_name )
# structure is dummy / version_name
return os.path.join('dummy' , self.version_name )
@property
def A_ ( self ):
return os.path.join(self.dummy_data_folder , 'dummy_data.zip' )
def A_ ( self ):
_lowerCamelCase : Union[str, Any] = (
self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
)
_lowerCamelCase : Any = cached_path(
lowercase , cache_dir=self.cache_dir , extract_compressed_file=lowercase , force_extract=lowercase )
return os.path.join(lowercase , self.dummy_file_name )
@property
def A_ ( self ):
return os.path.join(self.datasets_scripts_dir , self.dataset_name , self.dummy_zip_file )
@property
def A_ ( self ):
if self._bucket_url is None:
_lowerCamelCase : Optional[int] = hf_github_url(self.dataset_name , self.dummy_zip_file.replace(os.sep , '/' ) )
return self._bucket_url
@property
def A_ ( self ):
# return full path if its a dir
if os.path.isdir(self.dummy_file ):
return self.dummy_file
# else cut off path to file -> example `xsum`.
return "/".join(self.dummy_file.replace(os.sep , '/' ).split('/' )[:-1] )
def A_ ( self , lowercase , *lowercase ):
if self.load_existing_dummy_data:
# dummy data is downloaded and tested
_lowerCamelCase : Union[str, Any] = self.dummy_file
else:
# dummy data cannot be downloaded and only the path to dummy file is returned
_lowerCamelCase : Union[str, Any] = self.dummy_file_name
# special case when data_url is a dict
if isinstance(lowercase , lowercase ):
return self.create_dummy_data_dict(lowercase , lowercase )
elif isinstance(lowercase , (list, tuple) ):
return self.create_dummy_data_list(lowercase , lowercase )
else:
return self.create_dummy_data_single(lowercase , lowercase )
def A_ ( self , lowercase , *lowercase ):
return self.download_and_extract(lowercase )
def A_ ( self , lowercase , lowercase ):
return self.download_and_extract(lowercase )
def A_ ( self , lowercase , *lowercase , **lowercase ):
return path
def A_ ( self ):
return {}
def A_ ( self , lowercase , lowercase ):
_lowerCamelCase : Union[str, Any] = {}
for key, single_urls in data_url.items():
for download_callback in self.download_callbacks:
if isinstance(lowercase , lowercase ):
for single_url in single_urls:
download_callback(lowercase )
else:
_lowerCamelCase : Any = single_urls
download_callback(lowercase )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
if isinstance(lowercase , lowercase ):
_lowerCamelCase : str = [os.path.join(lowercase , urllib.parse.quote_plus(Path(lowercase ).name ) ) for x in single_urls]
else:
_lowerCamelCase : int = single_urls
_lowerCamelCase : Dict = os.path.join(lowercase , urllib.parse.quote_plus(Path(lowercase ).name ) )
_lowerCamelCase : List[str] = value
# make sure that values are unique
if all(isinstance(lowercase , lowercase ) for i in dummy_data_dict.values() ) and len(set(dummy_data_dict.values() ) ) < len(
dummy_data_dict.values() ):
# append key to value to make its name unique
_lowerCamelCase : List[Any] = {key: value + key for key, value in dummy_data_dict.items()}
return dummy_data_dict
def A_ ( self , lowercase , lowercase ):
_lowerCamelCase : List[str] = []
# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
_lowerCamelCase : Optional[Any] = all(bool(re.findall('[0-9]{3,}-of-[0-9]{3,}' , lowercase ) ) for url in data_url )
_lowerCamelCase : int = all(
url.startswith('https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed' ) for url in data_url )
if data_url and (is_tf_records or is_pubmed_records):
_lowerCamelCase : List[str] = [data_url[0]] * len(lowercase )
for single_url in data_url:
for download_callback in self.download_callbacks:
download_callback(lowercase )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
_lowerCamelCase : str = os.path.join(lowercase , urllib.parse.quote_plus(single_url.split('/' )[-1] ) )
dummy_data_list.append(lowercase )
return dummy_data_list
def A_ ( self , lowercase , lowercase ):
for download_callback in self.download_callbacks:
download_callback(lowercase )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
_lowerCamelCase : Any = os.path.join(lowercase , urllib.parse.quote_plus(data_url.split('/' )[-1] ) )
if os.path.exists(lowercase ) or not self.load_existing_dummy_data:
return value
else:
# Backward compatibility, maybe deprecate at one point.
# For many datasets with single url calls to dl_manager.download_and_extract,
# the dummy_data.zip file is actually the zipped downloaded file
# while now we expected the dummy_data.zip file to be a directory containing
# the downloaded file.
return path_to_dummy_data
def A_ ( self ):
pass
def A_ ( self ):
pass
def A_ ( self , lowercase ):
def _iter_archive_members(lowercase ):
# this preserves the order of the members inside the ZIP archive
_lowerCamelCase : Tuple = Path(self.dummy_file ).parent
_lowerCamelCase : str = path.relative_to(lowercase )
with ZipFile(self.local_path_to_dummy_data ) as zip_file:
_lowerCamelCase : Optional[Any] = zip_file.namelist()
for member in members:
if member.startswith(relative_path.as_posix() ):
yield dummy_parent_path.joinpath(lowercase )
_lowerCamelCase : Optional[Any] = Path(lowercase )
_lowerCamelCase : Tuple = _iter_archive_members(lowercase ) if self.use_local_dummy_data else path.rglob('*' )
for file_path in file_paths:
if file_path.is_file() and not file_path.name.startswith(('.', '__') ):
yield file_path.relative_to(lowercase ).as_posix(), file_path.open('rb' )
def A_ ( self , lowercase ):
if not isinstance(lowercase , lowercase ):
_lowerCamelCase : Optional[int] = [paths]
for path in paths:
if os.path.isfile(lowercase ):
if os.path.basename(lowercase ).startswith(('.', '__') ):
return
yield path
else:
for dirpath, dirnames, filenames in os.walk(lowercase ):
if os.path.basename(lowercase ).startswith(('.', '__') ):
continue
dirnames.sort()
for filename in sorted(lowercase ):
if filename.startswith(('.', '__') ):
continue
yield os.path.join(lowercase , lowercase )
| 492
|
"""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__ = {
"""facebook/deit-base-distilled-patch16-224""": (
"""https://huggingface.co/facebook/deit-base-patch16-224/resolve/main/config.json"""
),
# See all DeiT models at https://huggingface.co/models?filter=deit
}
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
lowerCamelCase__ = """deit"""
def __init__( self , lowercase=768 , lowercase=12 , lowercase=12 , lowercase=3072 , lowercase="gelu" , lowercase=0.0 , lowercase=0.0 , lowercase=0.02 , lowercase=1E-12 , lowercase=224 , lowercase=16 , lowercase=3 , lowercase=True , lowercase=16 , **lowercase , ):
super().__init__(**lowercase )
_lowerCamelCase : Optional[Any] = hidden_size
_lowerCamelCase : List[str] = num_hidden_layers
_lowerCamelCase : Tuple = num_attention_heads
_lowerCamelCase : Any = intermediate_size
_lowerCamelCase : int = hidden_act
_lowerCamelCase : Any = hidden_dropout_prob
_lowerCamelCase : str = attention_probs_dropout_prob
_lowerCamelCase : str = initializer_range
_lowerCamelCase : Union[str, Any] = layer_norm_eps
_lowerCamelCase : Optional[Any] = image_size
_lowerCamelCase : Optional[int] = patch_size
_lowerCamelCase : Dict = num_channels
_lowerCamelCase : Dict = qkv_bias
_lowerCamelCase : Dict = encoder_stride
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
lowerCamelCase__ = version.parse("""1.11""" )
@property
def A_ ( self ):
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def A_ ( self ):
return 1E-4
| 492
| 1
|
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
SCREAMING_SNAKE_CASE : Optional[int] = ["small", "medium", "large"]
SCREAMING_SNAKE_CASE : List[Any] = "lm_head.decoder.weight"
SCREAMING_SNAKE_CASE : List[Any] = "lm_head.weight"
def _lowerCamelCase ( SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : str ):
"""simple docstring"""
a_ : Optional[Any] = torch.load(SCREAMING_SNAKE_CASE_ )
a_ : str = d.pop(SCREAMING_SNAKE_CASE_ )
os.makedirs(SCREAMING_SNAKE_CASE_ , exist_ok=SCREAMING_SNAKE_CASE_ )
torch.save(SCREAMING_SNAKE_CASE_ , os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument("--dialogpt_path", default=".", type=str)
SCREAMING_SNAKE_CASE : Union[str, Any] = parser.parse_args()
for MODEL in DIALOGPT_MODELS:
SCREAMING_SNAKE_CASE : str = os.path.join(args.dialogpt_path, F"""{MODEL}_ft.pkl""")
SCREAMING_SNAKE_CASE : Optional[int] = F"""./DialoGPT-{MODEL}"""
convert_dialogpt_checkpoint(
checkpoint_path,
pytorch_dump_folder_path,
)
| 419
|
class snake_case__ :
def __init__( self , UpperCamelCase_ ) -> Tuple:
"""simple docstring"""
a_ : Any = n
a_ : Tuple = [None] * self.n
a_ : List[str] = 0 # index of the first element
a_ : Union[str, Any] = 0
a_ : Optional[int] = 0
def __len__( self ) -> int:
"""simple docstring"""
return self.size
def A ( self ) -> bool:
"""simple docstring"""
return self.size == 0
def A ( self ) -> Optional[Any]:
"""simple docstring"""
return False if self.is_empty() else self.array[self.front]
def A ( self , UpperCamelCase_ ) -> Tuple:
"""simple docstring"""
if self.size >= self.n:
raise Exception("""QUEUE IS FULL""" )
a_ : Optional[Any] = data
a_ : List[Any] = (self.rear + 1) % self.n
self.size += 1
return self
def A ( self ) -> str:
"""simple docstring"""
if self.size == 0:
raise Exception("""UNDERFLOW""" )
a_ : str = self.array[self.front]
a_ : Optional[int] = None
a_ : Optional[int] = (self.front + 1) % self.n
self.size -= 1
return temp
| 419
| 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,
FlaxDownBlockaD,
FlaxUNetMidBlockaDCrossAttn,
)
@flax.struct.dataclass
class UpperCamelCase__ (a ):
'''simple docstring'''
_UpperCamelCase = 42
_UpperCamelCase = 42
class UpperCamelCase__ (nn.Module ):
'''simple docstring'''
_UpperCamelCase = 42
_UpperCamelCase = (16, 32, 96, 256)
_UpperCamelCase = jnp.floataa
def UpperCamelCase_ ( self ):
lowerCamelCase__ = nn.Conv(
self.block_out_channels[0] ,kernel_size=(3, 3) ,padding=((1, 1), (1, 1)) ,dtype=self.dtype ,)
lowerCamelCase__ = []
for i in range(len(self.block_out_channels ) - 1 ):
lowerCamelCase__ = self.block_out_channels[i]
lowerCamelCase__ = self.block_out_channels[i + 1]
lowerCamelCase__ = nn.Conv(
_lowerCAmelCase ,kernel_size=(3, 3) ,padding=((1, 1), (1, 1)) ,dtype=self.dtype ,)
blocks.append(_lowerCAmelCase )
lowerCamelCase__ = nn.Conv(
_lowerCAmelCase ,kernel_size=(3, 3) ,strides=(2, 2) ,padding=((1, 1), (1, 1)) ,dtype=self.dtype ,)
blocks.append(_lowerCAmelCase )
lowerCamelCase__ = blocks
lowerCamelCase__ = nn.Conv(
self.conditioning_embedding_channels ,kernel_size=(3, 3) ,padding=((1, 1), (1, 1)) ,kernel_init=nn.initializers.zeros_init() ,bias_init=nn.initializers.zeros_init() ,dtype=self.dtype ,)
def __call__( self ,_lowerCAmelCase ):
lowerCamelCase__ = self.conv_in(_lowerCAmelCase )
lowerCamelCase__ = nn.silu(_lowerCAmelCase )
for block in self.blocks:
lowerCamelCase__ = block(_lowerCAmelCase )
lowerCamelCase__ = nn.silu(_lowerCAmelCase )
lowerCamelCase__ = self.conv_out(_lowerCAmelCase )
return embedding
@flax_register_to_config
class UpperCamelCase__ (nn.Module ,a ,a ):
'''simple docstring'''
_UpperCamelCase = 32
_UpperCamelCase = 4
_UpperCamelCase = (
'CrossAttnDownBlock2D',
'CrossAttnDownBlock2D',
'CrossAttnDownBlock2D',
'DownBlock2D',
)
_UpperCamelCase = False
_UpperCamelCase = (320, 640, 1280, 1280)
_UpperCamelCase = 2
_UpperCamelCase = 8
_UpperCamelCase = None
_UpperCamelCase = 1280
_UpperCamelCase = 0.0
_UpperCamelCase = False
_UpperCamelCase = jnp.floataa
_UpperCamelCase = True
_UpperCamelCase = 0
_UpperCamelCase = 'rgb'
_UpperCamelCase = (16, 32, 96, 256)
def UpperCamelCase_ ( self ,_lowerCAmelCase ):
# init input tensors
lowerCamelCase__ = (1, self.in_channels, self.sample_size, self.sample_size)
lowerCamelCase__ = jnp.zeros(_lowerCAmelCase ,dtype=jnp.floataa )
lowerCamelCase__ = jnp.ones((1,) ,dtype=jnp.intaa )
lowerCamelCase__ = jnp.zeros((1, 1, self.cross_attention_dim) ,dtype=jnp.floataa )
lowerCamelCase__ = (1, 3, self.sample_size * 8, self.sample_size * 8)
lowerCamelCase__ = jnp.zeros(_lowerCAmelCase ,dtype=jnp.floataa )
lowerCamelCase__ , lowerCamelCase__ = jax.random.split(_lowerCAmelCase )
lowerCamelCase__ = {"""params""": params_rng, """dropout""": dropout_rng}
return self.init(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )["params"]
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.block_out_channels
lowerCamelCase__ = block_out_channels[0] * 4
# 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.
lowerCamelCase__ = self.num_attention_heads or self.attention_head_dim
# input
lowerCamelCase__ = nn.Conv(
block_out_channels[0] ,kernel_size=(3, 3) ,strides=(1, 1) ,padding=((1, 1), (1, 1)) ,dtype=self.dtype ,)
# time
lowerCamelCase__ = FlaxTimesteps(
block_out_channels[0] ,flip_sin_to_cos=self.flip_sin_to_cos ,freq_shift=self.config.freq_shift )
lowerCamelCase__ = FlaxTimestepEmbedding(_lowerCAmelCase ,dtype=self.dtype )
lowerCamelCase__ = FlaxControlNetConditioningEmbedding(
conditioning_embedding_channels=block_out_channels[0] ,block_out_channels=self.conditioning_embedding_out_channels ,)
lowerCamelCase__ = self.only_cross_attention
if isinstance(_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = (only_cross_attention,) * len(self.down_block_types )
if isinstance(_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = (num_attention_heads,) * len(self.down_block_types )
# down
lowerCamelCase__ = []
lowerCamelCase__ = []
lowerCamelCase__ = block_out_channels[0]
lowerCamelCase__ = nn.Conv(
_lowerCAmelCase ,kernel_size=(1, 1) ,padding="""VALID""" ,kernel_init=nn.initializers.zeros_init() ,bias_init=nn.initializers.zeros_init() ,dtype=self.dtype ,)
controlnet_down_blocks.append(_lowerCAmelCase )
for i, down_block_type in enumerate(self.down_block_types ):
lowerCamelCase__ = output_channel
lowerCamelCase__ = block_out_channels[i]
lowerCamelCase__ = i == len(_lowerCAmelCase ) - 1
if down_block_type == "CrossAttnDownBlock2D":
lowerCamelCase__ = FlaxCrossAttnDownBlockaD(
in_channels=_lowerCAmelCase ,out_channels=_lowerCAmelCase ,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] ,dtype=self.dtype ,)
else:
lowerCamelCase__ = FlaxDownBlockaD(
in_channels=_lowerCAmelCase ,out_channels=_lowerCAmelCase ,dropout=self.dropout ,num_layers=self.layers_per_block ,add_downsample=not is_final_block ,dtype=self.dtype ,)
down_blocks.append(_lowerCAmelCase )
for _ in range(self.layers_per_block ):
lowerCamelCase__ = nn.Conv(
_lowerCAmelCase ,kernel_size=(1, 1) ,padding="""VALID""" ,kernel_init=nn.initializers.zeros_init() ,bias_init=nn.initializers.zeros_init() ,dtype=self.dtype ,)
controlnet_down_blocks.append(_lowerCAmelCase )
if not is_final_block:
lowerCamelCase__ = nn.Conv(
_lowerCAmelCase ,kernel_size=(1, 1) ,padding="""VALID""" ,kernel_init=nn.initializers.zeros_init() ,bias_init=nn.initializers.zeros_init() ,dtype=self.dtype ,)
controlnet_down_blocks.append(_lowerCAmelCase )
lowerCamelCase__ = down_blocks
lowerCamelCase__ = controlnet_down_blocks
# mid
lowerCamelCase__ = block_out_channels[-1]
lowerCamelCase__ = FlaxUNetMidBlockaDCrossAttn(
in_channels=_lowerCAmelCase ,dropout=self.dropout ,num_attention_heads=num_attention_heads[-1] ,use_linear_projection=self.use_linear_projection ,dtype=self.dtype ,)
lowerCamelCase__ = nn.Conv(
_lowerCAmelCase ,kernel_size=(1, 1) ,padding="""VALID""" ,kernel_init=nn.initializers.zeros_init() ,bias_init=nn.initializers.zeros_init() ,dtype=self.dtype ,)
def __call__( self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase = 1.0 ,_lowerCAmelCase = True ,_lowerCAmelCase = False ,):
lowerCamelCase__ = self.controlnet_conditioning_channel_order
if channel_order == "bgr":
lowerCamelCase__ = jnp.flip(_lowerCAmelCase ,axis=1 )
# 1. time
if not isinstance(_lowerCAmelCase ,jnp.ndarray ):
lowerCamelCase__ = jnp.array([timesteps] ,dtype=jnp.intaa )
elif isinstance(_lowerCAmelCase ,jnp.ndarray ) and len(timesteps.shape ) == 0:
lowerCamelCase__ = timesteps.astype(dtype=jnp.floataa )
lowerCamelCase__ = jnp.expand_dims(_lowerCAmelCase ,0 )
lowerCamelCase__ = self.time_proj(_lowerCAmelCase )
lowerCamelCase__ = self.time_embedding(_lowerCAmelCase )
# 2. pre-process
lowerCamelCase__ = jnp.transpose(_lowerCAmelCase ,(0, 2, 3, 1) )
lowerCamelCase__ = self.conv_in(_lowerCAmelCase )
lowerCamelCase__ = jnp.transpose(_lowerCAmelCase ,(0, 2, 3, 1) )
lowerCamelCase__ = self.controlnet_cond_embedding(_lowerCAmelCase )
sample += controlnet_cond
# 3. down
lowerCamelCase__ = (sample,)
for down_block in self.down_blocks:
if isinstance(_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ , lowerCamelCase__ = down_block(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,deterministic=not train )
else:
lowerCamelCase__ , lowerCamelCase__ = down_block(_lowerCAmelCase ,_lowerCAmelCase ,deterministic=not train )
down_block_res_samples += res_samples
# 4. mid
lowerCamelCase__ = self.mid_block(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,deterministic=not train )
# 5. contronet blocks
lowerCamelCase__ = ()
for down_block_res_sample, controlnet_block in zip(_lowerCAmelCase ,self.controlnet_down_blocks ):
lowerCamelCase__ = controlnet_block(_lowerCAmelCase )
controlnet_down_block_res_samples += (down_block_res_sample,)
lowerCamelCase__ = controlnet_down_block_res_samples
lowerCamelCase__ = self.controlnet_mid_block(_lowerCAmelCase )
# 6. scaling
lowerCamelCase__ = [sample * conditioning_scale for sample in down_block_res_samples]
mid_block_res_sample *= conditioning_scale
if not return_dict:
return (down_block_res_samples, mid_block_res_sample)
return FlaxControlNetOutput(
down_block_res_samples=_lowerCAmelCase ,mid_block_res_sample=_lowerCAmelCase )
| 713
|
'''simple docstring'''
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFViTMAEForPreTraining, TFViTMAEModel
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class UpperCamelCase__ :
'''simple docstring'''
def __init__( self ,_lowerCAmelCase ,_lowerCAmelCase=13 ,_lowerCAmelCase=30 ,_lowerCAmelCase=2 ,_lowerCAmelCase=3 ,_lowerCAmelCase=True ,_lowerCAmelCase=True ,_lowerCAmelCase=32 ,_lowerCAmelCase=2 ,_lowerCAmelCase=4 ,_lowerCAmelCase=37 ,_lowerCAmelCase="gelu" ,_lowerCAmelCase=0.1 ,_lowerCAmelCase=0.1 ,_lowerCAmelCase=10 ,_lowerCAmelCase=0.02 ,_lowerCAmelCase=3 ,_lowerCAmelCase=0.6 ,_lowerCAmelCase=None ,):
lowerCamelCase__ = parent
lowerCamelCase__ = batch_size
lowerCamelCase__ = image_size
lowerCamelCase__ = patch_size
lowerCamelCase__ = num_channels
lowerCamelCase__ = is_training
lowerCamelCase__ = use_labels
lowerCamelCase__ = hidden_size
lowerCamelCase__ = num_hidden_layers
lowerCamelCase__ = num_attention_heads
lowerCamelCase__ = intermediate_size
lowerCamelCase__ = hidden_act
lowerCamelCase__ = hidden_dropout_prob
lowerCamelCase__ = attention_probs_dropout_prob
lowerCamelCase__ = type_sequence_label_size
lowerCamelCase__ = initializer_range
lowerCamelCase__ = mask_ratio
lowerCamelCase__ = scope
# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
# (we add 1 for the [CLS] token)
lowerCamelCase__ = (image_size // patch_size) ** 2
lowerCamelCase__ = int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCamelCase__ = None
if self.use_labels:
lowerCamelCase__ = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
lowerCamelCase__ = self.get_config()
return config, pixel_values, labels
def UpperCamelCase_ ( self ):
return ViTMAEConfig(
image_size=self.image_size ,patch_size=self.patch_size ,num_channels=self.num_channels ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,decoder_hidden_size=self.hidden_size ,decoder_num_hidden_layers=self.num_hidden_layers ,decoder_num_attention_heads=self.num_attention_heads ,decoder_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 ,is_decoder=_lowerCAmelCase ,initializer_range=self.initializer_range ,mask_ratio=self.mask_ratio ,)
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = TFViTMAEModel(config=_lowerCAmelCase )
lowerCamelCase__ = model(_lowerCAmelCase ,training=_lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = TFViTMAEForPreTraining(_lowerCAmelCase )
lowerCamelCase__ = model(_lowerCAmelCase ,training=_lowerCAmelCase )
# expected sequence length = num_patches
lowerCamelCase__ = (self.image_size // self.patch_size) ** 2
lowerCamelCase__ = self.patch_size**2 * self.num_channels
self.parent.assertEqual(result.logits.shape ,(self.batch_size, num_patches, expected_num_channels) )
# test greyscale images
lowerCamelCase__ = 1
lowerCamelCase__ = TFViTMAEForPreTraining(_lowerCAmelCase )
lowerCamelCase__ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowerCamelCase__ = model(_lowerCAmelCase ,training=_lowerCAmelCase )
lowerCamelCase__ = self.patch_size**2
self.parent.assertEqual(result.logits.shape ,(self.batch_size, num_patches, expected_num_channels) )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.prepare_config_and_inputs()
((lowerCamelCase__) , (lowerCamelCase__) , (lowerCamelCase__)) = config_and_inputs
lowerCamelCase__ = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_tf
class UpperCamelCase__ (a ,a ,unittest.TestCase ):
'''simple docstring'''
_UpperCamelCase = (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
_UpperCamelCase = {'feature-extraction': TFViTMAEModel} if is_tf_available() else {}
_UpperCamelCase = False
_UpperCamelCase = False
_UpperCamelCase = False
_UpperCamelCase = False
def UpperCamelCase_ ( self ):
lowerCamelCase__ = TFViTMAEModelTester(self )
lowerCamelCase__ = ConfigTester(self ,config_class=_lowerCAmelCase ,has_text_modality=_lowerCAmelCase ,hidden_size=37 )
def UpperCamelCase_ ( self ):
self.config_tester.run_common_tests()
@unittest.skip(reason="""ViTMAE does not use inputs_embeds""" )
def UpperCamelCase_ ( self ):
pass
def UpperCamelCase_ ( self ):
lowerCamelCase__ , lowerCamelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase__ = model_class(_lowerCAmelCase )
self.assertIsInstance(model.get_input_embeddings() ,(tf.keras.layers.Layer) )
lowerCamelCase__ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_lowerCAmelCase ,tf.keras.layers.Layer ) )
def UpperCamelCase_ ( self ):
lowerCamelCase__ , lowerCamelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase__ = model_class(_lowerCAmelCase )
lowerCamelCase__ = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCamelCase__ = [*signature.parameters.keys()]
lowerCamelCase__ = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] ,_lowerCAmelCase )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCAmelCase )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*_lowerCAmelCase )
def UpperCamelCase_ ( self ):
# make the mask reproducible
np.random.seed(2 )
lowerCamelCase__ , lowerCamelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
lowerCamelCase__ = int((config.image_size // config.patch_size) ** 2 )
lowerCamelCase__ = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
lowerCamelCase__ = model_class(_lowerCAmelCase )
lowerCamelCase__ = self._prepare_for_class(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = model(_lowerCAmelCase ,noise=_lowerCAmelCase )
lowerCamelCase__ = copy.deepcopy(self._prepare_for_class(_lowerCAmelCase ,_lowerCAmelCase ) )
lowerCamelCase__ = model(**_lowerCAmelCase ,noise=_lowerCAmelCase )
lowerCamelCase__ = outputs_dict[0].numpy()
lowerCamelCase__ = outputs_keywords[0].numpy()
self.assertLess(np.sum(np.abs(output_dict - output_keywords ) ) ,1E-6 )
def UpperCamelCase_ ( self ):
# make the mask reproducible
np.random.seed(2 )
lowerCamelCase__ , lowerCamelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
lowerCamelCase__ = int((config.image_size // config.patch_size) ** 2 )
lowerCamelCase__ = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
def prepare_numpy_arrays(_lowerCAmelCase ):
lowerCamelCase__ = {}
for k, v in inputs_dict.items():
if tf.is_tensor(_lowerCAmelCase ):
lowerCamelCase__ = v.numpy()
else:
lowerCamelCase__ = np.array(_lowerCAmelCase )
return inputs_np_dict
for model_class in self.all_model_classes:
lowerCamelCase__ = model_class(_lowerCAmelCase )
lowerCamelCase__ = self._prepare_for_class(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = prepare_numpy_arrays(_lowerCAmelCase )
lowerCamelCase__ = model(_lowerCAmelCase ,noise=_lowerCAmelCase )
lowerCamelCase__ = model(**_lowerCAmelCase ,noise=_lowerCAmelCase )
self.assert_outputs_same(_lowerCAmelCase ,_lowerCAmelCase )
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ):
# make masks reproducible
np.random.seed(2 )
lowerCamelCase__ = int((tf_model.config.image_size // tf_model.config.patch_size) ** 2 )
lowerCamelCase__ = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
lowerCamelCase__ = tf.constant(_lowerCAmelCase )
# Add `noise` argument.
# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
lowerCamelCase__ = tf_noise
super().check_pt_tf_models(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )
def UpperCamelCase_ ( self ):
# make mask reproducible
np.random.seed(2 )
lowerCamelCase__ , lowerCamelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
lowerCamelCase__ = {
module_member
for model_class in self.all_model_classes
for module in (import_module(model_class.__module__ ),)
for module_member_name in dir(_lowerCAmelCase )
if module_member_name.endswith("""MainLayer""" )
# This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
and module_member_name[: -len("""MainLayer""" )] == model_class.__name__[: -len("""Model""" )]
for module_member in (getattr(_lowerCAmelCase ,_lowerCAmelCase ),)
if isinstance(_lowerCAmelCase ,_lowerCAmelCase )
and tf.keras.layers.Layer in module_member.__bases__
and getattr(_lowerCAmelCase ,"""_keras_serializable""" ,_lowerCAmelCase )
}
lowerCamelCase__ = int((config.image_size // config.patch_size) ** 2 )
lowerCamelCase__ = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
lowerCamelCase__ = tf.convert_to_tensor(_lowerCAmelCase )
inputs_dict.update({"""noise""": noise} )
for main_layer_class in tf_main_layer_classes:
lowerCamelCase__ = main_layer_class(_lowerCAmelCase )
lowerCamelCase__ = {
name: tf.keras.Input(tensor.shape[1:] ,dtype=tensor.dtype ) for name, tensor in inputs_dict.items()
}
lowerCamelCase__ = tf.keras.Model(_lowerCAmelCase ,outputs=main_layer(_lowerCAmelCase ) )
lowerCamelCase__ = model(_lowerCAmelCase )
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCamelCase__ = os.path.join(_lowerCAmelCase ,"""keras_model.h5""" )
model.save(_lowerCAmelCase )
lowerCamelCase__ = tf.keras.models.load_model(
_lowerCAmelCase ,custom_objects={main_layer_class.__name__: main_layer_class} )
assert isinstance(_lowerCAmelCase ,tf.keras.Model )
lowerCamelCase__ = model(_lowerCAmelCase )
self.assert_outputs_same(_lowerCAmelCase ,_lowerCAmelCase )
@slow
def UpperCamelCase_ ( self ):
# make mask reproducible
np.random.seed(2 )
lowerCamelCase__ , lowerCamelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
lowerCamelCase__ = int((config.image_size // config.patch_size) ** 2 )
lowerCamelCase__ = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
lowerCamelCase__ = model_class(_lowerCAmelCase )
lowerCamelCase__ = self._prepare_for_class(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = model(_lowerCAmelCase ,noise=_lowerCAmelCase )
if model_class.__name__ == "TFViTMAEModel":
lowerCamelCase__ = outputs.last_hidden_state.numpy()
lowerCamelCase__ = 0
else:
lowerCamelCase__ = outputs.logits.numpy()
lowerCamelCase__ = 0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_lowerCAmelCase ,saved_model=_lowerCAmelCase )
lowerCamelCase__ = model_class.from_pretrained(_lowerCAmelCase )
lowerCamelCase__ = model(_lowerCAmelCase ,noise=_lowerCAmelCase )
if model_class.__name__ == "TFViTMAEModel":
lowerCamelCase__ = after_outputs["""last_hidden_state"""].numpy()
lowerCamelCase__ = 0
else:
lowerCamelCase__ = after_outputs["""logits"""].numpy()
lowerCamelCase__ = 0
lowerCamelCase__ = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_lowerCAmelCase ,1E-5 )
def UpperCamelCase_ ( self ):
# make mask reproducible
np.random.seed(2 )
lowerCamelCase__ , lowerCamelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
lowerCamelCase__ = int((config.image_size // config.patch_size) ** 2 )
lowerCamelCase__ = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
lowerCamelCase__ = model_class(_lowerCAmelCase )
lowerCamelCase__ = self._prepare_for_class(_lowerCAmelCase ,_lowerCAmelCase )
lowerCamelCase__ = model(_lowerCAmelCase ,noise=_lowerCAmelCase )
lowerCamelCase__ = model.get_config()
# make sure that returned config is jsonifiable, which is required by keras
json.dumps(_lowerCAmelCase )
lowerCamelCase__ = model_class.from_config(model.get_config() )
# make sure it also accepts a normal config
lowerCamelCase__ = model_class.from_config(model.config )
lowerCamelCase__ = new_model(_lowerCAmelCase ) # Build model
new_model.set_weights(model.get_weights() )
lowerCamelCase__ = new_model(_lowerCAmelCase ,noise=_lowerCAmelCase )
self.assert_outputs_same(_lowerCAmelCase ,_lowerCAmelCase )
@unittest.skip(
reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
to get deterministic results.""" )
def UpperCamelCase_ ( self ):
pass
@unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load""" )
def UpperCamelCase_ ( self ):
pass
@slow
def UpperCamelCase_ ( self ):
lowerCamelCase__ = TFViTMAEModel.from_pretrained("""google/vit-base-patch16-224""" )
self.assertIsNotNone(_lowerCAmelCase )
def A__ ( ):
lowerCamelCase__ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
@require_vision
class UpperCamelCase__ (unittest.TestCase ):
'''simple docstring'''
@cached_property
def UpperCamelCase_ ( self ):
return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base""" ) if is_vision_available() else None
@slow
def UpperCamelCase_ ( self ):
# make random mask reproducible across the PT and TF model
np.random.seed(2 )
lowerCamelCase__ = TFViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base""" )
lowerCamelCase__ = self.default_image_processor
lowerCamelCase__ = prepare_img()
lowerCamelCase__ = image_processor(images=_lowerCAmelCase ,return_tensors="""tf""" )
# prepare a noise vector that will be also used for testing the TF model
# (this way we can ensure that the PT and TF models operate on the same inputs)
lowerCamelCase__ = ViTMAEConfig()
lowerCamelCase__ = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
lowerCamelCase__ = np.random.uniform(size=(1, num_patches) )
# forward pass
lowerCamelCase__ = model(**_lowerCAmelCase ,noise=_lowerCAmelCase )
# verify the logits
lowerCamelCase__ = tf.convert_to_tensor([1, 1_96, 7_68] )
self.assertEqual(outputs.logits.shape ,_lowerCAmelCase )
lowerCamelCase__ = tf.convert_to_tensor(
[[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]] )
tf.debugging.assert_near(outputs.logits[0, :3, :3] ,_lowerCAmelCase ,atol=1E-4 )
| 9
| 0
|
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import MgpstrProcessor, ViTImageProcessor
@require_torch
@require_vision
class a_ ( unittest.TestCase ):
lowercase = ViTImageProcessor if is_vision_available() else None
@property
def A__ ( self ) -> Tuple:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def A__ ( self ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase = (3, 32, 128)
UpperCamelCase = tempfile.mkdtemp()
# fmt: off
UpperCamelCase = ["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""]
# fmt: on
UpperCamelCase = dict(zip(_SCREAMING_SNAKE_CASE , range(len(_SCREAMING_SNAKE_CASE ) ) ) )
UpperCamelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(_SCREAMING_SNAKE_CASE ) + """\n""" )
UpperCamelCase = {
"""do_normalize""": False,
"""do_resize""": True,
"""image_processor_type""": """ViTImageProcessor""",
"""resample""": 3,
"""size""": {"""height""": 32, """width""": 128},
}
UpperCamelCase = os.path.join(self.tmpdirname , _SCREAMING_SNAKE_CASE )
with open(self.image_processor_file , """w""" , encoding="""utf-8""" ) as fp:
json.dump(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def A__ ( self , **_SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
return MgpstrTokenizer.from_pretrained(self.tmpdirname , **_SCREAMING_SNAKE_CASE )
def A__ ( self , **_SCREAMING_SNAKE_CASE ) -> Any:
"""simple docstring"""
return ViTImageProcessor.from_pretrained(self.tmpdirname , **_SCREAMING_SNAKE_CASE )
def A__ ( self ) -> str:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def A__ ( self ) -> int:
"""simple docstring"""
UpperCamelCase = np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )
UpperCamelCase = Image.fromarray(np.moveaxis(_SCREAMING_SNAKE_CASE , 0 , -1 ) )
return image_input
def A__ ( self ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = self.get_tokenizer()
UpperCamelCase = self.get_image_processor()
UpperCamelCase = MgpstrProcessor(tokenizer=_SCREAMING_SNAKE_CASE , image_processor=_SCREAMING_SNAKE_CASE )
processor.save_pretrained(self.tmpdirname )
UpperCamelCase = MgpstrProcessor.from_pretrained(self.tmpdirname , use_fast=_SCREAMING_SNAKE_CASE )
self.assertEqual(processor.char_tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.char_tokenizer , _SCREAMING_SNAKE_CASE )
self.assertEqual(processor.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor.image_processor , _SCREAMING_SNAKE_CASE )
def A__ ( self ) -> str:
"""simple docstring"""
UpperCamelCase = self.get_tokenizer()
UpperCamelCase = self.get_image_processor()
UpperCamelCase = MgpstrProcessor(tokenizer=_SCREAMING_SNAKE_CASE , image_processor=_SCREAMING_SNAKE_CASE )
processor.save_pretrained(self.tmpdirname )
UpperCamelCase = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
UpperCamelCase = self.get_image_processor(do_normalize=_SCREAMING_SNAKE_CASE , padding_value=1.0 )
UpperCamelCase = MgpstrProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=_SCREAMING_SNAKE_CASE , padding_value=1.0 )
self.assertEqual(processor.char_tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.char_tokenizer , _SCREAMING_SNAKE_CASE )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _SCREAMING_SNAKE_CASE )
def A__ ( self ) -> int:
"""simple docstring"""
UpperCamelCase = self.get_image_processor()
UpperCamelCase = self.get_tokenizer()
UpperCamelCase = MgpstrProcessor(tokenizer=_SCREAMING_SNAKE_CASE , image_processor=_SCREAMING_SNAKE_CASE )
UpperCamelCase = self.prepare_image_inputs()
UpperCamelCase = image_processor(_SCREAMING_SNAKE_CASE , return_tensors="""np""" )
UpperCamelCase = processor(images=_SCREAMING_SNAKE_CASE , return_tensors="""np""" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1e-2 )
def A__ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = self.get_image_processor()
UpperCamelCase = self.get_tokenizer()
UpperCamelCase = MgpstrProcessor(tokenizer=_SCREAMING_SNAKE_CASE , image_processor=_SCREAMING_SNAKE_CASE )
UpperCamelCase = """test"""
UpperCamelCase = processor(text=_SCREAMING_SNAKE_CASE )
UpperCamelCase = tokenizer(_SCREAMING_SNAKE_CASE )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def A__ ( self ) -> str:
"""simple docstring"""
UpperCamelCase = self.get_image_processor()
UpperCamelCase = self.get_tokenizer()
UpperCamelCase = MgpstrProcessor(tokenizer=_SCREAMING_SNAKE_CASE , image_processor=_SCREAMING_SNAKE_CASE )
UpperCamelCase = """test"""
UpperCamelCase = self.prepare_image_inputs()
UpperCamelCase = processor(text=_SCREAMING_SNAKE_CASE , images=_SCREAMING_SNAKE_CASE )
self.assertListEqual(list(inputs.keys() ) , ["""pixel_values""", """labels"""] )
# test if it raises when no input is passed
with pytest.raises(_SCREAMING_SNAKE_CASE ):
processor()
def A__ ( self ) -> List[Any]:
"""simple docstring"""
UpperCamelCase = self.get_image_processor()
UpperCamelCase = self.get_tokenizer()
UpperCamelCase = MgpstrProcessor(tokenizer=_SCREAMING_SNAKE_CASE , image_processor=_SCREAMING_SNAKE_CASE )
UpperCamelCase = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]
UpperCamelCase = processor.char_decode(_SCREAMING_SNAKE_CASE )
UpperCamelCase = tokenizer.batch_decode(_SCREAMING_SNAKE_CASE )
UpperCamelCase = [seq.replace(""" """ , """""" ) for seq in decoded_tok]
self.assertListEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def A__ ( self ) -> int:
"""simple docstring"""
UpperCamelCase = self.get_image_processor()
UpperCamelCase = self.get_tokenizer()
UpperCamelCase = MgpstrProcessor(tokenizer=_SCREAMING_SNAKE_CASE , image_processor=_SCREAMING_SNAKE_CASE )
UpperCamelCase = None
UpperCamelCase = self.prepare_image_inputs()
UpperCamelCase = processor(text=_SCREAMING_SNAKE_CASE , images=_SCREAMING_SNAKE_CASE )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
def A__ ( self ) -> Any:
"""simple docstring"""
UpperCamelCase = self.get_image_processor()
UpperCamelCase = self.get_tokenizer()
UpperCamelCase = MgpstrProcessor(tokenizer=_SCREAMING_SNAKE_CASE , image_processor=_SCREAMING_SNAKE_CASE )
UpperCamelCase = torch.randn(1 , 27 , 38 )
UpperCamelCase = torch.randn(1 , 27 , 50257 )
UpperCamelCase = torch.randn(1 , 27 , 30522 )
UpperCamelCase = processor.batch_decode([char_input, bpe_input, wp_input] )
self.assertListEqual(list(results.keys() ) , ["""generated_text""", """scores""", """char_preds""", """bpe_preds""", """wp_preds"""] )
| 301
|
'''simple docstring'''
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
SCREAMING_SNAKE_CASE__ = version.parse(version.parse(torch.__version__).base_version) < version.parse('1.11')
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=False , )-> Optional[int]:
output_path.parent.mkdir(parents=__UpperCamelCase , exist_ok=__UpperCamelCase )
# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
# so we check the torch version for backwards compatibility
if is_torch_less_than_1_11:
export(
__UpperCamelCase , __UpperCamelCase , f=output_path.as_posix() , input_names=__UpperCamelCase , output_names=__UpperCamelCase , dynamic_axes=__UpperCamelCase , do_constant_folding=__UpperCamelCase , use_external_data_format=__UpperCamelCase , enable_onnx_checker=__UpperCamelCase , opset_version=__UpperCamelCase , )
else:
export(
__UpperCamelCase , __UpperCamelCase , f=output_path.as_posix() , input_names=__UpperCamelCase , output_names=__UpperCamelCase , dynamic_axes=__UpperCamelCase , do_constant_folding=__UpperCamelCase , opset_version=__UpperCamelCase , )
@torch.no_grad()
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = False )-> Optional[Any]:
UpperCamelCase = torch.floataa if fpaa else torch.floataa
if fpaa and torch.cuda.is_available():
UpperCamelCase = """cuda"""
elif fpaa and not torch.cuda.is_available():
raise ValueError("""`float16` model export is only supported on GPUs with CUDA""" )
else:
UpperCamelCase = """cpu"""
UpperCamelCase = StableDiffusionPipeline.from_pretrained(__UpperCamelCase , torch_dtype=__UpperCamelCase ).to(__UpperCamelCase )
UpperCamelCase = Path(__UpperCamelCase )
# TEXT ENCODER
UpperCamelCase = pipeline.text_encoder.config.max_position_embeddings
UpperCamelCase = pipeline.text_encoder.config.hidden_size
UpperCamelCase = pipeline.tokenizer(
"""A sample prompt""" , padding="""max_length""" , max_length=pipeline.tokenizer.model_max_length , truncation=__UpperCamelCase , return_tensors="""pt""" , )
onnx_export(
pipeline.text_encoder , model_args=(text_input.input_ids.to(device=__UpperCamelCase , dtype=torch.intaa )) , output_path=output_path / """text_encoder""" / """model.onnx""" , ordered_input_names=["""input_ids"""] , output_names=["""last_hidden_state""", """pooler_output"""] , dynamic_axes={
"""input_ids""": {0: """batch""", 1: """sequence"""},
} , opset=__UpperCamelCase , )
del pipeline.text_encoder
# UNET
UpperCamelCase = pipeline.unet.config.in_channels
UpperCamelCase = pipeline.unet.config.sample_size
UpperCamelCase = output_path / """unet""" / """model.onnx"""
onnx_export(
pipeline.unet , model_args=(
torch.randn(2 , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ).to(device=__UpperCamelCase , dtype=__UpperCamelCase ),
torch.randn(2 ).to(device=__UpperCamelCase , dtype=__UpperCamelCase ),
torch.randn(2 , __UpperCamelCase , __UpperCamelCase ).to(device=__UpperCamelCase , dtype=__UpperCamelCase ),
False,
) , output_path=__UpperCamelCase , ordered_input_names=["""sample""", """timestep""", """encoder_hidden_states""", """return_dict"""] , output_names=["""out_sample"""] , dynamic_axes={
"""sample""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
"""timestep""": {0: """batch"""},
"""encoder_hidden_states""": {0: """batch""", 1: """sequence"""},
} , opset=__UpperCamelCase , use_external_data_format=__UpperCamelCase , )
UpperCamelCase = str(unet_path.absolute().as_posix() )
UpperCamelCase = os.path.dirname(__UpperCamelCase )
UpperCamelCase = onnx.load(__UpperCamelCase )
# clean up existing tensor files
shutil.rmtree(__UpperCamelCase )
os.mkdir(__UpperCamelCase )
# collate external tensor files into one
onnx.save_model(
__UpperCamelCase , __UpperCamelCase , save_as_external_data=__UpperCamelCase , all_tensors_to_one_file=__UpperCamelCase , location="""weights.pb""" , convert_attribute=__UpperCamelCase , )
del pipeline.unet
# VAE ENCODER
UpperCamelCase = pipeline.vae
UpperCamelCase = vae_encoder.config.in_channels
UpperCamelCase = vae_encoder.config.sample_size
# need to get the raw tensor output (sample) from the encoder
UpperCamelCase = lambda __UpperCamelCase , __UpperCamelCase : vae_encoder.encode(__UpperCamelCase , __UpperCamelCase )[0].sample()
onnx_export(
__UpperCamelCase , model_args=(
torch.randn(1 , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ).to(device=__UpperCamelCase , dtype=__UpperCamelCase ),
False,
) , output_path=output_path / """vae_encoder""" / """model.onnx""" , ordered_input_names=["""sample""", """return_dict"""] , output_names=["""latent_sample"""] , dynamic_axes={
"""sample""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
} , opset=__UpperCamelCase , )
# VAE DECODER
UpperCamelCase = pipeline.vae
UpperCamelCase = vae_decoder.config.latent_channels
UpperCamelCase = vae_decoder.config.out_channels
# forward only through the decoder part
UpperCamelCase = vae_encoder.decode
onnx_export(
__UpperCamelCase , model_args=(
torch.randn(1 , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ).to(device=__UpperCamelCase , dtype=__UpperCamelCase ),
False,
) , output_path=output_path / """vae_decoder""" / """model.onnx""" , ordered_input_names=["""latent_sample""", """return_dict"""] , output_names=["""sample"""] , dynamic_axes={
"""latent_sample""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
} , opset=__UpperCamelCase , )
del pipeline.vae
# SAFETY CHECKER
if pipeline.safety_checker is not None:
UpperCamelCase = pipeline.safety_checker
UpperCamelCase = safety_checker.config.vision_config.num_channels
UpperCamelCase = safety_checker.config.vision_config.image_size
UpperCamelCase = safety_checker.forward_onnx
onnx_export(
pipeline.safety_checker , model_args=(
torch.randn(
1 , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , ).to(device=__UpperCamelCase , dtype=__UpperCamelCase ),
torch.randn(1 , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ).to(device=__UpperCamelCase , dtype=__UpperCamelCase ),
) , output_path=output_path / """safety_checker""" / """model.onnx""" , ordered_input_names=["""clip_input""", """images"""] , output_names=["""out_images""", """has_nsfw_concepts"""] , dynamic_axes={
"""clip_input""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
"""images""": {0: """batch""", 1: """height""", 2: """width""", 3: """channels"""},
} , opset=__UpperCamelCase , )
del pipeline.safety_checker
UpperCamelCase = OnnxRuntimeModel.from_pretrained(output_path / """safety_checker""" )
UpperCamelCase = pipeline.feature_extractor
else:
UpperCamelCase = None
UpperCamelCase = None
UpperCamelCase = OnnxStableDiffusionPipeline(
vae_encoder=OnnxRuntimeModel.from_pretrained(output_path / """vae_encoder""" ) , vae_decoder=OnnxRuntimeModel.from_pretrained(output_path / """vae_decoder""" ) , text_encoder=OnnxRuntimeModel.from_pretrained(output_path / """text_encoder""" ) , tokenizer=pipeline.tokenizer , unet=OnnxRuntimeModel.from_pretrained(output_path / """unet""" ) , scheduler=pipeline.scheduler , safety_checker=__UpperCamelCase , feature_extractor=__UpperCamelCase , requires_safety_checker=safety_checker is not None , )
onnx_pipeline.save_pretrained(__UpperCamelCase )
print("""ONNX pipeline saved to""" , __UpperCamelCase )
del pipeline
del onnx_pipeline
UpperCamelCase = OnnxStableDiffusionPipeline.from_pretrained(__UpperCamelCase , provider="""CPUExecutionProvider""" )
print("""ONNX pipeline is loadable""" )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = argparse.ArgumentParser()
parser.add_argument(
'--model_path',
type=str,
required=True,
help='Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).',
)
parser.add_argument('--output_path', type=str, required=True, help='Path to the output model.')
parser.add_argument(
'--opset',
default=1_4,
type=int,
help='The version of the ONNX operator set to use.',
)
parser.add_argument('--fp16', action='store_true', default=False, help='Export the models in `float16` mode')
SCREAMING_SNAKE_CASE__ = parser.parse_args()
convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
| 301
| 1
|
'''simple docstring'''
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def A ( ):
print('''Making key files...''' )
make_key_files('''rsa''' , 1024 )
print('''Key files generation successful.''' )
def A ( A_ : int ):
print('''Generating prime p...''' )
snake_case : Dict = rabinMiller.generate_large_prime(A_ )
print('''Generating prime q...''' )
snake_case : Union[str, Any] = rabinMiller.generate_large_prime(A_ )
snake_case : Optional[Any] = p * q
print('''Generating e that is relatively prime to (p - 1) * (q - 1)...''' )
while True:
snake_case : int = random.randrange(2 ** (key_size - 1) , 2 ** (key_size) )
if cryptoMath.gcd(A_ , (p - 1) * (q - 1) ) == 1:
break
print('''Calculating d that is mod inverse of e...''' )
snake_case : List[Any] = cryptoMath.find_mod_inverse(A_ , (p - 1) * (q - 1) )
snake_case : int = (n, e)
snake_case : Optional[int] = (n, d)
return (public_key, private_key)
def A ( A_ : str , A_ : int ):
if os.path.exists(F"""{name}_pubkey.txt""" ) or os.path.exists(F"""{name}_privkey.txt""" ):
print('''\nWARNING:''' )
print(
F"""\"{name}_pubkey.txt\" or \"{name}_privkey.txt\" already exists. \n"""
'''Use a different name or delete these files and re-run this program.''' )
sys.exit()
snake_case, snake_case : int = generate_key(A_ )
print(F"""\nWriting public key to file {name}_pubkey.txt...""" )
with open(F"""{name}_pubkey.txt""" , '''w''' ) as out_file:
out_file.write(F"""{key_size},{public_key[0]},{public_key[1]}""" )
print(F"""Writing private key to file {name}_privkey.txt...""" )
with open(F"""{name}_privkey.txt""" , '''w''' ) as out_file:
out_file.write(F"""{key_size},{private_key[0]},{private_key[1]}""" )
if __name__ == "__main__":
main()
| 555
|
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
UpperCAmelCase = logging.get_logger(__name__)
class a ( __magic_name__ ):
def __init__( self : Union[str, Any], *SCREAMING_SNAKE_CASE_ : Tuple, **SCREAMING_SNAKE_CASE_ : Tuple ):
warnings.warn(
'''The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
''' Please use LayoutLMv2ImageProcessor instead.''', SCREAMING_SNAKE_CASE_, )
super().__init__(*SCREAMING_SNAKE_CASE_, **SCREAMING_SNAKE_CASE_ )
| 555
| 1
|
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config
# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)
a__ = [
'''cross_validation.py''',
'''gradient_accumulation.py''',
'''local_sgd.py''',
'''multi_process_metrics.py''',
'''memory.py''',
'''automatic_gradient_accumulation.py''',
'''fsdp_with_peak_mem_tracking.py''',
'''deepspeed_with_config_support.py''',
'''megatron_lm_gpt_pretraining.py''',
]
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def __lowercase ( self , _a , _a , _a = None , _a = None ) -> List[Any]:
_a : Union[str, Any] = None
_a : Optional[Any] = os.path.abspath(os.path.join('''examples''' , '''by_feature''' ) )
_a : Optional[int] = os.path.abspath('''examples''' )
for item in os.listdir(_a ):
if item not in EXCLUDE_EXAMPLES:
_a : Any = os.path.join(_a , _a )
if os.path.isfile(_a ) and ".py" in item_path:
with self.subTest(
tested_script=_a , feature_script=_a , tested_section='''main()''' if parser_only else '''training_function()''' , ):
_a : Optional[int] = compare_against_test(
os.path.join(_a , _a ) , _a , _a , _a )
_a : Union[str, Any] = '''\n'''.join(_a )
if special_strings is not None:
for string in special_strings:
_a : Union[str, Any] = diff.replace(_a , '''''' )
self.assertEqual(_a , '''''' )
def __lowercase ( self ) -> Optional[Any]:
self.one_complete_example('''complete_nlp_example.py''' , _a )
self.one_complete_example('''complete_nlp_example.py''' , _a )
def __lowercase ( self ) -> Union[str, Any]:
_a : Optional[int] = os.path.abspath(os.path.join('''examples''' , '''cv_example.py''' ) )
_a : int = [
''' ''' * 1_6 + '''{\n\n''',
''' ''' * 2_0 + '''"accuracy": eval_metric["accuracy"],\n\n''',
''' ''' * 2_0 + '''"f1": eval_metric["f1"],\n\n''',
''' ''' * 2_0 + '''"train_loss": total_loss.item() / len(train_dataloader),\n\n''',
''' ''' * 2_0 + '''"epoch": epoch,\n\n''',
''' ''' * 1_6 + '''},\n\n''',
''' ''' * 1_6 + '''step=epoch,\n''',
''' ''' * 1_2,
''' ''' * 8 + '''for step, batch in enumerate(active_dataloader):\n''',
]
self.one_complete_example('''complete_cv_example.py''' , _a , _a , _a )
self.one_complete_example('''complete_cv_example.py''' , _a , _a , _a )
@mock.patch.dict(os.environ , {"TESTING_MOCKED_DATALOADERS": "1"} )
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : Any = False
@classmethod
def __lowercase ( cls ) -> List[Any]:
super().setUpClass()
_a : str = tempfile.mkdtemp()
_a : str = os.path.join(cls._tmpdir , '''default_config.yml''' )
write_basic_config(save_location=cls.configPath )
_a : int = ['''accelerate''', '''launch''', '''--config_file''', cls.configPath]
@classmethod
def __lowercase ( cls ) -> Optional[int]:
super().tearDownClass()
shutil.rmtree(cls._tmpdir )
def __lowercase ( self ) -> Dict:
_a : Union[str, Any] = F"""
examples/by_feature/checkpointing.py
--checkpointing_steps epoch
--output_dir {self.tmpdir}
""".split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir , '''epoch_0''' ) ) )
def __lowercase ( self ) -> List[str]:
_a : Union[str, Any] = F"""
examples/by_feature/checkpointing.py
--checkpointing_steps 1
--output_dir {self.tmpdir}
""".split()
_a : List[str] = run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir , '''step_2''' ) ) )
def __lowercase ( self ) -> Any:
_a : Dict = F"""
examples/by_feature/checkpointing.py
--resume_from_checkpoint {os.path.join(self.tmpdir , 'epoch_0' )}
""".split()
_a : str = run_command(self._launch_args + testargs , return_stdout=_a )
self.assertNotIn('''epoch 0:''' , _a )
self.assertIn('''epoch 1:''' , _a )
def __lowercase ( self ) -> Dict:
_a : Optional[Any] = F"""
examples/by_feature/checkpointing.py
--resume_from_checkpoint {os.path.join(self.tmpdir , 'step_2' )}
""".split()
_a : Optional[int] = run_command(self._launch_args + testargs , return_stdout=_a )
if torch.cuda.is_available():
_a : List[Any] = torch.cuda.device_count()
else:
_a : Tuple = 1
if num_processes > 1:
self.assertNotIn('''epoch 0:''' , _a )
self.assertIn('''epoch 1:''' , _a )
else:
self.assertIn('''epoch 0:''' , _a )
self.assertIn('''epoch 1:''' , _a )
@slow
def __lowercase ( self ) -> Union[str, Any]:
_a : List[str] = '''
examples/by_feature/cross_validation.py
--num_folds 2
'''.split()
with mock.patch.dict(os.environ , {'''TESTING_MOCKED_DATALOADERS''': '''0'''} ):
_a : Tuple = run_command(self._launch_args + testargs , return_stdout=_a )
_a : int = re.findall('''({.+})''' , _a )
_a : int = [r for r in results if '''accuracy''' in r][-1]
_a : Optional[Any] = ast.literal_eval(_a )
self.assertGreaterEqual(results['''accuracy'''] , 0.75 )
def __lowercase ( self ) -> str:
_a : Optional[int] = ['''examples/by_feature/multi_process_metrics.py''']
run_command(self._launch_args + testargs )
@require_trackers
@mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''} )
def __lowercase ( self ) -> Union[str, Any]:
with tempfile.TemporaryDirectory() as tmpdir:
_a : str = F"""
examples/by_feature/tracking.py
--with_tracking
--project_dir {tmpdir}
""".split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(_a , '''tracking''' ) ) )
def __lowercase ( self ) -> Optional[int]:
_a : List[str] = ['''examples/by_feature/gradient_accumulation.py''']
run_command(self._launch_args + testargs )
def __lowercase ( self ) -> List[Any]:
_a : Union[str, Any] = ['''examples/by_feature/local_sgd.py''']
run_command(self._launch_args + testargs )
| 14
|
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
a__ = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
)
parser.add_argument(
'''--original_config_file''',
type=str,
required=True,
help='''The YAML config file corresponding to the original architecture.''',
)
parser.add_argument(
'''--num_in_channels''',
default=None,
type=int,
help='''The number of input channels. If `None` number of input channels will be automatically inferred.''',
)
parser.add_argument(
'''--image_size''',
default=512,
type=int,
help=(
'''The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'''
''' Base. Use 768 for Stable Diffusion v2.'''
),
)
parser.add_argument(
'''--extract_ema''',
action='''store_true''',
help=(
'''Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'''
''' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'''
''' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'''
),
)
parser.add_argument(
'''--upcast_attention''',
action='''store_true''',
help=(
'''Whether the attention computation should always be upcasted. This is necessary when running stable'''
''' diffusion 2.1.'''
),
)
parser.add_argument(
'''--from_safetensors''',
action='''store_true''',
help='''If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.''',
)
parser.add_argument(
'''--to_safetensors''',
action='''store_true''',
help='''Whether to store pipeline in safetensors format or not.''',
)
parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')
def __UpperCAmelCase ( __a : Any ) -> List[Any]:
"""simple docstring"""
if string == "True":
return True
elif string == "False":
return False
else:
raise ValueError(F"""could not parse string as bool {string}""" )
parser.add_argument(
'''--use_linear_projection''', help='''Override for use linear projection''', required=False, type=parse_bool
)
parser.add_argument('''--cross_attention_dim''', help='''Override for cross attention_dim''', required=False, type=int)
a__ = parser.parse_args()
a__ = download_controlnet_from_original_ckpt(
checkpoint_path=args.checkpoint_path,
original_config_file=args.original_config_file,
image_size=args.image_size,
extract_ema=args.extract_ema,
num_in_channels=args.num_in_channels,
upcast_attention=args.upcast_attention,
from_safetensors=args.from_safetensors,
device=args.device,
use_linear_projection=args.use_linear_projection,
cross_attention_dim=args.cross_attention_dim,
)
controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 14
| 1
|
def __lowerCAmelCase ( __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : list[int] , __SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
def count_of_possible_combinations(__SCREAMING_SNAKE_CASE : int ) -> int:
if target < 0:
return 0
if target == 0:
return 1
return sum(count_of_possible_combinations(target - item ) for item in array )
return count_of_possible_combinations(__SCREAMING_SNAKE_CASE )
def __lowerCAmelCase ( __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : list[int] , __SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
def count_of_possible_combinations_with_dp_array(
__SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : list[int] ) -> int:
if target < 0:
return 0
if target == 0:
return 1
if dp_array[target] != -1:
return dp_array[target]
__snake_case : int = sum(
count_of_possible_combinations_with_dp_array(target - item , __SCREAMING_SNAKE_CASE )
for item in array )
__snake_case : Tuple = answer
return answer
__snake_case : Optional[Any] = [-1] * (target + 1)
return count_of_possible_combinations_with_dp_array(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def __lowerCAmelCase ( __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : list[int] , __SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
__snake_case : int = [0] * (target + 1)
__snake_case : Any = 1
for i in range(1 , target + 1 ):
for j in range(__SCREAMING_SNAKE_CASE ):
if i - array[j] >= 0:
dp_array[i] += dp_array[i - array[j]]
return dp_array[target]
if __name__ == "__main__":
import doctest
doctest.testmod()
lowercase_ = 3
lowercase_ = 5
lowercase_ = [1, 2, 5]
print(combination_sum_iv(n, array, target))
| 390
|
lowercase_ = "0.18.2"
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_available,
is_note_seq_available,
is_onnx_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
is_transformers_available,
is_transformers_version,
is_unidecode_available,
logging,
)
try:
if not is_onnx_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_onnx_objects import * # noqa F403
else:
from .pipelines import OnnxRuntimeModel
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_pt_objects import * # noqa F403
else:
from .models import (
AutoencoderKL,
ControlNetModel,
ModelMixin,
PriorTransformer,
TaFilmDecoder,
TransformeraDModel,
UNetaDModel,
UNetaDConditionModel,
UNetaDModel,
UNetaDConditionModel,
VQModel,
)
from .optimization import (
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
get_scheduler,
)
from .pipelines import (
AudioPipelineOutput,
ConsistencyModelPipeline,
DanceDiffusionPipeline,
DDIMPipeline,
DDPMPipeline,
DiffusionPipeline,
DiTPipeline,
ImagePipelineOutput,
KarrasVePipeline,
LDMPipeline,
LDMSuperResolutionPipeline,
PNDMPipeline,
RePaintPipeline,
ScoreSdeVePipeline,
)
from .schedulers import (
CMStochasticIterativeScheduler,
DDIMInverseScheduler,
DDIMParallelScheduler,
DDIMScheduler,
DDPMParallelScheduler,
DDPMScheduler,
DEISMultistepScheduler,
DPMSolverMultistepInverseScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
HeunDiscreteScheduler,
IPNDMScheduler,
KarrasVeScheduler,
KDPMaAncestralDiscreteScheduler,
KDPMaDiscreteScheduler,
PNDMScheduler,
RePaintScheduler,
SchedulerMixin,
ScoreSdeVeScheduler,
UnCLIPScheduler,
UniPCMultistepScheduler,
VQDiffusionScheduler,
)
from .training_utils import EMAModel
try:
if not (is_torch_available() and is_scipy_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_scipy_objects import * # noqa F403
else:
from .schedulers import LMSDiscreteScheduler
try:
if not (is_torch_available() and is_torchsde_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_torchsde_objects import * # noqa F403
else:
from .schedulers import DPMSolverSDEScheduler
try:
if not (is_torch_available() and is_transformers_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipelines import (
AltDiffusionImgaImgPipeline,
AltDiffusionPipeline,
AudioLDMPipeline,
CycleDiffusionPipeline,
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
ImageTextPipelineOutput,
KandinskyImgaImgPipeline,
KandinskyInpaintPipeline,
KandinskyPipeline,
KandinskyPriorPipeline,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaControlnetPipeline,
KandinskyVaaImgaImgPipeline,
KandinskyVaaInpaintPipeline,
KandinskyVaaPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
KandinskyVaaPriorPipeline,
LDMTextToImagePipeline,
PaintByExamplePipeline,
SemanticStableDiffusionPipeline,
ShapEImgaImgPipeline,
ShapEPipeline,
StableDiffusionAttendAndExcitePipeline,
StableDiffusionControlNetImgaImgPipeline,
StableDiffusionControlNetInpaintPipeline,
StableDiffusionControlNetPipeline,
StableDiffusionDepthaImgPipeline,
StableDiffusionDiffEditPipeline,
StableDiffusionImageVariationPipeline,
StableDiffusionImgaImgPipeline,
StableDiffusionInpaintPipeline,
StableDiffusionInpaintPipelineLegacy,
StableDiffusionInstructPixaPixPipeline,
StableDiffusionLatentUpscalePipeline,
StableDiffusionLDMaDPipeline,
StableDiffusionModelEditingPipeline,
StableDiffusionPanoramaPipeline,
StableDiffusionParadigmsPipeline,
StableDiffusionPipeline,
StableDiffusionPipelineSafe,
StableDiffusionPixaPixZeroPipeline,
StableDiffusionSAGPipeline,
StableDiffusionUpscalePipeline,
StableUnCLIPImgaImgPipeline,
StableUnCLIPPipeline,
TextToVideoSDPipeline,
TextToVideoZeroPipeline,
UnCLIPImageVariationPipeline,
UnCLIPPipeline,
UniDiffuserModel,
UniDiffuserPipeline,
UniDiffuserTextDecoder,
VersatileDiffusionDualGuidedPipeline,
VersatileDiffusionImageVariationPipeline,
VersatileDiffusionPipeline,
VersatileDiffusionTextToImagePipeline,
VideoToVideoSDPipeline,
VQDiffusionPipeline,
)
try:
if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_invisible_watermark_objects import * # noqa F403
else:
from .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline
try:
if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_k_diffusion_objects import * # noqa F403
else:
from .pipelines import StableDiffusionKDiffusionPipeline
try:
if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_onnx_objects import * # noqa F403
else:
from .pipelines import (
OnnxStableDiffusionImgaImgPipeline,
OnnxStableDiffusionInpaintPipeline,
OnnxStableDiffusionInpaintPipelineLegacy,
OnnxStableDiffusionPipeline,
OnnxStableDiffusionUpscalePipeline,
StableDiffusionOnnxPipeline,
)
try:
if not (is_torch_available() and is_librosa_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_librosa_objects import * # noqa F403
else:
from .pipelines import AudioDiffusionPipeline, Mel
try:
if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403
else:
from .pipelines import SpectrogramDiffusionPipeline
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_flax_objects import * # noqa F403
else:
from .models.controlnet_flax import FlaxControlNetModel
from .models.modeling_flax_utils import FlaxModelMixin
from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel
from .models.vae_flax import FlaxAutoencoderKL
from .pipelines import FlaxDiffusionPipeline
from .schedulers import (
FlaxDDIMScheduler,
FlaxDDPMScheduler,
FlaxDPMSolverMultistepScheduler,
FlaxKarrasVeScheduler,
FlaxLMSDiscreteScheduler,
FlaxPNDMScheduler,
FlaxSchedulerMixin,
FlaxScoreSdeVeScheduler,
)
try:
if not (is_flax_available() and is_transformers_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_flax_and_transformers_objects import * # noqa F403
else:
from .pipelines import (
FlaxStableDiffusionControlNetPipeline,
FlaxStableDiffusionImgaImgPipeline,
FlaxStableDiffusionInpaintPipeline,
FlaxStableDiffusionPipeline,
)
try:
if not (is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_note_seq_objects import * # noqa F403
else:
from .pipelines import MidiProcessor
| 390
| 1
|
'''simple docstring'''
import torch
from transformers import AutoModel
class __UpperCAmelCase ( torch.nn.Module ):
def __init__( self , _lowerCamelCase="sayef/fsner-bert-base-uncased" ):
super(lowercase_ , self ).__init__()
lowerCAmelCase_ = AutoModel.from_pretrained(lowercase_ , return_dict=lowercase_ )
lowerCAmelCase_ = torch.nn.CosineSimilarity(3 , 1E-08 )
lowerCAmelCase_ = torch.nn.Softmax(dim=1 )
def UpperCAmelCase_ ( self , **_lowerCamelCase ):
return self.bert(**lowercase_ ).last_hidden_state
def UpperCAmelCase_ ( self , _lowerCamelCase ):
return token_embeddings.sum(2 , keepdim=lowercase_ )
def UpperCAmelCase_ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase=1 ):
return self.softmax(T * self.cos(lowercase_ , lowercase_ ) )
def UpperCAmelCase_ ( self , _lowerCamelCase , _lowerCamelCase ):
lowerCAmelCase_ = W_supports['''sizes'''].tolist()
lowerCAmelCase_ = W_supports['''start_token_id'''].item()
lowerCAmelCase_ = W_supports['''end_token_id'''].item()
del W_supports["sizes"]
del W_supports["start_token_id"]
del W_supports["end_token_id"]
lowerCAmelCase_ = self.BERT(**lowercase_ )
lowerCAmelCase_ = self.BERT(**lowercase_ )
lowerCAmelCase_ = None
lowerCAmelCase_ = None
lowerCAmelCase_ = W_supports['''input_ids'''] == start_token_id
lowerCAmelCase_ = W_supports['''input_ids'''] == end_token_id
for i, size in enumerate(lowercase_ ):
if i == 0:
lowerCAmelCase_ = 0
else:
lowerCAmelCase_ = support_sizes[i - 1]
lowerCAmelCase_ = S[s : s + size][start_token_masks[s : s + size]]
lowerCAmelCase_ = S[s : s + size][end_token_masks[s : s + size]]
lowerCAmelCase_ = torch.matmul(q[i] , s_start.T ).sum(1 ).softmax(0 )
lowerCAmelCase_ = torch.matmul(q[i] , s_end.T ).sum(1 ).softmax(0 )
if p_starts is not None:
lowerCAmelCase_ = torch.vstack((p_starts, p_start) )
lowerCAmelCase_ = torch.vstack((p_ends, p_end) )
else:
lowerCAmelCase_ = p_start
lowerCAmelCase_ = p_end
return p_starts, p_ends
| 274
|
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
class _UpperCAmelCase :
def __init__( self , lowercase_ ) -> None:
UpperCAmelCase = value
UpperCAmelCase = None
UpperCAmelCase = None
class _UpperCAmelCase :
def __init__( self , lowercase_ ) -> None:
UpperCAmelCase = tree
def a_ ( self , lowercase_ ) -> int:
if node is None:
return 0
return node.value + (
self.depth_first_search(node.left ) + self.depth_first_search(node.right )
)
def __iter__( self ) -> Iterator[int]:
yield self.depth_first_search(self.tree )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 373
| 0
|
from typing import TYPE_CHECKING
from ...utils import _LazyModule
lowercase_ = {"tokenization_bertweet": ["BertweetTokenizer"]}
if TYPE_CHECKING:
from .tokenization_bertweet import BertweetTokenizer
else:
import sys
lowercase_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 709
|
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
lowercase_ = "src/transformers"
lowercase_ = "docs/source/en/tasks"
def __lowerCAmelCase ( __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : Union[str, Any] ):
'''simple docstring'''
with open(__SCREAMING_SNAKE_CASE , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
__snake_case : Optional[Any] = f.readlines()
# Find the start prompt.
__snake_case : List[str] = 0
while not lines[start_index].startswith(__SCREAMING_SNAKE_CASE ):
start_index += 1
start_index += 1
__snake_case : List[str] = start_index
while not lines[end_index].startswith(__SCREAMING_SNAKE_CASE ):
end_index += 1
end_index -= 1
while len(lines[start_index] ) <= 1:
start_index += 1
while len(lines[end_index] ) <= 1:
end_index -= 1
end_index += 1
return "".join(lines[start_index:end_index] ), start_index, end_index, lines
# This is to make sure the transformers module imported is the one in the repo.
lowercase_ = direct_transformers_import(TRANSFORMERS_PATH)
lowercase_ = {
"asr.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
"audio_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
"language_modeling.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
"image_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
"masked_language_modeling.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
"multiple_choice.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
"object_detection.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
"question_answering.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
"semantic_segmentation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
"sequence_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
"summarization.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
"token_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
"translation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
"video_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
"document_question_answering.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
"monocular_depth_estimation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}
# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
lowercase_ = {
"summarization.md": ("nllb",),
"translation.md": ("nllb",),
}
def __lowerCAmelCase ( __SCREAMING_SNAKE_CASE : Tuple ):
'''simple docstring'''
__snake_case : Optional[int] = TASK_GUIDE_TO_MODELS[task_guide]
__snake_case : Tuple = SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(__SCREAMING_SNAKE_CASE , set() )
__snake_case : Optional[int] = {
code: name
for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
if (code in model_maping_names or code in special_model_types)
}
return ", ".join([F'''[{name}](../model_doc/{code})''' for code, name in model_names.items()] ) + "\n"
def __lowerCAmelCase ( __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : str=False ):
'''simple docstring'''
__snake_case , __snake_case , __snake_case , __snake_case : List[Any] = _find_text_in_file(
filename=os.path.join(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) , start_prompt="""<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->""" , end_prompt="""<!--End of the generated tip-->""" , )
__snake_case : Optional[int] = get_model_list_for_task(__SCREAMING_SNAKE_CASE )
if current_list != new_list:
if overwrite:
with open(os.path.join(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) , """w""" , encoding="""utf-8""" , newline="""\n""" ) as f:
f.writelines(lines[:start_index] + [new_list] + lines[end_index:] )
else:
raise ValueError(
F'''The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`'''
""" 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()
for task_guide in TASK_GUIDE_TO_MODELS.keys():
check_model_list_for_task(task_guide, args.fix_and_overwrite)
| 390
| 0
|
def _lowerCamelCase ( SCREAMING_SNAKE_CASE_ : str ):
"""simple docstring"""
a_ : Tuple = [int(SCREAMING_SNAKE_CASE_ ) for i in ip_va_address.split(""".""" ) if i.isdigit()]
return len(SCREAMING_SNAKE_CASE_ ) == 4 and all(0 <= int(SCREAMING_SNAKE_CASE_ ) <= 2_54 for octet in octets )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE : Optional[int] = input().strip()
SCREAMING_SNAKE_CASE : Optional[Any] = "valid" if is_ip_va_address_valid(ip) else "invalid"
print(F"""{ip} is a {valid_or_invalid} IP v4 address.""")
| 419
|
from __future__ import annotations
from cmath import sqrt
def _lowerCamelCase ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ):
"""simple docstring"""
if a == 0:
raise ValueError("""Coefficient 'a' must not be zero.""" )
a_ : Any = b * b - 4 * a * c
a_ : List[str] = (-b + sqrt(SCREAMING_SNAKE_CASE_ )) / (2 * a)
a_ : Union[str, Any] = (-b - sqrt(SCREAMING_SNAKE_CASE_ )) / (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 _lowerCamelCase ( ):
"""simple docstring"""
a_ , a_ : str = quadratic_roots(a=5 , b=6 , c=1 )
print(F"""The solutions are: {solutiona} and {solutiona}""" )
if __name__ == "__main__":
main()
| 419
| 1
|
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
lowercase_ : List[str] = logging.get_logger(__name__)
class __UpperCamelCase (_UpperCAmelCase ):
__A = ['''pixel_values''']
def __init__( self , _lowerCAmelCase = True , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = PILImageResampling.BILINEAR , _lowerCAmelCase = True , _lowerCAmelCase = 1 / 255 , _lowerCAmelCase = True , _lowerCAmelCase = None , _lowerCAmelCase = None , **_lowerCAmelCase , ) -> None:
'''simple docstring'''
super().__init__(**UpperCamelCase_ )
lowercase = size if size is not None else {'shortest_edge': 384}
lowercase = get_size_dict(UpperCamelCase_ , default_to_square=UpperCamelCase_ )
lowercase = do_resize
lowercase = size
# Default value set here for backwards compatibility where the value in config is None
lowercase = crop_pct if crop_pct is not None else 224 / 256
lowercase = resample
lowercase = do_rescale
lowercase = rescale_factor
lowercase = do_normalize
lowercase = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
lowercase = image_std if image_std is not None else IMAGENET_STANDARD_STD
def _a ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = PILImageResampling.BICUBIC , _lowerCAmelCase = None , **_lowerCAmelCase , ) -> np.ndarray:
'''simple docstring'''
lowercase = get_size_dict(UpperCamelCase_ , default_to_square=UpperCamelCase_ )
if "shortest_edge" not in size:
raise ValueError(F"""Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}""" )
lowercase = size['shortest_edge']
if shortest_edge < 384:
# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
lowercase = int(shortest_edge / crop_pct )
lowercase = get_resize_output_image_size(UpperCamelCase_ , size=UpperCamelCase_ , default_to_square=UpperCamelCase_ )
lowercase = resize(image=UpperCamelCase_ , size=UpperCamelCase_ , resample=UpperCamelCase_ , data_format=UpperCamelCase_ , **UpperCamelCase_ )
# then crop to (shortest_edge, shortest_edge)
return center_crop(image=UpperCamelCase_ , size=(shortest_edge, shortest_edge) , data_format=UpperCamelCase_ , **UpperCamelCase_ )
else:
# warping (no cropping) when evaluated at 384 or larger
return resize(
UpperCamelCase_ , size=(shortest_edge, shortest_edge) , resample=UpperCamelCase_ , data_format=UpperCamelCase_ , **UpperCamelCase_ )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = None , **_lowerCAmelCase , ) -> Dict:
'''simple docstring'''
return rescale(UpperCamelCase_ , scale=UpperCamelCase_ , data_format=UpperCamelCase_ , **UpperCamelCase_ )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = None , **_lowerCAmelCase , ) -> np.ndarray:
'''simple docstring'''
return normalize(UpperCamelCase_ , mean=UpperCamelCase_ , std=UpperCamelCase_ , data_format=UpperCamelCase_ , **UpperCamelCase_ )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = ChannelDimension.FIRST , **_lowerCAmelCase , ) -> PIL.Image.Image:
'''simple docstring'''
lowercase = do_resize if do_resize is not None else self.do_resize
lowercase = crop_pct if crop_pct is not None else self.crop_pct
lowercase = resample if resample is not None else self.resample
lowercase = do_rescale if do_rescale is not None else self.do_rescale
lowercase = rescale_factor if rescale_factor is not None else self.rescale_factor
lowercase = do_normalize if do_normalize is not None else self.do_normalize
lowercase = image_mean if image_mean is not None else self.image_mean
lowercase = image_std if image_std is not None else self.image_std
lowercase = size if size is not None else self.size
lowercase = get_size_dict(UpperCamelCase_ , default_to_square=UpperCamelCase_ )
lowercase = make_list_of_images(UpperCamelCase_ )
if not valid_images(UpperCamelCase_ ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None or resample is None:
raise ValueError("""Size and resample must be specified if do_resize is True.""" )
if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
raise ValueError("""crop_pct must be specified if size < 384.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
# All transformations expect numpy arrays.
lowercase = [to_numpy_array(UpperCamelCase_ ) for image in images]
if do_resize:
lowercase = [self.resize(image=UpperCamelCase_ , size=UpperCamelCase_ , crop_pct=UpperCamelCase_ , resample=UpperCamelCase_ ) for image in images]
if do_rescale:
lowercase = [self.rescale(image=UpperCamelCase_ , scale=UpperCamelCase_ ) for image in images]
if do_normalize:
lowercase = [self.normalize(image=UpperCamelCase_ , mean=UpperCamelCase_ , std=UpperCamelCase_ ) for image in images]
lowercase = [to_channel_dimension_format(UpperCamelCase_ , UpperCamelCase_ ) for image in images]
lowercase = {'pixel_values': images}
return BatchFeature(data=UpperCamelCase_ , tensor_type=UpperCamelCase_ )
| 704
|
'''simple docstring'''
from __future__ import annotations
import os
from typing import Any
import requests
lowercase_ : List[str] = '''https://api.github.com'''
# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
lowercase_ : Any = BASE_URL + '''/user'''
# https://github.com/settings/tokens
lowercase_ : Union[str, Any] = os.environ.get('''USER_TOKEN''', '''''')
def SCREAMING_SNAKE_CASE ( lowercase_ : str ):
lowercase = {
"""Authorization""": F"""token {auth_token}""",
"""Accept""": """application/vnd.github.v3+json""",
}
return requests.get(lowercase_ , headers=lowercase_ ).json()
if __name__ == "__main__": # pragma: no cover
if USER_TOKEN:
for key, value in fetch_github_info(USER_TOKEN).items():
print(f'''{key}: {value}''')
else:
raise ValueError('''\'USER_TOKEN\' field cannot be empty.''')
| 653
| 0
|
'''simple docstring'''
from __future__ import annotations
def lowercase_ ( __A : list[int] , __A : int ) -> int:
"""simple docstring"""
if len(__A ) < k or k < 0:
raise ValueError('''Invalid Input''' )
lowercase : List[Any] =sum(array[:k] )
for i in range(len(__A ) - k ):
lowercase : str =current_sum - array[i] + array[i + k]
lowercase : int =max(__A , __A )
return max_sum
if __name__ == "__main__":
from doctest import testmod
from random import randint
testmod()
SCREAMING_SNAKE_CASE = [randint(-1_000, 1_000) for i in range(100)]
SCREAMING_SNAKE_CASE = randint(0, 110)
print(f"""The maximum sum of {k} consecutive elements is {max_sum_in_array(array,k)}""")
| 94
|
def UpperCamelCase ( _A : int )-> int:
"""simple docstring"""
if not isinstance(_A , _A ):
raise ValueError("multiplicative_persistence() only accepts integral values" )
if num < 0:
raise ValueError("multiplicative_persistence() does not accept negative values" )
A__ = 0
A__ = str(_A )
while len(_A ) != 1:
A__ = [int(_A ) for i in num_string]
A__ = 1
for i in range(0 , len(_A ) ):
total *= numbers[i]
A__ = str(_A )
steps += 1
return steps
def UpperCamelCase ( _A : int )-> int:
"""simple docstring"""
if not isinstance(_A , _A ):
raise ValueError("additive_persistence() only accepts integral values" )
if num < 0:
raise ValueError("additive_persistence() does not accept negative values" )
A__ = 0
A__ = str(_A )
while len(_A ) != 1:
A__ = [int(_A ) for i in num_string]
A__ = 0
for i in range(0 , len(_A ) ):
total += numbers[i]
A__ = str(_A )
steps += 1
return steps
if __name__ == "__main__":
import doctest
doctest.testmod()
| 491
| 0
|
from __future__ import annotations
import math
def _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case ) -> float:
_UpperCAmelCase = u
for i in range(1 , __snake_case ):
_UpperCAmelCase = temp * (u - i)
return temp
def _SCREAMING_SNAKE_CASE ( ) -> None:
_UpperCAmelCase = int(input("""enter the numbers of values: """ ) )
_UpperCAmelCase = []
for _ in range(__snake_case ):
y.append([] )
for i in range(__snake_case ):
for j in range(__snake_case ):
y[i].append(__snake_case )
_UpperCAmelCase = 0
print("""enter the values of parameters in a list: """ )
_UpperCAmelCase = list(map(__snake_case , input().split() ) )
print("""enter the values of corresponding parameters: """ )
for i in range(__snake_case ):
_UpperCAmelCase = float(input() )
_UpperCAmelCase = int(input("""enter the value to interpolate: """ ) )
_UpperCAmelCase = (value - x[0]) / (x[1] - x[0])
# for calculating forward difference table
for i in range(1 , __snake_case ):
for j in range(n - i ):
_UpperCAmelCase = y[j + 1][i - 1] - y[j][i - 1]
_UpperCAmelCase = y[0][0]
for i in range(1 , __snake_case ):
summ += (ucal(__snake_case , __snake_case ) * y[0][i]) / math.factorial(__snake_case )
print(f"""the value at {value} is {summ}""" )
if __name__ == "__main__":
main()
| 402
|
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import CLIPSegProcessor, ViTImageProcessor
@require_vision
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase ( self : Optional[int] ) -> str:
"""simple docstring"""
_UpperCAmelCase = tempfile.mkdtemp()
# fmt: off
_UpperCAmelCase = ["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
# fmt: on
_UpperCAmelCase = dict(zip(lowerCamelCase , range(len(lowerCamelCase ) ) ) )
_UpperCAmelCase = ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
_UpperCAmelCase = {"""unk_token""": """<unk>"""}
_UpperCAmelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
_UpperCAmelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(lowerCamelCase ) + """\n""" )
with open(self.merges_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(lowerCamelCase ) )
_UpperCAmelCase = {
"""do_resize""": True,
"""size""": 20,
"""do_center_crop""": True,
"""crop_size""": 18,
"""do_normalize""": True,
"""image_mean""": [0.4814_5466, 0.457_8275, 0.4082_1073],
"""image_std""": [0.2686_2954, 0.2613_0258, 0.2757_7711],
}
_UpperCAmelCase = os.path.join(self.tmpdirname , lowerCamelCase )
with open(self.image_processor_file , """w""" , encoding="""utf-8""" ) as fp:
json.dump(lowerCamelCase , lowerCamelCase )
def lowerCamelCase ( self : Dict , **lowerCamelCase : List[str] ) -> int:
"""simple docstring"""
return CLIPTokenizer.from_pretrained(self.tmpdirname , **lowerCamelCase )
def lowerCamelCase ( self : List[Any] , **lowerCamelCase : Dict ) -> Dict:
"""simple docstring"""
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , **lowerCamelCase )
def lowerCamelCase ( self : Optional[Any] , **lowerCamelCase : List[Any] ) -> Optional[int]:
"""simple docstring"""
return ViTImageProcessor.from_pretrained(self.tmpdirname , **lowerCamelCase )
def lowerCamelCase ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def lowerCamelCase ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
_UpperCAmelCase = [Image.fromarray(np.moveaxis(lowerCamelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def lowerCamelCase ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = self.get_rust_tokenizer()
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = CLIPSegProcessor(tokenizer=lowerCamelCase , image_processor=lowerCamelCase )
processor_slow.save_pretrained(self.tmpdirname )
_UpperCAmelCase = CLIPSegProcessor.from_pretrained(self.tmpdirname , use_fast=lowerCamelCase )
_UpperCAmelCase = CLIPSegProcessor(tokenizer=lowerCamelCase , image_processor=lowerCamelCase )
processor_fast.save_pretrained(self.tmpdirname )
_UpperCAmelCase = CLIPSegProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , lowerCamelCase )
self.assertIsInstance(processor_fast.tokenizer , lowerCamelCase )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , lowerCamelCase )
self.assertIsInstance(processor_fast.image_processor , lowerCamelCase )
def lowerCamelCase ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase = CLIPSegProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
_UpperCAmelCase = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
_UpperCAmelCase = self.get_image_processor(do_normalize=lowerCamelCase , padding_value=1.0 )
_UpperCAmelCase = CLIPSegProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=lowerCamelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , lowerCamelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , lowerCamelCase )
def lowerCamelCase ( self : str ) -> Dict:
"""simple docstring"""
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = CLIPSegProcessor(tokenizer=lowerCamelCase , image_processor=lowerCamelCase )
_UpperCAmelCase = self.prepare_image_inputs()
_UpperCAmelCase = image_processor(lowerCamelCase , return_tensors="""np""" )
_UpperCAmelCase = processor(images=lowerCamelCase , return_tensors="""np""" )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def lowerCamelCase ( self : List[str] ) -> str:
"""simple docstring"""
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = CLIPSegProcessor(tokenizer=lowerCamelCase , image_processor=lowerCamelCase )
_UpperCAmelCase = """lower newer"""
_UpperCAmelCase = processor(text=lowerCamelCase )
_UpperCAmelCase = tokenizer(lowerCamelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def lowerCamelCase ( self : Optional[int] ) -> Any:
"""simple docstring"""
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = CLIPSegProcessor(tokenizer=lowerCamelCase , image_processor=lowerCamelCase )
_UpperCAmelCase = """lower newer"""
_UpperCAmelCase = self.prepare_image_inputs()
_UpperCAmelCase = processor(text=lowerCamelCase , images=lowerCamelCase )
self.assertListEqual(list(inputs.keys() ) , ["""input_ids""", """attention_mask""", """pixel_values"""] )
# test if it raises when no input is passed
with pytest.raises(lowerCamelCase ):
processor()
def lowerCamelCase ( self : int ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = CLIPSegProcessor(tokenizer=lowerCamelCase , image_processor=lowerCamelCase )
_UpperCAmelCase = self.prepare_image_inputs()
_UpperCAmelCase = self.prepare_image_inputs()
_UpperCAmelCase = processor(images=lowerCamelCase , visual_prompt=lowerCamelCase )
self.assertListEqual(list(inputs.keys() ) , ["""pixel_values""", """conditional_pixel_values"""] )
# test if it raises when no input is passed
with pytest.raises(lowerCamelCase ):
processor()
def lowerCamelCase ( self : List[str] ) -> int:
"""simple docstring"""
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = CLIPSegProcessor(tokenizer=lowerCamelCase , image_processor=lowerCamelCase )
_UpperCAmelCase = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
_UpperCAmelCase = processor.batch_decode(lowerCamelCase )
_UpperCAmelCase = tokenizer.batch_decode(lowerCamelCase )
self.assertListEqual(lowerCamelCase , lowerCamelCase )
| 402
| 1
|
"""simple docstring"""
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('ignore', category=UserWarning, module='torch.optim.lr_scheduler')
class _SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = True , lowerCamelCase__ = False ) -> Any:
lowercase__ : List[Any] = scheduler
lowercase__ : Optional[int] = optimizers if isinstance(lowerCamelCase__ , (list, tuple) ) else [optimizers]
lowercase__ : Dict = split_batches
lowercase__ : Tuple = step_with_optimizer
lowercase__ : int = GradientState()
def UpperCAmelCase__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Optional[Any]:
if not self.step_with_optimizer:
# No link between scheduler and optimizer -> just step
self.scheduler.step(*lowerCamelCase__ , **lowerCamelCase__ )
return
# Otherwise, first make sure the optimizer was stepped.
if not self.gradient_state.sync_gradients:
if self.gradient_state.adjust_scheduler:
self.scheduler._step_count += 1
return
for opt in self.optimizers:
if opt.step_was_skipped:
return
if self.split_batches:
# Split batches -> the training dataloader batch size is not changed so one step per training step
self.scheduler.step(*lowerCamelCase__ , **lowerCamelCase__ )
else:
# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
# num_processes steps per training step
lowercase__ : Union[str, Any] = AcceleratorState().num_processes
for _ in range(lowerCamelCase__ ):
# Special case when using OneCycle and `drop_last` was not used
if hasattr(self.scheduler , """total_steps""" ):
if self.scheduler._step_count <= self.scheduler.total_steps:
self.scheduler.step(*lowerCamelCase__ , **lowerCamelCase__ )
else:
self.scheduler.step(*lowerCamelCase__ , **lowerCamelCase__ )
def UpperCAmelCase__( self ) -> Union[str, Any]:
return self.scheduler.get_last_lr()
def UpperCAmelCase__( self ) -> List[Any]:
return self.scheduler.state_dict()
def UpperCAmelCase__( self , lowerCamelCase__ ) -> Optional[int]:
self.scheduler.load_state_dict(lowerCamelCase__ )
def UpperCAmelCase__( self ) -> int:
return self.scheduler.get_lr()
def UpperCAmelCase__( self , *lowerCamelCase__ , **lowerCamelCase__ ) -> Optional[int]:
return self.scheduler.print_lr(*lowerCamelCase__ , **lowerCamelCase__ )
| 200
|
'''simple docstring'''
# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a_ = {
'configuration_cpmant': ['CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CpmAntConfig'],
'tokenization_cpmant': ['CpmAntTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST',
'CpmAntForCausalLM',
'CpmAntModel',
'CpmAntPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
from .tokenization_cpmant import CpmAntTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_cpmant import (
CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
CpmAntForCausalLM,
CpmAntModel,
CpmAntPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 685
| 0
|
'''simple docstring'''
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import sqlalchemy
class a__ ( UpperCamelCase_ ):
"""simple docstring"""
def __init__(self , __lowercase , __lowercase , __lowercase = None , __lowercase = None , __lowercase = False , **__lowercase , ):
super().__init__(features=_a , cache_dir=_a , keep_in_memory=_a , **_a )
__lowerCAmelCase = Sql(
cache_dir=_a , features=_a , sql=_a , con=_a , **_a , )
def _snake_case (self ):
__lowerCAmelCase = None
__lowerCAmelCase = None
__lowerCAmelCase = None
__lowerCAmelCase = None
self.builder.download_and_prepare(
download_config=_a , download_mode=_a , verification_mode=_a , base_path=_a , )
# Build dataset for splits
__lowerCAmelCase = self.builder.as_dataset(
split='''train''' , verification_mode=_a , in_memory=self.keep_in_memory )
return dataset
class a__ :
"""simple docstring"""
def __init__(self , __lowercase , __lowercase , __lowercase , __lowercase = None , __lowercase = None , **__lowercase , ):
if num_proc is not None and num_proc <= 0:
raise ValueError(F"""num_proc {num_proc} must be an integer > 0.""" )
__lowerCAmelCase = dataset
__lowerCAmelCase = name
__lowerCAmelCase = con
__lowerCAmelCase = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
__lowerCAmelCase = num_proc
__lowerCAmelCase = to_sql_kwargs
def _snake_case (self ):
__lowerCAmelCase = self.to_sql_kwargs.pop('''sql''' , _a )
__lowerCAmelCase = self.to_sql_kwargs.pop('''con''' , _a )
__lowerCAmelCase = self.to_sql_kwargs.pop('''index''' , _a )
__lowerCAmelCase = self._write(index=_a , **self.to_sql_kwargs )
return written
def _snake_case (self , __lowercase ):
__lowerCAmelCase = args
__lowerCAmelCase = {**to_sql_kwargs, """if_exists""": """append"""} if offset > 0 else to_sql_kwargs
__lowerCAmelCase = query_table(
table=self.dataset.data , key=slice(_a , offset + self.batch_size ) , indices=self.dataset._indices , )
__lowerCAmelCase = batch.to_pandas()
__lowerCAmelCase = df.to_sql(self.name , self.con , index=_a , **_a )
return num_rows or len(_a )
def _snake_case (self , __lowercase , **__lowercase ):
__lowerCAmelCase = 0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 , len(self.dataset ) , self.batch_size ) , unit='''ba''' , disable=not logging.is_progress_bar_enabled() , desc='''Creating SQL from Arrow format''' , ):
written += self._batch_sql((offset, index, to_sql_kwargs) )
else:
__lowerCAmelCase = len(self.dataset ), self.batch_size
with multiprocessing.Pool(self.num_proc ) as pool:
for num_rows in logging.tqdm(
pool.imap(
self._batch_sql , [(offset, index, to_sql_kwargs) for offset in range(0 , _a , _a )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit='''ba''' , disable=not logging.is_progress_bar_enabled() , desc='''Creating SQL from Arrow format''' , ):
written += num_rows
return written
| 712
|
'''simple docstring'''
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
_UpperCAmelCase : str = {
"""text_branch""": """text_model""",
"""audio_branch""": """audio_model.audio_encoder""",
"""attn""": """attention.self""",
"""self.proj""": """output.dense""",
"""attention.self_mask""": """attn_mask""",
"""mlp.fc1""": """intermediate.dense""",
"""mlp.fc2""": """output.dense""",
"""norm1""": """layernorm_before""",
"""norm2""": """layernorm_after""",
"""bn0""": """batch_norm""",
}
_UpperCAmelCase : Optional[int] = AutoFeatureExtractor.from_pretrained("""laion/clap-htsat-unfused""", truncation="""rand_trunc""")
def __magic_name__( lowerCamelCase, lowerCamelCase=False):
__lowerCAmelCase , __lowerCAmelCase = create_model(
'''HTSAT-tiny''', '''roberta''', lowerCamelCase, precision='''fp32''', device='''cuda:0''' if torch.cuda.is_available() else '''cpu''', enable_fusion=lowerCamelCase, fusion_type='''aff_2d''' if enable_fusion else None, )
return model, model_cfg
def __magic_name__( lowerCamelCase):
__lowerCAmelCase = {}
__lowerCAmelCase = r'''.*sequential.(\d+).*'''
__lowerCAmelCase = r'''.*_projection.(\d+).*'''
for key, value in state_dict.items():
# check if any key needs to be modified
for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
if key_to_modify in key:
__lowerCAmelCase = key.replace(lowerCamelCase, lowerCamelCase)
if re.match(lowerCamelCase, lowerCamelCase):
# replace sequential layers with list
__lowerCAmelCase = re.match(lowerCamelCase, lowerCamelCase).group(1)
__lowerCAmelCase = key.replace(F"""sequential.{sequential_layer}.""", F"""layers.{int(lowerCamelCase)//3}.linear.""")
elif re.match(lowerCamelCase, lowerCamelCase):
__lowerCAmelCase = int(re.match(lowerCamelCase, lowerCamelCase).group(1))
# Because in CLAP they use `nn.Sequential`...
__lowerCAmelCase = 1 if projecton_layer == 0 else 2
__lowerCAmelCase = key.replace(F"""_projection.{projecton_layer}.""", F"""_projection.linear{transformers_projection_layer}.""")
if "audio" and "qkv" in key:
# split qkv into query key and value
__lowerCAmelCase = value
__lowerCAmelCase = mixed_qkv.size(0) // 3
__lowerCAmelCase = mixed_qkv[:qkv_dim]
__lowerCAmelCase = mixed_qkv[qkv_dim : qkv_dim * 2]
__lowerCAmelCase = mixed_qkv[qkv_dim * 2 :]
__lowerCAmelCase = query_layer
__lowerCAmelCase = key_layer
__lowerCAmelCase = value_layer
else:
__lowerCAmelCase = value
return model_state_dict
def __magic_name__( lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase=False):
__lowerCAmelCase , __lowerCAmelCase = init_clap(lowerCamelCase, enable_fusion=lowerCamelCase)
clap_model.eval()
__lowerCAmelCase = clap_model.state_dict()
__lowerCAmelCase = rename_state_dict(lowerCamelCase)
__lowerCAmelCase = ClapConfig()
__lowerCAmelCase = enable_fusion
__lowerCAmelCase = ClapModel(lowerCamelCase)
# ignore the spectrogram embedding layer
model.load_state_dict(lowerCamelCase, strict=lowerCamelCase)
model.save_pretrained(lowerCamelCase)
transformers_config.save_pretrained(lowerCamelCase)
if __name__ == "__main__":
_UpperCAmelCase : Any = 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("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument("""--enable_fusion""", action="""store_true""", help="""Whether to enable fusion or not""")
_UpperCAmelCase : Optional[int] = parser.parse_args()
convert_clap_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.enable_fusion)
| 474
| 0
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case_ : Optional[int] = {
'''configuration_upernet''': ['''UperNetConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ : Any = [
'''UperNetForSemanticSegmentation''',
'''UperNetPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_upernet import UperNetConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel
else:
import sys
snake_case_ : List[str] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 691
|
"""simple docstring"""
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
__A = 0b101100111110110010010000011110111011000110011110
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
__A = [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]
class _snake_case :
def __init__( self : Optional[Any] ):
__lowerCamelCase : Optional[int] = WATERMARK_BITS
__lowerCamelCase : Dict = WatermarkEncoder()
self.encoder.set_watermark("bits" , self.watermark )
def lowerCamelCase__ ( self : int , UpperCAmelCase : torch.FloatTensor ):
# can't encode images that are smaller than 256
if images.shape[-1] < 256:
return images
__lowerCamelCase : Optional[int] = (255 * (images / 2 + 0.5)).cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
__lowerCamelCase : Optional[Any] = [self.encoder.encode(UpperCAmelCase , "dwtDct" ) for image in images]
__lowerCamelCase : int = torch.from_numpy(np.array(UpperCAmelCase ) ).permute(0 , 3 , 1 , 2 )
__lowerCamelCase : int = torch.clamp(2 * (images / 255 - 0.5) , min=-1.0 , max=1.0 )
return images
| 646
| 0
|
'''simple docstring'''
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
_UpperCAmelCase = '''.'''
# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
_UpperCAmelCase = [
'''Assert''',
'''AssignVariableOp''',
'''EmptyTensorList''',
'''MergeV2Checkpoints''',
'''ReadVariableOp''',
'''ResourceGather''',
'''RestoreV2''',
'''SaveV2''',
'''ShardedFilename''',
'''StatefulPartitionedCall''',
'''StaticRegexFullMatch''',
'''VarHandleOp''',
]
def _lowerCamelCase ( _a , _a , _a ):
"""simple docstring"""
_lowerCamelCase = SavedModel()
_lowerCamelCase = []
with open(os.path.join(_lowercase , '''utils''' , '''tf_ops''' , '''onnx.json''' ) ) as f:
_lowerCamelCase = json.load(_lowercase )["opsets"]
for i in range(1 , opset + 1 ):
onnx_ops.extend(onnx_opsets[str(_lowercase )] )
with open(_lowercase , '''rb''' ) as f:
saved_model.ParseFromString(f.read() )
_lowerCamelCase = set()
# Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
for meta_graph in saved_model.meta_graphs:
# Add operations in the graph definition
model_op_names.update(node.op for node in meta_graph.graph_def.node )
# Go through the functions in the graph definition
for func in meta_graph.graph_def.library.function:
# Add operations in each function
model_op_names.update(node.op for node in func.node_def )
# Convert to list, sorted if you want
_lowerCamelCase = sorted(_lowercase )
_lowerCamelCase = []
for op in model_op_names:
if op not in onnx_ops and op not in INTERNAL_OPS:
incompatible_ops.append(_lowercase )
if strict and len(_lowercase ) > 0:
raise Exception(F'''Found the following incompatible ops for the opset {opset}:\n''' + incompatible_ops )
elif len(_lowercase ) > 0:
print(F'''Found the following incompatible ops for the opset {opset}:''' )
print(*_lowercase , sep='''\n''' )
else:
print(F'''The saved model {saved_model_path} can properly be converted with ONNX.''' )
if __name__ == "__main__":
_UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument("--saved_model_path", help="Path of the saved model to check (the .pb file).")
parser.add_argument(
"--opset", default=12, type=int, help="The ONNX opset against which the model has to be tested."
)
parser.add_argument(
"--framework", choices=["onnx"], default="onnx", help="Frameworks against which to test the saved model."
)
parser.add_argument(
"--strict", action="store_true", help="Whether make the checking strict (raise errors) or not (raise warnings)"
)
_UpperCAmelCase = parser.parse_args()
if args.framework == "onnx":
onnx_compliancy(args.saved_model_path, args.strict, args.opset)
| 709
|
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def _lowerCamelCase ( _a ):
"""simple docstring"""
random.seed(_a )
np.random.seed(_a )
torch.manual_seed(_a )
torch.cuda.manual_seed_all(_a )
# ^^ safe to call this function even if cuda is not available
class __magic_name__ :
"""simple docstring"""
def __init__( self , a__ , a__ = 0.9999 , a__ = 0.0 , a__ = 0 , a__ = False , a__ = 1.0 , a__ = 2 / 3 , a__ = None , a__ = None , **a__ , ):
if isinstance(a__ , torch.nn.Module ):
_lowerCamelCase = (
'''Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. '''
'''Please pass the parameters of the module instead.'''
)
deprecate(
'''passing a `torch.nn.Module` to `ExponentialMovingAverage`''' , '''1.0.0''' , a__ , standard_warn=a__ , )
_lowerCamelCase = parameters.parameters()
# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
_lowerCamelCase = True
if kwargs.get('''max_value''' , a__ ) is not None:
_lowerCamelCase = '''The `max_value` argument is deprecated. Please use `decay` instead.'''
deprecate('''max_value''' , '''1.0.0''' , a__ , standard_warn=a__ )
_lowerCamelCase = kwargs['''max_value''']
if kwargs.get('''min_value''' , a__ ) is not None:
_lowerCamelCase = '''The `min_value` argument is deprecated. Please use `min_decay` instead.'''
deprecate('''min_value''' , '''1.0.0''' , a__ , standard_warn=a__ )
_lowerCamelCase = kwargs['''min_value''']
_lowerCamelCase = list(a__ )
_lowerCamelCase = [p.clone().detach() for p in parameters]
if kwargs.get('''device''' , a__ ) is not None:
_lowerCamelCase = '''The `device` argument is deprecated. Please use `to` instead.'''
deprecate('''device''' , '''1.0.0''' , a__ , standard_warn=a__ )
self.to(device=kwargs['''device'''] )
_lowerCamelCase = None
_lowerCamelCase = decay
_lowerCamelCase = min_decay
_lowerCamelCase = update_after_step
_lowerCamelCase = use_ema_warmup
_lowerCamelCase = inv_gamma
_lowerCamelCase = power
_lowerCamelCase = 0
_lowerCamelCase = None # set in `step()`
_lowerCamelCase = model_cls
_lowerCamelCase = model_config
@classmethod
def _UpperCAmelCase ( cls , a__ , a__ ):
_lowerCamelCase , _lowerCamelCase = model_cls.load_config(a__ , return_unused_kwargs=a__ )
_lowerCamelCase = model_cls.from_pretrained(a__ )
_lowerCamelCase = cls(model.parameters() , model_cls=a__ , model_config=model.config )
ema_model.load_state_dict(a__ )
return ema_model
def _UpperCAmelCase ( self , a__ ):
if self.model_cls is None:
raise ValueError('''`save_pretrained` can only be used if `model_cls` was defined at __init__.''' )
if self.model_config is None:
raise ValueError('''`save_pretrained` can only be used if `model_config` was defined at __init__.''' )
_lowerCamelCase = self.model_cls.from_config(self.model_config )
_lowerCamelCase = self.state_dict()
state_dict.pop('''shadow_params''' , a__ )
model.register_to_config(**a__ )
self.copy_to(model.parameters() )
model.save_pretrained(a__ )
def _UpperCAmelCase ( self , a__ ):
_lowerCamelCase = max(0 , optimization_step - self.update_after_step - 1 )
if step <= 0:
return 0.0
if self.use_ema_warmup:
_lowerCamelCase = 1 - (1 + step / self.inv_gamma) ** -self.power
else:
_lowerCamelCase = (1 + step) / (10 + step)
_lowerCamelCase = min(a__ , self.decay )
# make sure decay is not smaller than min_decay
_lowerCamelCase = max(a__ , self.min_decay )
return cur_decay_value
@torch.no_grad()
def _UpperCAmelCase ( self , a__ ):
if isinstance(a__ , torch.nn.Module ):
_lowerCamelCase = (
'''Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. '''
'''Please pass the parameters of the module instead.'''
)
deprecate(
'''passing a `torch.nn.Module` to `ExponentialMovingAverage.step`''' , '''1.0.0''' , a__ , standard_warn=a__ , )
_lowerCamelCase = parameters.parameters()
_lowerCamelCase = list(a__ )
self.optimization_step += 1
# Compute the decay factor for the exponential moving average.
_lowerCamelCase = self.get_decay(self.optimization_step )
_lowerCamelCase = decay
_lowerCamelCase = 1 - decay
_lowerCamelCase = contextlib.nullcontext
if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
import deepspeed
for s_param, param in zip(self.shadow_params , a__ ):
if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
_lowerCamelCase = deepspeed.zero.GatheredParameters(a__ , modifier_rank=a__ )
with context_manager():
if param.requires_grad:
s_param.sub_(one_minus_decay * (s_param - param) )
else:
s_param.copy_(a__ )
def _UpperCAmelCase ( self , a__ ):
_lowerCamelCase = list(a__ )
for s_param, param in zip(self.shadow_params , a__ ):
param.data.copy_(s_param.to(param.device ).data )
def _UpperCAmelCase ( self , a__=None , a__=None ):
_lowerCamelCase = [
p.to(device=a__ , dtype=a__ ) if p.is_floating_point() else p.to(device=a__ )
for p in self.shadow_params
]
def _UpperCAmelCase ( self ):
return {
"decay": self.decay,
"min_decay": self.min_decay,
"optimization_step": self.optimization_step,
"update_after_step": self.update_after_step,
"use_ema_warmup": self.use_ema_warmup,
"inv_gamma": self.inv_gamma,
"power": self.power,
"shadow_params": self.shadow_params,
}
def _UpperCAmelCase ( self , a__ ):
_lowerCamelCase = [param.detach().cpu().clone() for param in parameters]
def _UpperCAmelCase ( self , a__ ):
if self.temp_stored_params is None:
raise RuntimeError('''This ExponentialMovingAverage has no `store()`ed weights ''' '''to `restore()`''' )
for c_param, param in zip(self.temp_stored_params , a__ ):
param.data.copy_(c_param.data )
# Better memory-wise.
_lowerCamelCase = None
def _UpperCAmelCase ( self , a__ ):
_lowerCamelCase = copy.deepcopy(a__ )
_lowerCamelCase = state_dict.get('''decay''' , self.decay )
if self.decay < 0.0 or self.decay > 1.0:
raise ValueError('''Decay must be between 0 and 1''' )
_lowerCamelCase = state_dict.get('''min_decay''' , self.min_decay )
if not isinstance(self.min_decay , a__ ):
raise ValueError('''Invalid min_decay''' )
_lowerCamelCase = state_dict.get('''optimization_step''' , self.optimization_step )
if not isinstance(self.optimization_step , a__ ):
raise ValueError('''Invalid optimization_step''' )
_lowerCamelCase = state_dict.get('''update_after_step''' , self.update_after_step )
if not isinstance(self.update_after_step , a__ ):
raise ValueError('''Invalid update_after_step''' )
_lowerCamelCase = state_dict.get('''use_ema_warmup''' , self.use_ema_warmup )
if not isinstance(self.use_ema_warmup , a__ ):
raise ValueError('''Invalid use_ema_warmup''' )
_lowerCamelCase = state_dict.get('''inv_gamma''' , self.inv_gamma )
if not isinstance(self.inv_gamma , (float, int) ):
raise ValueError('''Invalid inv_gamma''' )
_lowerCamelCase = state_dict.get('''power''' , self.power )
if not isinstance(self.power , (float, int) ):
raise ValueError('''Invalid power''' )
_lowerCamelCase = state_dict.get('''shadow_params''' , a__ )
if shadow_params is not None:
_lowerCamelCase = shadow_params
if not isinstance(self.shadow_params , a__ ):
raise ValueError('''shadow_params must be a list''' )
if not all(isinstance(a__ , torch.Tensor ) for p in self.shadow_params ):
raise ValueError('''shadow_params must all be Tensors''' )
| 297
| 0
|
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_: int =logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_: Dict ={
'asapp/sew-d-tiny-100k': 'https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json',
# See all SEW-D models at https://huggingface.co/models?filter=sew-d
}
class __A ( SCREAMING_SNAKE_CASE_ ):
a__ : int = "sew-d"
def __init__(self : List[Any] , __a : str=32 , __a : Tuple=768 , __a : Any=12 , __a : int=12 , __a : List[str]=3072 , __a : int=2 , __a : Tuple=512 , __a : List[str]=256 , __a : Optional[int]=True , __a : Optional[int]=True , __a : List[str]=("p2c", "c2p") , __a : Optional[Any]="layer_norm" , __a : int="gelu_python" , __a : Tuple=0.1 , __a : Dict=0.1 , __a : Any=0.1 , __a : Dict=0.0 , __a : Optional[int]=0.1 , __a : Optional[int]=0.02 , __a : str=1E-7 , __a : Tuple=1E-5 , __a : Any="group" , __a : Optional[int]="gelu" , __a : Any=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) , __a : Tuple=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) , __a : Tuple=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) , __a : Optional[int]=False , __a : Union[str, Any]=128 , __a : List[str]=16 , __a : Optional[Any]=True , __a : str=0.05 , __a : int=10 , __a : int=2 , __a : Union[str, Any]=0.0 , __a : List[str]=10 , __a : List[str]=0 , __a : Any="mean" , __a : List[str]=False , __a : str=False , __a : List[str]=256 , __a : Dict=0 , __a : Optional[Any]=1 , __a : str=2 , **__a : Optional[Any] , ):
super().__init__(**a__ , pad_token_id=a__ , bos_token_id=a__ , eos_token_id=a__ )
UpperCAmelCase_ = hidden_size
UpperCAmelCase_ = feat_extract_norm
UpperCAmelCase_ = feat_extract_activation
UpperCAmelCase_ = list(a__ )
UpperCAmelCase_ = list(a__ )
UpperCAmelCase_ = list(a__ )
UpperCAmelCase_ = conv_bias
UpperCAmelCase_ = num_conv_pos_embeddings
UpperCAmelCase_ = num_conv_pos_embedding_groups
UpperCAmelCase_ = len(self.conv_dim )
UpperCAmelCase_ = num_hidden_layers
UpperCAmelCase_ = intermediate_size
UpperCAmelCase_ = squeeze_factor
UpperCAmelCase_ = max_position_embeddings
UpperCAmelCase_ = position_buckets
UpperCAmelCase_ = share_att_key
UpperCAmelCase_ = relative_attention
UpperCAmelCase_ = norm_rel_ebd
UpperCAmelCase_ = list(a__ )
UpperCAmelCase_ = hidden_act
UpperCAmelCase_ = num_attention_heads
UpperCAmelCase_ = hidden_dropout
UpperCAmelCase_ = attention_dropout
UpperCAmelCase_ = activation_dropout
UpperCAmelCase_ = feat_proj_dropout
UpperCAmelCase_ = final_dropout
UpperCAmelCase_ = layer_norm_eps
UpperCAmelCase_ = feature_layer_norm_eps
UpperCAmelCase_ = initializer_range
UpperCAmelCase_ = vocab_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)`,"
f"""but is `len(config.conv_dim) = {len(self.conv_dim )}`, `len(config.conv_stride)"""
f"""= {len(self.conv_stride )}`, `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
UpperCAmelCase_ = mask_feature_min_masks
# ctc loss
UpperCAmelCase_ = ctc_loss_reduction
UpperCAmelCase_ = ctc_zero_infinity
# sequence classification
UpperCAmelCase_ = use_weighted_layer_sum
UpperCAmelCase_ = classifier_proj_size
@property
def _lowercase (self : Optional[int] ):
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 78
|
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_a : Optional[int] = logging.get_logger(__name__)
_a : int = {
'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json',
'google/bigbird-roberta-large': 'https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json',
'google/bigbird-base-trivia-itc': 'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json',
# See all BigBird models at https://huggingface.co/models?filter=big_bird
}
class __A ( SCREAMING_SNAKE_CASE_ ):
_UpperCamelCase : Any = "big_bird"
def __init__( self , a__=50358 , a__=768 , a__=12 , a__=12 , a__=3072 , a__="gelu_new" , a__=0.1 , a__=0.1 , a__=4096 , a__=2 , a__=0.0_2 , a__=1e-12 , a__=True , a__=0 , a__=1 , a__=2 , a__=66 , a__="block_sparse" , a__=True , a__=False , a__=64 , a__=3 , a__=None , **a__ , ):
super().__init__(
pad_token_id=a__ , bos_token_id=a__ , eos_token_id=a__ , sep_token_id=a__ , **a__ , )
_lowerCAmelCase : List[str] = vocab_size
_lowerCAmelCase : List[str] = max_position_embeddings
_lowerCAmelCase : Optional[int] = hidden_size
_lowerCAmelCase : Dict = num_hidden_layers
_lowerCAmelCase : str = num_attention_heads
_lowerCAmelCase : Any = intermediate_size
_lowerCAmelCase : Any = hidden_act
_lowerCAmelCase : str = hidden_dropout_prob
_lowerCAmelCase : int = attention_probs_dropout_prob
_lowerCAmelCase : str = initializer_range
_lowerCAmelCase : Optional[int] = type_vocab_size
_lowerCAmelCase : Optional[Any] = layer_norm_eps
_lowerCAmelCase : Optional[int] = use_cache
_lowerCAmelCase : List[Any] = rescale_embeddings
_lowerCAmelCase : Any = attention_type
_lowerCAmelCase : List[Any] = use_bias
_lowerCAmelCase : Dict = block_size
_lowerCAmelCase : Dict = num_random_blocks
_lowerCAmelCase : str = classifier_dropout
class __A ( SCREAMING_SNAKE_CASE_ ):
@property
def __A ( self ):
if self.task == "multiple-choice":
_lowerCAmelCase : Any = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
_lowerCAmelCase : List[Any] = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] )
| 213
| 0
|
'''simple docstring'''
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
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 torch import nn
from transformers import ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class lowerCamelCase :
'''simple docstring'''
def __init__( self : Optional[int] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : List[str]=13 , UpperCAmelCase__ : List[Any]=30 , UpperCAmelCase__ : List[Any]=2 , UpperCAmelCase__ : int=3 , UpperCAmelCase__ : Dict=True , UpperCAmelCase__ : Optional[int]=True , UpperCAmelCase__ : Tuple=32 , UpperCAmelCase__ : Optional[Any]=5 , UpperCAmelCase__ : Optional[Any]=4 , UpperCAmelCase__ : Optional[int]=37 , UpperCAmelCase__ : Dict="gelu" , UpperCAmelCase__ : str=0.1 , UpperCAmelCase__ : List[str]=0.1 , UpperCAmelCase__ : int=10 , UpperCAmelCase__ : List[str]=0.02 , UpperCAmelCase__ : Tuple=None , UpperCAmelCase__ : List[str]=2 , ) ->Dict:
UpperCAmelCase_ = parent
UpperCAmelCase_ = batch_size
UpperCAmelCase_ = image_size
UpperCAmelCase_ = patch_size
UpperCAmelCase_ = num_channels
UpperCAmelCase_ = is_training
UpperCAmelCase_ = use_labels
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_ = type_sequence_label_size
UpperCAmelCase_ = initializer_range
UpperCAmelCase_ = scope
UpperCAmelCase_ = encoder_stride
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
UpperCAmelCase_ = (image_size // patch_size) ** 2
UpperCAmelCase_ = num_patches + 1
def lowerCAmelCase__ ( self : str ) ->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.type_sequence_label_size )
UpperCAmelCase_ = self.get_config()
return config, pixel_values, labels
def lowerCAmelCase__ ( self : Any ) ->Optional[int]:
return ViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , 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 , is_decoder=UpperCAmelCase_ , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def lowerCAmelCase__ ( self : Optional[int] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Any ) ->Union[str, Any]:
UpperCAmelCase_ = ViTModel(config=UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
UpperCAmelCase_ = model(UpperCAmelCase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCAmelCase__ ( self : Optional[int] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Dict ) ->str:
UpperCAmelCase_ = ViTForMaskedImageModeling(config=UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
UpperCAmelCase_ = model(UpperCAmelCase_ )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
UpperCAmelCase_ = 1
UpperCAmelCase_ = ViTForMaskedImageModeling(UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
UpperCAmelCase_ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
UpperCAmelCase_ = model(UpperCAmelCase_ )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def lowerCAmelCase__ ( self : Optional[Any] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : int ) ->int:
UpperCAmelCase_ = self.type_sequence_label_size
UpperCAmelCase_ = ViTForImageClassification(UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
UpperCAmelCase_ = model(UpperCAmelCase_ , labels=UpperCAmelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
UpperCAmelCase_ = 1
UpperCAmelCase_ = ViTForImageClassification(UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
UpperCAmelCase_ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
UpperCAmelCase_ = model(UpperCAmelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def lowerCAmelCase__ ( self : Dict ) ->str:
UpperCAmelCase_ = self.prepare_config_and_inputs()
(
(
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) ,
) = config_and_inputs
UpperCAmelCase_ = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class lowerCamelCase ( __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase__ = (
(
ViTModel,
ViTForImageClassification,
ViTForMaskedImageModeling,
)
if is_torch_available()
else ()
)
lowerCAmelCase__ = (
{"""feature-extraction""": ViTModel, """image-classification""": ViTForImageClassification}
if is_torch_available()
else {}
)
lowerCAmelCase__ = True
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
def lowerCAmelCase__ ( self : Optional[Any] ) ->Any:
UpperCAmelCase_ = ViTModelTester(self )
UpperCAmelCase_ = ConfigTester(self , config_class=UpperCAmelCase_ , has_text_modality=UpperCAmelCase_ , hidden_size=37 )
def lowerCAmelCase__ ( self : Optional[int] ) ->Dict:
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViT does not use inputs_embeds''' )
def lowerCAmelCase__ ( self : Optional[int] ) ->str:
pass
def lowerCAmelCase__ ( self : str ) ->Any:
UpperCAmelCase_ , UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase_ = model_class(UpperCAmelCase_ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
UpperCAmelCase_ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(UpperCAmelCase_ , nn.Linear ) )
def lowerCAmelCase__ ( self : Dict ) ->Tuple:
UpperCAmelCase_ , UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase_ = model_class(UpperCAmelCase_ )
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] , UpperCAmelCase_ )
def lowerCAmelCase__ ( self : Dict ) ->Tuple:
UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase_ )
def lowerCAmelCase__ ( self : Tuple ) ->str:
UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*UpperCAmelCase_ )
def lowerCAmelCase__ ( self : List[str] ) ->Tuple:
UpperCAmelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_ )
@slow
def lowerCAmelCase__ ( self : List[str] ) ->Union[str, Any]:
for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase_ = ViTModel.from_pretrained(UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
def __lowerCamelCase ( ):
'''simple docstring'''
UpperCAmelCase_ = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def lowerCAmelCase__ ( self : List[Any] ) ->List[Any]:
return ViTImageProcessor.from_pretrained('''google/vit-base-patch16-224''' ) if is_vision_available() else None
@slow
def lowerCAmelCase__ ( self : Union[str, Any] ) ->List[str]:
UpperCAmelCase_ = ViTForImageClassification.from_pretrained('''google/vit-base-patch16-224''' ).to(UpperCAmelCase_ )
UpperCAmelCase_ = self.default_image_processor
UpperCAmelCase_ = prepare_img()
UpperCAmelCase_ = image_processor(images=UpperCAmelCase_ , return_tensors='''pt''' ).to(UpperCAmelCase_ )
# forward pass
with torch.no_grad():
UpperCAmelCase_ = model(**UpperCAmelCase_ )
# verify the logits
UpperCAmelCase_ = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , UpperCAmelCase_ )
UpperCAmelCase_ = torch.tensor([-0.2744, 0.8215, -0.0836] ).to(UpperCAmelCase_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , UpperCAmelCase_ , atol=1e-4 ) )
@slow
def lowerCAmelCase__ ( self : Tuple ) ->Optional[int]:
# ViT models have an `interpolate_pos_encoding` argument in their forward method,
# allowing to interpolate the pre-trained position embeddings in order to use
# the model on higher resolutions. The DINO model by Facebook AI leverages this
# to visualize self-attention on higher resolution images.
UpperCAmelCase_ = ViTModel.from_pretrained('''facebook/dino-vits8''' ).to(UpperCAmelCase_ )
UpperCAmelCase_ = ViTImageProcessor.from_pretrained('''facebook/dino-vits8''' , size=480 )
UpperCAmelCase_ = prepare_img()
UpperCAmelCase_ = image_processor(images=UpperCAmelCase_ , return_tensors='''pt''' )
UpperCAmelCase_ = inputs.pixel_values.to(UpperCAmelCase_ )
# forward pass
with torch.no_grad():
UpperCAmelCase_ = model(UpperCAmelCase_ , interpolate_pos_encoding=UpperCAmelCase_ )
# verify the logits
UpperCAmelCase_ = torch.Size((1, 3601, 384) )
self.assertEqual(outputs.last_hidden_state.shape , UpperCAmelCase_ )
UpperCAmelCase_ = torch.tensor(
[[4.2340, 4.3906, -6.6692], [4.5463, 1.8928, -6.7257], [4.4429, 0.8496, -5.8585]] ).to(UpperCAmelCase_ )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3] , UpperCAmelCase_ , atol=1e-4 ) )
@slow
@require_accelerate
@require_torch_gpu
def lowerCAmelCase__ ( self : Union[str, Any] ) ->Optional[int]:
UpperCAmelCase_ = ViTModel.from_pretrained('''facebook/dino-vits8''' , torch_dtype=torch.floataa , device_map='''auto''' )
UpperCAmelCase_ = self.default_image_processor
UpperCAmelCase_ = prepare_img()
UpperCAmelCase_ = image_processor(images=UpperCAmelCase_ , return_tensors='''pt''' )
UpperCAmelCase_ = inputs.pixel_values.to(UpperCAmelCase_ )
# forward pass to make sure inference works in fp16
with torch.no_grad():
UpperCAmelCase_ = model(UpperCAmelCase_ )
| 711
|
'''simple docstring'''
import collections
import os
import re
from pathlib import Path
lowercase__ : List[Any] = "src/transformers"
# Matches is_xxx_available()
lowercase__ : Optional[Any] = re.compile(R"is\_([a-z_]*)_available()")
# Catches a one-line _import_struct = {xxx}
lowercase__ : Any = re.compile(R"^_import_structure\s+=\s+\{([^\}]+)\}")
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
lowercase__ : Union[str, Any] = re.compile(R"\s+\"\S*\":\s+\[([^\]]*)\]")
# Catches a line if not is_foo_available
lowercase__ : Optional[int] = re.compile(R"^\s*if\s+not\s+is\_[a-z_]*\_available\(\)")
# Catches a line _import_struct["bla"].append("foo")
lowercase__ : List[str] = re.compile(R"^\s*_import_structure\[\"\S*\"\]\.append\(\"(\S*)\"\)")
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
lowercase__ : Any = re.compile(R"^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]")
# Catches a line with an object between quotes and a comma: "MyModel",
lowercase__ : List[Any] = re.compile(R"^\s+\"([^\"]+)\",")
# Catches a line with objects between brackets only: ["foo", "bar"],
lowercase__ : Optional[Any] = re.compile(R"^\s+\[([^\]]+)\]")
# Catches a line with from foo import bar, bla, boo
lowercase__ : Union[str, Any] = re.compile(R"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")
# Catches a line with try:
lowercase__ : int = re.compile(R"^\s*try:")
# Catches a line with else:
lowercase__ : Any = re.compile(R"^\s*else:")
def __lowerCamelCase ( _UpperCamelCase : Optional[Any] ):
'''simple docstring'''
if _re_test_backend.search(_UpperCamelCase ) is None:
return None
UpperCAmelCase_ = [b[0] for b in _re_backend.findall(_UpperCamelCase )]
backends.sort()
return "_and_".join(_UpperCamelCase )
def __lowerCamelCase ( _UpperCamelCase : int ):
'''simple docstring'''
with open(_UpperCamelCase , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
UpperCAmelCase_ = f.readlines()
UpperCAmelCase_ = 0
while line_index < len(_UpperCamelCase ) and not lines[line_index].startswith('''_import_structure = {''' ):
line_index += 1
# If this is a traditional init, just return.
if line_index >= len(_UpperCamelCase ):
return None
# First grab the objects without a specific backend in _import_structure
UpperCAmelCase_ = []
while not lines[line_index].startswith('''if TYPE_CHECKING''' ) and find_backend(lines[line_index] ) is None:
UpperCAmelCase_ = lines[line_index]
# If we have everything on a single line, let's deal with it.
if _re_one_line_import_struct.search(_UpperCamelCase ):
UpperCAmelCase_ = _re_one_line_import_struct.search(_UpperCamelCase ).groups()[0]
UpperCAmelCase_ = re.findall(R'''\[([^\]]+)\]''' , _UpperCamelCase )
for imp in imports:
objects.extend([obj[1:-1] for obj in imp.split(''', ''' )] )
line_index += 1
continue
UpperCAmelCase_ = _re_import_struct_key_value.search(_UpperCamelCase )
if single_line_import_search is not None:
UpperCAmelCase_ = [obj[1:-1] for obj in single_line_import_search.groups()[0].split(''', ''' ) if len(_UpperCamelCase ) > 0]
objects.extend(_UpperCamelCase )
elif line.startswith(''' ''' * 8 + '''"''' ):
objects.append(line[9:-3] )
line_index += 1
UpperCAmelCase_ = {'''none''': objects}
# Let's continue with backend-specific objects in _import_structure
while not lines[line_index].startswith('''if TYPE_CHECKING''' ):
# If the line is an if not is_backend_available, we grab all objects associated.
UpperCAmelCase_ = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
UpperCAmelCase_ = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
UpperCAmelCase_ = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(''' ''' * 4 ):
UpperCAmelCase_ = lines[line_index]
if _re_import_struct_add_one.search(_UpperCamelCase ) is not None:
objects.append(_re_import_struct_add_one.search(_UpperCamelCase ).groups()[0] )
elif _re_import_struct_add_many.search(_UpperCamelCase ) is not None:
UpperCAmelCase_ = _re_import_struct_add_many.search(_UpperCamelCase ).groups()[0].split(''', ''' )
UpperCAmelCase_ = [obj[1:-1] for obj in imports if len(_UpperCamelCase ) > 0]
objects.extend(_UpperCamelCase )
elif _re_between_brackets.search(_UpperCamelCase ) is not None:
UpperCAmelCase_ = _re_between_brackets.search(_UpperCamelCase ).groups()[0].split(''', ''' )
UpperCAmelCase_ = [obj[1:-1] for obj in imports if len(_UpperCamelCase ) > 0]
objects.extend(_UpperCamelCase )
elif _re_quote_object.search(_UpperCamelCase ) is not None:
objects.append(_re_quote_object.search(_UpperCamelCase ).groups()[0] )
elif line.startswith(''' ''' * 8 + '''"''' ):
objects.append(line[9:-3] )
elif line.startswith(''' ''' * 12 + '''"''' ):
objects.append(line[13:-3] )
line_index += 1
UpperCAmelCase_ = objects
else:
line_index += 1
# At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
UpperCAmelCase_ = []
while (
line_index < len(_UpperCamelCase )
and find_backend(lines[line_index] ) is None
and not lines[line_index].startswith('''else''' )
):
UpperCAmelCase_ = lines[line_index]
UpperCAmelCase_ = _re_import.search(_UpperCamelCase )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(''', ''' ) )
elif line.startswith(''' ''' * 8 ):
objects.append(line[8:-2] )
line_index += 1
UpperCAmelCase_ = {'''none''': objects}
# Let's continue with backend-specific objects
while line_index < len(_UpperCamelCase ):
# If the line is an if is_backend_available, we grab all objects associated.
UpperCAmelCase_ = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
UpperCAmelCase_ = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
UpperCAmelCase_ = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(''' ''' * 8 ):
UpperCAmelCase_ = lines[line_index]
UpperCAmelCase_ = _re_import.search(_UpperCamelCase )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(''', ''' ) )
elif line.startswith(''' ''' * 12 ):
objects.append(line[12:-2] )
line_index += 1
UpperCAmelCase_ = objects
else:
line_index += 1
return import_dict_objects, type_hint_objects
def __lowerCamelCase ( _UpperCamelCase : int , _UpperCamelCase : Optional[Any] ):
'''simple docstring'''
def find_duplicates(_UpperCamelCase : Tuple ):
return [k for k, v in collections.Counter(_UpperCamelCase ).items() if v > 1]
if list(import_dict_objects.keys() ) != list(type_hint_objects.keys() ):
return ["Both sides of the init do not have the same backends!"]
UpperCAmelCase_ = []
for key in import_dict_objects.keys():
UpperCAmelCase_ = find_duplicates(import_dict_objects[key] )
if duplicate_imports:
errors.append(F"""Duplicate _import_structure definitions for: {duplicate_imports}""" )
UpperCAmelCase_ = find_duplicates(type_hint_objects[key] )
if duplicate_type_hints:
errors.append(F"""Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}""" )
if sorted(set(import_dict_objects[key] ) ) != sorted(set(type_hint_objects[key] ) ):
UpperCAmelCase_ = '''base imports''' if key == '''none''' else F"""{key} backend"""
errors.append(F"""Differences for {name}:""" )
for a in type_hint_objects[key]:
if a not in import_dict_objects[key]:
errors.append(F""" {a} in TYPE_HINT but not in _import_structure.""" )
for a in import_dict_objects[key]:
if a not in type_hint_objects[key]:
errors.append(F""" {a} in _import_structure but not in TYPE_HINT.""" )
return errors
def __lowerCamelCase ( ):
'''simple docstring'''
UpperCAmelCase_ = []
for root, _, files in os.walk(_UpperCamelCase ):
if "__init__.py" in files:
UpperCAmelCase_ = os.path.join(_UpperCamelCase , '''__init__.py''' )
UpperCAmelCase_ = parse_init(_UpperCamelCase )
if objects is not None:
UpperCAmelCase_ = analyze_results(*_UpperCamelCase )
if len(_UpperCamelCase ) > 0:
UpperCAmelCase_ = F"""Problem in {fname}, both halves do not define the same objects.\n{errors[0]}"""
failures.append('''\n'''.join(_UpperCamelCase ) )
if len(_UpperCamelCase ) > 0:
raise ValueError('''\n\n'''.join(_UpperCamelCase ) )
def __lowerCamelCase ( ):
'''simple docstring'''
UpperCAmelCase_ = []
for path, directories, files in os.walk(_UpperCamelCase ):
for folder in directories:
# Ignore private modules
if folder.startswith('''_''' ):
directories.remove(_UpperCamelCase )
continue
# Ignore leftovers from branches (empty folders apart from pycache)
if len(list((Path(_UpperCamelCase ) / folder).glob('''*.py''' ) ) ) == 0:
continue
UpperCAmelCase_ = str((Path(_UpperCamelCase ) / folder).relative_to(_UpperCamelCase ) )
UpperCAmelCase_ = short_path.replace(os.path.sep , '''.''' )
submodules.append(_UpperCamelCase )
for fname in files:
if fname == "__init__.py":
continue
UpperCAmelCase_ = str((Path(_UpperCamelCase ) / fname).relative_to(_UpperCamelCase ) )
UpperCAmelCase_ = short_path.replace('''.py''' , '''''' ).replace(os.path.sep , '''.''' )
if len(submodule.split('''.''' ) ) == 1:
submodules.append(_UpperCamelCase )
return submodules
lowercase__ : Union[str, Any] = [
"convert_pytorch_checkpoint_to_tf2",
"modeling_flax_pytorch_utils",
"models.esm.openfold_utils",
]
def __lowerCamelCase ( ):
'''simple docstring'''
from transformers.utils import direct_transformers_import
UpperCAmelCase_ = direct_transformers_import(_UpperCamelCase )
UpperCAmelCase_ = set(transformers._import_structure.keys() )
# This contains all the base keys of the _import_structure object defined in the init, but if the user is missing
# some optional dependencies, they may not have all of them. Thus we read the init to read all additions and
# (potentiall re-) add them.
with open(os.path.join(_UpperCamelCase , '''__init__.py''' ) , '''r''' ) as f:
UpperCAmelCase_ = f.read()
import_structure_keys.update(set(re.findall(R'''import_structure\[\"([^\"]*)\"\]''' , _UpperCamelCase ) ) )
UpperCAmelCase_ = [
module
for module in get_transformers_submodules()
if module not in IGNORE_SUBMODULES and module not in import_structure_keys
]
if len(_UpperCamelCase ) > 0:
UpperCAmelCase_ = '''\n'''.join(F"""- {module}""" for module in module_not_registered )
raise ValueError(
'''The following submodules are not properly registed in the main init of Transformers:\n'''
F"""{list_of_modules}\n"""
'''Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.''' )
if __name__ == "__main__":
check_all_inits()
check_submodules()
| 43
| 0
|
'''simple docstring'''
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_decord,
require_tf,
require_torch,
require_torch_or_tf,
require_vision,
)
from .test_pipelines_common import ANY
@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
snake_case = MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
def _lowercase ( self , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
snake_case_ = hf_hub_download(
repo_id="nateraw/video-demo" , filename="archery.mp4" , repo_type="dataset" )
snake_case_ = VideoClassificationPipeline(model=UpperCAmelCase_ , image_processor=UpperCAmelCase_ , top_k=2 )
snake_case_ = [
example_video_filepath,
"https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4",
]
return video_classifier, examples
def _lowercase ( self , UpperCAmelCase_ , UpperCAmelCase_ ):
for example in examples:
snake_case_ = video_classifier(UpperCAmelCase_ )
self.assertEqual(
UpperCAmelCase_ , [
{"score": ANY(UpperCAmelCase_ ), "label": ANY(UpperCAmelCase_ )},
{"score": ANY(UpperCAmelCase_ ), "label": ANY(UpperCAmelCase_ )},
] , )
@require_torch
def _lowercase ( self ):
snake_case_ = "hf-internal-testing/tiny-random-VideoMAEForVideoClassification"
snake_case_ = VideoMAEFeatureExtractor(
size={"shortest_edge": 10} , crop_size={"height": 10, "width": 10} )
snake_case_ = pipeline(
"video-classification" , model=UpperCAmelCase_ , feature_extractor=UpperCAmelCase_ , frame_sampling_rate=4 )
snake_case_ = hf_hub_download(repo_id="nateraw/video-demo" , filename="archery.mp4" , repo_type="dataset" )
snake_case_ = video_classifier(UpperCAmelCase_ , top_k=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase_ , decimals=4 ) , [{"score": 0.5_199, "label": "LABEL_0"}, {"score": 0.4_801, "label": "LABEL_1"}] , )
snake_case_ = video_classifier(
[
video_file_path,
video_file_path,
] , top_k=2 , )
self.assertEqual(
nested_simplify(UpperCAmelCase_ , decimals=4 ) , [
[{"score": 0.5_199, "label": "LABEL_0"}, {"score": 0.4_801, "label": "LABEL_1"}],
[{"score": 0.5_199, "label": "LABEL_0"}, {"score": 0.4_801, "label": "LABEL_1"}],
] , )
@require_tf
def _lowercase ( self ):
pass
| 508
|
'''simple docstring'''
def __snake_case ( lowercase : int ):
if n == 1 or not isinstance(lowercase , lowercase ):
return 0
elif n == 2:
return 1
else:
snake_case_ = [0, 1]
for i in range(2 , n + 1 ):
sequence.append(sequence[i - 1] + sequence[i - 2] )
return sequence[n]
def __snake_case ( lowercase : int ):
snake_case_ = 0
snake_case_ = 2
while digits < n:
index += 1
snake_case_ = len(str(fibonacci(lowercase ) ) )
return index
def __snake_case ( lowercase : int = 1_000 ):
return fibonacci_digits_index(lowercase )
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 508
| 1
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'facebook/s2t-wav2vec2-large-en-de': (
'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'
),
# See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}
class __lowercase ( __lowerCamelCase ):
snake_case_ = """speech_to_text_2"""
snake_case_ = ["""past_key_values"""]
snake_case_ = {"""num_attention_heads""": """decoder_attention_heads""", """hidden_size""": """d_model"""}
def __init__( self : Optional[Any] ,A : Any=10_000 ,A : Tuple=6 ,A : Any=2_048 ,A : int=4 ,A : Dict=0.0 ,A : Dict=True ,A : int="relu" ,A : List[Any]=256 ,A : Optional[int]=0.1 ,A : List[str]=0.0 ,A : Any=0.0 ,A : str=0.0_2 ,A : int=2 ,A : str=True ,A : Union[str, Any]=1 ,A : Optional[int]=0 ,A : Any=2 ,A : Optional[Any]=1_024 ,**A : List[str] ,):
'''simple docstring'''
UpperCAmelCase__ : Tuple = vocab_size
UpperCAmelCase__ : List[Any] = d_model
UpperCAmelCase__ : List[str] = decoder_ffn_dim
UpperCAmelCase__ : Union[str, Any] = decoder_layers
UpperCAmelCase__ : Dict = decoder_attention_heads
UpperCAmelCase__ : Any = dropout
UpperCAmelCase__ : Union[str, Any] = attention_dropout
UpperCAmelCase__ : Any = activation_dropout
UpperCAmelCase__ : Dict = activation_function
UpperCAmelCase__ : int = init_std
UpperCAmelCase__ : Optional[Any] = decoder_layerdrop
UpperCAmelCase__ : Optional[int] = use_cache
UpperCAmelCase__ : int = decoder_layers
UpperCAmelCase__ : Optional[int] = scale_embedding # scale factor will be sqrt(d_model) if True
UpperCAmelCase__ : Union[str, Any] = max_target_positions
super().__init__(
pad_token_id=A ,bos_token_id=A ,eos_token_id=A ,decoder_start_token_id=A ,**A ,)
| 194
|
"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__UpperCAmelCase = abspath(join(dirname(dirname(__file__)), 'src'))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)
def lowerCAmelCase ( __UpperCamelCase ):
'''simple docstring'''
from diffusers.utils.testing_utils import pytest_addoption_shared
pytest_addoption_shared(__UpperCamelCase )
def lowerCAmelCase ( __UpperCamelCase ):
'''simple docstring'''
from diffusers.utils.testing_utils import pytest_terminal_summary_main
UpperCAmelCase__ : Optional[Any] = terminalreporter.config.getoption("""--make-reports""" )
if make_reports:
pytest_terminal_summary_main(__UpperCamelCase , id=__UpperCamelCase )
| 194
| 1
|
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
__A : Optional[Any] = "sshleifer/bart-tiny-random"
__A : Dict = "patrickvonplaten/t5-tiny-random"
@require_torch
class A_ (unittest.TestCase ):
@cached_property
def _lowercase ( self ):
'''simple docstring'''
return AutoConfig.from_pretrained(_A )
def _lowercase ( self ):
'''simple docstring'''
UpperCAmelCase , *UpperCAmelCase = create_student_by_copying_alternating_layers(_A , tempfile.mkdtemp() , e=1 , d=1 )
self.assertEqual(student.config.num_hidden_layers , 1 )
def _lowercase ( self ):
'''simple docstring'''
UpperCAmelCase , *UpperCAmelCase = create_student_by_copying_alternating_layers(_A , tempfile.mkdtemp() , e=1 , d=_A )
def _lowercase ( self ):
'''simple docstring'''
UpperCAmelCase , *UpperCAmelCase = create_student_by_copying_alternating_layers(_A , tempfile.mkdtemp() , e=1 , d=_A )
self.assertEqual(student.config.encoder_layers , 1 )
self.assertEqual(student.config.decoder_layers , self.teacher_config.encoder_layers )
def _lowercase ( self ):
'''simple docstring'''
UpperCAmelCase , *UpperCAmelCase = create_student_by_copying_alternating_layers(_A , tempfile.mkdtemp() , e=1 , d=1 )
self.assertEqual(student.config.encoder_layers , 1 )
self.assertEqual(student.config.decoder_layers , 1 )
def _lowercase ( self ):
'''simple docstring'''
with self.assertRaises(_A ):
create_student_by_copying_alternating_layers(_A , tempfile.mkdtemp() , e=_A , d=_A )
| 130
|
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401
from .text import text
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> str:
'''simple docstring'''
UpperCAmelCase = []
for line in lines:
UpperCAmelCase = re.sub(R'''#.*''' , '''''' , UpperCamelCase__ ) # remove comments
if line:
filtered_lines.append(UpperCamelCase__ )
UpperCAmelCase = '''\n'''.join(UpperCamelCase__ )
# Make a hash from all this code
UpperCAmelCase = full_str.encode('''utf-8''' )
return shaaaa(UpperCamelCase__ ).hexdigest()
# get importable module names and hash for caching
__A : List[str] = {
"csv": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
"json": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
"pandas": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
"parquet": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
"arrow": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
"text": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
"imagefolder": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
"audiofolder": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}
# Used to infer the module to use based on the data files extensions
__A : Tuple = {
".csv": ("csv", {}),
".tsv": ("csv", {"sep": "\t"}),
".json": ("json", {}),
".jsonl": ("json", {}),
".parquet": ("parquet", {}),
".arrow": ("arrow", {}),
".txt": ("text", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("imagefolder", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("imagefolder", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("audiofolder", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("audiofolder", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
__A : int = {"imagefolder", "audiofolder"}
# Used to filter data files based on extensions given a module name
__A : Dict[str, List[str]] = {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)
_MODULE_TO_EXTENSIONS["imagefolder"].append(".zip")
_MODULE_TO_EXTENSIONS["audiofolder"].append(".zip")
| 130
| 1
|
import copy
import re
class snake_case__ :
"""simple docstring"""
_SCREAMING_SNAKE_CASE = """hp"""
_SCREAMING_SNAKE_CASE = {}
_SCREAMING_SNAKE_CASE = None
@classmethod
def lowercase_ ( cls : Tuple, _snake_case : List[Any], _snake_case : List[Any] ) ->Tuple:
snake_case__ : Any = prefix
snake_case__ : Optional[Any] = defaults
cls.build_naming_info()
@staticmethod
def lowercase_ ( _snake_case : Tuple, _snake_case : Optional[int] ) ->Any:
if len(_snake_case ) == 0:
return ""
snake_case__ : Tuple = None
if any(char.isdigit() for char in word ):
raise Exception(F'''Parameters should not contain numbers: \'{word}\' contains a number''' )
if word in info["short_word"]:
return info["short_word"][word]
for prefix_len in range(1, len(_snake_case ) + 1 ):
snake_case__ : List[str] = word[:prefix_len]
if prefix in info["reverse_short_word"]:
continue
else:
snake_case__ : Optional[int] = prefix
break
if short_word is None:
# Paranoid fallback
def int_to_alphabetic(_snake_case : int ):
snake_case__ : Union[str, Any] = ''
while integer != 0:
snake_case__ : str = chr(ord('A' ) + integer % 1_0 ) + s
integer //= 1_0
return s
snake_case__ : int = 0
while True:
snake_case__ : Union[str, Any] = word + '#' + int_to_alphabetic(_snake_case )
if sword in info["reverse_short_word"]:
continue
else:
snake_case__ : Optional[int] = sword
break
snake_case__ : List[Any] = short_word
snake_case__ : List[str] = word
return short_word
@staticmethod
def lowercase_ ( _snake_case : str, _snake_case : int ) ->List[str]:
snake_case__ : Any = param_name.split('_' )
snake_case__ : int = [TrialShortNamer.shortname_for_word(_snake_case, _snake_case ) for word in words]
# We try to create a separatorless short name, but if there is a collision we have to fallback
# to a separated short name
snake_case__ : List[str] = ['', '_']
for separator in separators:
snake_case__ : Optional[int] = separator.join(_snake_case )
if shortname not in info["reverse_short_param"]:
snake_case__ : Union[str, Any] = shortname
snake_case__ : str = param_name
return shortname
return param_name
@staticmethod
def lowercase_ ( _snake_case : Union[str, Any], _snake_case : Optional[Any] ) ->str:
snake_case__ : str = TrialShortNamer.shortname_for_key(_snake_case, _snake_case )
snake_case__ : Dict = short_name
snake_case__ : List[Any] = param_name
@classmethod
def lowercase_ ( cls : Union[str, Any] ) ->List[Any]:
if cls.NAMING_INFO is not None:
return
snake_case__ : List[Any] = {
'short_word': {},
'reverse_short_word': {},
'short_param': {},
'reverse_short_param': {},
}
snake_case__ : Optional[int] = list(cls.DEFAULTS.keys() )
for k in field_keys:
cls.add_new_param_name(_snake_case, _snake_case )
snake_case__ : Dict = info
@classmethod
def lowercase_ ( cls : List[str], _snake_case : Tuple ) ->Tuple:
cls.build_naming_info()
assert cls.PREFIX is not None
snake_case__ : List[Any] = [copy.copy(cls.PREFIX )]
for k, v in params.items():
if k not in cls.DEFAULTS:
raise Exception(F'''You should provide a default value for the param name {k} with value {v}''' )
if v == cls.DEFAULTS[k]:
# The default value is not added to the name
continue
snake_case__ : Optional[int] = cls.NAMING_INFO['short_param'][k]
if isinstance(_snake_case, _snake_case ):
snake_case__ : List[Any] = 1 if v else 0
snake_case__ : List[Any] = '' if isinstance(_snake_case, (int, float) ) else '-'
snake_case__ : List[Any] = F'''{key}{sep}{v}'''
name.append(_snake_case )
return "_".join(_snake_case )
@classmethod
def lowercase_ ( cls : str, _snake_case : List[str] ) ->str:
snake_case__ : List[Any] = repr[len(cls.PREFIX ) + 1 :]
if repr == "":
snake_case__ : List[Any] = []
else:
snake_case__ : List[Any] = repr.split('_' )
snake_case__ : List[Any] = {}
for value in values:
if "-" in value:
snake_case__ : List[Any] = value.split('-' )
else:
snake_case__ : Tuple = re.sub('[0-9.]', '', _snake_case )
snake_case__ : Optional[Any] = float(re.sub('[^0-9.]', '', _snake_case ) )
snake_case__ : str = cls.NAMING_INFO['reverse_short_param'][p_k]
snake_case__ : Any = p_v
for k in cls.DEFAULTS:
if k not in parameters:
snake_case__ : Tuple = cls.DEFAULTS[k]
return parameters
| 709
|
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
a_ :Dict = None
a_ :List[str] = logging.get_logger(__name__)
a_ :Dict = "▁"
a_ :Dict = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}
a_ :Union[str, Any] = {
"vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"},
"tokenizer_file": {
"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"
},
}
a_ :List[Any] = {
"google/pegasus-xsum": 512,
}
class snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = VOCAB_FILES_NAMES
_SCREAMING_SNAKE_CASE = PRETRAINED_VOCAB_FILES_MAP
_SCREAMING_SNAKE_CASE = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_SCREAMING_SNAKE_CASE = PegasusTokenizer
_SCREAMING_SNAKE_CASE = ["""input_ids""", """attention_mask"""]
def __init__( self : Any, _snake_case : Any=None, _snake_case : Optional[Any]=None, _snake_case : Tuple="<pad>", _snake_case : Tuple="</s>", _snake_case : List[str]="<unk>", _snake_case : Any="<mask_2>", _snake_case : Optional[Any]="<mask_1>", _snake_case : Tuple=None, _snake_case : str=1_0_3, **_snake_case : Dict, ) ->List[Any]:
snake_case__ : Any = offset
if additional_special_tokens is not None:
if not isinstance(_snake_case, _snake_case ):
raise TypeError(
F'''additional_special_tokens should be of type {type(_snake_case )}, but is'''
F''' {type(_snake_case )}''' )
snake_case__ : int = (
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
F'''<unk_{i}>''' for i in range(len(_snake_case ), self.offset - 1 )
]
if len(set(_snake_case ) ) != len(_snake_case ):
raise ValueError(
'Please make sure that the provided additional_special_tokens do not contain an incorrectly'
F''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.''' )
snake_case__ : Optional[int] = additional_special_tokens_extended
else:
snake_case__ : Optional[Any] = [mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [F'''<unk_{i}>''' for i in range(2, self.offset )]
super().__init__(
_snake_case, tokenizer_file=_snake_case, pad_token=_snake_case, eos_token=_snake_case, unk_token=_snake_case, mask_token=_snake_case, mask_token_sent=_snake_case, offset=_snake_case, additional_special_tokens=_snake_case, **_snake_case, )
snake_case__ : str = vocab_file
snake_case__ : int = False if not self.vocab_file else True
def lowercase_ ( self : Optional[Any], _snake_case : str ) ->Dict:
snake_case__ : int = set(self.all_special_ids ) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id ) # <unk> is only sometimes special
if all_special_ids != set(range(len(self.additional_special_tokens ) + 3 ) ):
raise ValueError(
'There should be 3 special tokens: mask_token, pad_token, and eos_token +'
F''' {len(self.additional_special_tokens )} additional_special_tokens, but got {all_special_ids}''' )
return [1 if x in all_special_ids else 0 for x in seq]
def lowercase_ ( self : Dict, _snake_case : List, _snake_case : Optional[List] = None, _snake_case : bool = False ) ->List[int]:
if already_has_special_tokens:
return self._special_token_mask(_snake_case )
elif token_ids_a is None:
return self._special_token_mask(_snake_case ) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
def lowercase_ ( self : Any, _snake_case : Union[str, Any], _snake_case : Union[str, Any]=None ) ->List[int]:
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def lowercase_ ( self : Optional[Any], _snake_case : str, _snake_case : Optional[str] = None ) ->Tuple[str]:
if not self.can_save_slow_tokenizer:
raise ValueError(
'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
'tokenizer.' )
if not os.path.isdir(_snake_case ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
snake_case__ : List[Any] = 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 ):
copyfile(self.vocab_file, _snake_case )
return (out_vocab_file,)
| 243
| 0
|
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def a_ ( UpperCamelCase_ : Any ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase = int(number**0.5 )
return number == sq * sq
def a_ ( UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : List[str] , UpperCamelCase_ : List[Any] , UpperCamelCase_ : List[str] , UpperCamelCase_ : str ) -> Any:
"""simple docstring"""
lowerCamelCase = x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
lowerCamelCase = x_den * y_den * z_den
lowerCamelCase = gcd(lowercase__ , lowercase__ )
top //= hcf
bottom //= hcf
return top, bottom
def a_ ( UpperCamelCase_ : List[str] = 3_5 ) -> str:
"""simple docstring"""
lowerCamelCase = set()
lowerCamelCase = 42
lowerCamelCase = Fraction(0 )
lowerCamelCase = 42
for x_num in range(1 , order + 1 ):
for x_den in range(x_num + 1 , order + 1 ):
for y_num in range(1 , order + 1 ):
for y_den in range(y_num + 1 , order + 1 ):
# n=1
lowerCamelCase = x_num * y_den + x_den * y_num
lowerCamelCase = x_den * y_den
lowerCamelCase = gcd(lowercase__ , lowercase__ )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
lowerCamelCase = add_three(
lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ )
unique_s.add(lowercase__ )
# n=2
lowerCamelCase = (
x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
)
lowerCamelCase = x_den * x_den * y_den * y_den
if is_sq(lowercase__ ) and is_sq(lowercase__ ):
lowerCamelCase = int(sqrt(lowercase__ ) )
lowerCamelCase = int(sqrt(lowercase__ ) )
lowerCamelCase = gcd(lowercase__ , lowercase__ )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
lowerCamelCase = add_three(
lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ )
unique_s.add(lowercase__ )
# n=-1
lowerCamelCase = x_num * y_num
lowerCamelCase = x_den * y_num + x_num * y_den
lowerCamelCase = gcd(lowercase__ , lowercase__ )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
lowerCamelCase = add_three(
lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ )
unique_s.add(lowercase__ )
# n=2
lowerCamelCase = x_num * x_num * y_num * y_num
lowerCamelCase = (
x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
)
if is_sq(lowercase__ ) and is_sq(lowercase__ ):
lowerCamelCase = int(sqrt(lowercase__ ) )
lowerCamelCase = int(sqrt(lowercase__ ) )
lowerCamelCase = gcd(lowercase__ , lowercase__ )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
lowerCamelCase = add_three(
lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ )
unique_s.add(lowercase__ )
for num, den in unique_s:
total += Fraction(lowercase__ , lowercase__ )
return total.denominator + total.numerator
if __name__ == "__main__":
print(F'''{solution() = }''')
| 246
|
"""simple docstring"""
import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class lowerCAmelCase__ ( lowercase, unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ = MobileBertTokenizer
lowerCamelCase__ = MobileBertTokenizerFast
lowerCamelCase__ = True
lowerCamelCase__ = True
lowerCamelCase__ = filter_non_english
lowerCamelCase__ = """google/mobilebert-uncased"""
def A_ ( self ):
super().setUp()
_lowerCamelCase : Optional[int] = [
'[UNK]',
'[CLS]',
'[SEP]',
'[PAD]',
'[MASK]',
'want',
'##want',
'##ed',
'wa',
'un',
'runn',
'##ing',
',',
'low',
'lowest',
]
_lowerCamelCase : Union[str, Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
_lowerCamelCase : Any = [
(tokenizer_def[0], self.pre_trained_model_path, tokenizer_def[2]) # else the 'google/' prefix is stripped
for tokenizer_def in self.tokenizers_list
]
def A_ ( self , lowercase ):
_lowerCamelCase : Union[str, Any] = 'UNwant\u00E9d,running'
_lowerCamelCase : List[Any] = 'unwanted, running'
return input_text, output_text
def A_ ( self ):
_lowerCamelCase : Dict = self.tokenizer_class(self.vocab_file )
_lowerCamelCase : Union[str, Any] = tokenizer.tokenize('UNwant\u00E9d,running' )
self.assertListEqual(lowercase , ['un', '##want', '##ed', ',', 'runn', '##ing'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase ) , [9, 6, 7, 12, 10, 11] )
def A_ ( self ):
if not self.test_rust_tokenizer:
return
_lowerCamelCase : Any = self.get_tokenizer()
_lowerCamelCase : Any = self.get_rust_tokenizer()
_lowerCamelCase : int = 'UNwant\u00E9d,running'
_lowerCamelCase : Union[str, Any] = tokenizer.tokenize(lowercase )
_lowerCamelCase : List[Any] = rust_tokenizer.tokenize(lowercase )
self.assertListEqual(lowercase , lowercase )
_lowerCamelCase : Dict = tokenizer.encode(lowercase , add_special_tokens=lowercase )
_lowerCamelCase : Dict = rust_tokenizer.encode(lowercase , add_special_tokens=lowercase )
self.assertListEqual(lowercase , lowercase )
_lowerCamelCase : Optional[Any] = self.get_rust_tokenizer()
_lowerCamelCase : Optional[int] = tokenizer.encode(lowercase )
_lowerCamelCase : List[str] = rust_tokenizer.encode(lowercase )
self.assertListEqual(lowercase , lowercase )
# With lower casing
_lowerCamelCase : List[Any] = self.get_tokenizer(do_lower_case=lowercase )
_lowerCamelCase : int = self.get_rust_tokenizer(do_lower_case=lowercase )
_lowerCamelCase : Optional[Any] = 'UNwant\u00E9d,running'
_lowerCamelCase : Dict = tokenizer.tokenize(lowercase )
_lowerCamelCase : int = rust_tokenizer.tokenize(lowercase )
self.assertListEqual(lowercase , lowercase )
_lowerCamelCase : Dict = tokenizer.encode(lowercase , add_special_tokens=lowercase )
_lowerCamelCase : Dict = rust_tokenizer.encode(lowercase , add_special_tokens=lowercase )
self.assertListEqual(lowercase , lowercase )
_lowerCamelCase : Any = self.get_rust_tokenizer()
_lowerCamelCase : Union[str, Any] = tokenizer.encode(lowercase )
_lowerCamelCase : Dict = rust_tokenizer.encode(lowercase )
self.assertListEqual(lowercase , lowercase )
def A_ ( self ):
_lowerCamelCase : Dict = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz' ) , ['ah', '\u535A', '\u63A8', 'zz'] )
def A_ ( self ):
_lowerCamelCase : Optional[Any] = BasicTokenizer(do_lower_case=lowercase )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ' ) , ['hello', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def A_ ( self ):
_lowerCamelCase : List[str] = BasicTokenizer(do_lower_case=lowercase , strip_accents=lowercase )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hällo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['h\u00E9llo'] )
def A_ ( self ):
_lowerCamelCase : List[Any] = BasicTokenizer(do_lower_case=lowercase , strip_accents=lowercase )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hallo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def A_ ( self ):
_lowerCamelCase : int = BasicTokenizer(do_lower_case=lowercase )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hallo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def A_ ( self ):
_lowerCamelCase : List[str] = BasicTokenizer(do_lower_case=lowercase )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ' ) , ['HeLLo', '!', 'how', 'Are', 'yoU', '?'] )
def A_ ( self ):
_lowerCamelCase : Union[str, Any] = BasicTokenizer(do_lower_case=lowercase , strip_accents=lowercase )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HäLLo', '!', 'how', 'Are', 'yoU', '?'] )
def A_ ( self ):
_lowerCamelCase : Union[str, Any] = BasicTokenizer(do_lower_case=lowercase , strip_accents=lowercase )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HaLLo', '!', 'how', 'Are', 'yoU', '?'] )
def A_ ( self ):
_lowerCamelCase : int = BasicTokenizer(do_lower_case=lowercase , never_split=['[UNK]'] )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? [UNK]' ) , ['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]'] )
def A_ ( self ):
_lowerCamelCase : Union[str, Any] = ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']
_lowerCamelCase : Tuple = {}
for i, token in enumerate(lowercase ):
_lowerCamelCase : Union[str, Any] = i
_lowerCamelCase : str = WordpieceTokenizer(vocab=lowercase , unk_token='[UNK]' )
self.assertListEqual(tokenizer.tokenize('' ) , [] )
self.assertListEqual(tokenizer.tokenize('unwanted running' ) , ['un', '##want', '##ed', 'runn', '##ing'] )
self.assertListEqual(tokenizer.tokenize('unwantedX running' ) , ['[UNK]', 'runn', '##ing'] )
def A_ ( self ):
self.assertTrue(_is_whitespace(' ' ) )
self.assertTrue(_is_whitespace('\t' ) )
self.assertTrue(_is_whitespace('\r' ) )
self.assertTrue(_is_whitespace('\n' ) )
self.assertTrue(_is_whitespace('\u00A0' ) )
self.assertFalse(_is_whitespace('A' ) )
self.assertFalse(_is_whitespace('-' ) )
def A_ ( self ):
self.assertTrue(_is_control('\u0005' ) )
self.assertFalse(_is_control('A' ) )
self.assertFalse(_is_control(' ' ) )
self.assertFalse(_is_control('\t' ) )
self.assertFalse(_is_control('\r' ) )
def A_ ( self ):
self.assertTrue(_is_punctuation('-' ) )
self.assertTrue(_is_punctuation('$' ) )
self.assertTrue(_is_punctuation('`' ) )
self.assertTrue(_is_punctuation('.' ) )
self.assertFalse(_is_punctuation('A' ) )
self.assertFalse(_is_punctuation(' ' ) )
def A_ ( self ):
_lowerCamelCase : List[Any] = self.get_tokenizer()
_lowerCamelCase : Union[str, Any] = self.get_rust_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(lowercase ) for t in ['Test', '\xad', 'test']] , [['[UNK]'], [], ['[UNK]']] )
self.assertListEqual(
[rust_tokenizer.tokenize(lowercase ) for t in ['Test', '\xad', 'test']] , [['[UNK]'], [], ['[UNK]']] )
@slow
def A_ ( self ):
_lowerCamelCase : Optional[Any] = self.tokenizer_class.from_pretrained('google/mobilebert-uncased' )
_lowerCamelCase : List[str] = tokenizer.encode('sequence builders' , add_special_tokens=lowercase )
_lowerCamelCase : Dict = tokenizer.encode('multi-sequence build' , add_special_tokens=lowercase )
_lowerCamelCase : Optional[int] = tokenizer.build_inputs_with_special_tokens(lowercase )
_lowerCamelCase : List[Any] = tokenizer.build_inputs_with_special_tokens(lowercase , lowercase )
assert encoded_sentence == [101] + text + [102]
assert encoded_pair == [101] + text + [102] + text_a + [102]
def A_ ( self ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
_lowerCamelCase : int = self.rust_tokenizer_class.from_pretrained(lowercase , **lowercase )
_lowerCamelCase : int = F'''A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'''
_lowerCamelCase : int = tokenizer_r.encode_plus(
lowercase , return_attention_mask=lowercase , return_token_type_ids=lowercase , return_offsets_mapping=lowercase , add_special_tokens=lowercase , )
_lowerCamelCase : Tuple = tokenizer_r.do_lower_case if hasattr(lowercase , 'do_lower_case' ) else False
_lowerCamelCase : Dict = (
[
((0, 0), tokenizer_r.cls_token),
((0, 1), 'A'),
((1, 2), ','),
((3, 5), 'na'),
((5, 6), '##ï'),
((6, 8), '##ve'),
((9, 15), tokenizer_r.mask_token),
((16, 21), 'Allen'),
((21, 23), '##NL'),
((23, 24), '##P'),
((25, 33), 'sentence'),
((33, 34), '.'),
((0, 0), tokenizer_r.sep_token),
]
if not do_lower_case
else [
((0, 0), tokenizer_r.cls_token),
((0, 1), 'a'),
((1, 2), ','),
((3, 8), 'naive'),
((9, 15), tokenizer_r.mask_token),
((16, 21), 'allen'),
((21, 23), '##nl'),
((23, 24), '##p'),
((25, 33), 'sentence'),
((33, 34), '.'),
((0, 0), tokenizer_r.sep_token),
]
)
self.assertEqual(
[e[1] for e in expected_results] , tokenizer_r.convert_ids_to_tokens(tokens['input_ids'] ) )
self.assertEqual([e[0] for e in expected_results] , tokens['offset_mapping'] )
def A_ ( self ):
_lowerCamelCase : Tuple = ['的', '人', '有']
_lowerCamelCase : Optional[Any] = ''.join(lowercase )
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
_lowerCamelCase : int = True
_lowerCamelCase : Any = self.tokenizer_class.from_pretrained(lowercase , **lowercase )
_lowerCamelCase : Dict = self.rust_tokenizer_class.from_pretrained(lowercase , **lowercase )
_lowerCamelCase : List[str] = tokenizer_p.encode(lowercase , add_special_tokens=lowercase )
_lowerCamelCase : List[Any] = tokenizer_r.encode(lowercase , add_special_tokens=lowercase )
_lowerCamelCase : Any = tokenizer_r.convert_ids_to_tokens(lowercase )
_lowerCamelCase : Dict = tokenizer_p.convert_ids_to_tokens(lowercase )
# it is expected that each Chinese character is not preceded by "##"
self.assertListEqual(lowercase , lowercase )
self.assertListEqual(lowercase , lowercase )
_lowerCamelCase : Optional[Any] = False
_lowerCamelCase : Dict = self.rust_tokenizer_class.from_pretrained(lowercase , **lowercase )
_lowerCamelCase : Optional[int] = self.tokenizer_class.from_pretrained(lowercase , **lowercase )
_lowerCamelCase : Optional[Any] = tokenizer_r.encode(lowercase , add_special_tokens=lowercase )
_lowerCamelCase : str = tokenizer_p.encode(lowercase , add_special_tokens=lowercase )
_lowerCamelCase : List[Any] = tokenizer_r.convert_ids_to_tokens(lowercase )
_lowerCamelCase : Dict = tokenizer_p.convert_ids_to_tokens(lowercase )
# it is expected that only the first Chinese character is not preceded by "##".
_lowerCamelCase : List[str] = [
F'''##{token}''' if idx != 0 else token for idx, token in enumerate(lowercase )
]
self.assertListEqual(lowercase , lowercase )
self.assertListEqual(lowercase , lowercase )
| 630
| 0
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : Any = logging.get_logger(__name__)
A : Any = {
"google/canine-s": "https://huggingface.co/google/canine-s/resolve/main/config.json",
# See all CANINE models at https://huggingface.co/models?filter=canine
}
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = '''canine'''
def __init__( self : List[Any] , __magic_name__ : Optional[Any]=768 , __magic_name__ : Union[str, Any]=12 , __magic_name__ : str=12 , __magic_name__ : Union[str, Any]=3_072 , __magic_name__ : Union[str, Any]="gelu" , __magic_name__ : List[str]=0.1 , __magic_name__ : List[str]=0.1 , __magic_name__ : Optional[Any]=16_384 , __magic_name__ : Optional[Any]=16 , __magic_name__ : Optional[int]=0.02 , __magic_name__ : Optional[Any]=1e-12 , __magic_name__ : str=0 , __magic_name__ : Any=0XE_0_0_0 , __magic_name__ : Optional[int]=0XE_0_0_1 , __magic_name__ : Union[str, Any]=4 , __magic_name__ : str=4 , __magic_name__ : Dict=8 , __magic_name__ : Optional[int]=16_384 , __magic_name__ : Optional[Any]=128 , **__magic_name__ : Optional[int] , ) -> Optional[int]:
super().__init__(pad_token_id=__magic_name__ , bos_token_id=__magic_name__ , eos_token_id=__magic_name__ , **__magic_name__ )
SCREAMING_SNAKE_CASE_ = max_position_embeddings
SCREAMING_SNAKE_CASE_ = hidden_size
SCREAMING_SNAKE_CASE_ = num_hidden_layers
SCREAMING_SNAKE_CASE_ = num_attention_heads
SCREAMING_SNAKE_CASE_ = intermediate_size
SCREAMING_SNAKE_CASE_ = hidden_act
SCREAMING_SNAKE_CASE_ = hidden_dropout_prob
SCREAMING_SNAKE_CASE_ = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE_ = initializer_range
SCREAMING_SNAKE_CASE_ = type_vocab_size
SCREAMING_SNAKE_CASE_ = layer_norm_eps
# Character config:
SCREAMING_SNAKE_CASE_ = downsampling_rate
SCREAMING_SNAKE_CASE_ = upsampling_kernel_size
SCREAMING_SNAKE_CASE_ = num_hash_functions
SCREAMING_SNAKE_CASE_ = num_hash_buckets
SCREAMING_SNAKE_CASE_ = local_transformer_stride
| 700
|
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
A : Optional[Any] = logging.get_logger(__name__)
A : List[Any] = torch.device("cpu")
def a__ ( ):
SCREAMING_SNAKE_CASE_ = "http://images.cocodataset.org/val2017/000000039769.jpg"
SCREAMING_SNAKE_CASE_ = Image.open(requests.get(__UpperCamelCase , stream=__UpperCamelCase ).raw )
return im
def a__ ( __UpperCamelCase ):
if swiftformer_name == "swiftformer_xs":
return torch.tensor([-2.1_703E00, 2.1_107E00, -2.0_811E00, 8.8_685E-01, 2.4_360E-01] )
elif swiftformer_name == "swiftformer_s":
return torch.tensor([3.9_636E-01, 2.3_478E-01, -1.6_963E00, -1.7_381E00, -8.6_337E-01] )
elif swiftformer_name == "swiftformer_l1":
return torch.tensor([-4.2_768E-01, -4.7_429E-01, -1.0_897E00, -1.0_248E00, 3.5_523E-02] )
elif swiftformer_name == "swiftformer_l3":
return torch.tensor([-2.5_330E-01, 2.4_211E-01, -6.0_185E-01, -8.2_789E-01, -6.0_446E-02] )
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = dct.pop(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = val
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = []
for k in state_dict.keys():
SCREAMING_SNAKE_CASE_ = k
if ".pwconv" in k:
SCREAMING_SNAKE_CASE_ = k_new.replace(".pwconv" , ".point_wise_conv" )
if ".dwconv" in k:
SCREAMING_SNAKE_CASE_ = k_new.replace(".dwconv" , ".depth_wise_conv" )
if ".Proj." in k:
SCREAMING_SNAKE_CASE_ = k_new.replace(".Proj." , ".proj." )
if "patch_embed" in k_new:
SCREAMING_SNAKE_CASE_ = k_new.replace("patch_embed" , "swiftformer.patch_embed.patch_embedding" )
if "network" in k_new:
SCREAMING_SNAKE_CASE_ = k_new.split("." )
if ls[2].isdigit():
SCREAMING_SNAKE_CASE_ = "swiftformer.encoder.network." + ls[1] + ".blocks." + ls[2] + "." + ".".join(ls[3:] )
else:
SCREAMING_SNAKE_CASE_ = k_new.replace("network" , "swiftformer.encoder.network" )
rename_keys.append((k, k_new) )
return rename_keys
@torch.no_grad()
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = SwiftFormerConfig()
# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
SCREAMING_SNAKE_CASE_ = 1_0_0_0
SCREAMING_SNAKE_CASE_ = "huggingface/label-files"
SCREAMING_SNAKE_CASE_ = "imagenet-1k-id2label.json"
SCREAMING_SNAKE_CASE_ = json.load(open(hf_hub_download(__UpperCamelCase , __UpperCamelCase , repo_type="dataset" ) , "r" ) )
SCREAMING_SNAKE_CASE_ = {int(__UpperCamelCase ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE_ = idalabel
SCREAMING_SNAKE_CASE_ = {v: k for k, v in idalabel.items()}
# size of the architecture
if swiftformer_name == "swiftformer_xs":
SCREAMING_SNAKE_CASE_ = [3, 3, 6, 4]
SCREAMING_SNAKE_CASE_ = [4_8, 5_6, 1_1_2, 2_2_0]
elif swiftformer_name == "swiftformer_s":
SCREAMING_SNAKE_CASE_ = [3, 3, 9, 6]
SCREAMING_SNAKE_CASE_ = [4_8, 6_4, 1_6_8, 2_2_4]
elif swiftformer_name == "swiftformer_l1":
SCREAMING_SNAKE_CASE_ = [4, 3, 1_0, 5]
SCREAMING_SNAKE_CASE_ = [4_8, 9_6, 1_9_2, 3_8_4]
elif swiftformer_name == "swiftformer_l3":
SCREAMING_SNAKE_CASE_ = [4, 4, 1_2, 6]
SCREAMING_SNAKE_CASE_ = [6_4, 1_2_8, 3_2_0, 5_1_2]
# load state_dict of original model, remove and rename some keys
if original_ckpt:
if original_ckpt.startswith("https" ):
SCREAMING_SNAKE_CASE_ = torch.hub.load_state_dict_from_url(__UpperCamelCase , map_location="cpu" , check_hash=__UpperCamelCase )
else:
SCREAMING_SNAKE_CASE_ = torch.load(__UpperCamelCase , map_location="cpu" )
SCREAMING_SNAKE_CASE_ = checkpoint
SCREAMING_SNAKE_CASE_ = create_rename_keys(__UpperCamelCase )
for rename_key_src, rename_key_dest in rename_keys:
rename_key(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# load HuggingFace model
SCREAMING_SNAKE_CASE_ = SwiftFormerForImageClassification(__UpperCamelCase ).eval()
hf_model.load_state_dict(__UpperCamelCase )
# prepare test inputs
SCREAMING_SNAKE_CASE_ = prepare_img()
SCREAMING_SNAKE_CASE_ = ViTImageProcessor.from_pretrained("preprocessor_config" )
SCREAMING_SNAKE_CASE_ = processor(images=__UpperCamelCase , return_tensors="pt" )
# compare outputs from both models
SCREAMING_SNAKE_CASE_ = get_expected_output(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = hf_model(inputs["pixel_values"] ).logits
assert hf_logits.shape == torch.Size([1, 1_0_0_0] )
assert torch.allclose(hf_logits[0, 0:5] , __UpperCamelCase , atol=1E-3 )
Path(__UpperCamelCase ).mkdir(exist_ok=__UpperCamelCase )
print(F'''Saving model {swiftformer_name} to {pytorch_dump_folder_path}''' )
hf_model.save_pretrained(__UpperCamelCase )
if __name__ == "__main__":
A : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--swiftformer_name",
default="swiftformer_xs",
choices=["swiftformer_xs", "swiftformer_s", "swiftformer_l1", "swiftformer_l3"],
type=str,
help="Name of the SwiftFormer model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default="./converted_outputs/",
type=str,
help="Path to the output PyTorch model directory.",
)
parser.add_argument("--original_ckpt", default=None, type=str, help="Path to the original model checkpoint.")
A : List[Any] = parser.parse_args()
convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)
| 356
| 0
|
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : str = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Tuple = {
"facebook/data2vec-base-960h": "https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json",
# See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}
class lowerCAmelCase__ ( __lowercase ):
a__ : Optional[int] = """data2vec-audio"""
def __init__( self : Tuple , SCREAMING_SNAKE_CASE__ : int=32 , SCREAMING_SNAKE_CASE__ : Any=7_68 , SCREAMING_SNAKE_CASE__ : Any=12 , SCREAMING_SNAKE_CASE__ : Optional[int]=12 , SCREAMING_SNAKE_CASE__ : int=30_72 , SCREAMING_SNAKE_CASE__ : List[str]="gelu" , SCREAMING_SNAKE_CASE__ : str=0.1 , SCREAMING_SNAKE_CASE__ : Optional[int]=0.1 , SCREAMING_SNAKE_CASE__ : Tuple=0.1 , SCREAMING_SNAKE_CASE__ : Dict=0.0 , SCREAMING_SNAKE_CASE__ : str=0.1 , SCREAMING_SNAKE_CASE__ : int=0.1 , SCREAMING_SNAKE_CASE__ : str=0.02 , SCREAMING_SNAKE_CASE__ : int=1e-5 , SCREAMING_SNAKE_CASE__ : Union[str, Any]="gelu" , SCREAMING_SNAKE_CASE__ : List[Any]=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) , SCREAMING_SNAKE_CASE__ : Optional[int]=(5, 2, 2, 2, 2, 2, 2) , SCREAMING_SNAKE_CASE__ : Any=(10, 3, 3, 3, 3, 2, 2) , SCREAMING_SNAKE_CASE__ : List[str]=False , SCREAMING_SNAKE_CASE__ : List[str]=16 , SCREAMING_SNAKE_CASE__ : Tuple=19 , SCREAMING_SNAKE_CASE__ : str=5 , SCREAMING_SNAKE_CASE__ : Any=0.05 , SCREAMING_SNAKE_CASE__ : int=10 , SCREAMING_SNAKE_CASE__ : List[Any]=2 , SCREAMING_SNAKE_CASE__ : Any=0.0 , SCREAMING_SNAKE_CASE__ : Optional[Any]=10 , SCREAMING_SNAKE_CASE__ : Any=0 , SCREAMING_SNAKE_CASE__ : Dict="sum" , SCREAMING_SNAKE_CASE__ : List[Any]=False , SCREAMING_SNAKE_CASE__ : Tuple=False , SCREAMING_SNAKE_CASE__ : Union[str, Any]=2_56 , SCREAMING_SNAKE_CASE__ : Optional[Any]=(5_12, 5_12, 5_12, 5_12, 15_00) , SCREAMING_SNAKE_CASE__ : Tuple=(5, 3, 3, 1, 1) , SCREAMING_SNAKE_CASE__ : Optional[Any]=(1, 2, 3, 1, 1) , SCREAMING_SNAKE_CASE__ : int=5_12 , SCREAMING_SNAKE_CASE__ : List[Any]=0 , SCREAMING_SNAKE_CASE__ : Tuple=1 , SCREAMING_SNAKE_CASE__ : Optional[int]=2 , SCREAMING_SNAKE_CASE__ : Optional[int]=False , SCREAMING_SNAKE_CASE__ : List[Any]=3 , SCREAMING_SNAKE_CASE__ : int=2 , SCREAMING_SNAKE_CASE__ : str=3 , SCREAMING_SNAKE_CASE__ : Union[str, Any]=None , **SCREAMING_SNAKE_CASE__ : List[str] , ) -> Union[str, Any]:
super().__init__(**SCREAMING_SNAKE_CASE__ , pad_token_id=SCREAMING_SNAKE_CASE__ , bos_token_id=SCREAMING_SNAKE_CASE__ , eos_token_id=SCREAMING_SNAKE_CASE__ )
__lowerCamelCase = hidden_size
__lowerCamelCase = feat_extract_activation
__lowerCamelCase = list(SCREAMING_SNAKE_CASE__ )
__lowerCamelCase = list(SCREAMING_SNAKE_CASE__ )
__lowerCamelCase = list(SCREAMING_SNAKE_CASE__ )
__lowerCamelCase = conv_bias
__lowerCamelCase = num_conv_pos_embeddings
__lowerCamelCase = num_conv_pos_embedding_groups
__lowerCamelCase = conv_pos_kernel_size
__lowerCamelCase = len(self.conv_dim )
__lowerCamelCase = num_hidden_layers
__lowerCamelCase = intermediate_size
__lowerCamelCase = hidden_act
__lowerCamelCase = num_attention_heads
__lowerCamelCase = hidden_dropout
__lowerCamelCase = attention_dropout
__lowerCamelCase = activation_dropout
__lowerCamelCase = feat_proj_dropout
__lowerCamelCase = final_dropout
__lowerCamelCase = layerdrop
__lowerCamelCase = layer_norm_eps
__lowerCamelCase = initializer_range
__lowerCamelCase = vocab_size
__lowerCamelCase = use_weighted_layer_sum
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
__lowerCamelCase = mask_time_prob
__lowerCamelCase = mask_time_length
__lowerCamelCase = mask_time_min_masks
__lowerCamelCase = mask_feature_prob
__lowerCamelCase = mask_feature_length
__lowerCamelCase = mask_feature_min_masks
# ctc loss
__lowerCamelCase = ctc_loss_reduction
__lowerCamelCase = ctc_zero_infinity
# adapter
__lowerCamelCase = add_adapter
__lowerCamelCase = adapter_kernel_size
__lowerCamelCase = adapter_stride
__lowerCamelCase = num_adapter_layers
__lowerCamelCase = output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
__lowerCamelCase = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
__lowerCamelCase = list(SCREAMING_SNAKE_CASE__ )
__lowerCamelCase = list(SCREAMING_SNAKE_CASE__ )
__lowerCamelCase = list(SCREAMING_SNAKE_CASE__ )
__lowerCamelCase = xvector_output_dim
@property
def __A ( self : int ) -> Any:
return math.prod(self.conv_stride )
| 298
|
def __magic_name__ ( __lowerCAmelCase : Any , __lowerCAmelCase : Optional[int] ) -> Optional[Any]:
__lowerCamelCase = [1]
for i in range(2 , __lowerCAmelCase ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of bounds"
__lowerCamelCase = []
__lowerCamelCase = list(range(__lowerCAmelCase ) )
# Find permutation
while factorials:
__lowerCamelCase = factorials.pop()
__lowerCamelCase , __lowerCamelCase = divmod(__lowerCAmelCase , __lowerCAmelCase )
permutation.append(elements[number] )
elements.remove(elements[number] )
permutation.append(elements[0] )
return permutation
if __name__ == "__main__":
import doctest
doctest.testmod()
| 298
| 1
|
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
@slow
def A ( self : Union[str, Any] ):
"""simple docstring"""
__snake_case = XLMRobertaModel.from_pretrained("xlm-roberta-base" )
__snake_case = torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]] )
# The dog is cute and lives in the garden house
__snake_case = torch.Size((1, 12, 768) ) # batch_size, sequence_length, embedding_vector_dim
__snake_case = torch.tensor(
[[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]] )
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
# xlmr.eval()
# expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
with torch.no_grad():
__snake_case = model(snake_case_ )["last_hidden_state"].detach()
self.assertEqual(output.shape , snake_case_ )
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1] , snake_case_ , atol=1e-3 ) )
@slow
def A ( self : Tuple ):
"""simple docstring"""
__snake_case = XLMRobertaModel.from_pretrained("xlm-roberta-large" )
__snake_case = torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]] )
# The dog is cute and lives in the garden house
__snake_case = torch.Size((1, 12, 1_024) ) # batch_size, sequence_length, embedding_vector_dim
__snake_case = torch.tensor(
[[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]] )
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
# xlmr.eval()
# expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
with torch.no_grad():
__snake_case = model(snake_case_ )["last_hidden_state"].detach()
self.assertEqual(output.shape , snake_case_ )
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1] , snake_case_ , atol=1e-3 ) )
| 720
|
'''simple docstring'''
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEncoder,
BertModel,
BertPreTrainedModel,
)
a : Tuple = logging.getLogger(__name__)
class SCREAMING_SNAKE_CASE__ ( _UpperCamelCase ):
def A ( self : Union[str, Any] , a_ : List[str] , a_ : Optional[int] , a_ : List[str]=None , a_ : Any=None ):
"""simple docstring"""
__snake_case = self.layer[current_layer](a_ , a_ , head_mask[current_layer] )
__snake_case = layer_outputs[0]
return hidden_states
@add_start_docstrings(
"""The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top.""" , _UpperCamelCase , )
class SCREAMING_SNAKE_CASE__ ( _UpperCamelCase ):
def __init__( self : int , a_ : int ):
"""simple docstring"""
super().__init__(a_ )
__snake_case = BertEncoderWithPabee(a_ )
self.init_weights()
__snake_case = 0
__snake_case = 0
__snake_case = 0
__snake_case = 0
def A ( self : Optional[int] , a_ : Union[str, Any] ):
"""simple docstring"""
__snake_case = threshold
def A ( self : Optional[Any] , a_ : Union[str, Any] ):
"""simple docstring"""
__snake_case = patience
def A ( self : Any ):
"""simple docstring"""
__snake_case = 0
__snake_case = 0
def A ( self : Union[str, Any] ):
"""simple docstring"""
__snake_case = self.inference_layers_num / self.inference_instances_num
__snake_case = (
f'''*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ='''
f''' {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***'''
)
print(a_ )
@add_start_docstrings_to_model_forward(a_ )
def A ( self : Dict , a_ : Optional[Any]=None , a_ : Union[str, Any]=None , a_ : int=None , a_ : Optional[int]=None , a_ : int=None , a_ : Optional[Any]=None , a_ : Union[str, Any]=None , a_ : int=None , a_ : Any=None , a_ : Optional[Any]=None , a_ : Any=False , ):
"""simple docstring"""
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time" )
elif input_ids is not None:
__snake_case = input_ids.size()
elif inputs_embeds is not None:
__snake_case = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds" )
__snake_case = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
__snake_case = torch.ones(a_ , device=a_ )
if token_type_ids is None:
__snake_case = torch.zeros(a_ , dtype=torch.long , device=a_ )
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
__snake_case = self.get_extended_attention_mask(a_ , a_ , a_ )
# If a 2D ou 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if self.config.is_decoder and encoder_hidden_states is not None:
__snake_case , __snake_case , __snake_case = encoder_hidden_states.size()
__snake_case = (encoder_batch_size, encoder_sequence_length)
if encoder_attention_mask is None:
__snake_case = torch.ones(a_ , device=a_ )
__snake_case = self.invert_attention_mask(a_ )
else:
__snake_case = None
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
__snake_case = self.get_head_mask(a_ , self.config.num_hidden_layers )
__snake_case = self.embeddings(
input_ids=a_ , position_ids=a_ , token_type_ids=a_ , inputs_embeds=a_ )
__snake_case = embedding_output
if self.training:
__snake_case = []
for i in range(self.config.num_hidden_layers ):
__snake_case = self.encoder.adaptive_forward(
a_ , current_layer=a_ , attention_mask=a_ , head_mask=a_ )
__snake_case = self.pooler(a_ )
__snake_case = output_layers[i](output_dropout(a_ ) )
res.append(a_ )
elif self.patience == 0: # Use all layers for inference
__snake_case = self.encoder(
a_ , attention_mask=a_ , head_mask=a_ , encoder_hidden_states=a_ , encoder_attention_mask=a_ , )
__snake_case = self.pooler(encoder_outputs[0] )
__snake_case = [output_layers[self.config.num_hidden_layers - 1](a_ )]
else:
__snake_case = 0
__snake_case = None
__snake_case = 0
for i in range(self.config.num_hidden_layers ):
calculated_layer_num += 1
__snake_case = self.encoder.adaptive_forward(
a_ , current_layer=a_ , attention_mask=a_ , head_mask=a_ )
__snake_case = self.pooler(a_ )
__snake_case = output_layers[i](a_ )
if regression:
__snake_case = logits.detach()
if patient_result is not None:
__snake_case = patient_result.detach()
if (patient_result is not None) and torch.abs(patient_result - labels ) < self.regression_threshold:
patient_counter += 1
else:
__snake_case = 0
else:
__snake_case = logits.detach().argmax(dim=1 )
if patient_result is not None:
__snake_case = patient_result.detach().argmax(dim=1 )
if (patient_result is not None) and torch.all(labels.eq(a_ ) ):
patient_counter += 1
else:
__snake_case = 0
__snake_case = logits
if patient_counter == self.patience:
break
__snake_case = [patient_result]
self.inference_layers_num += calculated_layer_num
self.inference_instances_num += 1
return res
@add_start_docstrings(
"""Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of
the pooled output) e.g. for GLUE tasks. """ , _UpperCamelCase , )
class SCREAMING_SNAKE_CASE__ ( _UpperCamelCase ):
def __init__( self : List[str] , a_ : Tuple ):
"""simple docstring"""
super().__init__(a_ )
__snake_case = config.num_labels
__snake_case = BertModelWithPabee(a_ )
__snake_case = nn.Dropout(config.hidden_dropout_prob )
__snake_case = nn.ModuleList(
[nn.Linear(config.hidden_size , self.config.num_labels ) for _ in range(config.num_hidden_layers )] )
self.init_weights()
@add_start_docstrings_to_model_forward(a_ )
def A ( self : int , a_ : str=None , a_ : Tuple=None , a_ : Union[str, Any]=None , a_ : List[str]=None , a_ : Optional[int]=None , a_ : Union[str, Any]=None , a_ : Tuple=None , ):
"""simple docstring"""
__snake_case = self.bert(
input_ids=a_ , attention_mask=a_ , token_type_ids=a_ , position_ids=a_ , head_mask=a_ , inputs_embeds=a_ , output_dropout=self.dropout , output_layers=self.classifiers , regression=self.num_labels == 1 , )
__snake_case = (logits[-1],)
if labels is not None:
__snake_case = None
__snake_case = 0
for ix, logits_item in enumerate(a_ ):
if self.num_labels == 1:
# We are doing regression
__snake_case = MSELoss()
__snake_case = loss_fct(logits_item.view(-1 ) , labels.view(-1 ) )
else:
__snake_case = CrossEntropyLoss()
__snake_case = loss_fct(logits_item.view(-1 , self.num_labels ) , labels.view(-1 ) )
if total_loss is None:
__snake_case = loss
else:
total_loss += loss * (ix + 1)
total_weights += ix + 1
__snake_case = (total_loss / total_weights,) + outputs
return outputs
| 680
| 0
|
import math
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ ):
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowercase__ = f'''Input value of [number={number}] must be an integer'''
raise TypeError(SCREAMING_SNAKE_CASE_ )
if number < 1:
lowercase__ = f'''Input value of [number={number}] must be > 0'''
raise ValueError(SCREAMING_SNAKE_CASE_ )
elif number == 1:
return 3
elif number == 2:
return 5
else:
lowercase__ = int(math.log(number // 3 , 2 ) ) + 2
lowercase__ = [3, 5]
lowercase__ = 2
lowercase__ = 3
for block in range(1 , SCREAMING_SNAKE_CASE_ ):
for _ in range(SCREAMING_SNAKE_CASE_ ):
proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1] )
proth_index += 1
increment *= 2
return proth_list[number - 1]
if __name__ == "__main__":
import doctest
doctest.testmod()
for number in range(11):
lowercase_ = 0
try:
lowercase_ = proth(number)
except ValueError:
print(F'ValueError: there is no {number}th Proth number')
continue
print(F'The {number}th Proth number: {value}')
| 413
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A: Union[str, Any] = logging.get_logger(__name__)
A: Optional[int] = {
"facebook/nllb-moe-54B": "https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json",
}
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase__ ):
__lowerCAmelCase : str = 'nllb-moe'
__lowerCAmelCase : List[Any] = ['past_key_values']
__lowerCAmelCase : Dict = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self , _SCREAMING_SNAKE_CASE=128112 , _SCREAMING_SNAKE_CASE=1024 , _SCREAMING_SNAKE_CASE=12 , _SCREAMING_SNAKE_CASE=4096 , _SCREAMING_SNAKE_CASE=16 , _SCREAMING_SNAKE_CASE=12 , _SCREAMING_SNAKE_CASE=4096 , _SCREAMING_SNAKE_CASE=16 , _SCREAMING_SNAKE_CASE=0.05 , _SCREAMING_SNAKE_CASE=0.05 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE="relu" , _SCREAMING_SNAKE_CASE=1024 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=0.0 , _SCREAMING_SNAKE_CASE=0.02 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE="float32" , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=128 , _SCREAMING_SNAKE_CASE=64 , _SCREAMING_SNAKE_CASE=4 , _SCREAMING_SNAKE_CASE=4 , _SCREAMING_SNAKE_CASE=0.001 , _SCREAMING_SNAKE_CASE=0.001 , _SCREAMING_SNAKE_CASE="all" , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=1.0 , _SCREAMING_SNAKE_CASE=0.2 , _SCREAMING_SNAKE_CASE=1 , _SCREAMING_SNAKE_CASE=0 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=False , **_SCREAMING_SNAKE_CASE , ) -> Dict:
'''simple docstring'''
UpperCAmelCase : Optional[int] = vocab_size
UpperCAmelCase : Optional[int] = max_position_embeddings
UpperCAmelCase : str = d_model
UpperCAmelCase : Union[str, Any] = encoder_ffn_dim
UpperCAmelCase : int = encoder_layers
UpperCAmelCase : Dict = encoder_attention_heads
UpperCAmelCase : Tuple = decoder_ffn_dim
UpperCAmelCase : List[Any] = decoder_layers
UpperCAmelCase : Tuple = decoder_attention_heads
UpperCAmelCase : Any = dropout
UpperCAmelCase : Optional[int] = attention_dropout
UpperCAmelCase : Union[str, Any] = activation_dropout
UpperCAmelCase : Dict = activation_function
UpperCAmelCase : int = init_std
UpperCAmelCase : List[Any] = encoder_layerdrop
UpperCAmelCase : Optional[Any] = decoder_layerdrop
UpperCAmelCase : str = use_cache
UpperCAmelCase : List[Any] = encoder_layers
UpperCAmelCase : Union[str, Any] = scale_embedding # scale factor will be sqrt(d_model) if True
UpperCAmelCase : Optional[Any] = router_z_loss_coef
UpperCAmelCase : List[str] = router_aux_loss_coef
UpperCAmelCase : str = decoder_sparse_step
UpperCAmelCase : str = encoder_sparse_step
UpperCAmelCase : Optional[int] = num_experts
UpperCAmelCase : Optional[int] = expert_capacity
UpperCAmelCase : List[Any] = router_bias
if router_dtype not in ["float32", "float16", "bfloat16"]:
raise ValueError(F"`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}" )
UpperCAmelCase : int = router_dtype
UpperCAmelCase : Optional[int] = router_ignore_padding_tokens
UpperCAmelCase : Tuple = batch_prioritized_routing
UpperCAmelCase : Any = second_expert_policy
UpperCAmelCase : List[str] = normalize_router_prob_before_dropping
UpperCAmelCase : str = moe_eval_capacity_token_fraction
UpperCAmelCase : Union[str, Any] = moe_token_dropout
UpperCAmelCase : Any = output_router_logits
super().__init__(
pad_token_id=_SCREAMING_SNAKE_CASE , bos_token_id=_SCREAMING_SNAKE_CASE , eos_token_id=_SCREAMING_SNAKE_CASE , is_encoder_decoder=_SCREAMING_SNAKE_CASE , decoder_start_token_id=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
| 160
| 0
|
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFResNetForImageClassification, TFResNetModel
from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _lowercase :
'''simple docstring'''
def __init__( self :Optional[int] , lowerCAmelCase__ :Dict , lowerCAmelCase__ :Any=3 , lowerCAmelCase__ :Optional[Any]=32 , lowerCAmelCase__ :Any=3 , lowerCAmelCase__ :Any=10 , lowerCAmelCase__ :Any=[10, 20, 30, 40] , lowerCAmelCase__ :Any=[1, 1, 2, 1] , lowerCAmelCase__ :List[str]=True , lowerCAmelCase__ :List[Any]=True , lowerCAmelCase__ :Tuple="relu" , lowerCAmelCase__ :Union[str, Any]=3 , lowerCAmelCase__ :Any=None , ) -> Tuple:
__SCREAMING_SNAKE_CASE : str = parent
__SCREAMING_SNAKE_CASE : Union[str, Any] = batch_size
__SCREAMING_SNAKE_CASE : List[Any] = image_size
__SCREAMING_SNAKE_CASE : List[str] = num_channels
__SCREAMING_SNAKE_CASE : Dict = embeddings_size
__SCREAMING_SNAKE_CASE : Dict = hidden_sizes
__SCREAMING_SNAKE_CASE : List[str] = depths
__SCREAMING_SNAKE_CASE : List[str] = is_training
__SCREAMING_SNAKE_CASE : Tuple = use_labels
__SCREAMING_SNAKE_CASE : str = hidden_act
__SCREAMING_SNAKE_CASE : int = num_labels
__SCREAMING_SNAKE_CASE : int = scope
__SCREAMING_SNAKE_CASE : Tuple = len(lowerCAmelCase__ )
def __magic_name__( self :Optional[int] ) -> Union[str, Any]:
__SCREAMING_SNAKE_CASE : int = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__SCREAMING_SNAKE_CASE : str = None
if self.use_labels:
__SCREAMING_SNAKE_CASE : int = ids_tensor([self.batch_size] , self.num_labels )
__SCREAMING_SNAKE_CASE : Optional[Any] = self.get_config()
return config, pixel_values, labels
def __magic_name__( self :Tuple ) -> Optional[int]:
return ResNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , image_size=self.image_size , )
def __magic_name__( self :Dict , lowerCAmelCase__ :List[Any] , lowerCAmelCase__ :Tuple , lowerCAmelCase__ :Union[str, Any] ) -> str:
__SCREAMING_SNAKE_CASE : Tuple = TFResNetModel(config=lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Any = model(lowerCAmelCase__ )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def __magic_name__( self :Any , lowerCAmelCase__ :str , lowerCAmelCase__ :Optional[int] , lowerCAmelCase__ :int ) -> List[str]:
__SCREAMING_SNAKE_CASE : str = self.num_labels
__SCREAMING_SNAKE_CASE : str = TFResNetForImageClassification(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : List[Any] = model(lowerCAmelCase__ , labels=lowerCAmelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __magic_name__( self :str ) -> Dict:
__SCREAMING_SNAKE_CASE : Optional[Any] = self.prepare_config_and_inputs()
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[str] = config_and_inputs
__SCREAMING_SNAKE_CASE : Any = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_tf
class _lowercase ( A__ , A__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[Any] = (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
SCREAMING_SNAKE_CASE__ : Optional[Any] = (
{'''feature-extraction''': TFResNetModel, '''image-classification''': TFResNetForImageClassification}
if is_tf_available()
else {}
)
SCREAMING_SNAKE_CASE__ : List[str] = False
SCREAMING_SNAKE_CASE__ : List[str] = False
SCREAMING_SNAKE_CASE__ : int = False
SCREAMING_SNAKE_CASE__ : Optional[Any] = False
SCREAMING_SNAKE_CASE__ : Optional[int] = False
def __magic_name__( self :List[Any] ) -> str:
__SCREAMING_SNAKE_CASE : Union[str, Any] = TFResNetModelTester(self )
__SCREAMING_SNAKE_CASE : str = ConfigTester(self , config_class=lowerCAmelCase__ , has_text_modality=lowerCAmelCase__ )
def __magic_name__( self :List[str] ) -> int:
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 __magic_name__( self :Optional[int] ) -> Union[str, Any]:
return
@unittest.skip(reason='''ResNet does not use inputs_embeds''' )
def __magic_name__( self :Optional[int] ) -> int:
pass
@unittest.skip(reason='''ResNet does not support input and output embeddings''' )
def __magic_name__( self :str ) -> Any:
pass
def __magic_name__( self :Dict ) -> Optional[int]:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__SCREAMING_SNAKE_CASE : List[str] = model_class(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Optional[Any] = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__SCREAMING_SNAKE_CASE : Any = [*signature.parameters.keys()]
__SCREAMING_SNAKE_CASE : Optional[Any] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , lowerCAmelCase__ )
def __magic_name__( self :Any ) -> str:
__SCREAMING_SNAKE_CASE : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase__ )
def __magic_name__( self :Union[str, Any] ) -> Dict:
def check_hidden_states_output(lowerCAmelCase__ :List[str] , lowerCAmelCase__ :Any , lowerCAmelCase__ :Optional[Any] ):
__SCREAMING_SNAKE_CASE : str = model_class(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Optional[Any] = model(**self._prepare_for_class(lowerCAmelCase__ , lowerCAmelCase__ ) )
__SCREAMING_SNAKE_CASE : str = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
__SCREAMING_SNAKE_CASE : Optional[int] = self.model_tester.num_stages
self.assertEqual(len(lowerCAmelCase__ ) , expected_num_stages + 1 )
# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : str = self.model_tester.prepare_config_and_inputs_for_common()
__SCREAMING_SNAKE_CASE : Dict = ['''basic''', '''bottleneck''']
for model_class in self.all_model_classes:
for layer_type in layers_type:
__SCREAMING_SNAKE_CASE : List[str] = layer_type
__SCREAMING_SNAKE_CASE : int = True
check_hidden_states_output(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__SCREAMING_SNAKE_CASE : Dict = True
check_hidden_states_output(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
def __magic_name__( self :Tuple ) -> Optional[int]:
__SCREAMING_SNAKE_CASE : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__ )
@slow
def __magic_name__( self :int ) -> Optional[Any]:
for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE : List[Any] = TFResNetModel.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
def _UpperCamelCase ( ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_tf
@require_vision
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def __magic_name__( self :Union[str, Any] ) -> Any:
return (
AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def __magic_name__( self :Optional[int] ) -> List[Any]:
__SCREAMING_SNAKE_CASE : List[str] = TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
__SCREAMING_SNAKE_CASE : int = self.default_image_processor
__SCREAMING_SNAKE_CASE : Dict = prepare_img()
__SCREAMING_SNAKE_CASE : Optional[Any] = image_processor(images=lowerCAmelCase__ , return_tensors='''tf''' )
# forward pass
__SCREAMING_SNAKE_CASE : Optional[int] = model(**lowerCAmelCase__ )
# verify the logits
__SCREAMING_SNAKE_CASE : Optional[Any] = tf.TensorShape((1, 1_000) )
self.assertEqual(outputs.logits.shape , lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Tuple = tf.constant([-11.1069, -9.7877, -8.3777] )
self.assertTrue(np.allclose(outputs.logits[0, :3].numpy() , lowerCAmelCase__ , atol=1E-4 ) )
| 260
|
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _lowercase ( A__ ):
'''simple docstring'''
def __init__( self :List[str] , lowerCAmelCase__ :TransformeraDModel , lowerCAmelCase__ :AutoencoderKL , lowerCAmelCase__ :KarrasDiffusionSchedulers , lowerCAmelCase__ :Optional[Dict[int, str]] = None , ) -> List[Any]:
super().__init__()
self.register_modules(transformer=lowerCAmelCase__ , vae=lowerCAmelCase__ , scheduler=lowerCAmelCase__ )
# create a imagenet -> id dictionary for easier use
__SCREAMING_SNAKE_CASE : Dict = {}
if idalabel is not None:
for key, value in idalabel.items():
for label in value.split(''',''' ):
__SCREAMING_SNAKE_CASE : int = int(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Optional[int] = dict(sorted(self.labels.items() ) )
def __magic_name__( self :List[Any] , lowerCAmelCase__ :Union[str, List[str]] ) -> List[int]:
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
__SCREAMING_SNAKE_CASE : Optional[Any] = list(lowerCAmelCase__ )
for l in label:
if l not in self.labels:
raise ValueError(
f'''{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.''' )
return [self.labels[l] for l in label]
@torch.no_grad()
def __call__( self :Tuple , lowerCAmelCase__ :List[int] , lowerCAmelCase__ :float = 4.0 , lowerCAmelCase__ :Optional[Union[torch.Generator, List[torch.Generator]]] = None , lowerCAmelCase__ :int = 50 , lowerCAmelCase__ :Optional[str] = "pil" , lowerCAmelCase__ :bool = True , ) -> Union[ImagePipelineOutput, Tuple]:
__SCREAMING_SNAKE_CASE : Any = len(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.transformer.config.sample_size
__SCREAMING_SNAKE_CASE : Optional[Any] = self.transformer.config.in_channels
__SCREAMING_SNAKE_CASE : Union[str, Any] = randn_tensor(
shape=(batch_size, latent_channels, latent_size, latent_size) , generator=lowerCAmelCase__ , device=self.device , dtype=self.transformer.dtype , )
__SCREAMING_SNAKE_CASE : Optional[Any] = torch.cat([latents] * 2 ) if guidance_scale > 1 else latents
__SCREAMING_SNAKE_CASE : str = torch.tensor(lowerCAmelCase__ , device=self.device ).reshape(-1 )
__SCREAMING_SNAKE_CASE : Optional[Any] = torch.tensor([1_000] * batch_size , device=self.device )
__SCREAMING_SNAKE_CASE : Tuple = torch.cat([class_labels, class_null] , 0 ) if guidance_scale > 1 else class_labels
# set step values
self.scheduler.set_timesteps(lowerCAmelCase__ )
for t in self.progress_bar(self.scheduler.timesteps ):
if guidance_scale > 1:
__SCREAMING_SNAKE_CASE : Optional[int] = latent_model_input[: len(lowerCAmelCase__ ) // 2]
__SCREAMING_SNAKE_CASE : Optional[Any] = torch.cat([half, half] , dim=0 )
__SCREAMING_SNAKE_CASE : Dict = self.scheduler.scale_model_input(lowerCAmelCase__ , lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : List[str] = t
if not torch.is_tensor(lowerCAmelCase__ ):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
__SCREAMING_SNAKE_CASE : Optional[int] = latent_model_input.device.type == '''mps'''
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
__SCREAMING_SNAKE_CASE : Tuple = torch.floataa if is_mps else torch.floataa
else:
__SCREAMING_SNAKE_CASE : List[Any] = torch.intaa if is_mps else torch.intaa
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor([timesteps] , dtype=lowerCAmelCase__ , device=latent_model_input.device )
elif len(timesteps.shape ) == 0:
__SCREAMING_SNAKE_CASE : str = timesteps[None].to(latent_model_input.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
__SCREAMING_SNAKE_CASE : str = timesteps.expand(latent_model_input.shape[0] )
# predict noise model_output
__SCREAMING_SNAKE_CASE : Optional[Any] = self.transformer(
lowerCAmelCase__ , timestep=lowerCAmelCase__ , class_labels=lowerCAmelCase__ ).sample
# perform guidance
if guidance_scale > 1:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[str] = noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[int] = torch.split(lowerCAmelCase__ , len(lowerCAmelCase__ ) // 2 , dim=0 )
__SCREAMING_SNAKE_CASE : Union[str, Any] = uncond_eps + guidance_scale * (cond_eps - uncond_eps)
__SCREAMING_SNAKE_CASE : Any = torch.cat([half_eps, half_eps] , dim=0 )
__SCREAMING_SNAKE_CASE : Optional[Any] = torch.cat([eps, rest] , dim=1 )
# learned sigma
if self.transformer.config.out_channels // 2 == latent_channels:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : str = torch.split(lowerCAmelCase__ , lowerCAmelCase__ , dim=1 )
else:
__SCREAMING_SNAKE_CASE : Optional[Any] = noise_pred
# compute previous image: x_t -> x_t-1
__SCREAMING_SNAKE_CASE : Tuple = self.scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ).prev_sample
if guidance_scale > 1:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[int] = latent_model_input.chunk(2 , dim=0 )
else:
__SCREAMING_SNAKE_CASE : List[str] = latent_model_input
__SCREAMING_SNAKE_CASE : Dict = 1 / self.vae.config.scaling_factor * latents
__SCREAMING_SNAKE_CASE : Tuple = self.vae.decode(lowerCAmelCase__ ).sample
__SCREAMING_SNAKE_CASE : int = (samples / 2 + 0.5).clamp(0 , 1 )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
__SCREAMING_SNAKE_CASE : Dict = samples.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
__SCREAMING_SNAKE_CASE : str = self.numpy_to_pil(lowerCAmelCase__ )
if not return_dict:
return (samples,)
return ImagePipelineOutput(images=lowerCAmelCase__ )
| 260
| 1
|
"""simple docstring"""
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
_lowercase = version.parse(importlib_metadata.version('''nltk'''))
if NLTK_VERSION >= version.Version('''3.6.4'''):
from nltk import word_tokenize
_lowercase = '''\
@inproceedings{banarjee2005,
title = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},
author = {Banerjee, Satanjeev and Lavie, Alon},
booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},
month = jun,
year = {2005},
address = {Ann Arbor, Michigan},
publisher = {Association for Computational Linguistics},
url = {https://www.aclweb.org/anthology/W05-0909},
pages = {65--72},
}
'''
_lowercase = '''\
METEOR, an automatic metric for machine translation evaluation
that is based on a generalized concept of unigram matching between the
machine-produced translation and human-produced reference translations.
Unigrams can be matched based on their surface forms, stemmed forms,
and meanings; furthermore, METEOR can be easily extended to include more
advanced matching strategies. Once all generalized unigram matches
between the two strings have been found, METEOR computes a score for
this matching using a combination of unigram-precision, unigram-recall, and
a measure of fragmentation that is designed to directly capture how
well-ordered the matched words in the machine translation are in relation
to the reference.
METEOR gets an R correlation value of 0.347 with human evaluation on the Arabic
data and 0.331 on the Chinese data. This is shown to be an improvement on
using simply unigram-precision, unigram-recall and their harmonic F1
combination.
'''
_lowercase = '''
Computes METEOR score of translated segments against one or more references.
Args:
predictions: list of predictions to score. Each prediction
should be a string with tokens separated by spaces.
references: list of reference for each prediction. Each
reference should be a string with tokens separated by spaces.
alpha: Parameter for controlling relative weights of precision and recall. default: 0.9
beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3
gamma: Relative weight assigned to fragmentation penalty. default: 0.5
Returns:
\'meteor\': meteor score.
Examples:
>>> meteor = datasets.load_metric(\'meteor\')
>>> predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]
>>> references = ["It is a guide to action that ensures that the military will forever heed Party commands"]
>>> results = meteor.compute(predictions=predictions, references=references)
>>> print(round(results["meteor"], 4))
0.6944
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowerCAmelCase_ ( datasets.Metric ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( self : int ) -> Optional[int]:
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
{
'predictions': datasets.Value('string' ,id='sequence' ),
'references': datasets.Value('string' ,id='sequence' ),
} ) ,codebase_urls=['https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py'] ,reference_urls=[
'https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score',
'https://en.wikipedia.org/wiki/METEOR',
] ,)
def _SCREAMING_SNAKE_CASE ( self : Tuple ,A_ : List[Any] ) -> Tuple:
import nltk
nltk.download('wordnet' )
if NLTK_VERSION >= version.Version('3.6.5' ):
nltk.download('punkt' )
if NLTK_VERSION >= version.Version('3.6.6' ):
nltk.download('omw-1.4' )
def _SCREAMING_SNAKE_CASE ( self : Any ,A_ : Any ,A_ : Tuple ,A_ : Union[str, Any]=0.9 ,A_ : List[str]=3 ,A_ : Dict=0.5 ) -> Optional[Any]:
if NLTK_VERSION >= version.Version('3.6.5' ):
A = [
meteor_score.single_meteor_score(
word_tokenize(A_ ) ,word_tokenize(A_ ) ,alpha=A_ ,beta=A_ ,gamma=A_ )
for ref, pred in zip(A_ ,A_ )
]
else:
A = [
meteor_score.single_meteor_score(A_ ,A_ ,alpha=A_ ,beta=A_ ,gamma=A_ )
for ref, pred in zip(A_ ,A_ )
]
return {"meteor": np.mean(A_ )}
| 91
|
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging
_SCREAMING_SNAKE_CASE : Tuple = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : str = {
"bigscience/bloom": "https://huggingface.co/bigscience/bloom/resolve/main/config.json",
"bigscience/bloom-560m": "https://huggingface.co/bigscience/bloom-560m/blob/main/config.json",
"bigscience/bloom-1b1": "https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json",
"bigscience/bloom-1b7": "https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json",
"bigscience/bloom-3b": "https://huggingface.co/bigscience/bloom-3b/blob/main/config.json",
"bigscience/bloom-7b1": "https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json",
}
class _snake_case ( snake_case_ ):
'''simple docstring'''
__snake_case = "bloom"
__snake_case = ["past_key_values"]
__snake_case = {
"num_hidden_layers": "n_layer",
"num_attention_heads": "n_head",
}
def __init__( self: Union[str, Any] , __UpperCamelCase: Any=25_0880 , __UpperCamelCase: str=64 , __UpperCamelCase: Any=2 , __UpperCamelCase: str=8 , __UpperCamelCase: str=1E-5 , __UpperCamelCase: Optional[int]=0.0_2 , __UpperCamelCase: Any=True , __UpperCamelCase: Tuple=1 , __UpperCamelCase: Optional[Any]=2 , __UpperCamelCase: List[Any]=False , __UpperCamelCase: Any=0.0 , __UpperCamelCase: str=0.0 , __UpperCamelCase: List[str]=1 , __UpperCamelCase: str=False , **__UpperCamelCase: Optional[int] , ) -> List[Any]:
__magic_name__ : Any = vocab_size
# Backward compatibility with n_embed kwarg
__magic_name__ : Any = kwargs.pop("n_embed" , __UpperCamelCase )
__magic_name__ : str = hidden_size if n_embed is None else n_embed
__magic_name__ : Tuple = n_layer
__magic_name__ : Tuple = n_head
__magic_name__ : Optional[int] = layer_norm_epsilon
__magic_name__ : List[Any] = initializer_range
__magic_name__ : int = use_cache
__magic_name__ : str = pretraining_tp
__magic_name__ : Any = apply_residual_connection_post_layernorm
__magic_name__ : Union[str, Any] = hidden_dropout
__magic_name__ : Optional[int] = attention_dropout
__magic_name__ : Dict = bos_token_id
__magic_name__ : List[Any] = eos_token_id
__magic_name__ : Union[str, Any] = slow_but_exact
super().__init__(bos_token_id=__UpperCamelCase , eos_token_id=__UpperCamelCase , **__UpperCamelCase )
class _snake_case ( snake_case_ ):
'''simple docstring'''
__snake_case = version.parse("1.12" )
def __init__( self: str , __UpperCamelCase: PretrainedConfig , __UpperCamelCase: str = "default" , __UpperCamelCase: List[PatchingSpec] = None , __UpperCamelCase: bool = False , ) -> List[Any]:
super().__init__(__UpperCamelCase , task=__UpperCamelCase , patching_specs=__UpperCamelCase , use_past=__UpperCamelCase )
if not getattr(self._config , "pad_token_id" , __UpperCamelCase ):
# TODO: how to do that better?
__magic_name__ : Any = 0
@property
def lowerCAmelCase__ ( self: Any ) -> Mapping[str, Mapping[int, str]]:
__magic_name__ : List[str] = OrderedDict({"input_ids": {0: "batch", 1: "sequence"}} )
if self.use_past:
# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
self.fill_with_past_key_values_(__UpperCamelCase , direction="inputs" , inverted_values_shape=__UpperCamelCase )
__magic_name__ : Any = {0: "batch", 1: "past_sequence + sequence"}
else:
__magic_name__ : Optional[Any] = {0: "batch", 1: "sequence"}
return common_inputs
@property
def lowerCAmelCase__ ( self: Dict ) -> int:
return self._config.n_layer
@property
def lowerCAmelCase__ ( self: List[str] ) -> int:
return self._config.n_head
@property
def lowerCAmelCase__ ( self: Union[str, Any] ) -> float:
return 1E-3
def lowerCAmelCase__ ( self: Optional[int] , __UpperCamelCase: "PreTrainedTokenizer" , __UpperCamelCase: int = -1 , __UpperCamelCase: int = -1 , __UpperCamelCase: bool = False , __UpperCamelCase: Optional["TensorType"] = None , ) -> Mapping[str, Any]:
__magic_name__ : Dict = super(__UpperCamelCase , self ).generate_dummy_inputs(
__UpperCamelCase , batch_size=__UpperCamelCase , seq_length=__UpperCamelCase , is_pair=__UpperCamelCase , framework=__UpperCamelCase )
# We need to order the input in the way they appears in the forward()
__magic_name__ : Optional[int] = OrderedDict({"input_ids": common_inputs["input_ids"]} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." )
else:
import torch
__magic_name__ , __magic_name__ : Union[str, Any] = common_inputs["input_ids"].shape
# Not using the same length for past_key_values
__magic_name__ : Dict = seqlen + 2
__magic_name__ : Optional[Any] = self._config.hidden_size // self.num_attention_heads
__magic_name__ : List[str] = (
batch * self.num_attention_heads,
head_dim,
past_key_values_length,
)
__magic_name__ : Any = (
batch * self.num_attention_heads,
past_key_values_length,
head_dim,
)
__magic_name__ : str = [
(torch.zeros(__UpperCamelCase ), torch.zeros(__UpperCamelCase )) for _ in range(self.num_layers )
]
__magic_name__ : Tuple = common_inputs["attention_mask"]
if self.use_past:
__magic_name__ : Tuple = ordered_inputs["attention_mask"].dtype
__magic_name__ : Optional[Any] = torch.cat(
[ordered_inputs["attention_mask"], torch.ones(__UpperCamelCase , __UpperCamelCase , dtype=__UpperCamelCase )] , dim=1 )
return ordered_inputs
@property
def lowerCAmelCase__ ( self: Optional[int] ) -> int:
return 13
| 436
| 0
|
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar
class UpperCAmelCase ( unittest.TestCase ):
def _A ( self: Any ):
_a = logging.get_logger()
# the current default level is logging.WARNING
_a = logging.get_verbosity()
logging.set_verbosity_error()
self.assertEqual(logger.getEffectiveLevel() , logging.get_verbosity() )
logging.set_verbosity_warning()
self.assertEqual(logger.getEffectiveLevel() , logging.get_verbosity() )
logging.set_verbosity_info()
self.assertEqual(logger.getEffectiveLevel() , logging.get_verbosity() )
logging.set_verbosity_debug()
self.assertEqual(logger.getEffectiveLevel() , logging.get_verbosity() )
# restore to the original level
logging.set_verbosity(snake_case__ )
def _A ( self: List[str] ):
_a = logging.get_verbosity()
_a = logging.get_logger('''transformers.models.bart.tokenization_bart''' )
_a = "Testing 1, 2, 3"
# should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
if level_origin <= logging.WARNING:
with CaptureLogger(snake_case__ ) as cl:
logger.warning(snake_case__ )
self.assertEqual(cl.out , msg + '''\n''' )
# this is setting the level for all of `transformers.*` loggers
logging.set_verbosity_error()
# should not be able to log warnings
with CaptureLogger(snake_case__ ) as cl:
logger.warning(snake_case__ )
self.assertEqual(cl.out , '''''' )
# should be able to log warnings again
logging.set_verbosity_warning()
with CaptureLogger(snake_case__ ) as cl:
logger.warning(snake_case__ )
self.assertEqual(cl.out , msg + '''\n''' )
# restore to the original level
logging.set_verbosity(snake_case__ )
@mockenv(TRANSFORMERS_VERBOSITY='''error''' )
def _A ( self: int ):
transformers.utils.logging._reset_library_root_logger()
# this action activates the env var
_a = logging.get_logger('''transformers.models.bart.tokenization_bart''' )
_a = os.getenv('''TRANSFORMERS_VERBOSITY''' , snake_case__ )
_a = logging.log_levels[env_level_str]
_a = logging.get_verbosity()
self.assertEqual(
snake_case__ , snake_case__ , f"TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}" , )
# restore to the original level
_a = ""
transformers.utils.logging._reset_library_root_logger()
@mockenv(TRANSFORMERS_VERBOSITY='''super-error''' )
def _A ( self: Union[str, Any] ):
transformers.utils.logging._reset_library_root_logger()
_a = logging.logging.getLogger()
with CaptureLogger(snake_case__ ) as cl:
# this action activates the env var
logging.get_logger('''transformers.models.bart.tokenization_bart''' )
self.assertIn('''Unknown option TRANSFORMERS_VERBOSITY=super-error''' , cl.out )
# no need to restore as nothing was changed
def _A ( self: Optional[Any] ):
transformers.utils.logging._reset_library_root_logger()
_a = logging.get_logger('''transformers.models.bart.tokenization_bart''' )
_a = "Testing 1, 2, 3"
with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS='''1''' ):
# nothing should be logged as env var disables this method
with CaptureLogger(snake_case__ ) as cl:
logger.warning_advice(snake_case__ )
self.assertEqual(cl.out , '''''' )
with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS='''''' ):
# should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
with CaptureLogger(snake_case__ ) as cl:
logger.warning_advice(snake_case__ )
self.assertEqual(cl.out , msg + '''\n''' )
def __snake_case ( ) -> List[Any]:
disable_progress_bar()
assert are_progress_bars_disabled()
enable_progress_bar()
assert not are_progress_bars_disabled()
| 719
|
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
from .config import config_command_parser
from .config_args import default_config_file, load_config_from_file # noqa: F401
from .default import default_command_parser
from .update import update_command_parser
def __snake_case ( _UpperCamelCase=None ) -> List[str]:
_a = argparse.ArgumentParser(add_help=_UpperCamelCase , allow_abbrev=_UpperCamelCase )
# The main config parser
_a = config_command_parser(_UpperCamelCase )
# The subparser to add commands to
_a = config_parser.add_subparsers(title='''subcommands''' , dest='''subcommand''' )
# Then add other parsers with the parent parser
default_command_parser(_UpperCamelCase , parents=[parent_parser] )
update_command_parser(_UpperCamelCase , parents=[parent_parser] )
return config_parser
def __snake_case ( ) -> Optional[Any]:
_a = get_config_parser()
_a = config_parser.parse_args()
if not hasattr(_UpperCamelCase , '''func''' ):
config_parser.print_help()
exit(1 )
# Run
args.func(_UpperCamelCase )
if __name__ == "__main__":
main()
| 346
| 0
|
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
lowerCAmelCase = TypeVar('''KT''')
lowerCAmelCase = TypeVar('''VT''')
class A ( Generic[KT, VT] ):
def __init__(self , lowerCAmelCase = "root" , lowerCAmelCase = None ):
__lowercase= key
__lowercase= value
__lowercase= []
def __repr__(self ):
return f'Node({self.key}: {self.value})'
@property
def _A (self ):
return len(self.forward )
class A ( Generic[KT, VT] ):
def __init__(self , lowerCAmelCase = 0.5 , lowerCAmelCase = 1_6 ):
__lowercase= Node[KT, VT]()
__lowercase= 0
__lowercase= p
__lowercase= max_level
def __str__(self ):
__lowercase= list(self )
if len(lowerCAmelCase ) == 0:
return f'SkipList(level={self.level})'
__lowercase= max((len(str(lowerCAmelCase ) ) for item in items) , default=4 )
__lowercase= max(lowerCAmelCase , 4 ) + 4
__lowercase= self.head
__lowercase= []
__lowercase= node.forward.copy()
lines.append(f'[{node.key}]'.ljust(lowerCAmelCase , '-' ) + '* ' * len(lowerCAmelCase ) )
lines.append(' ' * label_size + '| ' * len(lowerCAmelCase ) )
while len(node.forward ) != 0:
__lowercase= node.forward[0]
lines.append(
f'[{node.key}]'.ljust(lowerCAmelCase , '-' )
+ ' '.join(str(n.key ) if n.key == node.key else '|' for n in forwards ) )
lines.append(' ' * label_size + '| ' * len(lowerCAmelCase ) )
__lowercase= node.forward
lines.append('None'.ljust(lowerCAmelCase ) + '* ' * len(lowerCAmelCase ) )
return f'SkipList(level={self.level})\n' + "\n".join(lowerCAmelCase )
def __iter__(self ):
__lowercase= self.head
while len(node.forward ) != 0:
yield node.forward[0].key
__lowercase= node.forward[0]
def _A (self ):
__lowercase= 1
while random() < self.p and level < self.max_level:
level += 1
return level
def _A (self , lowerCAmelCase ):
__lowercase= []
__lowercase= self.head
for i in reversed(range(self.level ) ):
# i < node.level - When node level is lesser than `i` decrement `i`.
# node.forward[i].key < key - Jumping to node with key value higher
# or equal to searched key would result
# in skipping searched key.
while i < node.level and node.forward[i].key < key:
__lowercase= node.forward[i]
# Each leftmost node (relative to searched node) will potentially have to
# be updated.
update_vector.append(lowerCAmelCase )
update_vector.reverse() # Note that we were inserting values in reverse order.
# len(node.forward) != 0 - If current node doesn't contain any further
# references then searched key is not present.
# node.forward[0].key == key - Next node key should be equal to search key
# if key is present.
if len(node.forward ) != 0 and node.forward[0].key == key:
return node.forward[0], update_vector
else:
return None, update_vector
def _A (self , lowerCAmelCase ):
__lowercase, __lowercase= self._locate_node(lowerCAmelCase )
if node is not None:
for i, update_node in enumerate(lowerCAmelCase ):
# Remove or replace all references to removed node.
if update_node.level > i and update_node.forward[i].key == key:
if node.level > i:
__lowercase= node.forward[i]
else:
__lowercase= update_node.forward[:i]
def _A (self , lowerCAmelCase , lowerCAmelCase ):
__lowercase, __lowercase= self._locate_node(lowerCAmelCase )
if node is not None:
__lowercase= value
else:
__lowercase= self.random_level()
if level > self.level:
# After level increase we have to add additional nodes to head.
for _ in range(self.level - 1 , lowerCAmelCase ):
update_vector.append(self.head )
__lowercase= level
__lowercase= Node(lowerCAmelCase , lowerCAmelCase )
for i, update_node in enumerate(update_vector[:level] ):
# Change references to pass through new node.
if update_node.level > i:
new_node.forward.append(update_node.forward[i] )
if update_node.level < i + 1:
update_node.forward.append(lowerCAmelCase )
else:
__lowercase= new_node
def _A (self , lowerCAmelCase ):
__lowercase, __lowercase= self._locate_node(lowerCAmelCase )
if node is not None:
return node.value
return None
def _lowerCamelCase( ) -> Optional[Any]:
'''simple docstring'''
__lowercase= SkipList()
skip_list.insert('Key1' , 3 )
skip_list.insert('Key2' , 1_2 )
skip_list.insert('Key3' , 4_1 )
skip_list.insert('Key4' , -1_9 )
__lowercase= skip_list.head
__lowercase= {}
while node.level != 0:
__lowercase= node.forward[0]
__lowercase= node.value
assert len(lowercase__ ) == 4
assert all_values["Key1"] == 3
assert all_values["Key2"] == 1_2
assert all_values["Key3"] == 4_1
assert all_values["Key4"] == -1_9
def _lowerCamelCase( ) -> Any:
'''simple docstring'''
__lowercase= SkipList()
skip_list.insert('Key1' , 1_0 )
skip_list.insert('Key1' , 1_2 )
skip_list.insert('Key5' , 7 )
skip_list.insert('Key7' , 1_0 )
skip_list.insert('Key10' , 5 )
skip_list.insert('Key7' , 7 )
skip_list.insert('Key5' , 5 )
skip_list.insert('Key10' , 1_0 )
__lowercase= skip_list.head
__lowercase= {}
while node.level != 0:
__lowercase= node.forward[0]
__lowercase= node.value
if len(lowercase__ ) != 4:
print()
assert len(lowercase__ ) == 4
assert all_values["Key1"] == 1_2
assert all_values["Key7"] == 7
assert all_values["Key5"] == 5
assert all_values["Key10"] == 1_0
def _lowerCamelCase( ) -> Tuple:
'''simple docstring'''
__lowercase= SkipList()
assert skip_list.find('Some key' ) is None
def _lowerCamelCase( ) -> Dict:
'''simple docstring'''
__lowercase= SkipList()
skip_list.insert('Key2' , 2_0 )
assert skip_list.find('Key2' ) == 2_0
skip_list.insert('Some Key' , 1_0 )
skip_list.insert('Key2' , 8 )
skip_list.insert('V' , 1_3 )
assert skip_list.find('Y' ) is None
assert skip_list.find('Key2' ) == 8
assert skip_list.find('Some Key' ) == 1_0
assert skip_list.find('V' ) == 1_3
def _lowerCamelCase( ) -> Tuple:
'''simple docstring'''
__lowercase= SkipList()
skip_list.delete('Some key' )
assert len(skip_list.head.forward ) == 0
def _lowerCamelCase( ) -> List[str]:
'''simple docstring'''
__lowercase= SkipList()
skip_list.insert('Key1' , 1_2 )
skip_list.insert('V' , 1_3 )
skip_list.insert('X' , 1_4 )
skip_list.insert('Key2' , 1_5 )
skip_list.delete('V' )
skip_list.delete('Key2' )
assert skip_list.find('V' ) is None
assert skip_list.find('Key2' ) is None
def _lowerCamelCase( ) -> Union[str, Any]:
'''simple docstring'''
__lowercase= SkipList()
skip_list.insert('Key1' , 1_2 )
skip_list.insert('V' , 1_3 )
skip_list.insert('X' , 1_4 )
skip_list.insert('Key2' , 1_5 )
skip_list.delete('V' )
assert skip_list.find('V' ) is None
assert skip_list.find('X' ) == 1_4
assert skip_list.find('Key1' ) == 1_2
assert skip_list.find('Key2' ) == 1_5
skip_list.delete('X' )
assert skip_list.find('V' ) is None
assert skip_list.find('X' ) is None
assert skip_list.find('Key1' ) == 1_2
assert skip_list.find('Key2' ) == 1_5
skip_list.delete('Key1' )
assert skip_list.find('V' ) is None
assert skip_list.find('X' ) is None
assert skip_list.find('Key1' ) is None
assert skip_list.find('Key2' ) == 1_5
skip_list.delete('Key2' )
assert skip_list.find('V' ) is None
assert skip_list.find('X' ) is None
assert skip_list.find('Key1' ) is None
assert skip_list.find('Key2' ) is None
def _lowerCamelCase( ) -> Tuple:
'''simple docstring'''
__lowercase= SkipList()
skip_list.insert('Key1' , 1_2 )
skip_list.insert('V' , 1_3 )
skip_list.insert('X' , 1_4_2 )
skip_list.insert('Key2' , 1_5 )
skip_list.delete('X' )
def traverse_keys(lowercase__ ):
yield node.key
for forward_node in node.forward:
yield from traverse_keys(lowercase__ )
assert len(set(traverse_keys(skip_list.head ) ) ) == 4
def _lowerCamelCase( ) -> Optional[Any]:
'''simple docstring'''
def is_sorted(lowercase__ ):
return all(next_item >= item for item, next_item in zip(lowercase__ , lst[1:] ) )
__lowercase= SkipList()
for i in range(1_0 ):
skip_list.insert(lowercase__ , lowercase__ )
assert is_sorted(list(lowercase__ ) )
skip_list.delete(5 )
skip_list.delete(8 )
skip_list.delete(2 )
assert is_sorted(list(lowercase__ ) )
skip_list.insert(-1_2 , -1_2 )
skip_list.insert(7_7 , 7_7 )
assert is_sorted(list(lowercase__ ) )
def _lowerCamelCase( ) -> Union[str, Any]:
'''simple docstring'''
for _ in range(1_0_0 ):
# Repeat test 100 times due to the probabilistic nature of skip list
# random values == random bugs
test_insert()
test_insert_overrides_existing_value()
test_searching_empty_list_returns_none()
test_search()
test_deleting_item_from_empty_list_do_nothing()
test_deleted_items_are_not_founded_by_find_method()
test_delete_removes_only_given_key()
test_delete_doesnt_leave_dead_nodes()
test_iter_always_yields_sorted_values()
def _lowerCamelCase( ) -> int:
'''simple docstring'''
__lowercase= SkipList()
skip_list.insert(2 , '2' )
skip_list.insert(4 , '4' )
skip_list.insert(6 , '4' )
skip_list.insert(4 , '5' )
skip_list.insert(8 , '4' )
skip_list.insert(9 , '4' )
skip_list.delete(4 )
print(lowercase__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 230
|
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class A ( A_ , unittest.TestCase ):
UpperCamelCase_ : Tuple =RobertaTokenizer
UpperCamelCase_ : int =RobertaTokenizerFast
UpperCamelCase_ : Tuple =True
UpperCamelCase_ : List[Any] ={'''cls_token''': '''<s>'''}
def _A (self ):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
__lowercase= [
'l',
'o',
'w',
'e',
'r',
's',
't',
'i',
'd',
'n',
'\u0120',
'\u0120l',
'\u0120n',
'\u0120lo',
'\u0120low',
'er',
'\u0120lowest',
'\u0120newer',
'\u0120wider',
'<unk>',
]
__lowercase= dict(zip(lowerCAmelCase , range(len(lowerCAmelCase ) ) ) )
__lowercase= ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
__lowercase= {'unk_token': '<unk>'}
__lowercase= os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
__lowercase= os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(lowerCAmelCase ) + '\n' )
with open(self.merges_file , 'w' , encoding='utf-8' ) as fp:
fp.write('\n'.join(lowerCAmelCase ) )
def _A (self , **lowerCAmelCase ):
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **lowerCAmelCase )
def _A (self , **lowerCAmelCase ):
kwargs.update(self.special_tokens_map )
return RobertaTokenizerFast.from_pretrained(self.tmpdirname , **lowerCAmelCase )
def _A (self , lowerCAmelCase ):
__lowercase= 'lower newer'
__lowercase= 'lower newer'
return input_text, output_text
def _A (self ):
__lowercase= self.tokenizer_class(self.vocab_file , self.merges_file , **self.special_tokens_map )
__lowercase= 'lower newer'
__lowercase= ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
__lowercase= tokenizer.tokenize(lowerCAmelCase ) # , add_prefix_space=True)
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
__lowercase= tokens + [tokenizer.unk_token]
__lowercase= [0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase ) , lowerCAmelCase )
def _A (self ):
__lowercase= self.get_tokenizer()
self.assertListEqual(tokenizer.encode('Hello world!' , add_special_tokens=lowerCAmelCase ) , [0, 3_1_4_1_4, 2_3_2, 3_2_8, 2] )
self.assertListEqual(
tokenizer.encode('Hello world! cécé herlolip 418' , add_special_tokens=lowerCAmelCase ) , [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2] , )
@slow
def _A (self ):
__lowercase= self.tokenizer_class.from_pretrained('roberta-base' )
__lowercase= tokenizer.encode('sequence builders' , add_special_tokens=lowerCAmelCase )
__lowercase= tokenizer.encode('multi-sequence build' , add_special_tokens=lowerCAmelCase )
__lowercase= tokenizer.encode(
'sequence builders' , add_special_tokens=lowerCAmelCase , add_prefix_space=lowerCAmelCase )
__lowercase= tokenizer.encode(
'sequence builders' , 'multi-sequence build' , add_special_tokens=lowerCAmelCase , add_prefix_space=lowerCAmelCase )
__lowercase= tokenizer.build_inputs_with_special_tokens(lowerCAmelCase )
__lowercase= tokenizer.build_inputs_with_special_tokens(lowerCAmelCase , lowerCAmelCase )
assert encoded_sentence == encoded_text_from_decode
assert encoded_pair == encoded_pair_from_decode
def _A (self ):
__lowercase= self.get_tokenizer()
__lowercase= 'Encode this sequence.'
__lowercase= tokenizer.byte_encoder[' '.encode('utf-8' )[0]]
# Testing encoder arguments
__lowercase= tokenizer.encode(lowerCAmelCase , add_special_tokens=lowerCAmelCase , add_prefix_space=lowerCAmelCase )
__lowercase= tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertNotEqual(lowerCAmelCase , lowerCAmelCase )
__lowercase= tokenizer.encode(lowerCAmelCase , add_special_tokens=lowerCAmelCase , add_prefix_space=lowerCAmelCase )
__lowercase= tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertEqual(lowerCAmelCase , lowerCAmelCase )
tokenizer.add_special_tokens({'bos_token': '<s>'} )
__lowercase= tokenizer.encode(lowerCAmelCase , add_special_tokens=lowerCAmelCase )
__lowercase= tokenizer.convert_ids_to_tokens(encoded[1] )[0]
self.assertNotEqual(lowerCAmelCase , lowerCAmelCase )
# Testing spaces after special tokens
__lowercase= '<mask>'
tokenizer.add_special_tokens(
{'mask_token': AddedToken(lowerCAmelCase , lstrip=lowerCAmelCase , rstrip=lowerCAmelCase )} ) # mask token has a left space
__lowercase= tokenizer.convert_tokens_to_ids(lowerCAmelCase )
__lowercase= 'Encode <mask> sequence'
__lowercase= 'Encode <mask>sequence'
__lowercase= tokenizer.encode(lowerCAmelCase )
__lowercase= encoded.index(lowerCAmelCase )
__lowercase= tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertEqual(lowerCAmelCase , lowerCAmelCase )
__lowercase= tokenizer.encode(lowerCAmelCase )
__lowercase= encoded.index(lowerCAmelCase )
__lowercase= tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertNotEqual(lowerCAmelCase , lowerCAmelCase )
def _A (self ):
pass
def _A (self ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
__lowercase= self.rust_tokenizer_class.from_pretrained(lowerCAmelCase , **lowerCAmelCase )
__lowercase= self.tokenizer_class.from_pretrained(lowerCAmelCase , **lowerCAmelCase )
__lowercase= 'A, <mask> AllenNLP sentence.'
__lowercase= tokenizer_r.encode_plus(lowerCAmelCase , add_special_tokens=lowerCAmelCase , return_token_type_ids=lowerCAmelCase )
__lowercase= tokenizer_p.encode_plus(lowerCAmelCase , add_special_tokens=lowerCAmelCase , return_token_type_ids=lowerCAmelCase )
# token_type_ids should put 0 everywhere
self.assertEqual(sum(tokens_r['token_type_ids'] ) , sum(tokens_p['token_type_ids'] ) )
# attention_mask should put 1 everywhere, so sum over length should be 1
self.assertEqual(
sum(tokens_r['attention_mask'] ) / len(tokens_r['attention_mask'] ) , sum(tokens_p['attention_mask'] ) / len(tokens_p['attention_mask'] ) , )
__lowercase= tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'] )
__lowercase= tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids'] )
# Rust correctly handles the space before the mask while python doesnt
self.assertSequenceEqual(tokens_p['input_ids'] , [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2] )
self.assertSequenceEqual(tokens_r['input_ids'] , [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2] )
self.assertSequenceEqual(
lowerCAmelCase , ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )
self.assertSequenceEqual(
lowerCAmelCase , ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )
def _A (self ):
for trim_offsets, add_prefix_space in itertools.product([True, False] , repeat=2 ):
__lowercase= self.rust_tokenizer_class.from_pretrained(
self.tmpdirname , use_fast=lowerCAmelCase , add_prefix_space=lowerCAmelCase , trim_offsets=lowerCAmelCase )
__lowercase= json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__() )
__lowercase= json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__() )
self.assertEqual(pre_tokenizer_state['add_prefix_space'] , lowerCAmelCase )
self.assertEqual(post_processor_state['add_prefix_space'] , lowerCAmelCase )
self.assertEqual(post_processor_state['trim_offsets'] , lowerCAmelCase )
def _A (self ):
# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
# `trim_offsets`
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
__lowercase= 'hello' # `hello` is a token in the vocabulary of `pretrained_name`
__lowercase= f'{text_of_1_token} {text_of_1_token}'
__lowercase= self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase , use_fast=lowerCAmelCase , add_prefix_space=lowerCAmelCase , trim_offsets=lowerCAmelCase )
__lowercase= tokenizer_r(lowerCAmelCase , return_offsets_mapping=lowerCAmelCase , add_special_tokens=lowerCAmelCase )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowerCAmelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowerCAmelCase ) + 1, len(lowerCAmelCase ) + 1 + len(lowerCAmelCase )) , )
__lowercase= self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase , use_fast=lowerCAmelCase , add_prefix_space=lowerCAmelCase , trim_offsets=lowerCAmelCase )
__lowercase= tokenizer_r(lowerCAmelCase , return_offsets_mapping=lowerCAmelCase , add_special_tokens=lowerCAmelCase )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowerCAmelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowerCAmelCase ) + 1, len(lowerCAmelCase ) + 1 + len(lowerCAmelCase )) , )
__lowercase= self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase , use_fast=lowerCAmelCase , add_prefix_space=lowerCAmelCase , trim_offsets=lowerCAmelCase )
__lowercase= tokenizer_r(lowerCAmelCase , return_offsets_mapping=lowerCAmelCase , add_special_tokens=lowerCAmelCase )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowerCAmelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowerCAmelCase ), len(lowerCAmelCase ) + 1 + len(lowerCAmelCase )) , )
__lowercase= self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase , use_fast=lowerCAmelCase , add_prefix_space=lowerCAmelCase , trim_offsets=lowerCAmelCase )
__lowercase= tokenizer_r(lowerCAmelCase , return_offsets_mapping=lowerCAmelCase , add_special_tokens=lowerCAmelCase )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowerCAmelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowerCAmelCase ), len(lowerCAmelCase ) + 1 + len(lowerCAmelCase )) , )
__lowercase= f' {text}'
# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
# pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
# )
# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
# self.assertEqual(
# encoding.offset_mapping[1],
# (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
# )
__lowercase= self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase , use_fast=lowerCAmelCase , add_prefix_space=lowerCAmelCase , trim_offsets=lowerCAmelCase )
__lowercase= tokenizer_r(lowerCAmelCase , return_offsets_mapping=lowerCAmelCase , add_special_tokens=lowerCAmelCase )
self.assertEqual(encoding.offset_mapping[0] , (1, 1 + len(lowerCAmelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(lowerCAmelCase ) + 1, 1 + len(lowerCAmelCase ) + 1 + len(lowerCAmelCase )) , )
__lowercase= self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase , use_fast=lowerCAmelCase , add_prefix_space=lowerCAmelCase , trim_offsets=lowerCAmelCase )
__lowercase= tokenizer_r(lowerCAmelCase , return_offsets_mapping=lowerCAmelCase , add_special_tokens=lowerCAmelCase )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(lowerCAmelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(lowerCAmelCase ), 1 + len(lowerCAmelCase ) + 1 + len(lowerCAmelCase )) , )
__lowercase= self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase , use_fast=lowerCAmelCase , add_prefix_space=lowerCAmelCase , trim_offsets=lowerCAmelCase )
__lowercase= tokenizer_r(lowerCAmelCase , return_offsets_mapping=lowerCAmelCase , add_special_tokens=lowerCAmelCase )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(lowerCAmelCase )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(lowerCAmelCase ), 1 + len(lowerCAmelCase ) + 1 + len(lowerCAmelCase )) , )
| 230
| 1
|
'''simple docstring'''
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
lowercase__ =(
"4S 3H 2C 7S 5H",
"9D 8H 2C 6S 7H",
"2D 6D 9D TH 7D",
"TC 8C 2S JH 6C",
"JH 8S TH AH QH",
"TS KS 5S 9S AC",
"KD 6S 9D TH AD",
"KS 8D 4D 9S 4S", # pair
"8C 4S KH JS 4D", # pair
"QH 8H KD JH 8S", # pair
"KC 4H KS 2H 8D", # pair
"KD 4S KC 3H 8S", # pair
"AH 8S AS KC JH", # pair
"3H 4C 4H 3S 2H", # 2 pairs
"5S 5D 2C KH KH", # 2 pairs
"3C KH 5D 5S KH", # 2 pairs
"AS 3C KH AD KH", # 2 pairs
"7C 7S 3S 7H 5S", # 3 of a kind
"7C 7S KH 2H 7H", # 3 of a kind
"AC KH QH AH AS", # 3 of a kind
"2H 4D 3C AS 5S", # straight (low ace)
"3C 5C 4C 2C 6H", # straight
"6S 8S 7S 5H 9H", # straight
"JS QS 9H TS KH", # straight
"QC KH TS JS AH", # straight (high ace)
"8C 9C 5C 3C TC", # flush
"3S 8S 9S 5S KS", # flush
"4C 5C 9C 8C KC", # flush
"JH 8H AH KH QH", # flush
"3D 2H 3H 2C 2D", # full house
"2H 2C 3S 3H 3D", # full house
"KH KC 3S 3H 3D", # full house
"JC 6H JS JD JH", # 4 of a kind
"JC 7H JS JD JH", # 4 of a kind
"JC KH JS JD JH", # 4 of a kind
"2S AS 4S 5S 3S", # straight flush (low ace)
"2D 6D 3D 4D 5D", # straight flush
"5C 6C 3C 7C 4C", # straight flush
"JH 9H TH KH QH", # straight flush
"JH AH TH KH QH", # royal flush (high ace straight flush)
)
lowercase__ =(
("2H 3H 4H 5H 6H", "KS AS TS QS JS", "Loss"),
("2H 3H 4H 5H 6H", "AS AD AC AH JD", "Win"),
("AS AH 2H AD AC", "JS JD JC JH 3D", "Win"),
("2S AH 2H AS AC", "JS JD JC JH AD", "Loss"),
("2S AH 2H AS AC", "2H 3H 5H 6H 7H", "Win"),
("AS 3S 4S 8S 2S", "2H 3H 5H 6H 7H", "Win"),
("2H 3H 5H 6H 7H", "2S 3H 4H 5S 6C", "Win"),
("2S 3H 4H 5S 6C", "3D 4C 5H 6H 2S", "Tie"),
("2S 3H 4H 5S 6C", "AH AC 5H 6H AS", "Win"),
("2S 2H 4H 5S 4C", "AH AC 5H 6H AS", "Loss"),
("2S 2H 4H 5S 4C", "AH AC 5H 6H 7S", "Win"),
("6S AD 7H 4S AS", "AH AC 5H 6H 7S", "Loss"),
("2S AH 4H 5S KC", "AH AC 5H 6H 7S", "Loss"),
("2S 3H 6H 7S 9C", "7H 3C TH 6H 9S", "Loss"),
("4S 5H 6H TS AC", "3S 5H 6H TS AC", "Win"),
("2S AH 4H 5S 6C", "AD 4C 5H 6H 2C", "Tie"),
("AS AH 3H AD AC", "AS AH 2H AD AC", "Win"),
("AH AC 5H 5C QS", "AH AC 5H 5C KS", "Loss"),
("AH AC 5H 5C QS", "KH KC 5H 5C QS", "Win"),
("7C 7S KH 2H 7H", "3C 3S AH 2H 3H", "Win"),
("3C 3S AH 2H 3H", "7C 7S KH 2H 7H", "Loss"),
("6H 5H 4H 3H 2H", "5H 4H 3H 2H AH", "Win"),
("5H 4H 3H 2H AH", "5H 4H 3H 2H AH", "Tie"),
("5H 4H 3H 2H AH", "6H 5H 4H 3H 2H", "Loss"),
("AH AD KS KC AC", "AH KD KH AC KC", "Win"),
("2H 4D 3C AS 5S", "2H 4D 3C 6S 5S", "Loss"),
("2H 3S 3C 3H 2S", "3S 3C 2S 2H 2D", "Win"),
("4D 6D 5D 2D JH", "3S 8S 3H TC KH", "Loss"),
("4S 6C 8S 3S 7S", "AD KS 2D 7D 7C", "Loss"),
("6S 4C 7H 8C 3H", "5H JC AH 9D 9C", "Loss"),
("9D 9H JH TC QH", "3C 2S JS 5C 7H", "Win"),
("2H TC 8S AD 9S", "4H TS 7H 2C 5C", "Win"),
("9D 3S 2C 7S 7C", "JC TD 3C TC 9H", "Loss"),
)
lowercase__ =(
("2H 3H 4H 5H 6H", True),
("AS AH 2H AD AC", False),
("2H 3H 5H 6H 7H", True),
("KS AS TS QS JS", True),
("8H 9H QS JS TH", False),
("AS 3S 4S 8S 2S", True),
)
lowercase__ =(
("2H 3H 4H 5H 6H", True),
("AS AH 2H AD AC", False),
("2H 3H 5H 6H 7H", False),
("KS AS TS QS JS", True),
("8H 9H QS JS TH", True),
)
lowercase__ =(
("2H 4D 3C AS 5S", True, [5, 4, 3, 2, 14]),
("2H 5D 3C AS 5S", False, [14, 5, 5, 3, 2]),
("JH QD KC AS TS", False, [14, 13, 12, 11, 10]),
("9D 3S 2C 7S 7C", False, [9, 7, 7, 3, 2]),
)
lowercase__ =(
("JH AH TH KH QH", 0),
("JH 9H TH KH QH", 0),
("JC KH JS JD JH", 7),
("KH KC 3S 3H 3D", 6),
("8C 9C 5C 3C TC", 0),
("JS QS 9H TS KH", 0),
("7C 7S KH 2H 7H", 3),
("3C KH 5D 5S KH", 2),
("QH 8H KD JH 8S", 1),
("2D 6D 9D TH 7D", 0),
)
lowercase__ =(
("JH AH TH KH QH", 23),
("JH 9H TH KH QH", 22),
("JC KH JS JD JH", 21),
("KH KC 3S 3H 3D", 20),
("8C 9C 5C 3C TC", 19),
("JS QS 9H TS KH", 18),
("7C 7S KH 2H 7H", 17),
("3C KH 5D 5S KH", 16),
("QH 8H KD JH 8S", 15),
("2D 6D 9D TH 7D", 14),
)
def UpperCamelCase_ ( ):
a_ , a_ = randrange(len(__lowerCAmelCase ) ), randrange(len(__lowerCAmelCase ) )
a_ = ["""Loss""", """Tie""", """Win"""][(play >= oppo) + (play > oppo)]
a_ , a_ = SORTED_HANDS[play], SORTED_HANDS[oppo]
return hand, other, expected
def UpperCamelCase_ ( A__ = 1_00 ):
return (generate_random_hand() for _ in range(__lowerCAmelCase ))
@pytest.mark.parametrize("""hand, expected""" , __lowerCAmelCase )
def UpperCamelCase_ ( A__ , A__ ):
assert PokerHand(__lowerCAmelCase )._is_flush() == expected
@pytest.mark.parametrize("""hand, expected""" , __lowerCAmelCase )
def UpperCamelCase_ ( A__ , A__ ):
assert PokerHand(__lowerCAmelCase )._is_straight() == expected
@pytest.mark.parametrize("""hand, expected, card_values""" , __lowerCAmelCase )
def UpperCamelCase_ ( A__ , A__ , A__ ):
a_ = PokerHand(__lowerCAmelCase )
assert player._is_five_high_straight() == expected
assert player._card_values == card_values
@pytest.mark.parametrize("""hand, expected""" , __lowerCAmelCase )
def UpperCamelCase_ ( A__ , A__ ):
assert PokerHand(__lowerCAmelCase )._is_same_kind() == expected
@pytest.mark.parametrize("""hand, expected""" , __lowerCAmelCase )
def UpperCamelCase_ ( A__ , A__ ):
assert PokerHand(__lowerCAmelCase )._hand_type == expected
@pytest.mark.parametrize("""hand, other, expected""" , __lowerCAmelCase )
def UpperCamelCase_ ( A__ , A__ , A__ ):
assert PokerHand(__lowerCAmelCase ).compare_with(PokerHand(__lowerCAmelCase ) ) == expected
@pytest.mark.parametrize("""hand, other, expected""" , generate_random_hands() )
def UpperCamelCase_ ( A__ , A__ , A__ ):
assert PokerHand(__lowerCAmelCase ).compare_with(PokerHand(__lowerCAmelCase ) ) == expected
def UpperCamelCase_ ( ):
a_ = [PokerHand(__lowerCAmelCase ) for hand in SORTED_HANDS]
a_ = poker_hands.copy()
shuffle(__lowerCAmelCase )
a_ = chain(sorted(__lowerCAmelCase ) )
for index, hand in enumerate(__lowerCAmelCase ):
assert hand == poker_hands[index]
def UpperCamelCase_ ( ):
a_ = [PokerHand("""2D AC 3H 4H 5S""" ), PokerHand("""2S 3H 4H 5S 6C""" )]
pokerhands.sort(reverse=__lowerCAmelCase )
assert pokerhands[0].__str__() == "2S 3H 4H 5S 6C"
def UpperCamelCase_ ( ):
a_ = PokerHand("""2C 4S AS 3D 5C""" )
a_ = True
a_ = [5, 4, 3, 2, 14]
for _ in range(10 ):
assert pokerhand._is_five_high_straight() == expected
assert pokerhand._card_values == expected_card_values
def UpperCamelCase_ ( ):
a_ = 0
a_ = os.path.abspath(os.path.dirname(__lowerCAmelCase ) )
a_ = os.path.join(__lowerCAmelCase , """poker_hands.txt""" )
with open(__lowerCAmelCase ) as file_hand:
for line in file_hand:
a_ = line[:14].strip()
a_ = line[15:].strip()
a_ , a_ = PokerHand(__lowerCAmelCase ), PokerHand(__lowerCAmelCase )
a_ = player.compare_with(__lowerCAmelCase )
if output == "Win":
answer += 1
assert answer == 3_76
| 712
|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowercase__ =logging.get_logger(__name__)
lowercase__ ={
'microsoft/focalnet-tiny': 'https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json',
}
class a_ ( UpperCamelCase__ , UpperCamelCase__ ):
lowerCamelCase__ : int = 'focalnet'
def __init__( self , UpperCAmelCase=2_24 , UpperCAmelCase=4 , UpperCAmelCase=3 , UpperCAmelCase=96 , UpperCAmelCase=False , UpperCAmelCase=[1_92, 3_84, 7_68, 7_68] , UpperCAmelCase=[2, 2, 6, 2] , UpperCAmelCase=[2, 2, 2, 2] , UpperCAmelCase=[3, 3, 3, 3] , UpperCAmelCase="gelu" , UpperCAmelCase=4.0 , UpperCAmelCase=0.0 , UpperCAmelCase=0.1 , UpperCAmelCase=False , UpperCAmelCase=1e-4 , UpperCAmelCase=False , UpperCAmelCase=False , UpperCAmelCase=False , UpperCAmelCase=0.02 , UpperCAmelCase=1e-5 , UpperCAmelCase=32 , UpperCAmelCase=None , UpperCAmelCase=None , **UpperCAmelCase , ):
super().__init__(**UpperCAmelCase )
a_ = image_size
a_ = patch_size
a_ = num_channels
a_ = embed_dim
a_ = use_conv_embed
a_ = hidden_sizes
a_ = depths
a_ = focal_levels
a_ = focal_windows
a_ = hidden_act
a_ = mlp_ratio
a_ = hidden_dropout_prob
a_ = drop_path_rate
a_ = use_layerscale
a_ = layerscale_value
a_ = use_post_layernorm
a_ = use_post_layernorm_in_modulation
a_ = normalize_modulator
a_ = initializer_range
a_ = layer_norm_eps
a_ = encoder_stride
a_ = ["""stem"""] + [f'''stage{idx}''' for idx in range(1 , len(self.depths ) + 1 )]
a_ , a_ = get_aligned_output_features_output_indices(
out_features=UpperCAmelCase , out_indices=UpperCAmelCase , stage_names=self.stage_names )
| 511
| 0
|
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block
@dataclass
class lowerCAmelCase_ ( a__ ):
UpperCAmelCase__ : torch.FloatTensor
class lowerCAmelCase_ ( a__ , a__ ):
@register_to_config
def __init__( self, SCREAMING_SNAKE_CASE_ = 6_5536, SCREAMING_SNAKE_CASE_ = None, SCREAMING_SNAKE_CASE_ = 2, SCREAMING_SNAKE_CASE_ = 2, SCREAMING_SNAKE_CASE_ = 0, SCREAMING_SNAKE_CASE_ = "fourier", SCREAMING_SNAKE_CASE_ = True, SCREAMING_SNAKE_CASE_ = False, SCREAMING_SNAKE_CASE_ = 0.0, SCREAMING_SNAKE_CASE_ = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D"), SCREAMING_SNAKE_CASE_ = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip"), SCREAMING_SNAKE_CASE_ = "UNetMidBlock1D", SCREAMING_SNAKE_CASE_ = None, SCREAMING_SNAKE_CASE_ = (32, 32, 64), SCREAMING_SNAKE_CASE_ = None, SCREAMING_SNAKE_CASE_ = 8, SCREAMING_SNAKE_CASE_ = 1, SCREAMING_SNAKE_CASE_ = False, ) -> Any:
super().__init__()
UpperCamelCase : Optional[Any] = sample_size
# time
if time_embedding_type == "fourier":
UpperCamelCase : Any = GaussianFourierProjection(
embedding_size=8, set_W_to_weight=SCREAMING_SNAKE_CASE_, log=SCREAMING_SNAKE_CASE_, flip_sin_to_cos=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = 2 * block_out_channels[0]
elif time_embedding_type == "positional":
UpperCamelCase : Optional[Any] = Timesteps(
block_out_channels[0], flip_sin_to_cos=SCREAMING_SNAKE_CASE_, downscale_freq_shift=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = block_out_channels[0]
if use_timestep_embedding:
UpperCamelCase : List[Any] = block_out_channels[0] * 4
UpperCamelCase : Any = TimestepEmbedding(
in_channels=SCREAMING_SNAKE_CASE_, time_embed_dim=SCREAMING_SNAKE_CASE_, act_fn=SCREAMING_SNAKE_CASE_, out_dim=block_out_channels[0], )
UpperCamelCase : Tuple = nn.ModuleList([] )
UpperCamelCase : Dict = None
UpperCamelCase : Tuple = nn.ModuleList([] )
UpperCamelCase : Optional[Any] = None
# down
UpperCamelCase : Any = in_channels
for i, down_block_type in enumerate(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : int = output_channel
UpperCamelCase : Union[str, Any] = block_out_channels[i]
if i == 0:
input_channel += extra_in_channels
UpperCamelCase : str = i == len(SCREAMING_SNAKE_CASE_ ) - 1
UpperCamelCase : Tuple = get_down_block(
SCREAMING_SNAKE_CASE_, num_layers=SCREAMING_SNAKE_CASE_, in_channels=SCREAMING_SNAKE_CASE_, out_channels=SCREAMING_SNAKE_CASE_, temb_channels=block_out_channels[0], add_downsample=not is_final_block or downsample_each_block, )
self.down_blocks.append(SCREAMING_SNAKE_CASE_ )
# mid
UpperCamelCase : Dict = get_mid_block(
SCREAMING_SNAKE_CASE_, in_channels=block_out_channels[-1], mid_channels=block_out_channels[-1], out_channels=block_out_channels[-1], embed_dim=block_out_channels[0], num_layers=SCREAMING_SNAKE_CASE_, add_downsample=SCREAMING_SNAKE_CASE_, )
# up
UpperCamelCase : Optional[Any] = list(reversed(SCREAMING_SNAKE_CASE_ ) )
UpperCamelCase : Tuple = reversed_block_out_channels[0]
if out_block_type is None:
UpperCamelCase : List[Any] = out_channels
else:
UpperCamelCase : Tuple = block_out_channels[0]
for i, up_block_type in enumerate(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Tuple = output_channel
UpperCamelCase : Union[str, Any] = (
reversed_block_out_channels[i + 1] if i < len(SCREAMING_SNAKE_CASE_ ) - 1 else final_upsample_channels
)
UpperCamelCase : Dict = i == len(SCREAMING_SNAKE_CASE_ ) - 1
UpperCamelCase : List[str] = get_up_block(
SCREAMING_SNAKE_CASE_, num_layers=SCREAMING_SNAKE_CASE_, in_channels=SCREAMING_SNAKE_CASE_, out_channels=SCREAMING_SNAKE_CASE_, temb_channels=block_out_channels[0], add_upsample=not is_final_block, )
self.up_blocks.append(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = output_channel
# out
UpperCamelCase : List[str] = norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4, 32 )
UpperCamelCase : Tuple = get_out_block(
out_block_type=SCREAMING_SNAKE_CASE_, num_groups_out=SCREAMING_SNAKE_CASE_, embed_dim=block_out_channels[0], out_channels=SCREAMING_SNAKE_CASE_, act_fn=SCREAMING_SNAKE_CASE_, fc_dim=block_out_channels[-1] // 4, )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ = True, ) -> Union[UNetaDOutput, Tuple]:
UpperCamelCase : Optional[Any] = timestep
if not torch.is_tensor(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Tuple = torch.tensor([timesteps], dtype=torch.long, device=sample.device )
elif torch.is_tensor(SCREAMING_SNAKE_CASE_ ) and len(timesteps.shape ) == 0:
UpperCamelCase : List[str] = timesteps[None].to(sample.device )
UpperCamelCase : Tuple = self.time_proj(SCREAMING_SNAKE_CASE_ )
if self.config.use_timestep_embedding:
UpperCamelCase : str = self.time_mlp(SCREAMING_SNAKE_CASE_ )
else:
UpperCamelCase : Dict = timestep_embed[..., None]
UpperCamelCase : int = timestep_embed.repeat([1, 1, sample.shape[2]] ).to(sample.dtype )
UpperCamelCase : Optional[int] = timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]) )
# 2. down
UpperCamelCase : str = ()
for downsample_block in self.down_blocks:
UpperCamelCase , UpperCamelCase : Tuple = downsample_block(hidden_states=SCREAMING_SNAKE_CASE_, temb=SCREAMING_SNAKE_CASE_ )
down_block_res_samples += res_samples
# 3. mid
if self.mid_block:
UpperCamelCase : Tuple = self.mid_block(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
# 4. up
for i, upsample_block in enumerate(self.up_blocks ):
UpperCamelCase : Optional[Any] = down_block_res_samples[-1:]
UpperCamelCase : Optional[int] = down_block_res_samples[:-1]
UpperCamelCase : Any = upsample_block(SCREAMING_SNAKE_CASE_, res_hidden_states_tuple=SCREAMING_SNAKE_CASE_, temb=SCREAMING_SNAKE_CASE_ )
# 5. post-process
if self.out_block:
UpperCamelCase : Any = self.out_block(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
if not return_dict:
return (sample,)
return UNetaDOutput(sample=SCREAMING_SNAKE_CASE_ )
| 40
|
"""simple docstring"""
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 102
| 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
a_ = sys.version_info >= (3, 10)
def _a ( UpperCamelCase_ : Dict=None , UpperCamelCase_ : Any=None ) -> str:
"""simple docstring"""
return field(default_factory=lambda: default , metadata=UpperCamelCase_ )
@dataclass
class lowercase__ :
a_ =42
a_ =42
a_ =42
a_ =42
@dataclass
class lowercase__ :
a_ =42
a_ =field(default="""toto""", metadata={"""help""": """help message"""} )
@dataclass
class lowercase__ :
a_ =False
a_ =True
a_ =None
class lowercase__ ( _UpperCAmelCase ):
a_ ="""titi"""
a_ ="""toto"""
class lowercase__ ( _UpperCAmelCase ):
a_ ="""titi"""
a_ ="""toto"""
a_ =42
@dataclass
class lowercase__ :
a_ ="""toto"""
def UpperCAmelCase ( self )-> Tuple:
'''simple docstring'''
lowerCAmelCase__ = BasicEnum(self.foo )
@dataclass
class lowercase__ :
a_ ="""toto"""
def UpperCAmelCase ( self )-> Any:
'''simple docstring'''
lowerCAmelCase__ = MixedTypeEnum(self.foo )
@dataclass
class lowercase__ :
a_ =None
a_ =field(default=_UpperCAmelCase, metadata={"""help""": """help message"""} )
a_ =None
a_ =list_field(default=[] )
a_ =list_field(default=[] )
@dataclass
class lowercase__ :
a_ =list_field(default=[] )
a_ =list_field(default=[1, 2, 3] )
a_ =list_field(default=["""Hallo""", """Bonjour""", """Hello"""] )
a_ =list_field(default=[0.1, 0.2, 0.3] )
@dataclass
class lowercase__ :
a_ =field()
a_ =field()
a_ =field()
def UpperCAmelCase ( self )-> Optional[Any]:
'''simple docstring'''
lowerCAmelCase__ = BasicEnum(self.required_enum )
@dataclass
class lowercase__ :
a_ =42
a_ =field()
a_ =None
a_ =field(default="""toto""", metadata={"""help""": """help message"""} )
a_ =list_field(default=["""Hallo""", """Bonjour""", """Hello"""] )
if is_python_no_less_than_3_10:
@dataclass
class lowercase__ :
a_ =False
a_ =True
a_ =None
@dataclass
class lowercase__ :
a_ =None
a_ =field(default=_UpperCAmelCase, metadata={"""help""": """help message"""} )
a_ =None
a_ =list_field(default=[] )
a_ =list_field(default=[] )
class lowercase__ ( unittest.TestCase ):
def UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase )-> Optional[Any]:
'''simple docstring'''
self.assertEqual(len(a._actions ) , len(b._actions ) )
for x, y in zip(a._actions , b._actions ):
lowerCAmelCase__ = {k: v for k, v in vars(__UpperCAmelCase ).items() if k != "container"}
lowerCAmelCase__ = {k: v for k, v in vars(__UpperCAmelCase ).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" , __UpperCAmelCase ) and yy.get("choices" , __UpperCAmelCase ):
for expected_choice in yy["choices"] + xx["choices"]:
self.assertEqual(xx["type"](__UpperCAmelCase ) , yy["type"](__UpperCAmelCase ) )
del xx["type"], yy["type"]
self.assertEqual(__UpperCAmelCase , __UpperCAmelCase )
def UpperCAmelCase ( self )-> Tuple:
'''simple docstring'''
lowerCAmelCase__ = HfArgumentParser(__UpperCAmelCase )
lowerCAmelCase__ = argparse.ArgumentParser()
expected.add_argument("--foo" , type=__UpperCAmelCase , required=__UpperCAmelCase )
expected.add_argument("--bar" , type=__UpperCAmelCase , required=__UpperCAmelCase )
expected.add_argument("--baz" , type=__UpperCAmelCase , required=__UpperCAmelCase )
expected.add_argument("--flag" , type=__UpperCAmelCase , default=__UpperCAmelCase , const=__UpperCAmelCase , nargs="?" )
self.argparsersEqual(__UpperCAmelCase , __UpperCAmelCase )
lowerCAmelCase__ = ["--foo", "1", "--baz", "quux", "--bar", "0.5"]
((lowerCAmelCase__ ) , ) = parser.parse_args_into_dataclasses(__UpperCAmelCase , look_for_args_file=__UpperCAmelCase )
self.assertFalse(example.flag )
def UpperCAmelCase ( self )-> Tuple:
'''simple docstring'''
lowerCAmelCase__ = HfArgumentParser(__UpperCAmelCase )
lowerCAmelCase__ = argparse.ArgumentParser()
expected.add_argument("--foo" , default=42 , type=__UpperCAmelCase )
expected.add_argument("--baz" , default="toto" , type=__UpperCAmelCase , help="help message" )
self.argparsersEqual(__UpperCAmelCase , __UpperCAmelCase )
def UpperCAmelCase ( self )-> int:
'''simple docstring'''
lowerCAmelCase__ = argparse.ArgumentParser()
expected.add_argument("--foo" , type=__UpperCAmelCase , default=__UpperCAmelCase , const=__UpperCAmelCase , nargs="?" )
expected.add_argument("--baz" , type=__UpperCAmelCase , default=__UpperCAmelCase , const=__UpperCAmelCase , 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=__UpperCAmelCase , dest="baz" )
expected.add_argument("--opt" , type=__UpperCAmelCase , default=__UpperCAmelCase )
lowerCAmelCase__ = [WithDefaultBoolExample]
if is_python_no_less_than_3_10:
dataclass_types.append(__UpperCAmelCase )
for dataclass_type in dataclass_types:
lowerCAmelCase__ = HfArgumentParser(__UpperCAmelCase )
self.argparsersEqual(__UpperCAmelCase , __UpperCAmelCase )
lowerCAmelCase__ = parser.parse_args([] )
self.assertEqual(__UpperCAmelCase , Namespace(foo=__UpperCAmelCase , baz=__UpperCAmelCase , opt=__UpperCAmelCase ) )
lowerCAmelCase__ = parser.parse_args(["--foo", "--no_baz"] )
self.assertEqual(__UpperCAmelCase , Namespace(foo=__UpperCAmelCase , baz=__UpperCAmelCase , opt=__UpperCAmelCase ) )
lowerCAmelCase__ = parser.parse_args(["--foo", "--baz"] )
self.assertEqual(__UpperCAmelCase , Namespace(foo=__UpperCAmelCase , baz=__UpperCAmelCase , opt=__UpperCAmelCase ) )
lowerCAmelCase__ = parser.parse_args(["--foo", "True", "--baz", "True", "--opt", "True"] )
self.assertEqual(__UpperCAmelCase , Namespace(foo=__UpperCAmelCase , baz=__UpperCAmelCase , opt=__UpperCAmelCase ) )
lowerCAmelCase__ = parser.parse_args(["--foo", "False", "--baz", "False", "--opt", "False"] )
self.assertEqual(__UpperCAmelCase , Namespace(foo=__UpperCAmelCase , baz=__UpperCAmelCase , opt=__UpperCAmelCase ) )
def UpperCAmelCase ( self )-> List[Any]:
'''simple docstring'''
lowerCAmelCase__ = HfArgumentParser(__UpperCAmelCase )
lowerCAmelCase__ = argparse.ArgumentParser()
expected.add_argument(
"--foo" , default="toto" , choices=["titi", "toto", 42] , type=make_choice_type_function(["titi", "toto", 42] ) , )
self.argparsersEqual(__UpperCAmelCase , __UpperCAmelCase )
lowerCAmelCase__ = parser.parse_args([] )
self.assertEqual(args.foo , "toto" )
lowerCAmelCase__ = parser.parse_args_into_dataclasses([] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.toto )
lowerCAmelCase__ = parser.parse_args(["--foo", "titi"] )
self.assertEqual(args.foo , "titi" )
lowerCAmelCase__ = parser.parse_args_into_dataclasses(["--foo", "titi"] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.titi )
lowerCAmelCase__ = parser.parse_args(["--foo", "42"] )
self.assertEqual(args.foo , 42 )
lowerCAmelCase__ = parser.parse_args_into_dataclasses(["--foo", "42"] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.fourtytwo )
def UpperCAmelCase ( self )-> Any:
'''simple docstring'''
@dataclass
class lowercase__ :
a_ ="""toto"""
lowerCAmelCase__ = HfArgumentParser(__UpperCAmelCase )
lowerCAmelCase__ = argparse.ArgumentParser()
expected.add_argument(
"--foo" , default="toto" , choices=("titi", "toto", 42) , type=make_choice_type_function(["titi", "toto", 42] ) , )
self.argparsersEqual(__UpperCAmelCase , __UpperCAmelCase )
lowerCAmelCase__ = parser.parse_args([] )
self.assertEqual(args.foo , "toto" )
lowerCAmelCase__ = parser.parse_args(["--foo", "titi"] )
self.assertEqual(args.foo , "titi" )
lowerCAmelCase__ = parser.parse_args(["--foo", "42"] )
self.assertEqual(args.foo , 42 )
def UpperCAmelCase ( self )-> List[str]:
'''simple docstring'''
lowerCAmelCase__ = HfArgumentParser(__UpperCAmelCase )
lowerCAmelCase__ = argparse.ArgumentParser()
expected.add_argument("--foo_int" , nargs="+" , default=[] , type=__UpperCAmelCase )
expected.add_argument("--bar_int" , nargs="+" , default=[1, 2, 3] , type=__UpperCAmelCase )
expected.add_argument("--foo_str" , nargs="+" , default=["Hallo", "Bonjour", "Hello"] , type=__UpperCAmelCase )
expected.add_argument("--foo_float" , nargs="+" , default=[0.1, 0.2, 0.3] , type=__UpperCAmelCase )
self.argparsersEqual(__UpperCAmelCase , __UpperCAmelCase )
lowerCAmelCase__ = parser.parse_args([] )
self.assertEqual(
__UpperCAmelCase , Namespace(foo_int=[] , bar_int=[1, 2, 3] , foo_str=["Hallo", "Bonjour", "Hello"] , foo_float=[0.1, 0.2, 0.3] ) , )
lowerCAmelCase__ = parser.parse_args("--foo_int 1 --bar_int 2 3 --foo_str a b c --foo_float 0.1 0.7".split() )
self.assertEqual(__UpperCAmelCase , Namespace(foo_int=[1] , bar_int=[2, 3] , foo_str=["a", "b", "c"] , foo_float=[0.1, 0.7] ) )
def UpperCAmelCase ( self )-> Any:
'''simple docstring'''
lowerCAmelCase__ = argparse.ArgumentParser()
expected.add_argument("--foo" , default=__UpperCAmelCase , type=__UpperCAmelCase )
expected.add_argument("--bar" , default=__UpperCAmelCase , type=__UpperCAmelCase , help="help message" )
expected.add_argument("--baz" , default=__UpperCAmelCase , type=__UpperCAmelCase )
expected.add_argument("--ces" , nargs="+" , default=[] , type=__UpperCAmelCase )
expected.add_argument("--des" , nargs="+" , default=[] , type=__UpperCAmelCase )
lowerCAmelCase__ = [OptionalExample]
if is_python_no_less_than_3_10:
dataclass_types.append(__UpperCAmelCase )
for dataclass_type in dataclass_types:
lowerCAmelCase__ = HfArgumentParser(__UpperCAmelCase )
self.argparsersEqual(__UpperCAmelCase , __UpperCAmelCase )
lowerCAmelCase__ = parser.parse_args([] )
self.assertEqual(__UpperCAmelCase , Namespace(foo=__UpperCAmelCase , bar=__UpperCAmelCase , baz=__UpperCAmelCase , ces=[] , des=[] ) )
lowerCAmelCase__ = parser.parse_args("--foo 12 --bar 3.14 --baz 42 --ces a b c --des 1 2 3".split() )
self.assertEqual(__UpperCAmelCase , Namespace(foo=12 , bar=3.14 , baz="42" , ces=["a", "b", "c"] , des=[1, 2, 3] ) )
def UpperCAmelCase ( self )-> int:
'''simple docstring'''
lowerCAmelCase__ = HfArgumentParser(__UpperCAmelCase )
lowerCAmelCase__ = argparse.ArgumentParser()
expected.add_argument("--required_list" , nargs="+" , type=__UpperCAmelCase , required=__UpperCAmelCase )
expected.add_argument("--required_str" , type=__UpperCAmelCase , required=__UpperCAmelCase )
expected.add_argument(
"--required_enum" , type=make_choice_type_function(["titi", "toto"] ) , choices=["titi", "toto"] , required=__UpperCAmelCase , )
self.argparsersEqual(__UpperCAmelCase , __UpperCAmelCase )
def UpperCAmelCase ( self )-> Tuple:
'''simple docstring'''
lowerCAmelCase__ = HfArgumentParser(__UpperCAmelCase )
lowerCAmelCase__ = argparse.ArgumentParser()
expected.add_argument("--foo" , type=__UpperCAmelCase , required=__UpperCAmelCase )
expected.add_argument(
"--required_enum" , type=make_choice_type_function(["titi", "toto"] ) , choices=["titi", "toto"] , required=__UpperCAmelCase , )
expected.add_argument("--opt" , type=__UpperCAmelCase , default=__UpperCAmelCase )
expected.add_argument("--baz" , default="toto" , type=__UpperCAmelCase , help="help message" )
expected.add_argument("--foo_str" , nargs="+" , default=["Hallo", "Bonjour", "Hello"] , type=__UpperCAmelCase )
self.argparsersEqual(__UpperCAmelCase , __UpperCAmelCase )
def UpperCAmelCase ( self )-> Tuple:
'''simple docstring'''
lowerCAmelCase__ = HfArgumentParser(__UpperCAmelCase )
lowerCAmelCase__ = {
"foo": 12,
"bar": 3.14,
"baz": "42",
"flag": True,
}
lowerCAmelCase__ = parser.parse_dict(__UpperCAmelCase )[0]
lowerCAmelCase__ = BasicExample(**__UpperCAmelCase )
self.assertEqual(__UpperCAmelCase , __UpperCAmelCase )
def UpperCAmelCase ( self )-> List[Any]:
'''simple docstring'''
lowerCAmelCase__ = HfArgumentParser(__UpperCAmelCase )
lowerCAmelCase__ = {
"foo": 12,
"bar": 3.14,
"baz": "42",
"flag": True,
"extra": 42,
}
self.assertRaises(__UpperCAmelCase , parser.parse_dict , __UpperCAmelCase , allow_extra_keys=__UpperCAmelCase )
def UpperCAmelCase ( self )-> int:
'''simple docstring'''
lowerCAmelCase__ = HfArgumentParser(__UpperCAmelCase )
lowerCAmelCase__ = {
"foo": 12,
"bar": 3.14,
"baz": "42",
"flag": True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
lowerCAmelCase__ = os.path.join(__UpperCAmelCase , "temp_json" )
os.mkdir(__UpperCAmelCase )
with open(temp_local_path + ".json" , "w+" ) as f:
json.dump(__UpperCAmelCase , __UpperCAmelCase )
lowerCAmelCase__ = parser.parse_yaml_file(Path(temp_local_path + ".json" ) )[0]
lowerCAmelCase__ = BasicExample(**__UpperCAmelCase )
self.assertEqual(__UpperCAmelCase , __UpperCAmelCase )
def UpperCAmelCase ( self )-> Tuple:
'''simple docstring'''
lowerCAmelCase__ = HfArgumentParser(__UpperCAmelCase )
lowerCAmelCase__ = {
"foo": 12,
"bar": 3.14,
"baz": "42",
"flag": True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
lowerCAmelCase__ = os.path.join(__UpperCAmelCase , "temp_yaml" )
os.mkdir(__UpperCAmelCase )
with open(temp_local_path + ".yaml" , "w+" ) as f:
yaml.dump(__UpperCAmelCase , __UpperCAmelCase )
lowerCAmelCase__ = parser.parse_yaml_file(Path(temp_local_path + ".yaml" ) )[0]
lowerCAmelCase__ = BasicExample(**__UpperCAmelCase )
self.assertEqual(__UpperCAmelCase , __UpperCAmelCase )
def UpperCAmelCase ( self )-> Any:
'''simple docstring'''
lowerCAmelCase__ = HfArgumentParser(__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
| 701
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
'''SEW_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''SEWForCTC''',
'''SEWForSequenceClassification''',
'''SEWModel''',
'''SEWPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_sew import (
SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
SEWForCTC,
SEWForSequenceClassification,
SEWModel,
SEWPreTrainedModel,
)
else:
import sys
a_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 115
| 0
|
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
SCREAMING_SNAKE_CASE_ : Optional[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ : List[Any] = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
SCREAMING_SNAKE_CASE_ : List[str] = {
'''vocab_file''': {
'''squeezebert/squeezebert-uncased''': (
'''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt'''
),
'''squeezebert/squeezebert-mnli''': '''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt''',
'''squeezebert/squeezebert-mnli-headless''': (
'''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt'''
),
},
'''tokenizer_file''': {
'''squeezebert/squeezebert-uncased''': (
'''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json'''
),
'''squeezebert/squeezebert-mnli''': (
'''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json'''
),
'''squeezebert/squeezebert-mnli-headless''': (
'''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json'''
),
},
}
SCREAMING_SNAKE_CASE_ : int = {
'''squeezebert/squeezebert-uncased''': 512,
'''squeezebert/squeezebert-mnli''': 512,
'''squeezebert/squeezebert-mnli-headless''': 512,
}
SCREAMING_SNAKE_CASE_ : int = {
'''squeezebert/squeezebert-uncased''': {'''do_lower_case''': True},
'''squeezebert/squeezebert-mnli''': {'''do_lower_case''': True},
'''squeezebert/squeezebert-mnli-headless''': {'''do_lower_case''': True},
}
class snake_case_ ( UpperCAmelCase_ ):
'''simple docstring'''
__UpperCamelCase = VOCAB_FILES_NAMES
__UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
__UpperCamelCase = PRETRAINED_INIT_CONFIGURATION
__UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCamelCase = SqueezeBertTokenizer
def __init__( self : Union[str, Any] , __lowerCamelCase : List[Any]=None , __lowerCamelCase : List[Any]=None , __lowerCamelCase : List[Any]=True , __lowerCamelCase : Tuple="[UNK]" , __lowerCamelCase : Optional[Any]="[SEP]" , __lowerCamelCase : List[str]="[PAD]" , __lowerCamelCase : List[Any]="[CLS]" , __lowerCamelCase : Tuple="[MASK]" , __lowerCamelCase : Dict=True , __lowerCamelCase : Union[str, Any]=None , **__lowerCamelCase : Tuple , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(
__lowerCamelCase , tokenizer_file=__lowerCamelCase , do_lower_case=__lowerCamelCase , unk_token=__lowerCamelCase , sep_token=__lowerCamelCase , pad_token=__lowerCamelCase , cls_token=__lowerCamelCase , mask_token=__lowerCamelCase , tokenize_chinese_chars=__lowerCamelCase , strip_accents=__lowerCamelCase , **__lowerCamelCase , )
__lowercase = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('lowercase' , __lowerCamelCase ) != do_lower_case
or normalizer_state.get('strip_accents' , __lowerCamelCase ) != strip_accents
or normalizer_state.get('handle_chinese_chars' , __lowerCamelCase ) != tokenize_chinese_chars
):
__lowercase = getattr(__lowerCamelCase , normalizer_state.pop('type' ) )
__lowercase = do_lower_case
__lowercase = strip_accents
__lowercase = tokenize_chinese_chars
__lowercase = normalizer_class(**__lowerCamelCase )
__lowercase = do_lower_case
def UpperCAmelCase ( self : List[str] , __lowerCamelCase : Tuple , __lowerCamelCase : Any=None ) -> Union[str, Any]:
'''simple docstring'''
__lowercase = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def UpperCAmelCase ( self : Optional[Any] , __lowerCamelCase : List[int] , __lowerCamelCase : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
__lowercase = [self.sep_token_id]
__lowercase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def UpperCAmelCase ( self : Optional[int] , __lowerCamelCase : str , __lowerCamelCase : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
__lowercase = self._tokenizer.model.save(__lowerCamelCase , name=__lowerCamelCase )
return tuple(__lowerCamelCase )
| 375
|
def SCREAMING_SNAKE_CASE ( snake_case , snake_case , snake_case ) -> int:
def count_of_possible_combinations(snake_case ) -> int:
if target < 0:
return 0
if target == 0:
return 1
return sum(count_of_possible_combinations(target - item ) for item in array )
return count_of_possible_combinations(snake_case )
def SCREAMING_SNAKE_CASE ( snake_case , snake_case , snake_case ) -> int:
def count_of_possible_combinations_with_dp_array(
snake_case , snake_case ) -> int:
if target < 0:
return 0
if target == 0:
return 1
if dp_array[target] != -1:
return dp_array[target]
__lowercase = sum(
count_of_possible_combinations_with_dp_array(target - item , snake_case )
for item in array )
__lowercase = answer
return answer
__lowercase = [-1] * (target + 1)
return count_of_possible_combinations_with_dp_array(snake_case , snake_case )
def SCREAMING_SNAKE_CASE ( snake_case , snake_case , snake_case ) -> int:
__lowercase = [0] * (target + 1)
__lowercase = 1
for i in range(1 , target + 1 ):
for j in range(snake_case ):
if i - array[j] >= 0:
dp_array[i] += dp_array[i - array[j]]
return dp_array[target]
if __name__ == "__main__":
import doctest
doctest.testmod()
SCREAMING_SNAKE_CASE_ : Tuple = 3
SCREAMING_SNAKE_CASE_ : Dict = 5
SCREAMING_SNAKE_CASE_ : Any = [1, 2, 5]
print(combination_sum_iv(n, array, target))
| 375
| 1
|
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ = logging.get_logger(__name__)
snake_case__ = {
'facebook/wav2vec2-base-960h': 'https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json',
# See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}
class UpperCamelCase ( lowercase__ ):
'''simple docstring'''
A_ = 'wav2vec2'
def __init__( self , A_=32 , A_=7_68 , A_=12 , A_=12 , A_=30_72 , A_="gelu" , A_=0.1 , A_=0.1 , A_=0.1 , A_=0.0 , A_=0.0 , A_=0.1 , A_=0.1 , A_=0.02 , A_=1E-5 , A_="group" , A_="gelu" , A_=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) , A_=(5, 2, 2, 2, 2, 2, 2) , A_=(10, 3, 3, 3, 3, 2, 2) , A_=False , A_=1_28 , A_=16 , A_=False , A_=True , A_=0.05 , A_=10 , A_=2 , A_=0.0 , A_=10 , A_=0 , A_=3_20 , A_=2 , A_=0.1 , A_=1_00 , A_=2_56 , A_=2_56 , A_=0.1 , A_="sum" , A_=False , A_=False , A_=2_56 , A_=(5_12, 5_12, 5_12, 5_12, 15_00) , A_=(5, 3, 3, 1, 1) , A_=(1, 2, 3, 1, 1) , A_=5_12 , A_=0 , A_=1 , A_=2 , A_=False , A_=3 , A_=2 , A_=3 , A_=None , A_=None , **A_ , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(**UpperCAmelCase__ , pad_token_id=UpperCAmelCase__ , bos_token_id=UpperCAmelCase__ , eos_token_id=UpperCAmelCase__ )
_lowerCamelCase = hidden_size
_lowerCamelCase = feat_extract_norm
_lowerCamelCase = feat_extract_activation
_lowerCamelCase = list(UpperCAmelCase__ )
_lowerCamelCase = list(UpperCAmelCase__ )
_lowerCamelCase = list(UpperCAmelCase__ )
_lowerCamelCase = conv_bias
_lowerCamelCase = num_conv_pos_embeddings
_lowerCamelCase = num_conv_pos_embedding_groups
_lowerCamelCase = len(self.conv_dim )
_lowerCamelCase = num_hidden_layers
_lowerCamelCase = intermediate_size
_lowerCamelCase = hidden_act
_lowerCamelCase = num_attention_heads
_lowerCamelCase = hidden_dropout
_lowerCamelCase = attention_dropout
_lowerCamelCase = activation_dropout
_lowerCamelCase = feat_proj_dropout
_lowerCamelCase = final_dropout
_lowerCamelCase = layerdrop
_lowerCamelCase = layer_norm_eps
_lowerCamelCase = initializer_range
_lowerCamelCase = vocab_size
_lowerCamelCase = do_stable_layer_norm
_lowerCamelCase = use_weighted_layer_sum
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
_lowerCamelCase = apply_spec_augment
_lowerCamelCase = mask_time_prob
_lowerCamelCase = mask_time_length
_lowerCamelCase = mask_time_min_masks
_lowerCamelCase = mask_feature_prob
_lowerCamelCase = mask_feature_length
_lowerCamelCase = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
_lowerCamelCase = num_codevectors_per_group
_lowerCamelCase = num_codevector_groups
_lowerCamelCase = contrastive_logits_temperature
_lowerCamelCase = feat_quantizer_dropout
_lowerCamelCase = num_negatives
_lowerCamelCase = codevector_dim
_lowerCamelCase = proj_codevector_dim
_lowerCamelCase = diversity_loss_weight
# ctc loss
_lowerCamelCase = ctc_loss_reduction
_lowerCamelCase = ctc_zero_infinity
# adapter
_lowerCamelCase = add_adapter
_lowerCamelCase = adapter_kernel_size
_lowerCamelCase = adapter_stride
_lowerCamelCase = num_adapter_layers
_lowerCamelCase = output_hidden_size or hidden_size
_lowerCamelCase = adapter_attn_dim
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
_lowerCamelCase = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
_lowerCamelCase = list(UpperCAmelCase__ )
_lowerCamelCase = list(UpperCAmelCase__ )
_lowerCamelCase = list(UpperCAmelCase__ )
_lowerCamelCase = xvector_output_dim
@property
def UpperCamelCase_ ( self ) -> str:
"""simple docstring"""
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 700
|
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_xpu,
require_torch_min_version,
require_tpu,
require_xpu,
skip,
slow,
)
from .training import RegressionDataset, RegressionModel, RegressionModelaXPU
from .scripts import test_script, test_sync, test_ops # isort: skip
| 638
| 0
|
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
__snake_case : Dict = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
__snake_case : Dict = {
'vocab_file': {
'unc-nlp/lxmert-base-uncased': 'https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt',
},
'tokenizer_file': {
'unc-nlp/lxmert-base-uncased': (
'https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json'
),
},
}
__snake_case : str = {
'unc-nlp/lxmert-base-uncased': 512,
}
__snake_case : int = {
'unc-nlp/lxmert-base-uncased': {'do_lower_case': True},
}
class lowerCamelCase ( lowercase_ ):
'''simple docstring'''
__snake_case = VOCAB_FILES_NAMES
__snake_case = PRETRAINED_VOCAB_FILES_MAP
__snake_case = PRETRAINED_INIT_CONFIGURATION
__snake_case = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__snake_case = LxmertTokenizer
def __init__( self : Optional[Any] , lowerCAmelCase_ : Optional[Any]=None , lowerCAmelCase_ : Any=None , lowerCAmelCase_ : int=True , lowerCAmelCase_ : Optional[Any]="[UNK]" , lowerCAmelCase_ : List[Any]="[SEP]" , lowerCAmelCase_ : Dict="[PAD]" , lowerCAmelCase_ : Optional[int]="[CLS]" , lowerCAmelCase_ : int="[MASK]" , lowerCAmelCase_ : int=True , lowerCAmelCase_ : Dict=None , **lowerCAmelCase_ : Optional[Any] , ) -> List[str]:
'''simple docstring'''
super().__init__(
lowerCAmelCase_ , tokenizer_file=lowerCAmelCase_ , do_lower_case=lowerCAmelCase_ , unk_token=lowerCAmelCase_ , sep_token=lowerCAmelCase_ , pad_token=lowerCAmelCase_ , cls_token=lowerCAmelCase_ , mask_token=lowerCAmelCase_ , tokenize_chinese_chars=lowerCAmelCase_ , strip_accents=lowerCAmelCase_ , **lowerCAmelCase_ , )
A__ : List[str] =json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("""lowercase""" , lowerCAmelCase_ ) != do_lower_case
or normalizer_state.get("""strip_accents""" , lowerCAmelCase_ ) != strip_accents
or normalizer_state.get("""handle_chinese_chars""" , lowerCAmelCase_ ) != tokenize_chinese_chars
):
A__ : Tuple =getattr(lowerCAmelCase_ , normalizer_state.pop("""type""" ) )
A__ : Optional[int] =do_lower_case
A__ : List[str] =strip_accents
A__ : str =tokenize_chinese_chars
A__ : int =normalizer_class(**lowerCAmelCase_ )
A__ : Dict =do_lower_case
def lowercase__ ( self : List[Any] , lowerCAmelCase_ : str , lowerCAmelCase_ : Dict=None ) -> Optional[int]:
'''simple docstring'''
A__ : int =[self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def lowercase__ ( self : Dict , lowerCAmelCase_ : List[int] , lowerCAmelCase_ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
A__ : Tuple =[self.sep_token_id]
A__ : Optional[Any] =[self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def lowercase__ ( self : List[Any] , lowerCAmelCase_ : str , lowerCAmelCase_ : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
A__ : Union[str, Any] =self._tokenizer.model.save(lowerCAmelCase_ , name=lowerCAmelCase_ )
return tuple(lowerCAmelCase_ )
| 215
|
'''simple docstring'''
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class lowerCamelCase ( lowercase_ , lowercase_ ):
'''simple docstring'''
__snake_case = 1
@register_to_config
def __init__( self : int , lowerCAmelCase_ : int = 10_00 , lowerCAmelCase_ : Optional[Union[np.ndarray, List[float]]] = None ) -> Union[str, Any]:
'''simple docstring'''
# set `betas`, `alphas`, `timesteps`
self.set_timesteps(lowerCAmelCase_ )
# standard deviation of the initial noise distribution
A__ : Union[str, Any] =1.0
# For now we only support F-PNDM, i.e. the runge-kutta method
# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
# mainly at formula (9), (12), (13) and the Algorithm 2.
A__ : str =4
# running values
A__ : Optional[int] =[]
def lowercase__ ( self : Optional[Any] , lowerCAmelCase_ : int , lowerCAmelCase_ : Union[str, torch.device] = None ) -> Tuple:
'''simple docstring'''
A__ : int =num_inference_steps
A__ : str =torch.linspace(1 , 0 , num_inference_steps + 1 )[:-1]
A__ : Optional[int] =torch.cat([steps, torch.tensor([0.0] )] )
if self.config.trained_betas is not None:
A__ : Tuple =torch.tensor(self.config.trained_betas , dtype=torch.floataa )
else:
A__ : Optional[Any] =torch.sin(steps * math.pi / 2 ) ** 2
A__ : Optional[Any] =(1.0 - self.betas**2) ** 0.5
A__ : Union[str, Any] =(torch.atana(self.betas , self.alphas ) / math.pi * 2)[:-1]
A__ : str =timesteps.to(lowerCAmelCase_ )
A__ : str =[]
def lowercase__ ( self : Optional[int] , lowerCAmelCase_ : torch.FloatTensor , lowerCAmelCase_ : int , lowerCAmelCase_ : torch.FloatTensor , lowerCAmelCase_ : bool = True , ) -> Union[SchedulerOutput, Tuple]:
'''simple docstring'''
if self.num_inference_steps is None:
raise ValueError(
"""Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler""" )
A__ : Optional[int] =(self.timesteps == timestep).nonzero().item()
A__ : List[str] =timestep_index + 1
A__ : List[Any] =sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
self.ets.append(lowerCAmelCase_ )
if len(self.ets ) == 1:
A__ : Union[str, Any] =self.ets[-1]
elif len(self.ets ) == 2:
A__ : Union[str, Any] =(3 * self.ets[-1] - self.ets[-2]) / 2
elif len(self.ets ) == 3:
A__ : int =(23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
else:
A__ : Dict =(1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])
A__ : str =self._get_prev_sample(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=lowerCAmelCase_ )
def lowercase__ ( self : Union[str, Any] , lowerCAmelCase_ : torch.FloatTensor , *lowerCAmelCase_ : Union[str, Any] , **lowerCAmelCase_ : int ) -> torch.FloatTensor:
'''simple docstring'''
return sample
def lowercase__ ( self : List[str] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : List[Any] ) -> Dict:
'''simple docstring'''
A__ : Tuple =self.alphas[timestep_index]
A__ : List[Any] =self.betas[timestep_index]
A__ : int =self.alphas[prev_timestep_index]
A__ : List[str] =self.betas[prev_timestep_index]
A__ : int =(sample - sigma * ets) / max(lowerCAmelCase_ , 1e-8 )
A__ : Dict =next_alpha * pred + ets * next_sigma
return prev_sample
def __len__( self : str ) -> Optional[int]:
'''simple docstring'''
return self.config.num_train_timesteps
| 215
| 1
|
'''simple docstring'''
import torch
def _lowercase ( ) -> List[str]:
if torch.cuda.is_available():
__A : Optional[int] = torch.cuda.device_count()
else:
__A : Any = 0
print(f"""Successfully ran on {num_gpus} GPUs""" )
if __name__ == "__main__":
main()
| 702
|
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitConfig,
ViTHybridConfig,
ViTHybridForImageClassification,
ViTHybridImageProcessor,
ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ : Tuple = logging.get_logger(__name__)
def _lowercase ( UpperCamelCase__ : Optional[Any], UpperCamelCase__ : Tuple=False ):
__A : List[str] = []
# fmt: off
# stem:
rename_keys.append(('cls_token', 'vit.embeddings.cls_token') )
rename_keys.append(('pos_embed', 'vit.embeddings.position_embeddings') )
rename_keys.append(('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight') )
rename_keys.append(('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias') )
# backbone
rename_keys.append(('patch_embed.backbone.stem.conv.weight', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight') )
rename_keys.append(('patch_embed.backbone.stem.norm.weight', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight') )
rename_keys.append(('patch_embed.backbone.stem.norm.bias', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias') )
for stage_idx in range(len(config.backbone_config.depths ) ):
for layer_idx in range(config.backbone_config.depths[stage_idx] ):
rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight""") )
rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight""") )
rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias""") )
rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight""") )
rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight""") )
rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias""") )
rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight""") )
rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight""") )
rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias""") )
rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight""") )
rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight""") )
rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias""") )
# transformer encoder
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f"""blocks.{i}.norm1.weight""", f"""vit.encoder.layer.{i}.layernorm_before.weight""") )
rename_keys.append((f"""blocks.{i}.norm1.bias""", f"""vit.encoder.layer.{i}.layernorm_before.bias""") )
rename_keys.append((f"""blocks.{i}.attn.proj.weight""", f"""vit.encoder.layer.{i}.attention.output.dense.weight""") )
rename_keys.append((f"""blocks.{i}.attn.proj.bias""", f"""vit.encoder.layer.{i}.attention.output.dense.bias""") )
rename_keys.append((f"""blocks.{i}.norm2.weight""", f"""vit.encoder.layer.{i}.layernorm_after.weight""") )
rename_keys.append((f"""blocks.{i}.norm2.bias""", f"""vit.encoder.layer.{i}.layernorm_after.bias""") )
rename_keys.append((f"""blocks.{i}.mlp.fc1.weight""", f"""vit.encoder.layer.{i}.intermediate.dense.weight""") )
rename_keys.append((f"""blocks.{i}.mlp.fc1.bias""", f"""vit.encoder.layer.{i}.intermediate.dense.bias""") )
rename_keys.append((f"""blocks.{i}.mlp.fc2.weight""", f"""vit.encoder.layer.{i}.output.dense.weight""") )
rename_keys.append((f"""blocks.{i}.mlp.fc2.bias""", f"""vit.encoder.layer.{i}.output.dense.bias""") )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
('norm.weight', 'layernorm.weight'),
('norm.bias', 'layernorm.bias'),
('pre_logits.fc.weight', 'pooler.dense.weight'),
('pre_logits.fc.bias', 'pooler.dense.bias'),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
__A : List[str] = [(pair[0], pair[1][4:]) if pair[1].startswith('vit' ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
('norm.weight', 'vit.layernorm.weight'),
('norm.bias', 'vit.layernorm.bias'),
('head.weight', 'classifier.weight'),
('head.bias', 'classifier.bias'),
] )
# fmt: on
return rename_keys
def _lowercase ( UpperCamelCase__ : Any, UpperCamelCase__ : int, UpperCamelCase__ : List[Any]=False ):
for i in range(config.num_hidden_layers ):
if base_model:
__A : Optional[Any] = ''
else:
__A : int = 'vit.'
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
__A : str = state_dict.pop(f"""blocks.{i}.attn.qkv.weight""" )
__A : str = state_dict.pop(f"""blocks.{i}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
__A : List[Any] = in_proj_weight[
: config.hidden_size, :
]
__A : List[Any] = in_proj_bias[: config.hidden_size]
__A : Optional[int] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
__A : int = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
__A : Dict = in_proj_weight[
-config.hidden_size :, :
]
__A : Optional[Any] = in_proj_bias[-config.hidden_size :]
def _lowercase ( UpperCamelCase__ : Optional[Any] ):
__A : Any = ['head.weight', 'head.bias']
for k in ignore_keys:
state_dict.pop(UpperCamelCase__, UpperCamelCase__ )
def _lowercase ( UpperCamelCase__ : Any, UpperCamelCase__ : Any, UpperCamelCase__ : Union[str, Any] ):
__A : Dict = dct.pop(UpperCamelCase__ )
__A : Optional[Any] = val
def _lowercase ( ):
__A : Tuple = 'http://images.cocodataset.org/val2017/000000039769.jpg'
__A : Optional[Any] = Image.open(requests.get(UpperCamelCase__, stream=UpperCamelCase__ ).raw )
return im
@torch.no_grad()
def _lowercase ( UpperCamelCase__ : Tuple, UpperCamelCase__ : int, UpperCamelCase__ : Any=False ):
__A : Optional[Any] = BitConfig(
global_padding='same', layer_type='bottleneck', depths=(3, 4, 9), out_features=['stage3'], embedding_dynamic_padding=UpperCamelCase__, )
__A : str = ViTHybridConfig(backbone_config=UpperCamelCase__, image_size=384, num_labels=1000 )
__A : Union[str, Any] = False
# load original model from timm
__A : List[Any] = timm.create_model(UpperCamelCase__, pretrained=UpperCamelCase__ )
timm_model.eval()
# load state_dict of original model, remove and rename some keys
__A : Optional[int] = timm_model.state_dict()
if base_model:
remove_classification_head_(UpperCamelCase__ )
__A : List[str] = create_rename_keys(UpperCamelCase__, UpperCamelCase__ )
for src, dest in rename_keys:
rename_key(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ )
read_in_q_k_v(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ )
__A : List[str] = 'huggingface/label-files'
__A : Optional[Any] = 'imagenet-1k-id2label.json'
__A : Optional[int] = json.load(open(hf_hub_download(UpperCamelCase__, UpperCamelCase__, repo_type='dataset' ), 'r' ) )
__A : Optional[int] = {int(UpperCamelCase__ ): v for k, v in idalabel.items()}
__A : List[Any] = idalabel
__A : Tuple = {v: k for k, v in idalabel.items()}
# load HuggingFace model
if vit_name[-5:] == "in21k":
__A : List[Any] = ViTHybridModel(UpperCamelCase__ ).eval()
else:
__A : int = ViTHybridForImageClassification(UpperCamelCase__ ).eval()
model.load_state_dict(UpperCamelCase__ )
# create image processor
__A : Tuple = create_transform(**resolve_data_config({}, model=UpperCamelCase__ ) )
__A : Union[str, Any] = transform.transforms
__A : str = {
'bilinear': PILImageResampling.BILINEAR,
'bicubic': PILImageResampling.BICUBIC,
'nearest': PILImageResampling.NEAREST,
}
__A : List[str] = ViTHybridImageProcessor(
do_resize=UpperCamelCase__, size={'shortest_edge': timm_transforms[0].size}, resample=pillow_resamplings[timm_transforms[0].interpolation.value], do_center_crop=UpperCamelCase__, crop_size={'height': timm_transforms[1].size[0], 'width': timm_transforms[1].size[1]}, do_normalize=UpperCamelCase__, image_mean=timm_transforms[-1].mean.tolist(), image_std=timm_transforms[-1].std.tolist(), )
__A : Optional[Any] = prepare_img()
__A : Any = transform(UpperCamelCase__ ).unsqueeze(0 )
__A : Dict = processor(UpperCamelCase__, return_tensors='pt' ).pixel_values
# verify pixel values
assert torch.allclose(UpperCamelCase__, UpperCamelCase__ )
# verify logits
with torch.no_grad():
__A : Union[str, Any] = model(UpperCamelCase__ )
__A : Dict = outputs.logits
print('Predicted class:', logits.argmax(-1 ).item() )
if base_model:
__A : Tuple = timm_model.forward_features(UpperCamelCase__ )
assert timm_pooled_output.shape == outputs.pooler_output.shape
assert torch.allclose(UpperCamelCase__, outputs.pooler_output, atol=1E-3 )
else:
__A : Any = timm_model(UpperCamelCase__ )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(UpperCamelCase__, outputs.logits, atol=1E-3 )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
Path(UpperCamelCase__ ).mkdir(exist_ok=UpperCamelCase__ )
print(f"""Saving model {vit_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(UpperCamelCase__ )
print(f"""Saving processor to {pytorch_dump_folder_path}""" )
processor.save_pretrained(UpperCamelCase__ )
if push_to_hub:
print(f"""Pushing model and processor to the hub {vit_name}""" )
model.push_to_hub(f"""ybelkada/{vit_name}""" )
processor.push_to_hub(f"""ybelkada/{vit_name}""" )
if __name__ == "__main__":
UpperCAmelCase_ : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--vit_name',
default='vit_base_r50_s16_384',
type=str,
help='Name of the hybrid ViT timm model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
parser.add_argument(
'--push_to_hub', action='store_true', help='Whether to upload the model to the HuggingFace hub.'
)
UpperCAmelCase_ : Union[str, Any] = parser.parse_args()
convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 540
| 0
|
'''simple docstring'''
from string import ascii_uppercase
_UpperCAmelCase : List[str] = {str(ord(c) - 55): c for c in ascii_uppercase}
def UpperCamelCase ( lowercase_ : int , lowercase_ : int ) -> str:
'''simple docstring'''
if isinstance(lowercase_ , lowercase_ ):
raise TypeError('''int() can\'t convert non-string with explicit base''' )
if num < 0:
raise ValueError('''parameter must be positive int''' )
if isinstance(lowercase_ , lowercase_ ):
raise TypeError('''\'str\' object cannot be interpreted as an integer''' )
if isinstance(lowercase_ , lowercase_ ):
raise TypeError('''\'float\' object cannot be interpreted as an integer''' )
if base in (0, 1):
raise ValueError('''base must be >= 2''' )
if base > 3_6:
raise ValueError('''base must be <= 36''' )
lowercase =''''''
lowercase =0
lowercase =0
while div != 1:
lowercase , lowercase =divmod(lowercase_ , lowercase_ )
if base >= 1_1 and 9 < mod < 3_6:
lowercase =ALPHABET_VALUES[str(lowercase_ )]
else:
lowercase =str(lowercase_ )
new_value += actual_value
lowercase =num // base
lowercase =div
if div == 0:
return str(new_value[::-1] )
elif div == 1:
new_value += str(lowercase_ )
return str(new_value[::-1] )
return new_value[::-1]
if __name__ == "__main__":
import doctest
doctest.testmod()
for base in range(2, 37):
for num in range(10_00):
assert int(decimal_to_any(num, base), base) == num, (
num,
base,
decimal_to_any(num, base),
int(decimal_to_any(num, base), base),
)
| 72
|
"""simple docstring"""
def _lowerCamelCase( a ):
return " ".join(
"".join(word[::-1] ) if len(a ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_long_words("""Hey wollef sroirraw"""))
| 528
| 0
|
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase ( _snake_case ): # This function is recursive
UpperCAmelCase__ : Optional[int] = len(_snake_case )
# If the array contains only one element, we return it (it's the stop condition of
# recursion)
if array_length <= 1:
return array
# Else
UpperCAmelCase__ : Union[str, Any] = array[0]
UpperCAmelCase__ : Any = False
UpperCAmelCase__ : str = 1
UpperCAmelCase__ : list[int] = []
while not is_found and i < array_length:
if array[i] < pivot:
UpperCAmelCase__ : Dict = True
UpperCAmelCase__ : Any = [element for element in array[i:] if element >= array[i]]
UpperCAmelCase__ : str = longest_subsequence(_snake_case )
if len(_snake_case ) > len(_snake_case ):
UpperCAmelCase__ : Optional[Any] = temp_array
else:
i += 1
UpperCAmelCase__ : Union[str, Any] = [element for element in array[1:] if element >= pivot]
UpperCAmelCase__ : Optional[Any] = [pivot, *longest_subsequence(_snake_case )]
if len(_snake_case ) > len(_snake_case ):
return temp_array
else:
return longest_subseq
if __name__ == "__main__":
import doctest
doctest.testmod()
| 254
|
"""simple docstring"""
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def lowerCamelCase ( _snake_case ):
def wrapper(*_snake_case ,**_snake_case ):
UpperCAmelCase__ : str = timeit.default_timer()
UpperCAmelCase__ : Dict = func(*_snake_case ,**_snake_case )
UpperCAmelCase__ : Dict = timeit.default_timer() - starttime
return delta
UpperCAmelCase__ : Dict = func.__name__
return wrapper
def lowerCamelCase ( _snake_case ,_snake_case=100 ,_snake_case=None ):
UpperCAmelCase__ : int = []
UpperCAmelCase__ : List[Any] = seq_shapes or {}
for i in range(_snake_case ):
UpperCAmelCase__ : Tuple = {}
for col_id, (k, v) in enumerate(features.items() ):
if isinstance(_snake_case ,_ArrayXD ):
UpperCAmelCase__ : Union[str, Any] = np.random.rand(*v.shape ).astype(v.dtype )
elif isinstance(_snake_case ,datasets.Value ):
if v.dtype == "string":
UpperCAmelCase__ : List[Any] = 'The small grey turtle was surprisingly fast when challenged.'
else:
UpperCAmelCase__ : List[str] = np.random.randint(10 ,size=1 ).astype(v.dtype ).item()
elif isinstance(_snake_case ,datasets.Sequence ):
while isinstance(_snake_case ,datasets.Sequence ):
UpperCAmelCase__ : str = v.feature
UpperCAmelCase__ : Optional[Any] = seq_shapes[k]
UpperCAmelCase__ : Union[str, Any] = np.random.rand(*_snake_case ).astype(v.dtype )
UpperCAmelCase__ : str = data
dummy_data.append((i, example) )
return dummy_data
def lowerCamelCase ( _snake_case ,_snake_case ,_snake_case=100 ,_snake_case=None ):
UpperCAmelCase__ : Any = generate_examples(_snake_case ,num_examples=_snake_case ,seq_shapes=_snake_case )
with ArrowWriter(features=_snake_case ,path=_snake_case ) as writer:
for key, record in dummy_data:
UpperCAmelCase__ : int = features.encode_example(_snake_case )
writer.write(_snake_case )
UpperCAmelCase__ , UpperCAmelCase__ : Union[str, Any] = writer.finalize()
if not num_final_examples == num_examples:
raise ValueError(
F'''Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.''' )
UpperCAmelCase__ : str = datasets.Dataset.from_file(filename=_snake_case ,info=datasets.DatasetInfo(features=_snake_case ) )
return dataset
| 254
| 1
|
'''simple docstring'''
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
from .text_encoder import MultilingualCLIP
lowerCAmelCase__ = logging.get_logger(__name__) # pylint: disable=invalid-name
lowerCAmelCase__ = '''
Examples:
```py
>>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline
>>> import torch
>>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior")
>>> pipe_prior.to("cuda")
>>> prompt = "red cat, 4k photo"
>>> out = pipe_prior(prompt)
>>> image_emb = out.image_embeds
>>> negative_image_emb = out.negative_image_embeds
>>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")
>>> pipe.to("cuda")
>>> image = pipe(
... prompt,
... image_embeds=image_emb,
... negative_image_embeds=negative_image_emb,
... height=768,
... width=768,
... num_inference_steps=100,
... ).images
>>> image[0].save("cat.png")
```
'''
def _A ( A__ , A__ , A__=8 ):
"""simple docstring"""
__lowercase = h // scale_factor**2
if h % scale_factor**2 != 0:
new_h += 1
__lowercase = w // scale_factor**2
if w % scale_factor**2 != 0:
new_w += 1
return new_h * scale_factor, new_w * scale_factor
class lowercase_ (lowerCamelCase__ ):
"""simple docstring"""
def __init__( self : List[Any] ,lowercase__ : MultilingualCLIP ,lowercase__ : XLMRobertaTokenizer ,lowercase__ : UNetaDConditionModel ,lowercase__ : Union[DDIMScheduler, DDPMScheduler] ,lowercase__ : VQModel ,):
super().__init__()
self.register_modules(
text_encoder=lowercase__ ,tokenizer=lowercase__ ,unet=lowercase__ ,scheduler=lowercase__ ,movq=lowercase__ ,)
__lowercase = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def SCREAMING_SNAKE_CASE ( self : Tuple ,lowercase__ : Dict ,lowercase__ : List[Any] ,lowercase__ : Dict ,lowercase__ : Union[str, Any] ,lowercase__ : int ,lowercase__ : Dict ):
if latents is None:
__lowercase = randn_tensor(lowercase__ ,generator=lowercase__ ,device=lowercase__ ,dtype=lowercase__ )
else:
if latents.shape != shape:
raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}" )
__lowercase = latents.to(lowercase__ )
__lowercase = latents * scheduler.init_noise_sigma
return latents
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ,lowercase__ : Union[str, Any] ,lowercase__ : Optional[Any] ,lowercase__ : Any ,lowercase__ : List[Any] ,lowercase__ : Tuple=None ,):
__lowercase = len(lowercase__ ) if isinstance(lowercase__ ,lowercase__ ) else 1
# get prompt text embeddings
__lowercase = self.tokenizer(
lowercase__ ,padding='''max_length''' ,truncation=lowercase__ ,max_length=7_7 ,return_attention_mask=lowercase__ ,add_special_tokens=lowercase__ ,return_tensors='''pt''' ,)
__lowercase = text_inputs.input_ids
__lowercase = self.tokenizer(lowercase__ ,padding='''longest''' ,return_tensors='''pt''' ).input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(lowercase__ ,lowercase__ ):
__lowercase = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] )
logger.warning(
'''The following part of your input was truncated because CLIP can only handle sequences up to'''
F" {self.tokenizer.model_max_length} tokens: {removed_text}" )
__lowercase = text_input_ids.to(lowercase__ )
__lowercase = text_inputs.attention_mask.to(lowercase__ )
__lowercase , __lowercase = self.text_encoder(
input_ids=lowercase__ ,attention_mask=lowercase__ )
__lowercase = prompt_embeds.repeat_interleave(lowercase__ ,dim=0 )
__lowercase = text_encoder_hidden_states.repeat_interleave(lowercase__ ,dim=0 )
__lowercase = text_mask.repeat_interleave(lowercase__ ,dim=0 )
if do_classifier_free_guidance:
__lowercase = 42
if negative_prompt is None:
__lowercase = [''''''] * batch_size
elif type(lowercase__ ) is not type(lowercase__ ):
raise TypeError(
F"`negative_prompt` should be the same type to `prompt`, but got {type(lowercase__ )} !="
F" {type(lowercase__ )}." )
elif isinstance(lowercase__ ,lowercase__ ):
__lowercase = [negative_prompt]
elif batch_size != len(lowercase__ ):
raise ValueError(
F"`negative_prompt`: {negative_prompt} has batch size {len(lowercase__ )}, but `prompt`:"
F" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
''' the batch size of `prompt`.''' )
else:
__lowercase = negative_prompt
__lowercase = self.tokenizer(
lowercase__ ,padding='''max_length''' ,max_length=7_7 ,truncation=lowercase__ ,return_attention_mask=lowercase__ ,add_special_tokens=lowercase__ ,return_tensors='''pt''' ,)
__lowercase = uncond_input.input_ids.to(lowercase__ )
__lowercase = uncond_input.attention_mask.to(lowercase__ )
__lowercase , __lowercase = self.text_encoder(
input_ids=lowercase__ ,attention_mask=lowercase__ )
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
__lowercase = negative_prompt_embeds.shape[1]
__lowercase = negative_prompt_embeds.repeat(1 ,lowercase__ )
__lowercase = negative_prompt_embeds.view(batch_size * num_images_per_prompt ,lowercase__ )
__lowercase = uncond_text_encoder_hidden_states.shape[1]
__lowercase = uncond_text_encoder_hidden_states.repeat(1 ,lowercase__ ,1 )
__lowercase = uncond_text_encoder_hidden_states.view(
batch_size * num_images_per_prompt ,lowercase__ ,-1 )
__lowercase = uncond_text_mask.repeat_interleave(lowercase__ ,dim=0 )
# done duplicates
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
__lowercase = torch.cat([negative_prompt_embeds, prompt_embeds] )
__lowercase = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states] )
__lowercase = torch.cat([uncond_text_mask, text_mask] )
return prompt_embeds, text_encoder_hidden_states, text_mask
def SCREAMING_SNAKE_CASE ( self : List[str] ,lowercase__ : List[Any]=0 ):
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError('''Please install accelerate via `pip install accelerate`''' )
__lowercase = torch.device(F"cuda:{gpu_id}" )
__lowercase = [
self.unet,
self.text_encoder,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(lowercase__ ,lowercase__ )
def SCREAMING_SNAKE_CASE ( self : Dict ,lowercase__ : Union[str, Any]=0 ):
if is_accelerate_available() and is_accelerate_version('''>=''' ,'''0.17.0.dev0''' ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.''' )
__lowercase = torch.device(F"cuda:{gpu_id}" )
if self.device.type != "cpu":
self.to('''cpu''' ,silence_dtype_warnings=lowercase__ )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
__lowercase = None
for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
__lowercase , __lowercase = cpu_offload_with_hook(lowercase__ ,lowercase__ ,prev_module_hook=lowercase__ )
if self.safety_checker is not None:
__lowercase , __lowercase = cpu_offload_with_hook(self.safety_checker ,lowercase__ ,prev_module_hook=lowercase__ )
# We'll offload the last model manually.
__lowercase = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def SCREAMING_SNAKE_CASE ( self : Dict ):
if not hasattr(self.unet ,'''_hf_hook''' ):
return self.device
for module in self.unet.modules():
if (
hasattr(lowercase__ ,'''_hf_hook''' )
and hasattr(module._hf_hook ,'''execution_device''' )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(lowercase__ )
def __call__( self : Union[str, Any] ,lowercase__ : Union[str, List[str]] ,lowercase__ : Union[torch.FloatTensor, List[torch.FloatTensor]] ,lowercase__ : Union[torch.FloatTensor, List[torch.FloatTensor]] ,lowercase__ : Optional[Union[str, List[str]]] = None ,lowercase__ : int = 5_1_2 ,lowercase__ : int = 5_1_2 ,lowercase__ : int = 1_0_0 ,lowercase__ : float = 4.0 ,lowercase__ : int = 1 ,lowercase__ : Optional[Union[torch.Generator, List[torch.Generator]]] = None ,lowercase__ : Optional[torch.FloatTensor] = None ,lowercase__ : Optional[str] = "pil" ,lowercase__ : bool = True ,):
if isinstance(lowercase__ ,lowercase__ ):
__lowercase = 1
elif isinstance(lowercase__ ,lowercase__ ):
__lowercase = len(lowercase__ )
else:
raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(lowercase__ )}" )
__lowercase = self._execution_device
__lowercase = batch_size * num_images_per_prompt
__lowercase = guidance_scale > 1.0
__lowercase , __lowercase , __lowercase = self._encode_prompt(
lowercase__ ,lowercase__ ,lowercase__ ,lowercase__ ,lowercase__ )
if isinstance(lowercase__ ,lowercase__ ):
__lowercase = torch.cat(lowercase__ ,dim=0 )
if isinstance(lowercase__ ,lowercase__ ):
__lowercase = torch.cat(lowercase__ ,dim=0 )
if do_classifier_free_guidance:
__lowercase = image_embeds.repeat_interleave(lowercase__ ,dim=0 )
__lowercase = negative_image_embeds.repeat_interleave(lowercase__ ,dim=0 )
__lowercase = torch.cat([negative_image_embeds, image_embeds] ,dim=0 ).to(
dtype=prompt_embeds.dtype ,device=lowercase__ )
self.scheduler.set_timesteps(lowercase__ ,device=lowercase__ )
__lowercase = self.scheduler.timesteps
__lowercase = self.unet.config.in_channels
__lowercase , __lowercase = get_new_h_w(lowercase__ ,lowercase__ ,self.movq_scale_factor )
# create initial latent
__lowercase = self.prepare_latents(
(batch_size, num_channels_latents, height, width) ,text_encoder_hidden_states.dtype ,lowercase__ ,lowercase__ ,lowercase__ ,self.scheduler ,)
for i, t in enumerate(self.progress_bar(lowercase__ ) ):
# expand the latents if we are doing classifier free guidance
__lowercase = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
__lowercase = {'''text_embeds''': prompt_embeds, '''image_embeds''': image_embeds}
__lowercase = self.unet(
sample=lowercase__ ,timestep=lowercase__ ,encoder_hidden_states=lowercase__ ,added_cond_kwargs=lowercase__ ,return_dict=lowercase__ ,)[0]
if do_classifier_free_guidance:
__lowercase , __lowercase = noise_pred.split(latents.shape[1] ,dim=1 )
__lowercase , __lowercase = noise_pred.chunk(2 )
__lowercase , __lowercase = variance_pred.chunk(2 )
__lowercase = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
__lowercase = torch.cat([noise_pred, variance_pred_text] ,dim=1 )
if not (
hasattr(self.scheduler.config ,'''variance_type''' )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
__lowercase , __lowercase = noise_pred.split(latents.shape[1] ,dim=1 )
# compute the previous noisy sample x_t -> x_t-1
__lowercase = self.scheduler.step(
lowercase__ ,lowercase__ ,lowercase__ ,generator=lowercase__ ,).prev_sample
# post-processing
__lowercase = self.movq.decode(lowercase__ ,force_not_quantize=lowercase__ )['''sample''']
if output_type not in ["pt", "np", "pil"]:
raise ValueError(F"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}" )
if output_type in ["np", "pil"]:
__lowercase = image * 0.5 + 0.5
__lowercase = image.clamp(0 ,1 )
__lowercase = image.cpu().permute(0 ,2 ,3 ,1 ).float().numpy()
if output_type == "pil":
__lowercase = self.numpy_to_pil(lowercase__ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=lowercase__ )
| 41
|
from __future__ import annotations
from typing import Generic, TypeVar
__a : str = TypeVar("T")
class __lowercase ( Generic[T] ):
'''simple docstring'''
def __init__( self : Any , UpperCamelCase_ : T ):
"""simple docstring"""
__A = data
__A = self
__A = 0
class __lowercase ( Generic[T] ):
'''simple docstring'''
def __init__( self : Optional[Any] ):
"""simple docstring"""
__A = {}
def lowerCAmelCase_ ( self : Union[str, Any] , UpperCamelCase_ : T ):
"""simple docstring"""
__A = DisjointSetTreeNode(UpperCamelCase_ )
def lowerCAmelCase_ ( self : List[Any] , UpperCamelCase_ : T ):
"""simple docstring"""
__A = self.map[data]
if elem_ref != elem_ref.parent:
__A = self.find_set(elem_ref.parent.data )
return elem_ref.parent
def lowerCAmelCase_ ( self : int , UpperCamelCase_ : DisjointSetTreeNode[T] , UpperCamelCase_ : DisjointSetTreeNode[T] ):
"""simple docstring"""
if nodea.rank > nodea.rank:
__A = nodea
else:
__A = nodea
if nodea.rank == nodea.rank:
nodea.rank += 1
def lowerCAmelCase_ ( self : Tuple , UpperCamelCase_ : T , UpperCamelCase_ : T ):
"""simple docstring"""
self.link(self.find_set(UpperCamelCase_ ) , self.find_set(UpperCamelCase_ ) )
class __lowercase ( Generic[T] ):
'''simple docstring'''
def __init__( self : Optional[int] ):
"""simple docstring"""
__A = {}
def lowerCAmelCase_ ( self : int , UpperCamelCase_ : T ):
"""simple docstring"""
if node not in self.connections:
__A = {}
def lowerCAmelCase_ ( self : Dict , UpperCamelCase_ : T , UpperCamelCase_ : T , UpperCamelCase_ : int ):
"""simple docstring"""
self.add_node(UpperCamelCase_ )
self.add_node(UpperCamelCase_ )
__A = weight
__A = weight
def lowerCAmelCase_ ( self : Dict ):
"""simple docstring"""
__A = []
__A = set()
for start in self.connections:
for end in self.connections[start]:
if (start, end) not in seen:
seen.add((end, start) )
edges.append((start, end, self.connections[start][end]) )
edges.sort(key=lambda UpperCamelCase_ : x[2] )
# creating the disjoint set
__A = DisjointSetTree[T]()
for node in self.connections:
disjoint_set.make_set(UpperCamelCase_ )
# MST generation
__A = 0
__A = 0
__A = GraphUndirectedWeighted[T]()
while num_edges < len(self.connections ) - 1:
__A , __A , __A = edges[index]
index += 1
__A = disjoint_set.find_set(UpperCamelCase_ )
__A = disjoint_set.find_set(UpperCamelCase_ )
if parent_u != parent_v:
num_edges += 1
graph.add_edge(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
disjoint_set.union(UpperCamelCase_ , UpperCamelCase_ )
return graph
| 637
| 0
|
from functools import reduce
A =(
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'66896648950445244523161731856403098711121722383113'
'62229893423380308135336276614282806444486645238749'
'30358907296290491560440772390713810515859307960866'
'70172427121883998797908792274921901699720888093776'
'65727333001053367881220235421809751254540594752243'
'52584907711670556013604839586446706324415722155397'
'53697817977846174064955149290862569321978468622482'
'83972241375657056057490261407972968652414535100474'
'82166370484403199890008895243450658541227588666881'
'16427171479924442928230863465674813919123162824586'
'17866458359124566529476545682848912883142607690042'
'24219022671055626321111109370544217506941658960408'
'07198403850962455444362981230987879927244284909188'
'84580156166097919133875499200524063689912560717606'
'05886116467109405077541002256983155200055935729725'
'71636269561882670428252483600823257530420752963450'
)
def snake_case_ (_a : str = N ):
return max(
# mypy cannot properly interpret reduce
int(reduce(lambda _a , _a : str(int(_a ) * int(_a ) ) , n[i : i + 1_3] ) )
for i in range(len(_a ) - 1_2 ) )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 701
|
'''simple docstring'''
def snake_case_ (_a : list[list[int]] , _a : int , _a : int , _a : list[int] ):
# 1. Validate that path exists between current and next vertices
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
# 2. Validate that next vertex is not already in path
return not any(vertex == next_ver for vertex in path )
def snake_case_ (_a : list[list[int]] , _a : list[int] , _a : int ):
# Base Case
if curr_ind == len(_a ):
# return whether path exists between current and starting vertices
return graph[path[curr_ind - 1]][path[0]] == 1
# Recursive Step
for next_ver in range(0 , len(_a ) ):
if valid_connection(_a , _a , _a , _a ):
# Insert current vertex into path as next transition
UpperCAmelCase = next_ver
# Validate created path
if util_hamilton_cycle(_a , _a , curr_ind + 1 ):
return True
# Backtrack
UpperCAmelCase = -1
return False
def snake_case_ (_a : list[list[int]] , _a : int = 0 ):
UpperCAmelCase = [-1] * (len(_a ) + 1)
# initialize start and end of path with starting index
UpperCAmelCase = UpperCAmelCase = start_index
# evaluate and if we find answer return path either return empty array
return path if util_hamilton_cycle(_a , _a , 1 ) else []
| 358
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE_ = {
'configuration_lxmert': ['LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LxmertConfig'],
'tokenization_lxmert': ['LxmertTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = ['LxmertTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = [
'LxmertEncoder',
'LxmertForPreTraining',
'LxmertForQuestionAnswering',
'LxmertModel',
'LxmertPreTrainedModel',
'LxmertVisualFeatureEncoder',
'LxmertXLayer',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = [
'TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFLxmertForPreTraining',
'TFLxmertMainLayer',
'TFLxmertModel',
'TFLxmertPreTrainedModel',
'TFLxmertVisualFeatureEncoder',
]
if TYPE_CHECKING:
from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
from .tokenization_lxmert import LxmertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_lxmert_fast import LxmertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_lxmert import (
LxmertEncoder,
LxmertForPreTraining,
LxmertForQuestionAnswering,
LxmertModel,
LxmertPreTrainedModel,
LxmertVisualFeatureEncoder,
LxmertXLayer,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_lxmert import (
TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLxmertForPreTraining,
TFLxmertMainLayer,
TFLxmertModel,
TFLxmertPreTrainedModel,
TFLxmertVisualFeatureEncoder,
)
else:
import sys
SCREAMING_SNAKE_CASE_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 34
|
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int
def snake_case_ (__A : Any , __A : List[str] ) -> Optional[Any]:
if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
# expert layer
__lowerCAmelCase : Union[str, Any] = flax_key_tuple[:-1] + ("""weight""",)
__lowerCAmelCase : Tuple = torch.permute(__A , (0, 2, 1) )
elif flax_key_tuple[-1] == "kernel" and ".".join(__A ):
# linear layer
__lowerCAmelCase : Optional[Any] = flax_key_tuple[:-1] + ("""weight""",)
__lowerCAmelCase : str = flax_tensor.T
elif flax_key_tuple[-1] in ["scale", "embedding"]:
__lowerCAmelCase : Union[str, Any] = flax_key_tuple[:-1] + ("""weight""",)
return flax_key_tuple, flax_tensor
def snake_case_ (__A : Union[str, Any] , __A : int , __A : Union[str, Any] ) -> List[str]:
if "metadata" in layer:
__lowerCAmelCase : Union[str, Any] = layer.split("""metadata""" )
__lowerCAmelCase : Tuple = """""".join(split_layer[0] )[:-1]
__lowerCAmelCase : int = [tuple(("""metadata""" + split_layer[1]).split("""/""" ) )]
elif "kvstore" in layer:
__lowerCAmelCase : int = layer.split("""kvstore""" )
__lowerCAmelCase : int = """""".join(split_layer[0] )[:-1]
__lowerCAmelCase : List[Any] = [tuple(("""kvstore""" + split_layer[1]).split("""/""" ) )]
else:
__lowerCAmelCase : List[str] = layer.split("""/""" )
__lowerCAmelCase : Tuple = """/""".join(split_layer[:-1] )
__lowerCAmelCase : Dict = (split_layer[-1],)
if "kvstore/path" in layer:
__lowerCAmelCase : Optional[Any] = f'''{switch_checkpoint_path}/{checkpoint_info[layer]}'''
elif "kvstore/driver" in layer:
__lowerCAmelCase : Dict = """file"""
else:
__lowerCAmelCase : int = checkpoint_info[layer]
return curr_real_layer_name, split_layer, content
def snake_case_ (__A : Dict , __A : str ) -> List[str]:
__lowerCAmelCase : Union[str, Any] = rename_keys(__A )
__lowerCAmelCase : Tuple = {}
for k, v in current_block.items():
__lowerCAmelCase : Optional[int] = v
__lowerCAmelCase : List[Any] = new_current_block
torch.save(__A , __A )
def snake_case_ (__A : Optional[Any] , __A : List[Any] , __A : List[str] , __A : int , __A : str = WEIGHTS_NAME ) -> List[str]:
__lowerCAmelCase : str = convert_file_size_to_int(__A )
__lowerCAmelCase : List[Any] = []
__lowerCAmelCase : Any = {}
__lowerCAmelCase : str = 0
__lowerCAmelCase : List[str] = 0
os.makedirs(__A , exist_ok=__A )
with gfile.GFile(switch_checkpoint_path + """/checkpoint""" , """rb""" ) as fp:
__lowerCAmelCase : Any = serialization.msgpack_restore(fp.read() )["""optimizer"""]["""target"""]
__lowerCAmelCase : List[Any] = flatten_dict(__A , sep="""/""" )
__lowerCAmelCase : List[Any] = {}
for layer in checkpoint_info.keys():
__lowerCAmelCase ,__lowerCAmelCase ,__lowerCAmelCase : Optional[int] = get_key_and_tensorstore_dict(
__A , __A , __A )
if curr_real_layer_name in all_layers:
__lowerCAmelCase : List[str] = content
else:
__lowerCAmelCase : Optional[int] = {split_layer[-1]: content}
for key in all_layers.keys():
# open tensorstore file
__lowerCAmelCase : Dict = ts.open(unflatten_dict(all_layers[key] ) ).result().read().result()
__lowerCAmelCase : Tuple = torch.tensor(__A )
__lowerCAmelCase : List[str] = raw_weights.numel() * dtype_byte_size(raw_weights.dtype )
# use the renaming pattern from the small conversion scripts
__lowerCAmelCase ,__lowerCAmelCase : Union[str, Any] = rename_base_flax_keys(tuple(key.split("""/""" ) ) , __A )
__lowerCAmelCase : int = """/""".join(__A )
# If this weight is going to tip up over the maximal size, we split.
if current_block_size + weight_size > max_shard_size:
__lowerCAmelCase : List[str] = os.path.join(
__A , weights_name.replace(""".bin""" , f'''-{len(__A )+1:05d}-of-???.bin''' ) )
rename_and_save_block(__A , __A )
sharded_state_dicts.append(current_block.keys() )
del current_block
__lowerCAmelCase : List[str] = {}
__lowerCAmelCase : Tuple = 0
__lowerCAmelCase : Any = raw_weights.to(getattr(__A , __A ) )
current_block_size += weight_size
total_size += weight_size
# Add the last block
__lowerCAmelCase : Tuple = os.path.join(__A , weights_name.replace(""".bin""" , f'''-{len(__A )+1:05d}-of-???.bin''' ) )
rename_and_save_block(__A , __A )
sharded_state_dicts.append(current_block.keys() )
# If we only have one shard, we return it
if len(__A ) == 1:
return {weights_name: sharded_state_dicts[0]}, None
# Otherwise, let's build the index
__lowerCAmelCase : Optional[int] = {}
__lowerCAmelCase : int = {}
for idx, shard in enumerate(__A ):
__lowerCAmelCase : Optional[int] = weights_name.replace(
""".bin""" , f'''-{idx+1:05d}-of-{len(__A ):05d}.bin''' ) # len(sharded_state_dicts):05d}
__lowerCAmelCase : Optional[int] = os.path.join(__A , weights_name.replace(""".bin""" , f'''-{idx+1:05d}-of-???.bin''' ) )
os.rename(__A , os.path.join(__A , __A ) )
__lowerCAmelCase : List[Any] = shard
for key in shard:
__lowerCAmelCase : Optional[Any] = shard_file
# Add the metadata
__lowerCAmelCase : Dict = {"""total_size""": total_size}
__lowerCAmelCase : Any = {"""metadata""": metadata, """weight_map""": weight_map}
with open(os.path.join(__A , __A ) , """w""" , encoding="""utf-8""" ) as f:
__lowerCAmelCase : Optional[Any] = json.dumps(__A , indent=2 , sort_keys=__A ) + """\n"""
f.write(__A )
return metadata, index
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--switch_t5x_checkpoint_path""",
default="""/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600""",
type=str,
required=False,
help="""Path to a directory containing a folder per layer. Follows the original Google format.""",
)
parser.add_argument("""--max_shard_size""", default="""10GB""", required=False, help="""Max shard size""")
parser.add_argument("""--dtype""", default="""bfloat16""", type=str, required=False, help="""dtype of the saved model""")
parser.add_argument(
"""--pytorch_dump_folder_path""",
default="""/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted""",
type=str,
required=False,
help="""Path to the output pytorch model.""",
)
__UpperCAmelCase = parser.parse_args()
shard_on_the_fly(
args.switch_tax_checkpoint_path,
args.pytorch_dump_folder_path,
args.max_shard_size,
args.dtype,
)
def snake_case_ () -> Union[str, Any]:
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer
__lowerCAmelCase : str = SwitchTransformersConfig.from_pretrained("""google/switch-base-8""" )
config.save_pretrained("""/home/arthur_huggingface_co/transformers/switch_converted""" )
__lowerCAmelCase : Dict = SwitchTransformersForConditionalGeneration.from_pretrained(
"""/home/arthur_huggingface_co/transformers/switch_converted""" , device_map="""auto""" )
__lowerCAmelCase : Union[str, Any] = TaTokenizer.from_pretrained("""t5-small""" )
__lowerCAmelCase : str = """A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."""
__lowerCAmelCase : Optional[Any] = tokenizer(__A , return_tensors="""pt""" ).input_ids
__lowerCAmelCase : List[Any] = model.generate(__A , decoder_start_token_id=0 )
print(tokenizer.decode(out[0] ) )
| 651
| 0
|
'''simple docstring'''
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
lowerCAmelCase : Any = argparse.ArgumentParser(
description=(
'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'
' Distillation'
)
)
parser.add_argument('--model_type', default='roberta', choices=['roberta', 'gpt2'])
parser.add_argument('--model_name', default='roberta-large', type=str)
parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_roberta_048131723.pth', type=str)
parser.add_argument('--vocab_transform', action='store_true')
lowerCAmelCase : int = parser.parse_args()
if args.model_type == "roberta":
lowerCAmelCase : int = RobertaForMaskedLM.from_pretrained(args.model_name)
lowerCAmelCase : int = 'roberta'
elif args.model_type == "gpt2":
lowerCAmelCase : Optional[int] = GPTaLMHeadModel.from_pretrained(args.model_name)
lowerCAmelCase : Optional[int] = 'transformer'
lowerCAmelCase : str = model.state_dict()
lowerCAmelCase : List[str] = {}
# Embeddings #
if args.model_type == "gpt2":
for param_name in ["wte.weight", "wpe.weight"]:
lowerCAmelCase : Any = state_dict[f"""{prefix}.{param_name}"""]
else:
for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
lowerCAmelCase : Tuple = f"""{prefix}.embeddings.{w}.weight"""
lowerCAmelCase : str = state_dict[param_name]
for w in ["weight", "bias"]:
lowerCAmelCase : List[Any] = f"""{prefix}.embeddings.LayerNorm.{w}"""
lowerCAmelCase : str = state_dict[param_name]
# Transformer Blocks #
lowerCAmelCase : Any = 0
for teacher_idx in [0, 2, 4, 7, 9, 11]:
if args.model_type == "gpt2":
for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
for w in ["weight", "bias"]:
lowerCAmelCase : int = state_dict[
f"""{prefix}.h.{teacher_idx}.{layer}.{w}"""
]
lowerCAmelCase : Union[str, Any] = state_dict[f"""{prefix}.h.{teacher_idx}.attn.bias"""]
else:
for layer in [
"attention.self.query",
"attention.self.key",
"attention.self.value",
"attention.output.dense",
"attention.output.LayerNorm",
"intermediate.dense",
"output.dense",
"output.LayerNorm",
]:
for w in ["weight", "bias"]:
lowerCAmelCase : Optional[Any] = state_dict[
f"""{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}"""
]
std_idx += 1
# Language Modeling Head ###s
if args.model_type == "roberta":
for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
lowerCAmelCase : Any = state_dict[f"""{layer}"""]
if args.vocab_transform:
for w in ["weight", "bias"]:
lowerCAmelCase : List[str] = state_dict[f"""lm_head.dense.{w}"""]
lowerCAmelCase : Any = state_dict[f"""lm_head.layer_norm.{w}"""]
elif args.model_type == "gpt2":
for w in ["weight", "bias"]:
lowerCAmelCase : Dict = state_dict[f"""{prefix}.ln_f.{w}"""]
lowerCAmelCase : Tuple = state_dict['lm_head.weight']
print(f"""N layers selected for distillation: {std_idx}""")
print(f"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")
print(f"""Save transferred checkpoint to {args.dump_checkpoint}.""")
torch.save(compressed_sd, args.dump_checkpoint)
| 719
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase : Optional[Any] = {
'configuration_rembert': ['REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RemBertConfig', 'RemBertOnnxConfig']
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase : Dict = ['RemBertTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase : List[Any] = ['RemBertTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase : Optional[Any] = [
'REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'RemBertForCausalLM',
'RemBertForMaskedLM',
'RemBertForMultipleChoice',
'RemBertForQuestionAnswering',
'RemBertForSequenceClassification',
'RemBertForTokenClassification',
'RemBertLayer',
'RemBertModel',
'RemBertPreTrainedModel',
'load_tf_weights_in_rembert',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase : Any = [
'TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFRemBertForCausalLM',
'TFRemBertForMaskedLM',
'TFRemBertForMultipleChoice',
'TFRemBertForQuestionAnswering',
'TFRemBertForSequenceClassification',
'TFRemBertForTokenClassification',
'TFRemBertLayer',
'TFRemBertModel',
'TFRemBertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_rembert import RemBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_rembert_fast import RemBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_rembert import (
REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
RemBertForCausalLM,
RemBertForMaskedLM,
RemBertForMultipleChoice,
RemBertForQuestionAnswering,
RemBertForSequenceClassification,
RemBertForTokenClassification,
RemBertLayer,
RemBertModel,
RemBertPreTrainedModel,
load_tf_weights_in_rembert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_rembert import (
TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRemBertForCausalLM,
TFRemBertForMaskedLM,
TFRemBertForMultipleChoice,
TFRemBertForQuestionAnswering,
TFRemBertForSequenceClassification,
TFRemBertForTokenClassification,
TFRemBertLayer,
TFRemBertModel,
TFRemBertPreTrainedModel,
)
else:
import sys
lowerCAmelCase : Tuple = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 432
| 0
|
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class A_ ( __lowercase ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE : torch.FloatTensor
_SCREAMING_SNAKE_CASE : Optional[torch.FloatTensor] = None
def _lowerCamelCase ( __A : List[Any] , __A : int=0.999 , __A : List[Any]="cosine" , ) -> str:
if alpha_transform_type == "cosine":
def alpha_bar_fn(__A : Dict ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(__A : str ):
return math.exp(t * -12.0 )
else:
raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}''' )
_UpperCAmelCase : str = []
for i in range(__A ):
_UpperCAmelCase : Dict = i / num_diffusion_timesteps
_UpperCAmelCase : Any = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(__A ) / alpha_bar_fn(__A ) , __A ) )
return torch.tensor(__A , dtype=torch.floataa )
class A_ ( __lowercase , __lowercase ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE : Tuple = 1
@register_to_config
def __init__( self , _A = 1000 , _A = 0.0001 , _A = 0.02 , _A = "linear" , _A = None , _A = True , _A = True , _A = 0 , _A = "epsilon" , _A = 1.0 , **_A , ) -> Union[str, Any]:
"""simple docstring"""
if kwargs.get('''set_alpha_to_one''' , _A) is not None:
_UpperCAmelCase : Optional[Any] = (
'''The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'''
)
deprecate('''set_alpha_to_one''' , '''1.0.0''' , _A , standard_warn=_A)
_UpperCAmelCase : str = kwargs['''set_alpha_to_one''']
if trained_betas is not None:
_UpperCAmelCase : Dict = torch.tensor(_A , dtype=torch.floataa)
elif beta_schedule == "linear":
_UpperCAmelCase : Dict = torch.linspace(_A , _A , _A , dtype=torch.floataa)
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
_UpperCAmelCase : Optional[int] = (
torch.linspace(beta_start**0.5 , beta_end**0.5 , _A , dtype=torch.floataa) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
_UpperCAmelCase : Optional[int] = betas_for_alpha_bar(_A)
else:
raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}''')
_UpperCAmelCase : Tuple = 1.0 - self.betas
_UpperCAmelCase : str = torch.cumprod(self.alphas , dim=0)
# At every step in inverted ddim, we are looking into the next alphas_cumprod
# For the final step, there is no next alphas_cumprod, and the index is out of bounds
# `set_alpha_to_zero` decides whether we set this parameter simply to zero
# in this case, self.step() just output the predicted noise
# or whether we use the final alpha of the "non-previous" one.
_UpperCAmelCase : Dict = torch.tensor(0.0) if set_alpha_to_zero else self.alphas_cumprod[-1]
# standard deviation of the initial noise distribution
_UpperCAmelCase : int = 1.0
# setable values
_UpperCAmelCase : Tuple = None
_UpperCAmelCase : str = torch.from_numpy(np.arange(0 , _A).copy().astype(np.intaa))
def snake_case__ ( self , _A , _A = None) -> torch.FloatTensor:
"""simple docstring"""
return sample
def snake_case__ ( self , _A , _A = None) -> int:
"""simple docstring"""
if num_inference_steps > self.config.num_train_timesteps:
raise ValueError(
f'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
f''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
f''' maximal {self.config.num_train_timesteps} timesteps.''')
_UpperCAmelCase : List[Any] = num_inference_steps
_UpperCAmelCase : List[str] = self.config.num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
_UpperCAmelCase : Tuple = (np.arange(0 , _A) * step_ratio).round().copy().astype(np.intaa)
_UpperCAmelCase : Any = torch.from_numpy(_A).to(_A)
self.timesteps += self.config.steps_offset
def snake_case__ ( self , _A , _A , _A , _A = 0.0 , _A = False , _A = None , _A = True , ) -> Union[DDIMSchedulerOutput, Tuple]:
"""simple docstring"""
_UpperCAmelCase : Any = timestep + self.config.num_train_timesteps // self.num_inference_steps
# 2. compute alphas, betas
# change original implementation to exactly match noise levels for analogous forward process
_UpperCAmelCase : str = self.alphas_cumprod[timestep]
_UpperCAmelCase : int = (
self.alphas_cumprod[prev_timestep]
if prev_timestep < self.config.num_train_timesteps
else self.final_alpha_cumprod
)
_UpperCAmelCase : Optional[int] = 1 - alpha_prod_t
# 3. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
if self.config.prediction_type == "epsilon":
_UpperCAmelCase : Dict = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
_UpperCAmelCase : List[Any] = model_output
elif self.config.prediction_type == "sample":
_UpperCAmelCase : Optional[int] = model_output
_UpperCAmelCase : Optional[int] = (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
elif self.config.prediction_type == "v_prediction":
_UpperCAmelCase : Any = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
_UpperCAmelCase : str = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
else:
raise ValueError(
f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'''
''' `v_prediction`''')
# 4. Clip or threshold "predicted x_0"
if self.config.clip_sample:
_UpperCAmelCase : int = pred_original_sample.clamp(
-self.config.clip_sample_range , self.config.clip_sample_range)
# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
_UpperCAmelCase : Union[str, Any] = (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon
# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
_UpperCAmelCase : Optional[Any] = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
if not return_dict:
return (prev_sample, pred_original_sample)
return DDIMSchedulerOutput(prev_sample=_A , pred_original_sample=_A)
def __len__( self) -> Union[str, Any]:
"""simple docstring"""
return self.config.num_train_timesteps
| 485
|
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
import torch
from transformers.modeling_outputs import BaseModelOutput
from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
@add_end_docstrings(__lowercase )
class A_ ( __lowercase ):
'''simple docstring'''
def __init__( self , **_A) -> List[Any]:
"""simple docstring"""
super().__init__(**_A)
if self.framework == "tf":
raise ValueError(f'''The {self.__class__} is only available in PyTorch.''')
requires_backends(self , '''vision''')
self.check_model_type(_A)
def __call__( self , _A , _A = None , **_A , ) -> Optional[Any]:
"""simple docstring"""
if "text_queries" in kwargs:
_UpperCAmelCase : int = kwargs.pop('''text_queries''')
if isinstance(_A , (str, Image.Image)):
_UpperCAmelCase : int = {'''image''': image, '''candidate_labels''': candidate_labels}
else:
_UpperCAmelCase : int = image
_UpperCAmelCase : Dict = super().__call__(_A , **_A)
return results
def snake_case__ ( self , **_A) -> Any:
"""simple docstring"""
_UpperCAmelCase : int = {}
if "threshold" in kwargs:
_UpperCAmelCase : List[Any] = kwargs['''threshold''']
if "top_k" in kwargs:
_UpperCAmelCase : List[str] = kwargs['''top_k''']
return {}, {}, postprocess_params
def snake_case__ ( self , _A) -> str:
"""simple docstring"""
_UpperCAmelCase : List[Any] = load_image(inputs['''image'''])
_UpperCAmelCase : Optional[Any] = inputs['''candidate_labels''']
if isinstance(_A , _A):
_UpperCAmelCase : Tuple = candidate_labels.split(''',''')
_UpperCAmelCase : Union[str, Any] = torch.tensor([[image.height, image.width]] , dtype=torch.intaa)
for i, candidate_label in enumerate(_A):
_UpperCAmelCase : List[Any] = self.tokenizer(_A , return_tensors=self.framework)
_UpperCAmelCase : str = self.image_processor(_A , return_tensors=self.framework)
yield {
"is_last": i == len(_A) - 1,
"target_size": target_size,
"candidate_label": candidate_label,
**text_inputs,
**image_features,
}
def snake_case__ ( self , _A) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = model_inputs.pop('''target_size''')
_UpperCAmelCase : List[Any] = model_inputs.pop('''candidate_label''')
_UpperCAmelCase : Optional[Any] = model_inputs.pop('''is_last''')
_UpperCAmelCase : Dict = self.model(**_A)
_UpperCAmelCase : str = {'''target_size''': target_size, '''candidate_label''': candidate_label, '''is_last''': is_last, **outputs}
return model_outputs
def snake_case__ ( self , _A , _A=0.1 , _A=None) -> int:
"""simple docstring"""
_UpperCAmelCase : List[str] = []
for model_output in model_outputs:
_UpperCAmelCase : int = model_output['''candidate_label''']
_UpperCAmelCase : Any = BaseModelOutput(_A)
_UpperCAmelCase : Optional[int] = self.image_processor.post_process_object_detection(
outputs=_A , threshold=_A , target_sizes=model_output['''target_size'''])[0]
for index in outputs["scores"].nonzero():
_UpperCAmelCase : Optional[int] = outputs['''scores'''][index].item()
_UpperCAmelCase : Dict = self._get_bounding_box(outputs['''boxes'''][index][0])
_UpperCAmelCase : Any = {'''score''': score, '''label''': label, '''box''': box}
results.append(_A)
_UpperCAmelCase : int = sorted(_A , key=lambda _A: x["score"] , reverse=_A)
if top_k:
_UpperCAmelCase : str = results[:top_k]
return results
def snake_case__ ( self , _A) -> Dict[str, int]:
"""simple docstring"""
if self.framework != "pt":
raise ValueError('''The ZeroShotObjectDetectionPipeline is only available in PyTorch.''')
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : Tuple = box.int().tolist()
_UpperCAmelCase : Optional[Any] = {
'''xmin''': xmin,
'''ymin''': ymin,
'''xmax''': xmax,
'''ymax''': ymax,
}
return bbox
| 485
| 1
|
'''simple docstring'''
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def UpperCamelCase_( _A :str , _A :str , _A :Optional[str] = None )-> str:
if version.parse(hfh.__version__ ).release < version.parse("0.11.0" ).release:
# old versions of hfh don't url-encode the file path
UpperCamelCase__ = quote(_A )
return hfh.hf_hub_url(_A , _A , repo_type="dataset" , revision=_A )
| 721
|
def UpperCamelCase_( _A :int , _A :int )-> str:
if number < 0 or shift_amount < 0:
raise ValueError("both inputs must be positive integers" )
UpperCamelCase__ = str(bin(_A ) )
binary_number += "0" * shift_amount
return binary_number
def UpperCamelCase_( _A :int , _A :int )-> str:
if number < 0 or shift_amount < 0:
raise ValueError("both inputs must be positive integers" )
UpperCamelCase__ = str(bin(_A ) )[2:]
if shift_amount >= len(_A ):
return "0b0"
UpperCamelCase__ = binary_number[: len(_A ) - shift_amount]
return "0b" + shifted_binary_number
def UpperCamelCase_( _A :int , _A :int )-> str:
if number >= 0: # Get binary representation of positive number
UpperCamelCase__ = "0" + str(bin(_A ) ).strip("-" )[2:]
else: # Get binary (2's complement) representation of negative number
UpperCamelCase__ = len(bin(_A )[3:] ) # Find 2's complement of number
UpperCamelCase__ = bin(abs(_A ) - (1 << binary_number_length) )[3:]
UpperCamelCase__ = (
"1" + "0" * (binary_number_length - len(_A )) + binary_number
)
if shift_amount >= len(_A ):
return "0b" + binary_number[0] * len(_A )
return (
"0b"
+ binary_number[0] * shift_amount
+ binary_number[: len(_A ) - shift_amount]
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 185
| 0
|
"""simple docstring"""
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch
if is_faiss_available():
import faiss
@require_faiss
class __lowerCamelCase ( A__ ):
'''simple docstring'''
def lowerCamelCase ( self : Union[str, Any] ):
lowerCAmelCase_ : Optional[Any] = tempfile.mkdtemp()
lowerCAmelCase_ : List[Any] = 8
# DPR tok
lowerCAmelCase_ : Any = [
"[UNK]",
"[CLS]",
"[SEP]",
"[PAD]",
"[MASK]",
"want",
"##want",
"##ed",
"wa",
"un",
"runn",
"##ing",
",",
"low",
"lowest",
]
lowerCAmelCase_ : Optional[int] = os.path.join(self.tmpdirname , "dpr_tokenizer" )
os.makedirs(a_ , exist_ok=a_ )
lowerCAmelCase_ : Tuple = os.path.join(a_ , DPR_VOCAB_FILES_NAMES["vocab_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )
# BART tok
lowerCAmelCase_ : Any = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
]
lowerCAmelCase_ : Tuple = dict(zip(a_ , range(len(a_ ) ) ) )
lowerCAmelCase_ : Optional[Any] = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
lowerCAmelCase_ : Optional[int] = {"unk_token": "<unk>"}
lowerCAmelCase_ : int = os.path.join(self.tmpdirname , "bart_tokenizer" )
os.makedirs(a_ , exist_ok=a_ )
lowerCAmelCase_ : Tuple = os.path.join(a_ , BART_VOCAB_FILES_NAMES["vocab_file"] )
lowerCAmelCase_ : Any = os.path.join(a_ , BART_VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as fp:
fp.write(json.dumps(a_ ) + "\n" )
with open(self.merges_file , "w" , encoding="utf-8" ) as fp:
fp.write("\n".join(a_ ) )
def lowerCamelCase ( self : Any ):
return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , "dpr_tokenizer" ) )
def lowerCamelCase ( self : Optional[Any] ):
return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , "dpr_tokenizer" ) )
def lowerCamelCase ( self : List[str] ):
return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname , "bart_tokenizer" ) )
def lowerCamelCase ( self : Dict ):
shutil.rmtree(self.tmpdirname )
def lowerCamelCase ( self : Optional[int] ):
lowerCAmelCase_ : str = Dataset.from_dict(
{
"id": ["0", "1"],
"text": ["foo", "bar"],
"title": ["Foo", "Bar"],
"embeddings": [np.ones(self.retrieval_vector_size ), 2 * np.ones(self.retrieval_vector_size )],
} )
dataset.add_faiss_index("embeddings" , string_factory="Flat" , metric_type=faiss.METRIC_INNER_PRODUCT )
return dataset
def lowerCamelCase ( self : Optional[int] ):
lowerCAmelCase_ : Optional[int] = self.get_dummy_dataset()
lowerCAmelCase_ : List[str] = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , )
with patch("transformers.models.rag.retrieval_rag.load_dataset" ) as mock_load_dataset:
lowerCAmelCase_ : Optional[Any] = dataset
lowerCAmelCase_ : int = RagRetriever(
a_ , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , )
return retriever
def lowerCamelCase ( self : Any , a_ : bool ):
lowerCAmelCase_ : Dict = self.get_dummy_dataset()
lowerCAmelCase_ : Dict = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , index_name="custom" , )
if from_disk:
lowerCAmelCase_ : Any = os.path.join(self.tmpdirname , "dataset" )
lowerCAmelCase_ : str = os.path.join(self.tmpdirname , "index.faiss" )
dataset.get_index("embeddings" ).save(os.path.join(self.tmpdirname , "index.faiss" ) )
dataset.drop_index("embeddings" )
dataset.save_to_disk(os.path.join(self.tmpdirname , "dataset" ) )
del dataset
lowerCAmelCase_ : Tuple = RagRetriever(
a_ , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , )
else:
lowerCAmelCase_ : Optional[Any] = RagRetriever(
a_ , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , index=CustomHFIndex(config.retrieval_vector_size , a_ ) , )
return retriever
def lowerCamelCase ( self : List[str] ):
lowerCAmelCase_ : Optional[int] = Dataset.from_dict(
{
"id": ["0", "1"],
"text": ["foo", "bar"],
"title": ["Foo", "Bar"],
"embeddings": [np.ones(self.retrieval_vector_size + 1 ), 2 * np.ones(self.retrieval_vector_size + 1 )],
} )
dataset.add_faiss_index("embeddings" , string_factory="Flat" , metric_type=faiss.METRIC_INNER_PRODUCT )
lowerCAmelCase_ : Dict = os.path.join(self.tmpdirname , "hf_bert_base.hnswSQ8_correct_phi_128.c_index" )
dataset.save_faiss_index("embeddings" , index_file_name + ".index.dpr" )
pickle.dump(dataset["id"] , open(index_file_name + ".index_meta.dpr" , "wb" ) )
lowerCAmelCase_ : Any = os.path.join(self.tmpdirname , "psgs_w100.tsv.pkl" )
lowerCAmelCase_ : Any = {sample["id"]: [sample["text"], sample["title"]] for sample in dataset}
pickle.dump(a_ , open(a_ , "wb" ) )
lowerCAmelCase_ : Tuple = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , index_name="legacy" , index_path=self.tmpdirname , )
lowerCAmelCase_ : Optional[int] = RagRetriever(
a_ , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() )
return retriever
def lowerCamelCase ( self : Any ):
lowerCAmelCase_ : Union[str, Any] = 1
lowerCAmelCase_ : Any = self.get_dummy_canonical_hf_index_retriever()
lowerCAmelCase_ : List[str] = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Union[str, Any] = retriever.retrieve(a_ , n_docs=a_ )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(a_ ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ["embeddings", "id", "text", "title"] )
self.assertEqual(len(doc_dicts[0]["id"] ) , a_ )
self.assertEqual(doc_dicts[0]["id"][0] , "1" ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]["id"][0] , "0" ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def lowerCamelCase ( self : List[Any] ):
lowerCAmelCase_ : Tuple = self.get_dummy_canonical_hf_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
with patch("transformers.models.rag.retrieval_rag.load_dataset" ) as mock_load_dataset:
lowerCAmelCase_ : Union[str, Any] = self.get_dummy_dataset()
retriever.save_pretrained(a_ )
lowerCAmelCase_ : List[str] = RagRetriever.from_pretrained(a_ )
self.assertIsInstance(a_ , a_ )
lowerCAmelCase_ : Any = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
lowerCAmelCase_ : str = retriever.retrieve(a_ , n_docs=1 )
self.assertTrue(out is not None )
def lowerCamelCase ( self : List[Any] ):
lowerCAmelCase_ : Dict = 1
lowerCAmelCase_ : List[Any] = self.get_dummy_custom_hf_index_retriever(from_disk=a_ )
lowerCAmelCase_ : Any = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Optional[Any] = retriever.retrieve(a_ , n_docs=a_ )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(a_ ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ["embeddings", "id", "text", "title"] )
self.assertEqual(len(doc_dicts[0]["id"] ) , a_ )
self.assertEqual(doc_dicts[0]["id"][0] , "1" ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]["id"][0] , "0" ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def lowerCamelCase ( self : Any ):
lowerCAmelCase_ : List[str] = self.get_dummy_custom_hf_index_retriever(from_disk=a_ )
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(a_ )
lowerCAmelCase_ : Union[str, Any] = RagRetriever.from_pretrained(a_ )
self.assertIsInstance(a_ , a_ )
lowerCAmelCase_ : Optional[Any] = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
lowerCAmelCase_ : Tuple = retriever.retrieve(a_ , n_docs=1 )
self.assertTrue(out is not None )
def lowerCamelCase ( self : List[str] ):
lowerCAmelCase_ : Optional[int] = 1
lowerCAmelCase_ : Optional[int] = self.get_dummy_custom_hf_index_retriever(from_disk=a_ )
lowerCAmelCase_ : int = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Tuple = retriever.retrieve(a_ , n_docs=a_ )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(a_ ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ["embeddings", "id", "text", "title"] )
self.assertEqual(len(doc_dicts[0]["id"] ) , a_ )
self.assertEqual(doc_dicts[0]["id"][0] , "1" ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]["id"][0] , "0" ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def lowerCamelCase ( self : Any ):
lowerCAmelCase_ : Optional[int] = self.get_dummy_custom_hf_index_retriever(from_disk=a_ )
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(a_ )
lowerCAmelCase_ : str = RagRetriever.from_pretrained(a_ )
self.assertIsInstance(a_ , a_ )
lowerCAmelCase_ : Optional[Any] = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
lowerCAmelCase_ : Dict = retriever.retrieve(a_ , n_docs=1 )
self.assertTrue(out is not None )
def lowerCamelCase ( self : str ):
lowerCAmelCase_ : List[str] = 1
lowerCAmelCase_ : List[str] = self.get_dummy_legacy_index_retriever()
lowerCAmelCase_ : List[Any] = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Any = retriever.retrieve(a_ , n_docs=a_ )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(a_ ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ["text", "title"] )
self.assertEqual(len(doc_dicts[0]["text"] ) , a_ )
self.assertEqual(doc_dicts[0]["text"][0] , "bar" ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]["text"][0] , "foo" ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def lowerCamelCase ( self : str ):
lowerCAmelCase_ : Dict = self.get_dummy_legacy_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(a_ )
lowerCAmelCase_ : List[Any] = RagRetriever.from_pretrained(a_ )
self.assertIsInstance(a_ , a_ )
lowerCAmelCase_ : int = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
lowerCAmelCase_ : List[str] = retriever.retrieve(a_ , n_docs=1 )
self.assertTrue(out is not None )
@require_torch
@require_tokenizers
@require_sentencepiece
def lowerCamelCase ( self : Optional[int] ):
import torch
lowerCAmelCase_ : List[Any] = 1
lowerCAmelCase_ : Dict = self.get_dummy_canonical_hf_index_retriever()
lowerCAmelCase_ : Optional[int] = [[5, 7], [10, 11]]
lowerCAmelCase_ : Dict = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
lowerCAmelCase_ : List[str] = retriever(a_ , a_ , prefix=retriever.config.generator.prefix , n_docs=a_ )
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : str = (
out["context_input_ids"],
out["context_attention_mask"],
out["retrieved_doc_embeds"],
)
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertIsInstance(a_ , a_ )
self.assertIsInstance(a_ , a_ )
self.assertIsInstance(a_ , np.ndarray )
lowerCAmelCase_ : int = retriever(
a_ , a_ , prefix=retriever.config.generator.prefix , n_docs=a_ , return_tensors="pt" , )
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Any = ( # noqa: F841
out["context_input_ids"],
out["context_attention_mask"],
out["retrieved_doc_embeds"],
out["doc_ids"],
)
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertIsInstance(a_ , torch.Tensor )
self.assertIsInstance(a_ , torch.Tensor )
self.assertIsInstance(a_ , torch.Tensor )
@require_torch
@require_tokenizers
@require_sentencepiece
def lowerCamelCase ( self : List[str] ):
lowerCAmelCase_ : Optional[int] = self.get_dpr_ctx_encoder_tokenizer()
lowerCAmelCase_ : int = 1
lowerCAmelCase_ : Tuple = self.get_dummy_custom_hf_index_retriever(from_disk=a_ )
retriever.set_ctx_encoder_tokenizer(a_ )
lowerCAmelCase_ : Tuple = [[5, 7], [10, 11]]
lowerCAmelCase_ : str = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
lowerCAmelCase_ : Tuple = retriever(a_ , a_ , prefix=retriever.config.generator.prefix , n_docs=a_ )
self.assertEqual(
len(a_ ) , 6 ) # check whether the retriever output consist of 6 attributes including tokenized docs
self.assertEqual(
all(k in out for k in ("tokenized_doc_ids", "tokenized_doc_attention_mask") ) , a_ ) # check for doc token related keys in dictionary.
| 610
|
"""simple docstring"""
import math
def __lowerCamelCase ( __UpperCamelCase ) -> int:
"""simple docstring"""
if not isinstance(__UpperCamelCase , __UpperCamelCase ):
lowerCAmelCase_ : Any = f'''Input value of [number={number}] must be an integer'''
raise TypeError(__UpperCamelCase )
if number < 1:
lowerCAmelCase_ : Any = f'''Input value of [number={number}] must be > 0'''
raise ValueError(__UpperCamelCase )
elif number == 1:
return 3
elif number == 2:
return 5
else:
lowerCAmelCase_ : Any = int(math.log(number // 3 , 2 ) ) + 2
lowerCAmelCase_ : Dict = [3, 5]
lowerCAmelCase_ : Union[str, Any] = 2
lowerCAmelCase_ : List[Any] = 3
for block in range(1 , __UpperCamelCase ):
for _ in range(__UpperCamelCase ):
proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1] )
proth_index += 1
increment *= 2
return proth_list[number - 1]
if __name__ == "__main__":
import doctest
doctest.testmod()
for number in range(11):
lowercase__ = 0
try:
lowercase__ = proth(number)
except ValueError:
print(F"""ValueError: there is no {number}th Proth number""")
continue
print(F"""The {number}th Proth number: {value}""")
| 610
| 1
|
from __future__ import annotations
from math import pi
def __lowerCAmelCase ( __snake_case , __snake_case , __snake_case ):
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if inductance < 0:
raise ValueError("Inductance cannot be negative" )
if frequency < 0:
raise ValueError("Frequency cannot be negative" )
if reactance < 0:
raise ValueError("Inductive reactance cannot be negative" )
if inductance == 0:
return {"inductance": reactance / (2 * pi * frequency)}
elif frequency == 0:
return {"frequency": reactance / (2 * pi * inductance)}
elif reactance == 0:
return {"reactance": 2 * pi * frequency * inductance}
else:
raise ValueError("Exactly one argument must be 0" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 703
|
import os
import sys
import unittest
lowerCamelCase : int = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
lowerCamelCase : Any = os.path.join(git_repo_path, '''src''', '''diffusers''')
class _UpperCamelCase (unittest.TestCase ):
def __UpperCAmelCase ( self )-> Optional[int]:
__lowerCAmelCase = find_backend(" if not is_torch_available():" )
self.assertEqual(__UpperCamelCase , "torch" )
# backend_with_underscore = find_backend(" if not is_tensorflow_text_available():")
# self.assertEqual(backend_with_underscore, "tensorflow_text")
__lowerCAmelCase = find_backend(" if not (is_torch_available() and is_transformers_available()):" )
self.assertEqual(__UpperCamelCase , "torch_and_transformers" )
# double_backend_with_underscore = find_backend(
# " if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
# )
# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")
__lowerCAmelCase = find_backend(
" if not (is_torch_available() and is_transformers_available() and is_onnx_available()):" )
self.assertEqual(__UpperCamelCase , "torch_and_transformers_and_onnx" )
def __UpperCAmelCase ( self )-> Dict:
__lowerCAmelCase = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn("torch" , __UpperCamelCase )
self.assertIn("torch_and_transformers" , __UpperCamelCase )
self.assertIn("flax_and_transformers" , __UpperCamelCase )
self.assertIn("torch_and_transformers_and_onnx" , __UpperCamelCase )
# Likewise, we can't assert on the exact content of a key
self.assertIn("UNet2DModel" , objects["torch"] )
self.assertIn("FlaxUNet2DConditionModel" , objects["flax"] )
self.assertIn("StableDiffusionPipeline" , objects["torch_and_transformers"] )
self.assertIn("FlaxStableDiffusionPipeline" , objects["flax_and_transformers"] )
self.assertIn("LMSDiscreteScheduler" , objects["torch_and_scipy"] )
self.assertIn("OnnxStableDiffusionPipeline" , objects["torch_and_transformers_and_onnx"] )
def __UpperCAmelCase ( self )-> Any:
__lowerCAmelCase = create_dummy_object("CONSTANT" , "'torch'" )
self.assertEqual(__UpperCamelCase , "\nCONSTANT = None\n" )
__lowerCAmelCase = create_dummy_object("function" , "'torch'" )
self.assertEqual(
__UpperCamelCase , "\ndef function(*args, **kwargs):\n requires_backends(function, 'torch')\n" )
__lowerCAmelCase = "\nclass FakeClass(metaclass=DummyObject):\n _backends = 'torch'\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, 'torch')\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, 'torch')\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, 'torch')\n"
__lowerCAmelCase = create_dummy_object("FakeClass" , "'torch'" )
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
def __UpperCAmelCase ( self )-> Tuple:
__lowerCAmelCase = "# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n requires_backends(function, [\"torch\"])\n\n\nclass FakeClass(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n"
__lowerCAmelCase = create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]} )
self.assertEqual(dummy_files["torch"] , __UpperCamelCase )
| 290
| 0
|
'''simple docstring'''
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
DPMSolverMultistepScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
PIL_INTERPOLATION,
randn_tensor,
)
def __A ( a_ : Optional[Any] ,a_ : int ,a_ : Optional[int] ):
if isinstance(__snake_case ,torch.Tensor ):
return image
elif isinstance(__snake_case ,PIL.Image.Image ):
lowerCAmelCase : Optional[int] = [image]
if isinstance(image[0] ,PIL.Image.Image ):
lowerCAmelCase : Any = [np.array(i.resize((w, h) ,resample=PIL_INTERPOLATION["lanczos"] ) )[None, :] for i in image]
lowerCAmelCase : List[str] = np.concatenate(__snake_case ,axis=0 )
lowerCAmelCase : Tuple = np.array(__snake_case ).astype(np.floataa ) / 2_5_5.0
lowerCAmelCase : List[str] = image.transpose(0 ,3 ,1 ,2 )
lowerCAmelCase : Tuple = 2.0 * image - 1.0
lowerCAmelCase : Tuple = torch.from_numpy(__snake_case )
elif isinstance(image[0] ,torch.Tensor ):
lowerCAmelCase : Optional[Any] = torch.cat(__snake_case ,dim=0 )
return image
def __A ( a_ : List[Any] ,a_ : Any ,a_ : Optional[int] ,a_ : Any=0.9_9_9_5 ):
if not isinstance(__snake_case ,np.ndarray ):
lowerCAmelCase : Optional[int] = True
lowerCAmelCase : Union[str, Any] = va.device
lowerCAmelCase : List[str] = va.cpu().numpy()
lowerCAmelCase : Optional[Any] = va.cpu().numpy()
lowerCAmelCase : Any = np.sum(va * va / (np.linalg.norm(__snake_case ) * np.linalg.norm(__snake_case )) )
if np.abs(__snake_case ) > DOT_THRESHOLD:
lowerCAmelCase : List[str] = (1 - t) * va + t * va
else:
lowerCAmelCase : int = np.arccos(__snake_case )
lowerCAmelCase : Optional[int] = np.sin(__snake_case )
lowerCAmelCase : Optional[Any] = theta_a * t
lowerCAmelCase : int = np.sin(__snake_case )
lowerCAmelCase : Optional[Any] = np.sin(theta_a - theta_t ) / sin_theta_a
lowerCAmelCase : Tuple = sin_theta_t / sin_theta_a
lowerCAmelCase : str = sa * va + sa * va
if inputs_are_torch:
lowerCAmelCase : Optional[int] = torch.from_numpy(__snake_case ).to(__snake_case )
return va
def __A ( a_ : Union[str, Any] ,a_ : List[str] ):
lowerCAmelCase : Any = F.normalize(__snake_case ,dim=-1 )
lowerCAmelCase : List[str] = F.normalize(__snake_case ,dim=-1 )
return (x - y).norm(dim=-1 ).div(2 ).arcsin().pow(2 ).mul(2 )
def __A ( a_ : Optional[int] ,a_ : Dict ):
for param in model.parameters():
lowerCAmelCase : List[Any] = value
class lowerCamelCase ( _A ):
def __init__( self , a_ , a_ , a_ , a_ , a_ , a_ , a_ , a_=None , a_=None , a_=None , ):
super().__init__()
self.register_modules(
vae=a_ , text_encoder=a_ , clip_model=a_ , tokenizer=a_ , unet=a_ , scheduler=a_ , feature_extractor=a_ , coca_model=a_ , coca_tokenizer=a_ , coca_transform=a_ , )
lowerCAmelCase : Union[str, Any] = (
feature_extractor.size
if isinstance(feature_extractor.size , a_ )
else feature_extractor.size["shortest_edge"]
)
lowerCAmelCase : Any = transforms.Normalize(mean=feature_extractor.image_mean , std=feature_extractor.image_std )
set_requires_grad(self.text_encoder , a_ )
set_requires_grad(self.clip_model , a_ )
def _lowerCamelCase ( self , a_ = "auto" ):
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
lowerCAmelCase : Dict = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(a_ )
def _lowerCamelCase ( self ):
self.enable_attention_slicing(a_ )
def _lowerCamelCase ( self ):
set_requires_grad(self.vae , a_ )
def _lowerCamelCase ( self ):
set_requires_grad(self.vae , a_ )
def _lowerCamelCase ( self ):
set_requires_grad(self.unet , a_ )
def _lowerCamelCase ( self ):
set_requires_grad(self.unet , a_ )
def _lowerCamelCase ( self , a_ , a_ , a_ ):
lowerCAmelCase : List[Any] = min(int(num_inference_steps * strength ) , a_ )
lowerCAmelCase : Dict = max(num_inference_steps - init_timestep , 0 )
lowerCAmelCase : Optional[Any] = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def _lowerCamelCase ( self , a_ , a_ , a_ , a_ , a_ , a_=None ):
if not isinstance(a_ , torch.Tensor ):
raise ValueError(F'''`image` has to be of type `torch.Tensor` but is {type(a_ )}''' )
lowerCAmelCase : List[str] = image.to(device=a_ , dtype=a_ )
if isinstance(a_ , a_ ):
lowerCAmelCase : Optional[Any] = [
self.vae.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(a_ )
]
lowerCAmelCase : Optional[Any] = torch.cat(a_ , dim=0 )
else:
lowerCAmelCase : List[Any] = self.vae.encode(a_ ).latent_dist.sample(a_ )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
lowerCAmelCase : List[str] = 0.18215 * init_latents
lowerCAmelCase : Optional[int] = init_latents.repeat_interleave(a_ , dim=0 )
lowerCAmelCase : str = randn_tensor(init_latents.shape , generator=a_ , device=a_ , dtype=a_ )
# get latents
lowerCAmelCase : Any = self.scheduler.add_noise(a_ , a_ , a_ )
lowerCAmelCase : Dict = init_latents
return latents
def _lowerCamelCase ( self , a_ ):
lowerCAmelCase : Optional[Any] = self.coca_transform(a_ ).unsqueeze(0 )
with torch.no_grad(), torch.cuda.amp.autocast():
lowerCAmelCase : Optional[int] = self.coca_model.generate(transformed_image.to(device=self.device , dtype=self.coca_model.dtype ) )
lowerCAmelCase : Optional[Any] = self.coca_tokenizer.decode(generated[0].cpu().numpy() )
return generated.split("<end_of_text>" )[0].replace("<start_of_text>" , "" ).rstrip(" .," )
def _lowerCamelCase ( self , a_ , a_ ):
lowerCAmelCase : int = self.feature_extractor.preprocess(a_ )
lowerCAmelCase : List[str] = torch.from_numpy(clip_image_input["pixel_values"][0] ).unsqueeze(0 ).to(self.device ).half()
lowerCAmelCase : str = self.clip_model.get_image_features(a_ )
lowerCAmelCase : Dict = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=a_ )
lowerCAmelCase : Any = image_embeddings_clip.repeat_interleave(a_ , dim=0 )
return image_embeddings_clip
@torch.enable_grad()
def _lowerCamelCase ( self , a_ , a_ , a_ , a_ , a_ , a_ , a_ , ):
lowerCAmelCase : List[str] = latents.detach().requires_grad_()
lowerCAmelCase : Tuple = self.scheduler.scale_model_input(a_ , a_ )
# predict the noise residual
lowerCAmelCase : Tuple = self.unet(a_ , a_ , encoder_hidden_states=a_ ).sample
if isinstance(self.scheduler , (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler) ):
lowerCAmelCase : List[Any] = self.scheduler.alphas_cumprod[timestep]
lowerCAmelCase : Any = 1 - alpha_prod_t
# compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
lowerCAmelCase : Any = (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5
lowerCAmelCase : Any = torch.sqrt(a_ )
lowerCAmelCase : Union[str, Any] = pred_original_sample * (fac) + latents * (1 - fac)
elif isinstance(self.scheduler , a_ ):
lowerCAmelCase : Optional[Any] = self.scheduler.sigmas[index]
lowerCAmelCase : Optional[Any] = latents - sigma * noise_pred
else:
raise ValueError(F'''scheduler type {type(self.scheduler )} not supported''' )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
lowerCAmelCase : int = 1 / 0.18215 * sample
lowerCAmelCase : Optional[int] = self.vae.decode(a_ ).sample
lowerCAmelCase : str = (image / 2 + 0.5).clamp(0 , 1 )
lowerCAmelCase : Any = transforms.Resize(self.feature_extractor_size )(a_ )
lowerCAmelCase : Optional[Any] = self.normalize(a_ ).to(latents.dtype )
lowerCAmelCase : Optional[int] = self.clip_model.get_image_features(a_ )
lowerCAmelCase : Optional[Any] = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=a_ )
lowerCAmelCase : Optional[Any] = spherical_dist_loss(a_ , a_ ).mean() * clip_guidance_scale
lowerCAmelCase : str = -torch.autograd.grad(a_ , a_ )[0]
if isinstance(self.scheduler , a_ ):
lowerCAmelCase : Dict = latents.detach() + grads * (sigma**2)
lowerCAmelCase : Optional[int] = noise_pred_original
else:
lowerCAmelCase : str = noise_pred_original - torch.sqrt(a_ ) * grads
return noise_pred, latents
@torch.no_grad()
def __call__( self , a_ , a_ , a_ = None , a_ = None , a_ = 512 , a_ = 512 , a_ = 0.6 , a_ = 50 , a_ = 7.5 , a_ = 1 , a_ = 0.0 , a_ = 100 , a_ = None , a_ = "pil" , a_ = True , a_ = 0.8 , a_ = 0.1 , a_ = 0.1 , ):
if isinstance(a_ , a_ ) and len(a_ ) != batch_size:
raise ValueError(F'''You have passed {batch_size} batch_size, but only {len(a_ )} generators.''' )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(F'''`height` and `width` have to be divisible by 8 but are {height} and {width}.''' )
if isinstance(a_ , torch.Generator ) and batch_size > 1:
lowerCAmelCase : Optional[int] = [generator] + [None] * (batch_size - 1)
lowerCAmelCase : Union[str, Any] = [
("model", self.coca_model is None),
("tokenizer", self.coca_tokenizer is None),
("transform", self.coca_transform is None),
]
lowerCAmelCase : Union[str, Any] = [x[0] for x in coca_is_none if x[1]]
lowerCAmelCase : List[Any] = ", ".join(a_ )
# generate prompts with coca model if prompt is None
if content_prompt is None:
if len(a_ ):
raise ValueError(
F'''Content prompt is None and CoCa [{coca_is_none_str}] is None.'''
F'''Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.''' )
lowerCAmelCase : List[Any] = self.get_image_description(a_ )
if style_prompt is None:
if len(a_ ):
raise ValueError(
F'''Style prompt is None and CoCa [{coca_is_none_str}] is None.'''
F''' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.''' )
lowerCAmelCase : Optional[int] = self.get_image_description(a_ )
# get prompt text embeddings for content and style
lowerCAmelCase : str = self.tokenizer(
a_ , padding="max_length" , max_length=self.tokenizer.model_max_length , truncation=a_ , return_tensors="pt" , )
lowerCAmelCase : Any = self.text_encoder(content_text_input.input_ids.to(self.device ) )[0]
lowerCAmelCase : List[Any] = self.tokenizer(
a_ , padding="max_length" , max_length=self.tokenizer.model_max_length , truncation=a_ , return_tensors="pt" , )
lowerCAmelCase : Dict = self.text_encoder(style_text_input.input_ids.to(self.device ) )[0]
lowerCAmelCase : Tuple = slerp(a_ , a_ , a_ )
# duplicate text embeddings for each generation per prompt
lowerCAmelCase : Dict = text_embeddings.repeat_interleave(a_ , dim=0 )
# set timesteps
lowerCAmelCase : Dict = "offset" in set(inspect.signature(self.scheduler.set_timesteps ).parameters.keys() )
lowerCAmelCase : Optional[int] = {}
if accepts_offset:
lowerCAmelCase : List[Any] = 1
self.scheduler.set_timesteps(a_ , **a_ )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
self.scheduler.timesteps.to(self.device )
lowerCAmelCase , lowerCAmelCase : Dict = self.get_timesteps(a_ , a_ , self.device )
lowerCAmelCase : List[str] = timesteps[:1].repeat(a_ )
# Preprocess image
lowerCAmelCase : Dict = preprocess(a_ , a_ , a_ )
lowerCAmelCase : int = self.prepare_latents(
a_ , a_ , a_ , text_embeddings.dtype , self.device , a_ )
lowerCAmelCase : int = preprocess(a_ , a_ , a_ )
lowerCAmelCase : Union[str, Any] = self.prepare_latents(
a_ , a_ , a_ , text_embeddings.dtype , self.device , a_ )
lowerCAmelCase : Optional[Any] = slerp(a_ , a_ , a_ )
if clip_guidance_scale > 0:
lowerCAmelCase : List[Any] = self.get_clip_image_embeddings(a_ , a_ )
lowerCAmelCase : Optional[Any] = self.get_clip_image_embeddings(a_ , a_ )
lowerCAmelCase : Dict = slerp(
a_ , a_ , a_ )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
lowerCAmelCase : Optional[int] = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
lowerCAmelCase : List[str] = content_text_input.input_ids.shape[-1]
lowerCAmelCase : List[Any] = self.tokenizer([""] , padding="max_length" , max_length=a_ , return_tensors="pt" )
lowerCAmelCase : Any = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt
lowerCAmelCase : str = uncond_embeddings.repeat_interleave(a_ , dim=0 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
lowerCAmelCase : Tuple = torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
lowerCAmelCase : List[Any] = (batch_size, self.unet.config.in_channels, height // 8, width // 8)
lowerCAmelCase : Union[str, Any] = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not work reproducibly on mps
lowerCAmelCase : int = torch.randn(a_ , generator=a_ , device="cpu" , dtype=a_ ).to(
self.device )
else:
lowerCAmelCase : Dict = torch.randn(a_ , generator=a_ , device=self.device , dtype=a_ )
else:
if latents.shape != latents_shape:
raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''' )
lowerCAmelCase : Union[str, Any] = latents.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
lowerCAmelCase : int = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
lowerCAmelCase : int = "eta" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
lowerCAmelCase : Optional[int] = {}
if accepts_eta:
lowerCAmelCase : int = eta
# check if the scheduler accepts generator
lowerCAmelCase : int = "generator" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
if accepts_generator:
lowerCAmelCase : Any = generator
with self.progress_bar(total=a_ ):
for i, t in enumerate(a_ ):
# expand the latents if we are doing classifier free guidance
lowerCAmelCase : Dict = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
lowerCAmelCase : Any = self.scheduler.scale_model_input(a_ , a_ )
# predict the noise residual
lowerCAmelCase : Any = self.unet(a_ , a_ , encoder_hidden_states=a_ ).sample
# perform classifier free guidance
if do_classifier_free_guidance:
lowerCAmelCase , lowerCAmelCase : Optional[int] = noise_pred.chunk(2 )
lowerCAmelCase : List[str] = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# perform clip guidance
if clip_guidance_scale > 0:
lowerCAmelCase : str = (
text_embeddings.chunk(2 )[1] if do_classifier_free_guidance else text_embeddings
)
lowerCAmelCase , lowerCAmelCase : int = self.cond_fn(
a_ , a_ , a_ , a_ , a_ , a_ , a_ , )
# compute the previous noisy sample x_t -> x_t-1
lowerCAmelCase : int = self.scheduler.step(a_ , a_ , a_ , **a_ ).prev_sample
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
lowerCAmelCase : Tuple = 1 / 0.18215 * latents
lowerCAmelCase : Any = self.vae.decode(a_ ).sample
lowerCAmelCase : Any = (image / 2 + 0.5).clamp(0 , 1 )
lowerCAmelCase : List[Any] = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
lowerCAmelCase : Tuple = self.numpy_to_pil(a_ )
if not return_dict:
return (image, None)
return StableDiffusionPipelineOutput(images=a_ , nsfw_content_detected=a_ )
| 525
|
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."""
)
| 317
| 0
|
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase__ = get_tests_dir("""fixtures/test_sentencepiece.model""")
if is_torch_available():
from transformers.models.mbart.modeling_mbart import shift_tokens_right
UpperCAmelCase__ = 2_5_0_0_0_4
UpperCAmelCase__ = 2_5_0_0_2_0
@require_sentencepiece
@require_tokenizers
class a ( lowerCAmelCase_ , unittest.TestCase ):
_snake_case : Optional[int] = MBartTokenizer
_snake_case : Dict = MBartTokenizerFast
_snake_case : int = True
_snake_case : Dict = True
def lowerCAmelCase_ ( self : str ):
super().setUp()
# We have a SentencePiece fixture for testing
_UpperCAmelCase = MBartTokenizer(__lowerCAmelCase , keep_accents=__lowerCAmelCase )
tokenizer.save_pretrained(self.tmpdirname )
def lowerCAmelCase_ ( self : Optional[Any] ):
_UpperCAmelCase = MBartTokenizer(__lowerCAmelCase , keep_accents=__lowerCAmelCase )
_UpperCAmelCase = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(__lowerCAmelCase , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__lowerCAmelCase ) , [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(
__lowerCAmelCase , [
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(__lowerCAmelCase )
self.assertListEqual(
__lowerCAmelCase , [
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]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
_UpperCAmelCase = tokenizer.convert_ids_to_tokens(__lowerCAmelCase )
self.assertListEqual(
__lowerCAmelCase , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""<unk>""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""<unk>""",
""".""",
] , )
def lowerCAmelCase_ ( self : int ):
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
_UpperCAmelCase = (self.rust_tokenizer_class, """hf-internal-testing/tiny-random-mbart""", {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
_UpperCAmelCase = self.rust_tokenizer_class.from_pretrained(__lowerCAmelCase , **__lowerCAmelCase )
_UpperCAmelCase = self.tokenizer_class.from_pretrained(__lowerCAmelCase , **__lowerCAmelCase )
_UpperCAmelCase = tempfile.mkdtemp()
_UpperCAmelCase = tokenizer_r.save_pretrained(__lowerCAmelCase )
_UpperCAmelCase = tokenizer_p.save_pretrained(__lowerCAmelCase )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files ) )
_UpperCAmelCase = tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f )
self.assertSequenceEqual(__lowerCAmelCase , __lowerCAmelCase )
# Checks everything loads correctly in the same way
_UpperCAmelCase = tokenizer_r.from_pretrained(__lowerCAmelCase )
_UpperCAmelCase = tokenizer_p.from_pretrained(__lowerCAmelCase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__lowerCAmelCase , __lowerCAmelCase ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(__lowerCAmelCase )
# Save tokenizer rust, legacy_format=True
_UpperCAmelCase = tempfile.mkdtemp()
_UpperCAmelCase = tokenizer_r.save_pretrained(__lowerCAmelCase , legacy_format=__lowerCAmelCase )
_UpperCAmelCase = tokenizer_p.save_pretrained(__lowerCAmelCase )
# Checks it save with the same files
self.assertSequenceEqual(__lowerCAmelCase , __lowerCAmelCase )
# Checks everything loads correctly in the same way
_UpperCAmelCase = tokenizer_r.from_pretrained(__lowerCAmelCase )
_UpperCAmelCase = tokenizer_p.from_pretrained(__lowerCAmelCase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__lowerCAmelCase , __lowerCAmelCase ) )
shutil.rmtree(__lowerCAmelCase )
# Save tokenizer rust, legacy_format=False
_UpperCAmelCase = tempfile.mkdtemp()
_UpperCAmelCase = tokenizer_r.save_pretrained(__lowerCAmelCase , legacy_format=__lowerCAmelCase )
_UpperCAmelCase = tokenizer_p.save_pretrained(__lowerCAmelCase )
# Checks it saved the tokenizer.json file
self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
_UpperCAmelCase = tokenizer_r.from_pretrained(__lowerCAmelCase )
_UpperCAmelCase = tokenizer_p.from_pretrained(__lowerCAmelCase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__lowerCAmelCase , __lowerCAmelCase ) )
shutil.rmtree(__lowerCAmelCase )
@require_torch
@require_sentencepiece
@require_tokenizers
class a ( unittest.TestCase ):
_snake_case : List[str] = 'facebook/mbart-large-en-ro'
_snake_case : Union[str, Any] = [
' UN Chief Says There Is No Military Solution in Syria',
' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
]
_snake_case : Any = [
'Şeful ONU declară că nu există o soluţie militară în Siria',
'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
]
_snake_case : Dict = [82_74, 12_78_73, 2_59_16, 7, 86_22, 20_71, 4_38, 6_74_85, 53, 18_78_95, 23, 5_17_12, 2, EN_CODE]
@classmethod
def lowerCAmelCase_ ( cls : Optional[Any] ):
_UpperCAmelCase = MBartTokenizer.from_pretrained(
cls.checkpoint_name , src_lang="""en_XX""" , tgt_lang="""ro_RO""" )
_UpperCAmelCase = 1
return cls
def lowerCAmelCase_ ( self : Any ):
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ar_AR"""] , 25_0001 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""en_EN"""] , 25_0004 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ro_RO"""] , 25_0020 )
def lowerCAmelCase_ ( self : Union[str, Any] ):
_UpperCAmelCase = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , __lowerCAmelCase )
def lowerCAmelCase_ ( self : Dict ):
self.assertIn(__lowerCAmelCase , self.tokenizer.all_special_ids )
_UpperCAmelCase = [RO_CODE, 884, 9019, 96, 9, 916, 8_6792, 36, 1_8743, 1_5596, 5, 2]
_UpperCAmelCase = self.tokenizer.decode(__lowerCAmelCase , skip_special_tokens=__lowerCAmelCase )
_UpperCAmelCase = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=__lowerCAmelCase )
self.assertEqual(__lowerCAmelCase , __lowerCAmelCase )
self.assertNotIn(self.tokenizer.eos_token , __lowerCAmelCase )
def lowerCAmelCase_ ( self : int ):
_UpperCAmelCase = ["""this is gunna be a long sentence """ * 20]
assert isinstance(src_text[0] , __lowerCAmelCase )
_UpperCAmelCase = 10
_UpperCAmelCase = self.tokenizer(__lowerCAmelCase , max_length=__lowerCAmelCase , truncation=__lowerCAmelCase ).input_ids[0]
self.assertEqual(ids[-2] , 2 )
self.assertEqual(ids[-1] , __lowerCAmelCase )
self.assertEqual(len(__lowerCAmelCase ) , __lowerCAmelCase )
def lowerCAmelCase_ ( self : int ):
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""] ) , [25_0026, 25_0001] )
def lowerCAmelCase_ ( self : Union[str, Any] ):
_UpperCAmelCase = tempfile.mkdtemp()
_UpperCAmelCase = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(__lowerCAmelCase )
_UpperCAmelCase = MBartTokenizer.from_pretrained(__lowerCAmelCase )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , __lowerCAmelCase )
@require_torch
def lowerCAmelCase_ ( self : Any ):
_UpperCAmelCase = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=__lowerCAmelCase , return_tensors="""pt""" )
_UpperCAmelCase = shift_tokens_right(batch["""labels"""] , self.tokenizer.pad_token_id )
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
assert batch.decoder_input_ids[1][-1] == 2
assert batch.labels[1][-2:].tolist() == [2, RO_CODE]
@require_torch
def lowerCAmelCase_ ( self : Optional[Any] ):
_UpperCAmelCase = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=__lowerCAmelCase , truncation=__lowerCAmelCase , max_length=len(self.expected_src_tokens ) , return_tensors="""pt""" , )
_UpperCAmelCase = shift_tokens_right(batch["""labels"""] , self.tokenizer.pad_token_id )
self.assertIsInstance(__lowerCAmelCase , __lowerCAmelCase )
self.assertEqual((2, 14) , batch.input_ids.shape )
self.assertEqual((2, 14) , batch.attention_mask.shape )
_UpperCAmelCase = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , __lowerCAmelCase )
self.assertEqual(2 , batch.decoder_input_ids[0, -1] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id, EN_CODE] )
def lowerCAmelCase_ ( self : Optional[int] ):
_UpperCAmelCase = self.tokenizer(self.src_text , padding=__lowerCAmelCase , truncation=__lowerCAmelCase , max_length=3 , return_tensors="""pt""" )
_UpperCAmelCase = self.tokenizer(
text_target=self.tgt_text , padding=__lowerCAmelCase , truncation=__lowerCAmelCase , max_length=10 , return_tensors="""pt""" )
_UpperCAmelCase = targets["""input_ids"""]
_UpperCAmelCase = shift_tokens_right(__lowerCAmelCase , self.tokenizer.pad_token_id )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def lowerCAmelCase_ ( self : Optional[Any] ):
_UpperCAmelCase = self.tokenizer._build_translation_inputs(
"""A test""" , return_tensors="""pt""" , src_lang="""en_XX""" , tgt_lang="""ar_AR""" )
self.assertEqual(
nested_simplify(__lowerCAmelCase ) , {
# A, test, EOS, en_XX
"""input_ids""": [[62, 3034, 2, 25_0004]],
"""attention_mask""": [[1, 1, 1, 1]],
# ar_AR
"""forced_bos_token_id""": 25_0001,
} , )
| 275
|
"""simple docstring"""
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def __UpperCAmelCase ( lowercase ,lowercase ):
"""simple docstring"""
_UpperCAmelCase = []
for part_id in partition_order:
_UpperCAmelCase = df.where(f'''SPARK_PARTITION_ID() = {part_id}''' ).collect()
for row_idx, row in enumerate(lowercase ):
expected_row_ids_and_row_dicts.append((f'''{part_id}_{row_idx}''', row.asDict()) )
return expected_row_ids_and_row_dicts
@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase ( ):
"""simple docstring"""
_UpperCAmelCase = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
_UpperCAmelCase = spark.range(1_00 ).repartition(1 )
_UpperCAmelCase = Spark(lowercase )
# The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
# that each partition can hold 2 rows.
spark_builder._repartition_df_if_needed(max_shard_size=16 )
# Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
assert spark_builder.df.rdd.getNumPartitions() == 50
@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase ( ):
"""simple docstring"""
_UpperCAmelCase = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
_UpperCAmelCase = spark.range(10 ).repartition(2 )
_UpperCAmelCase = [1, 0]
_UpperCAmelCase = _generate_iterable_examples(lowercase ,lowercase ) # Reverse the partitions.
_UpperCAmelCase = _get_expected_row_ids_and_row_dicts_for_partition_order(lowercase ,lowercase )
for i, (row_id, row_dict) in enumerate(generate_fn() ):
_UpperCAmelCase , _UpperCAmelCase = expected_row_ids_and_row_dicts[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase ( ):
"""simple docstring"""
_UpperCAmelCase = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
_UpperCAmelCase = spark.range(10 ).repartition(1 )
_UpperCAmelCase = SparkExamplesIterable(lowercase )
assert it.n_shards == 1
for i, (row_id, row_dict) in enumerate(lowercase ):
assert row_id == f'''0_{i}'''
assert row_dict == {"id": i}
@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase ( ):
"""simple docstring"""
_UpperCAmelCase = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
_UpperCAmelCase = spark.range(30 ).repartition(3 )
# Mock the generator so that shuffle reverses the partition indices.
with patch("""numpy.random.Generator""" ) as generator_mock:
_UpperCAmelCase = lambda lowercase : x.reverse()
_UpperCAmelCase = _get_expected_row_ids_and_row_dicts_for_partition_order(lowercase ,[2, 1, 0] )
_UpperCAmelCase = SparkExamplesIterable(lowercase ).shuffle_data_sources(lowercase )
assert shuffled_it.n_shards == 3
for i, (row_id, row_dict) in enumerate(lowercase ):
_UpperCAmelCase , _UpperCAmelCase = expected_row_ids_and_row_dicts[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase ( ):
"""simple docstring"""
_UpperCAmelCase = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
_UpperCAmelCase = spark.range(20 ).repartition(4 )
# Partitions 0 and 2
_UpperCAmelCase = SparkExamplesIterable(lowercase ).shard_data_sources(worker_id=0 ,num_workers=2 )
assert shard_it_a.n_shards == 2
_UpperCAmelCase = _get_expected_row_ids_and_row_dicts_for_partition_order(lowercase ,[0, 2] )
for i, (row_id, row_dict) in enumerate(lowercase ):
_UpperCAmelCase , _UpperCAmelCase = expected_row_ids_and_row_dicts_a[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
# Partitions 1 and 3
_UpperCAmelCase = SparkExamplesIterable(lowercase ).shard_data_sources(worker_id=1 ,num_workers=2 )
assert shard_it_a.n_shards == 2
_UpperCAmelCase = _get_expected_row_ids_and_row_dicts_for_partition_order(lowercase ,[1, 3] )
for i, (row_id, row_dict) in enumerate(lowercase ):
_UpperCAmelCase , _UpperCAmelCase = expected_row_ids_and_row_dicts_a[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase ( ):
"""simple docstring"""
_UpperCAmelCase = pyspark.sql.SparkSession.builder.master("""local[*]""" ).appName("""pyspark""" ).getOrCreate()
_UpperCAmelCase = spark.range(1_00 ).repartition(1 )
_UpperCAmelCase = Spark(lowercase )
# Choose a small max_shard_size for maximum partitioning.
spark_builder._repartition_df_if_needed(max_shard_size=1 )
# The new number of partitions should not be greater than the number of rows.
assert spark_builder.df.rdd.getNumPartitions() == 1_00
| 275
| 1
|
'''simple docstring'''
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
snake_case_ : List[Any] = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(), gbellmf(), gaussmf(), etc).
snake_case_ : Optional[Any] = [0, 25, 50]
snake_case_ : str = [25, 50, 75]
snake_case_ : Tuple = fuzz.membership.trimf(X, abca)
snake_case_ : Optional[Any] = fuzz.membership.trimf(X, abca)
# Compute the different operations using inbuilt functions.
snake_case_ : List[Any] = np.ones(75)
snake_case_ : List[Any] = np.zeros((75,))
# 1. Union = max(µA(x), µB(x))
snake_case_ : int = fuzz.fuzzy_or(X, young, X, middle_aged)[1]
# 2. Intersection = min(µA(x), µB(x))
snake_case_ : List[Any] = fuzz.fuzzy_and(X, young, X, middle_aged)[1]
# 3. Complement (A) = (1- min(µA(x))
snake_case_ : Any = fuzz.fuzzy_not(young)
# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
snake_case_ : Optional[Any] = fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
snake_case_ : Optional[Any] = young + middle_aged - (young * middle_aged)
# 6. Algebraic Product = (µA(x) * µB(x))
snake_case_ : Tuple = young * middle_aged
# 7. Bounded Sum = min[1,(µA(x), µB(x))]
snake_case_ : Any = fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
# 8. Bounded difference = min[0,(µA(x), µB(x))]
snake_case_ : Optional[int] = fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]
# max-min composition
# max-product composition
# Plot each set A, set B and each operation result using plot() and subplot().
from matplotlib import pyplot as plt
plt.figure()
plt.subplot(4, 3, 1)
plt.plot(X, young)
plt.title('Young')
plt.grid(True)
plt.subplot(4, 3, 2)
plt.plot(X, middle_aged)
plt.title('Middle aged')
plt.grid(True)
plt.subplot(4, 3, 3)
plt.plot(X, union)
plt.title('union')
plt.grid(True)
plt.subplot(4, 3, 4)
plt.plot(X, intersection)
plt.title('intersection')
plt.grid(True)
plt.subplot(4, 3, 5)
plt.plot(X, complement_a)
plt.title('complement_a')
plt.grid(True)
plt.subplot(4, 3, 6)
plt.plot(X, difference)
plt.title('difference a/b')
plt.grid(True)
plt.subplot(4, 3, 7)
plt.plot(X, alg_sum)
plt.title('alg_sum')
plt.grid(True)
plt.subplot(4, 3, 8)
plt.plot(X, alg_product)
plt.title('alg_product')
plt.grid(True)
plt.subplot(4, 3, 9)
plt.plot(X, bdd_sum)
plt.title('bdd_sum')
plt.grid(True)
plt.subplot(4, 3, 10)
plt.plot(X, bdd_difference)
plt.title('bdd_difference')
plt.grid(True)
plt.subplots_adjust(hspace=0.5)
plt.show()
| 195
|
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ : Optional[int] = logging.get_logger(__name__)
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
lowerCamelCase : Tuple = []
for i in range(encoder_config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(f'''encoder.deit.blocks.{i}.norm1.weight''', f'''encoder.encoder.layer.{i}.layernorm_before.weight''') )
rename_keys.append((f'''encoder.deit.blocks.{i}.norm1.bias''', f'''encoder.encoder.layer.{i}.layernorm_before.bias''') )
rename_keys.append(
(f'''encoder.deit.blocks.{i}.attn.proj.weight''', f'''encoder.encoder.layer.{i}.attention.output.dense.weight''') )
rename_keys.append(
(f'''encoder.deit.blocks.{i}.attn.proj.bias''', f'''encoder.encoder.layer.{i}.attention.output.dense.bias''') )
rename_keys.append(
(f'''encoder.deit.blocks.{i}.norm2.weight''', f'''encoder.encoder.layer.{i}.layernorm_after.weight''') )
rename_keys.append((f'''encoder.deit.blocks.{i}.norm2.bias''', f'''encoder.encoder.layer.{i}.layernorm_after.bias''') )
rename_keys.append(
(f'''encoder.deit.blocks.{i}.mlp.fc1.weight''', f'''encoder.encoder.layer.{i}.intermediate.dense.weight''') )
rename_keys.append(
(f'''encoder.deit.blocks.{i}.mlp.fc1.bias''', f'''encoder.encoder.layer.{i}.intermediate.dense.bias''') )
rename_keys.append(
(f'''encoder.deit.blocks.{i}.mlp.fc2.weight''', f'''encoder.encoder.layer.{i}.output.dense.weight''') )
rename_keys.append((f'''encoder.deit.blocks.{i}.mlp.fc2.bias''', f'''encoder.encoder.layer.{i}.output.dense.bias''') )
# cls token, position embeddings and patch embeddings of encoder
rename_keys.extend(
[
("encoder.deit.cls_token", "encoder.embeddings.cls_token"),
("encoder.deit.pos_embed", "encoder.embeddings.position_embeddings"),
("encoder.deit.patch_embed.proj.weight", "encoder.embeddings.patch_embeddings.projection.weight"),
("encoder.deit.patch_embed.proj.bias", "encoder.embeddings.patch_embeddings.projection.bias"),
("encoder.deit.norm.weight", "encoder.layernorm.weight"),
("encoder.deit.norm.bias", "encoder.layernorm.bias"),
] )
return rename_keys
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> int:
for i in range(encoder_config.num_hidden_layers ):
# queries, keys and values (only weights, no biases)
lowerCamelCase : Dict = state_dict.pop(f'''encoder.deit.blocks.{i}.attn.qkv.weight''' )
lowerCamelCase : Dict = in_proj_weight[
: encoder_config.hidden_size, :
]
lowerCamelCase : int = in_proj_weight[
encoder_config.hidden_size : encoder_config.hidden_size * 2, :
]
lowerCamelCase : int = in_proj_weight[
-encoder_config.hidden_size :, :
]
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> Optional[Any]:
lowerCamelCase : Optional[int] = dct.pop(_SCREAMING_SNAKE_CASE )
lowerCamelCase : Optional[Any] = val
def A ( _SCREAMING_SNAKE_CASE ) -> int:
if "handwritten" in checkpoint_url:
lowerCamelCase : Optional[Any] = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg" # industry
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-12.jpg" # have
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-10.jpg" # let
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg" #
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122.jpg"
elif "printed" in checkpoint_url or "stage1" in checkpoint_url:
lowerCamelCase : Optional[int] = "https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg"
lowerCamelCase : Dict = Image.open(requests.get(_SCREAMING_SNAKE_CASE ,stream=_SCREAMING_SNAKE_CASE ).raw ).convert("RGB" )
return im
@torch.no_grad()
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> Tuple:
lowerCamelCase : Optional[Any] = ViTConfig(image_size=384 ,qkv_bias=_SCREAMING_SNAKE_CASE )
lowerCamelCase : Optional[Any] = TrOCRConfig()
# size of the architecture
if "base" in checkpoint_url:
lowerCamelCase : Tuple = 768
elif "large" in checkpoint_url:
# use ViT-large encoder
lowerCamelCase : str = 1024
lowerCamelCase : Any = 4096
lowerCamelCase : str = 24
lowerCamelCase : Optional[Any] = 16
lowerCamelCase : Any = 1024
else:
raise ValueError("Should either find 'base' or 'large' in checkpoint URL" )
# the large-printed + stage1 checkpoints uses sinusoidal position embeddings, no layernorm afterwards
if "large-printed" in checkpoint_url or "stage1" in checkpoint_url:
lowerCamelCase : Tuple = False
lowerCamelCase : Union[str, Any] = "relu"
lowerCamelCase : str = 1024
lowerCamelCase : Optional[int] = True
lowerCamelCase : Any = False
lowerCamelCase : Any = False
# load HuggingFace model
lowerCamelCase : Dict = ViTModel(_SCREAMING_SNAKE_CASE ,add_pooling_layer=_SCREAMING_SNAKE_CASE )
lowerCamelCase : Any = TrOCRForCausalLM(_SCREAMING_SNAKE_CASE )
lowerCamelCase : Optional[Any] = VisionEncoderDecoderModel(encoder=_SCREAMING_SNAKE_CASE ,decoder=_SCREAMING_SNAKE_CASE )
model.eval()
# load state_dict of original model, rename some keys
lowerCamelCase : Union[str, Any] = torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE ,map_location="cpu" ,check_hash=_SCREAMING_SNAKE_CASE )["model"]
lowerCamelCase : Dict = create_rename_keys(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
for src, dest in rename_keys:
rename_key(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
read_in_q_k_v(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
# remove parameters we don't need
del state_dict["encoder.deit.head.weight"]
del state_dict["encoder.deit.head.bias"]
del state_dict["decoder.version"]
# add prefix to decoder keys
for key, val in state_dict.copy().items():
lowerCamelCase : List[str] = state_dict.pop(_SCREAMING_SNAKE_CASE )
if key.startswith("decoder" ) and "output_projection" not in key:
lowerCamelCase : Optional[int] = val
else:
lowerCamelCase : int = val
# load state dict
model.load_state_dict(_SCREAMING_SNAKE_CASE )
# Check outputs on an image
lowerCamelCase : Union[str, Any] = ViTImageProcessor(size=encoder_config.image_size )
lowerCamelCase : Any = RobertaTokenizer.from_pretrained("roberta-large" )
lowerCamelCase : Tuple = TrOCRProcessor(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
lowerCamelCase : Tuple = processor(images=prepare_img(_SCREAMING_SNAKE_CASE ) ,return_tensors="pt" ).pixel_values
# verify logits
lowerCamelCase : Any = torch.tensor([[model.config.decoder.decoder_start_token_id]] )
lowerCamelCase : int = model(pixel_values=_SCREAMING_SNAKE_CASE ,decoder_input_ids=_SCREAMING_SNAKE_CASE )
lowerCamelCase : Tuple = outputs.logits
lowerCamelCase : Any = torch.Size([1, 1, 5_0265] )
if "trocr-base-handwritten" in checkpoint_url:
lowerCamelCase : Tuple = torch.tensor(
[-1.4502, -4.6683, -0.5347, -2.9291, 9.1435, -3.0571, 8.9764, 1.7560, 8.7358, -1.5311] )
elif "trocr-large-handwritten" in checkpoint_url:
lowerCamelCase : Dict = torch.tensor(
[-2.6437, -1.3129, -2.2596, -5.3455, 6.3539, 1.7604, 5.4991, 1.4702, 5.6113, 2.0170] )
elif "trocr-base-printed" in checkpoint_url:
lowerCamelCase : Optional[Any] = torch.tensor(
[-5.6816, -5.8388, 1.1398, -6.9034, 6.8505, -2.4393, 1.2284, -1.0232, -1.9661, -3.9210] )
elif "trocr-large-printed" in checkpoint_url:
lowerCamelCase : List[Any] = torch.tensor(
[-6.0162, -7.0959, 4.4155, -5.1063, 7.0468, -3.1631, 2.6466, -0.3081, -0.8106, -1.7535] )
if "stage1" not in checkpoint_url:
assert logits.shape == expected_shape, "Shape of logits not as expected"
assert torch.allclose(logits[0, 0, :10] ,_SCREAMING_SNAKE_CASE ,atol=1e-3 ), "First elements of logits not as expected"
Path(_SCREAMING_SNAKE_CASE ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE )
print(f'''Saving model to {pytorch_dump_folder_path}''' )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
print(f'''Saving processor to {pytorch_dump_folder_path}''' )
processor.save_pretrained(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ : int = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_url',
default='https://layoutlm.blob.core.windows.net/trocr/model_zoo/fairseq/trocr-base-handwritten.pt',
type=str,
help='URL to the original PyTorch checkpoint (.pth file).',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
)
SCREAMING_SNAKE_CASE__ : List[str] = parser.parse_args()
convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 311
| 0
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__UpperCamelCase : Any = {
'configuration_perceiver': ['PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PerceiverConfig', 'PerceiverOnnxConfig'],
'tokenization_perceiver': ['PerceiverTokenizer'],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : str = ['PerceiverFeatureExtractor']
__UpperCamelCase : Any = ['PerceiverImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : str = [
'PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST',
'PerceiverForImageClassificationConvProcessing',
'PerceiverForImageClassificationFourier',
'PerceiverForImageClassificationLearned',
'PerceiverForMaskedLM',
'PerceiverForMultimodalAutoencoding',
'PerceiverForOpticalFlow',
'PerceiverForSequenceClassification',
'PerceiverLayer',
'PerceiverModel',
'PerceiverPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
from .tokenization_perceiver import PerceiverTokenizer
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_perceiver import PerceiverFeatureExtractor
from .image_processing_perceiver import PerceiverImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_perceiver import (
PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
PerceiverForImageClassificationConvProcessing,
PerceiverForImageClassificationFourier,
PerceiverForImageClassificationLearned,
PerceiverForMaskedLM,
PerceiverForMultimodalAutoencoding,
PerceiverForOpticalFlow,
PerceiverForSequenceClassification,
PerceiverLayer,
PerceiverModel,
PerceiverPreTrainedModel,
)
else:
import sys
__UpperCamelCase : Union[str, Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 458
|
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
)
logging.set_verbosity_info()
__UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
__UpperCamelCase : List[str] = {
'post_extract_proj': 'feature_projection.projection',
'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
'fc2': 'encoder.layers.*.feed_forward.output_dense',
'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',
}
__UpperCamelCase : Dict = [
'lm_head',
'quantizer.weight_proj',
'quantizer.codevectors',
'project_q',
'project_hid',
]
def _UpperCAmelCase ( UpperCAmelCase : Optional[int] , UpperCAmelCase : List[str] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Any ):
"""simple docstring"""
for attribute in key.split(""".""" ):
__lowerCamelCase : Any = getattr(UpperCAmelCase , UpperCAmelCase )
if weight_type is not None:
__lowerCamelCase : List[Any] = getattr(UpperCAmelCase , UpperCAmelCase ).shape
else:
__lowerCamelCase : Optional[int] = hf_pointer.shape
assert hf_shape == value.shape, (
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":
__lowerCamelCase : List[Any] = value
elif weight_type == "weight_g":
__lowerCamelCase : Tuple = value
elif weight_type == "weight_v":
__lowerCamelCase : List[Any] = value
elif weight_type == "bias":
__lowerCamelCase : Optional[int] = value
else:
__lowerCamelCase : Dict = value
logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" )
def _UpperCAmelCase ( UpperCAmelCase : List[Any] , UpperCAmelCase : Optional[Any] ):
"""simple docstring"""
__lowerCamelCase : Tuple = []
__lowerCamelCase : Union[str, Any] = fairseq_model.state_dict()
__lowerCamelCase : Optional[Any] = hf_model.feature_extractor
__lowerCamelCase : List[str] = hf_model.adapter
for name, value in fairseq_dict.items():
__lowerCamelCase : List[str] = False
if "conv_layers" in name:
load_conv_layer(
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , hf_model.config.feat_extract_norm == """group""" , )
__lowerCamelCase : Any = True
elif any(x in name for x in ["""adaptor""", """w2v_encoder.proj.""", """w2v_proj_ln."""] ):
load_adapter(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
__lowerCamelCase : Dict = True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
__lowerCamelCase : Union[str, Any] = True
if "*" in mapped_key:
__lowerCamelCase : int = name.split(UpperCAmelCase )[0].split(""".""" )[-2]
__lowerCamelCase : Dict = mapped_key.replace("""*""" , UpperCAmelCase )
if "weight_g" in name:
__lowerCamelCase : int = """weight_g"""
elif "weight_v" in name:
__lowerCamelCase : List[str] = """weight_v"""
elif "bias" in name:
__lowerCamelCase : str = """bias"""
elif "weight" in name:
__lowerCamelCase : List[str] = """weight"""
else:
__lowerCamelCase : int = None
set_recursively(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
continue
if not is_used:
unused_weights.append(UpperCAmelCase )
logger.warning(f"""Unused weights: {unused_weights}""" )
def _UpperCAmelCase ( UpperCAmelCase : Dict , UpperCAmelCase : Optional[int] , UpperCAmelCase : Any , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Union[str, Any] ):
"""simple docstring"""
__lowerCamelCase : List[str] = full_name.split("""conv_layers.""" )[-1]
__lowerCamelCase : Tuple = name.split(""".""" )
__lowerCamelCase : Tuple = int(items[0] )
__lowerCamelCase : str = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
)
__lowerCamelCase : Dict = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
)
__lowerCamelCase : List[str] = 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:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
" found."
)
__lowerCamelCase : Any = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
)
__lowerCamelCase : str = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(UpperCAmelCase )
def _UpperCAmelCase ( UpperCAmelCase : str , UpperCAmelCase : int , UpperCAmelCase : List[Any] , UpperCAmelCase : List[Any] ):
"""simple docstring"""
__lowerCamelCase : Union[str, Any] = full_name.split("""adaptor.""" )[-1]
__lowerCamelCase : Any = name.split(""".""" )
if items[1].isdigit():
__lowerCamelCase : Dict = int(items[1] )
else:
__lowerCamelCase : List[str] = None
if "adaptor" not in full_name:
if "proj_ln" in full_name:
# has to be layer norm
if "bias" in name:
assert (
value.shape == adapter.proj_layer_norm.bias.data.shape
), f"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found."""
__lowerCamelCase : str = value
logger.info(f"""Adapter proj layer norm bias was initialized from {full_name}.""" )
if "weight" in name:
assert (
value.shape == adapter.proj_layer_norm.weight.data.shape
), f"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found."""
__lowerCamelCase : str = value
else:
# has to be projection layer
if "bias" in name:
assert (
value.shape == adapter.proj.bias.data.shape
), f"""{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found."""
__lowerCamelCase : Optional[int] = value
logger.info(f"""Adapter proj layer bias was initialized from {full_name}.""" )
if "weight" in name:
assert (
value.shape == adapter.proj.weight.data.shape
), f"""{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found."""
__lowerCamelCase : List[str] = value
logger.info(f"""Adapter proj layer weight was initialized from {full_name}.""" )
elif isinstance(UpperCAmelCase , UpperCAmelCase ):
if "bias" in name:
assert (
value.shape == adapter.layers[layer_id].conv.bias.data.shape
), f"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found."""
__lowerCamelCase : int = value
logger.info(f"""Adapter layer {layer_id} bias was initialized from {full_name}.""" )
elif "weight" in name:
assert (
value.shape == adapter.layers[layer_id].conv.weight.data.shape
), f"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found."""
__lowerCamelCase : Tuple = value
logger.info(f"""Adapter layer {layer_id} bias was initialized from {full_name}.""" )
else:
unused_weights.append(UpperCAmelCase )
def _UpperCAmelCase ( UpperCAmelCase : List[Any] ):
"""simple docstring"""
__lowerCamelCase , __lowerCamelCase : Tuple = emb.weight.shape
__lowerCamelCase : Any = nn.Linear(UpperCAmelCase , UpperCAmelCase , bias=UpperCAmelCase )
__lowerCamelCase : List[Any] = emb.weight.data
return lin_layer
@torch.no_grad()
def _UpperCAmelCase ( UpperCAmelCase : Optional[Any] , UpperCAmelCase : List[str] , UpperCAmelCase : List[Any] , UpperCAmelCase : str , UpperCAmelCase : int , UpperCAmelCase : Dict , UpperCAmelCase : Tuple , UpperCAmelCase : Optional[Any] , UpperCAmelCase : int , UpperCAmelCase : int , UpperCAmelCase : Optional[Any] , ):
"""simple docstring"""
__lowerCamelCase : int = WavaVecaConfig.from_pretrained(
UpperCAmelCase , add_adapter=UpperCAmelCase , adapter_stride=UpperCAmelCase , adapter_kernel_size=UpperCAmelCase , use_auth_token=UpperCAmelCase , output_hidden_size=UpperCAmelCase , )
__lowerCamelCase : Optional[int] = MBartConfig.from_pretrained(UpperCAmelCase )
# load model
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase : Dict = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={
"""config_yaml""": config_yaml_path,
"""data""": """/""".join(dict_path.split("""/""" )[:-1] ),
"""w2v_path""": checkpoint_path,
"""load_pretrained_decoder_from""": None,
} , )
__lowerCamelCase : Union[str, Any] = model[0].eval()
# load feature extractor
__lowerCamelCase : Any = WavaVecaFeatureExtractor.from_pretrained(UpperCAmelCase , use_auth_token=UpperCAmelCase )
# set weights for wav2vec2 encoder
__lowerCamelCase : Tuple = WavaVecaModel(UpperCAmelCase )
recursively_load_weights_wavaveca(model.encoder , UpperCAmelCase )
# load decoder weights
__lowerCamelCase : Dict = MBartForCausalLM(UpperCAmelCase )
__lowerCamelCase , __lowerCamelCase : int = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() , strict=UpperCAmelCase )
logger.warning(f"""The following keys are missing when loading the decoder weights: {missing_keys}""" )
logger.warning(f"""The following keys are unexpected when loading the decoder weights: {unexpected_keys}""" )
__lowerCamelCase : List[Any] = SpeechEncoderDecoderModel(encoder=UpperCAmelCase , decoder=UpperCAmelCase )
__lowerCamelCase : Union[str, Any] = False
__lowerCamelCase : List[str] = MBartaaTokenizer(UpperCAmelCase )
tokenizer.save_pretrained(UpperCAmelCase )
__lowerCamelCase : int = hf_wavavec.config.to_dict()
__lowerCamelCase : str = tokenizer.pad_token_id
__lowerCamelCase : Optional[Any] = tokenizer.bos_token_id
__lowerCamelCase : Dict = tokenizer.eos_token_id
__lowerCamelCase : Tuple = """mbart50"""
__lowerCamelCase : List[str] = """wav2vec2"""
__lowerCamelCase : List[str] = tokenizer.eos_token_id
__lowerCamelCase : Optional[int] = 250_004
__lowerCamelCase : Dict = tokenizer.eos_token_id
__lowerCamelCase : Optional[Any] = SpeechEncoderDecoderConfig.from_dict(UpperCAmelCase )
hf_wavavec.save_pretrained(UpperCAmelCase )
feature_extractor.save_pretrained(UpperCAmelCase )
if __name__ == "__main__":
__UpperCamelCase : Dict = 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_yaml_path', default=None, type=str, help='Path to yaml file of fine-tuned model')
parser.add_argument(
'--encoder_config_path',
default='facebook/wav2vec2-xls-r-1b',
type=str,
help='Path to hf encoder wav2vec2 checkpoint config',
)
parser.add_argument(
'--decoder_config_path',
default='facebook/mbart-large-50-one-to-many-mmt',
type=str,
help='Path to hf decoder checkpoint config',
)
parser.add_argument('--add_adapter', default=True, type=bool, help='whethere to add model adapter layers')
parser.add_argument('--adapter_stride', default=2, type=int, help='stride of adapter layers')
parser.add_argument('--adapter_kernel_size', default=3, type=int, help='kernel size of adapter layers')
parser.add_argument('--encoder_output_dim', default=1024, type=int, help='encoder output dim')
parser.add_argument('--start_token_id', default=250004, type=int, help='`decoder_start_token_id` of model config')
__UpperCamelCase : int = parser.parse_args()
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.dict_path,
args.config_yaml_path,
encoder_config_path=args.encoder_config_path,
decoder_config_path=args.decoder_config_path,
add_adapter=args.add_adapter,
adapter_kernel_size=args.adapter_kernel_size,
adapter_stride=args.adapter_stride,
decoder_start_token_id=args.start_token_id,
encoder_output_dim=args.encoder_output_dim,
)
| 458
| 1
|
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {'''vocab_file''': '''vocab.txt''', '''emoji_file''': '''emoji.json'''}
__UpperCAmelCase = {
'''vocab_file''': {
'''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt''',
},
'''emoji_file''': {
'''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json''',
},
}
__UpperCAmelCase = {
'''abeja/gpt-neox-japanese-2.7b''': 2_048,
}
def UpperCamelCase ( snake_case__ : int , snake_case__ : int ) -> Dict:
with open(snake_case_ , 'r' , encoding='utf-8' ) as f:
UpperCamelCase : List[Any] = json.loads(f.read() )
UpperCamelCase : Optional[int] = collections.OrderedDict()
UpperCamelCase : Dict = collections.OrderedDict()
UpperCamelCase : List[Any] = collections.OrderedDict()
with open(snake_case_ , 'r' , encoding='utf-8' ) as f:
UpperCamelCase : Optional[Any] = f.readlines()
UpperCamelCase : List[str] = [[t.rstrip('\n' )] if (t == ''',''' or ''',''' not in t) else t.rstrip('\n' ).split(',' ) for t in token]
for idx, b in enumerate(snake_case_ ):
UpperCamelCase : List[str] = b
UpperCamelCase : List[Any] = idx
for wd in b:
UpperCamelCase : Optional[int] = idx
return vocab, raw_vocab, ids_to_tokens, emoji
class lowerCAmelCase_ ( SCREAMING_SNAKE_CASE__ ):
UpperCAmelCase__ : List[str] = VOCAB_FILES_NAMES
UpperCAmelCase__ : Tuple = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase__ : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase__ : Union[str, Any] = ["input_ids", "attention_mask"]
def __init__( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_="<|endoftext|>", SCREAMING_SNAKE_CASE_="<|endoftext|>", SCREAMING_SNAKE_CASE_="<|startoftext|>", SCREAMING_SNAKE_CASE_="<|endoftext|>", SCREAMING_SNAKE_CASE_=False, **SCREAMING_SNAKE_CASE_, ) -> Any:
super().__init__(
unk_token=__lowerCamelCase, pad_token=__lowerCamelCase, bos_token=__lowerCamelCase, eos_token=__lowerCamelCase, do_clean_text=__lowerCamelCase, **__lowerCamelCase, )
if not os.path.isfile(__lowerCamelCase ):
raise ValueError(
F"""Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"""
' model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`' )
if not os.path.isfile(__lowerCamelCase ):
raise ValueError(
F"""Can't find a emoji file at path '{emoji_file}'. To load the emoji information from a Google"""
' pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`' )
UpperCamelCase : Optional[Any] = do_clean_text
UpperCamelCase : Dict = load_vocab_and_emoji(__lowerCamelCase, __lowerCamelCase )
UpperCamelCase : Optional[int] = SubWordJapaneseTokenizer(
vocab=self.vocab, ids_to_tokens=self.ids_to_tokens, emoji=self.emoji )
@property
def snake_case_ ( self ) -> List[Any]:
return len(self.raw_vocab )
def snake_case_ ( self ) -> int:
return dict(self.raw_vocab, **self.added_tokens_encoder )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_ ) -> List[str]:
return self.subword_tokenizer.tokenize(__lowerCamelCase, clean=self.do_clean_text )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
return self.vocab.get(__lowerCamelCase, self.vocab.get(self.unk_token ) )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
return self.subword_tokenizer.convert_id_to_token(__lowerCamelCase )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
UpperCamelCase : Optional[Any] = ''''''.join(__lowerCamelCase ).strip()
return out_string
def snake_case_ ( self, SCREAMING_SNAKE_CASE_ ) -> Dict:
UpperCamelCase : Union[str, Any] = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(__lowerCamelCase, add_special_tokens=__lowerCamelCase ) + [self.eos_token_id] )
if len(__lowerCamelCase ) > self.model_max_length:
UpperCamelCase : Any = input_ids[-self.model_max_length :]
return input_ids
def snake_case_ ( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ = None ) -> Dict:
UpperCamelCase : Any = 0
if os.path.isdir(__lowerCamelCase ):
UpperCamelCase : List[str] = os.path.join(
__lowerCamelCase, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
UpperCamelCase : Optional[int] = os.path.join(
__lowerCamelCase, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['emoji_file'] )
else:
UpperCamelCase : Optional[int] = (
(filename_prefix + '''-''' if filename_prefix else '''''') + save_directory + VOCAB_FILES_NAMES['''vocab_file''']
)
UpperCamelCase : List[str] = (
(filename_prefix + '''-''' if filename_prefix else '''''') + save_directory + VOCAB_FILES_NAMES['''emoji_file''']
)
with open(__lowerCamelCase, 'w', encoding='utf-8' ) as writer:
for token_index, token in self.ids_to_tokens.items():
if index != token_index:
logger.warning(
F"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
' Please check that the vocabulary is not corrupted!' )
UpperCamelCase : List[Any] = token_index
writer.write(','.join(__lowerCamelCase ) + '\n' )
index += 1
with open(__lowerCamelCase, 'w', encoding='utf-8' ) as writer:
json.dump(self.emoji, __lowerCamelCase )
return vocab_file, emoji_file
class lowerCAmelCase_ ( SCREAMING_SNAKE_CASE__ ):
def __init__( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
UpperCamelCase : Optional[int] = vocab # same as swe
UpperCamelCase : Tuple = ids_to_tokens # same as bpe
UpperCamelCase : Union[str, Any] = emoji
UpperCamelCase : List[Any] = np.max([len(__lowerCamelCase ) for w in self.vocab.keys()] )
UpperCamelCase : Tuple = re.compile(R'(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)' )
UpperCamelCase : Optional[int] = re.compile(R'[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*' )
UpperCamelCase : Tuple = re.compile(R'[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}' )
UpperCamelCase : Dict = re.compile(
R'([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*' )
UpperCamelCase : Optional[Any] = re.compile(
R'(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*' )
UpperCamelCase : Dict = re.compile(
R'((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*' )
UpperCamelCase : List[str] = '''─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿'''
UpperCamelCase : Optional[int] = '''▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟'''
UpperCamelCase : List[Any] = str.maketrans({k: '<BLOCK>' for k in keisen + blocks} )
def __len__( self ) -> List[str]:
return len(self.ids_to_tokens )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_ ) -> str:
UpperCamelCase : int = self.content_repattera.sub('<URL>', __lowerCamelCase )
UpperCamelCase : int = self.content_repattera.sub('<EMAIL>', __lowerCamelCase )
UpperCamelCase : List[str] = self.content_repattera.sub('<TEL>', __lowerCamelCase )
UpperCamelCase : Dict = self.content_repattera.sub('<DATE>', __lowerCamelCase )
UpperCamelCase : int = self.content_repattera.sub('<DATE>', __lowerCamelCase )
UpperCamelCase : Union[str, Any] = self.content_repattera.sub('<PRICE>', __lowerCamelCase )
UpperCamelCase : List[str] = content.translate(self.content_transa )
while "<BLOCK><BLOCK>" in content:
UpperCamelCase : Tuple = content.replace('<BLOCK><BLOCK>', '<BLOCK>' )
return content
def snake_case_ ( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_=False ) -> int:
UpperCamelCase : Tuple = text.replace(' ', '<SP>' )
UpperCamelCase : Optional[Any] = text.replace(' ', '<SP>' )
UpperCamelCase : Union[str, Any] = text.replace('\r\n', '<BR>' )
UpperCamelCase : int = text.replace('\n', '<BR>' )
UpperCamelCase : Optional[Any] = text.replace('\r', '<BR>' )
UpperCamelCase : Dict = text.replace('\t', '<TAB>' )
UpperCamelCase : List[str] = text.replace('—', 'ー' )
UpperCamelCase : Dict = text.replace('−', 'ー' )
for k, v in self.emoji["emoji"].items():
if k in text:
UpperCamelCase : str = text.replace(__lowerCamelCase, __lowerCamelCase )
if clean:
UpperCamelCase : Any = self.clean_text(__lowerCamelCase )
def check_simbol(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Union[str, Any] = x.encode()
if len(__lowerCamelCase ) == 1 and len(__lowerCamelCase ) == 2:
UpperCamelCase : Tuple = (int(e[0] ) << 8) + int(e[1] )
if (
(c >= 0XC2A1 and c <= 0XC2BF)
or (c >= 0XC780 and c <= 0XC783)
or (c >= 0XCAB9 and c <= 0XCBBF)
or (c >= 0XCC80 and c <= 0XCDA2)
):
return True
return False
def checkuae(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = x.encode()
if len(__lowerCamelCase ) == 1 and len(__lowerCamelCase ) == 3:
UpperCamelCase : Dict = (int(e[0] ) << 16) + (int(e[1] ) << 8) + int(e[2] )
if c >= 0XE28080 and c <= 0XE2B07F:
return True
return False
UpperCamelCase : str = 0
UpperCamelCase : Any = []
while pos < len(__lowerCamelCase ):
UpperCamelCase : List[str] = min(len(__lowerCamelCase ), pos + self.maxlen + 1 ) if text[pos] == '''<''' else pos + 3
UpperCamelCase : int = [] # (token_id, token, pos)
for e in range(__lowerCamelCase, __lowerCamelCase, -1 ):
UpperCamelCase : Tuple = text[pos:e]
if wd in self.vocab:
if wd[0] == "<" and len(__lowerCamelCase ) > 2:
UpperCamelCase : Dict = [(self.vocab[wd], wd, e)]
break
else:
candidates.append((self.vocab[wd], wd, e) )
if len(__lowerCamelCase ) > 0:
# the smallest token_id is adopted
UpperCamelCase : Optional[int] = sorted(__lowerCamelCase, key=lambda SCREAMING_SNAKE_CASE_ : x[0] )[0]
result.append(__lowerCamelCase )
UpperCamelCase : Optional[Any] = e
else:
UpperCamelCase : Optional[int] = pos + 1
UpperCamelCase : Any = text[pos:end]
if check_simbol(__lowerCamelCase ):
result.append('<KIGOU>' )
elif checkuae(__lowerCamelCase ):
result.append('<U2000U2BFF>' )
else:
for i in wd.encode('utf-8' ):
result.append('<|byte%d|>' % i )
UpperCamelCase : Dict = end
return result
def snake_case_ ( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_="\n" ) -> Optional[Any]:
UpperCamelCase : List[Any] = []
UpperCamelCase : Optional[Any] = []
UpperCamelCase : Dict = self.ids_to_tokens[index][0]
if word[:6] == "<|byte" and word[-2:] == "|>":
byte_tokens.append(int(word[6:-2] ) )
else:
if len(__lowerCamelCase ) > 0:
words.append(bytearray(__lowerCamelCase ).decode('utf-8', errors='replace' ) )
UpperCamelCase : int = []
if word[:7] == "<|emoji" and word[-2:] == "|>":
words.append(self.emoji['emoji_inv'][word] )
elif word == "<SP>":
words.append(' ' )
elif word == "<BR>":
words.append(__lowerCamelCase )
elif word == "<TAB>":
words.append('\t' )
elif word == "<BLOCK>":
words.append('▀' )
elif word == "<KIGOU>":
words.append('ǀ' )
elif word == "<U2000U2BFF>":
words.append('‖' )
else:
words.append(__lowerCamelCase )
if len(__lowerCamelCase ) > 0:
words.append(bytearray(__lowerCamelCase ).decode('utf-8', errors='replace' ) )
UpperCamelCase : Dict = ''''''.join(__lowerCamelCase )
return text
| 40
|
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
class __snake_case ( unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase__( self ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def UpperCamelCase__( self ):
'''simple docstring'''
torch.manual_seed(0 )
__A : List[Any] = UNetaDModel(
sample_size=(32, 64) , in_channels=1 , out_channels=1 , layers_per_block=2 , block_out_channels=(128, 128) , down_block_types=('''AttnDownBlock2D''', '''DownBlock2D''') , up_block_types=('''UpBlock2D''', '''AttnUpBlock2D''') , )
return model
@property
def UpperCamelCase__( self ):
'''simple docstring'''
torch.manual_seed(0 )
__A : str = UNetaDConditionModel(
sample_size=(64, 32) , in_channels=1 , out_channels=1 , layers_per_block=2 , block_out_channels=(128, 128) , down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''') , up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''') , cross_attention_dim=10 , )
return model
@property
def UpperCamelCase__( self ):
'''simple docstring'''
torch.manual_seed(0 )
__A : int = AutoencoderKL(
sample_size=(128, 64) , in_channels=1 , out_channels=1 , latent_channels=1 , layers_per_block=2 , block_out_channels=(128, 128) , down_block_types=('''DownEncoderBlock2D''', '''DownEncoderBlock2D''') , up_block_types=('''UpDecoderBlock2D''', '''UpDecoderBlock2D''') , )
__A : int = UNetaDModel(
sample_size=(64, 32) , in_channels=1 , out_channels=1 , layers_per_block=2 , block_out_channels=(128, 128) , down_block_types=('''AttnDownBlock2D''', '''DownBlock2D''') , up_block_types=('''UpBlock2D''', '''AttnUpBlock2D''') , )
return vqvae, unet
@slow
def UpperCamelCase__( self ):
'''simple docstring'''
__A : str = '''cpu''' # ensure determinism for the device-dependent torch.Generator
__A : Union[str, Any] = Mel(
x_res=self.dummy_unet.config.sample_size[1] , y_res=self.dummy_unet.config.sample_size[0] , )
__A : List[Any] = DDPMScheduler()
__A : Optional[Any] = AudioDiffusionPipeline(vqvae=__lowerCamelCase , unet=self.dummy_unet , mel=__lowerCamelCase , scheduler=__lowerCamelCase )
__A : Tuple = pipe.to(__lowerCamelCase )
pipe.set_progress_bar_config(disable=__lowerCamelCase )
__A : Tuple = torch.Generator(device=__lowerCamelCase ).manual_seed(42 )
__A : List[str] = pipe(generator=__lowerCamelCase , steps=4 )
__A : Union[str, Any] = output.audios[0]
__A : str = output.images[0]
__A : List[Any] = torch.Generator(device=__lowerCamelCase ).manual_seed(42 )
__A : Any = pipe(generator=__lowerCamelCase , steps=4 , return_dict=__lowerCamelCase )
__A : int = output[0][0]
assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
assert (
image.height == self.dummy_unet.config.sample_size[0]
and image.width == self.dummy_unet.config.sample_size[1]
)
__A : Tuple = np.frombuffer(image.tobytes() , dtype='''uint8''' )[:10]
__A : str = np.frombuffer(image_from_tuple.tobytes() , dtype='''uint8''' )[:10]
__A : Any = np.array([69, 255, 255, 255, 0, 0, 77, 181, 12, 127] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() == 0
__A : int = Mel(
x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1] , y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0] , )
__A : Any = DDIMScheduler()
__A : Optional[Any] = self.dummy_vqvae_and_unet
__A : int = AudioDiffusionPipeline(
vqvae=self.dummy_vqvae_and_unet[0] , unet=dummy_vqvae_and_unet[1] , mel=__lowerCamelCase , scheduler=__lowerCamelCase )
__A : Any = pipe.to(__lowerCamelCase )
pipe.set_progress_bar_config(disable=__lowerCamelCase )
np.random.seed(0 )
__A : str = np.random.uniform(-1 , 1 , ((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,) )
__A : Union[str, Any] = torch.Generator(device=__lowerCamelCase ).manual_seed(42 )
__A : int = pipe(raw_audio=__lowerCamelCase , generator=__lowerCamelCase , start_step=5 , steps=10 )
__A : Any = output.images[0]
assert (
image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
)
__A : str = np.frombuffer(image.tobytes() , dtype='''uint8''' )[:10]
__A : Optional[Any] = np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
__A : Union[str, Any] = self.dummy_unet_condition
__A : List[str] = AudioDiffusionPipeline(
vqvae=self.dummy_vqvae_and_unet[0] , unet=__lowerCamelCase , mel=__lowerCamelCase , scheduler=__lowerCamelCase )
__A : Dict = pipe.to(__lowerCamelCase )
pipe.set_progress_bar_config(disable=__lowerCamelCase )
np.random.seed(0 )
__A : int = torch.rand((1, 1, 10) )
__A : Optional[Any] = pipe(generator=__lowerCamelCase , encoding=__lowerCamelCase )
__A : str = output.images[0]
__A : Optional[Any] = np.frombuffer(image.tobytes() , dtype='''uint8''' )[:10]
__A : Tuple = np.array([107, 103, 120, 127, 142, 122, 113, 122, 97, 111] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
@slow
@require_torch_gpu
class __snake_case ( unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase__( self ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase__( self ):
'''simple docstring'''
__A : int = torch_device
__A : List[Any] = DiffusionPipeline.from_pretrained('''teticio/audio-diffusion-ddim-256''' )
__A : List[Any] = pipe.to(__lowerCamelCase )
pipe.set_progress_bar_config(disable=__lowerCamelCase )
__A : Dict = torch.Generator(device=__lowerCamelCase ).manual_seed(42 )
__A : str = pipe(generator=__lowerCamelCase )
__A : Dict = output.audios[0]
__A : List[Any] = output.images[0]
assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
__A : Optional[int] = np.frombuffer(image.tobytes() , dtype='''uint8''' )[:10]
__A : Optional[Any] = np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
| 177
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__lowercase : Union[str, Any] = {
'''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : str = ['''BloomTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : List[str] = [
'''BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''BloomForCausalLM''',
'''BloomModel''',
'''BloomPreTrainedModel''',
'''BloomForSequenceClassification''',
'''BloomForTokenClassification''',
'''BloomForQuestionAnswering''',
]
if TYPE_CHECKING:
from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bloom_fast import BloomTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bloom import (
BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
BloomForCausalLM,
BloomForQuestionAnswering,
BloomForSequenceClassification,
BloomForTokenClassification,
BloomModel,
BloomPreTrainedModel,
)
else:
import sys
__lowercase : int = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 716
|
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import CLIPSegProcessor, ViTImageProcessor
@require_vision
class _A ( unittest.TestCase ):
"""simple docstring"""
def lowercase ( self : Optional[int] ) -> List[str]:
__snake_case = tempfile.mkdtemp()
# fmt: off
__snake_case = ['''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''lo''', '''l</w>''', '''w</w>''', '''r</w>''', '''t</w>''', '''low</w>''', '''er</w>''', '''lowest</w>''', '''newer</w>''', '''wider''', '''<unk>''', '''<|startoftext|>''', '''<|endoftext|>''']
# fmt: on
__snake_case = dict(zip(A_ , range(len(A_ ) ) ) )
__snake_case = ['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
__snake_case = {'''unk_token''': '''<unk>'''}
__snake_case = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
__snake_case = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(A_ ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(A_ ) )
__snake_case = {
'''do_resize''': True,
'''size''': 20,
'''do_center_crop''': True,
'''crop_size''': 18,
'''do_normalize''': True,
'''image_mean''': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
'''image_std''': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
}
__snake_case = os.path.join(self.tmpdirname , A_ )
with open(self.image_processor_file , '''w''' , encoding='''utf-8''' ) as fp:
json.dump(A_ , A_ )
def lowercase ( self : Optional[Any] , **A_ : Dict ) -> Any:
return CLIPTokenizer.from_pretrained(self.tmpdirname , **A_ )
def lowercase ( self : Optional[int] , **A_ : str ) -> str:
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , **A_ )
def lowercase ( self : Any , **A_ : Tuple ) -> Tuple:
return ViTImageProcessor.from_pretrained(self.tmpdirname , **A_ )
def lowercase ( self : Optional[int] ) -> Optional[Any]:
shutil.rmtree(self.tmpdirname )
def lowercase ( self : int ) -> Optional[Any]:
__snake_case = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
__snake_case = [Image.fromarray(np.moveaxis(A_ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def lowercase ( self : Optional[Any] ) -> Optional[Any]:
__snake_case = self.get_tokenizer()
__snake_case = self.get_rust_tokenizer()
__snake_case = self.get_image_processor()
__snake_case = CLIPSegProcessor(tokenizer=A_ , image_processor=A_ )
processor_slow.save_pretrained(self.tmpdirname )
__snake_case = CLIPSegProcessor.from_pretrained(self.tmpdirname , use_fast=A_ )
__snake_case = CLIPSegProcessor(tokenizer=A_ , image_processor=A_ )
processor_fast.save_pretrained(self.tmpdirname )
__snake_case = CLIPSegProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , A_ )
self.assertIsInstance(processor_fast.tokenizer , A_ )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , A_ )
self.assertIsInstance(processor_fast.image_processor , A_ )
def lowercase ( self : Union[str, Any] ) -> Any:
__snake_case = CLIPSegProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
__snake_case = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
__snake_case = self.get_image_processor(do_normalize=A_ , padding_value=1.0 )
__snake_case = CLIPSegProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=A_ , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , A_ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , A_ )
def lowercase ( self : Any ) -> str:
__snake_case = self.get_image_processor()
__snake_case = self.get_tokenizer()
__snake_case = CLIPSegProcessor(tokenizer=A_ , image_processor=A_ )
__snake_case = self.prepare_image_inputs()
__snake_case = image_processor(A_ , return_tensors='''np''' )
__snake_case = processor(images=A_ , return_tensors='''np''' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def lowercase ( self : List[str] ) -> List[Any]:
__snake_case = self.get_image_processor()
__snake_case = self.get_tokenizer()
__snake_case = CLIPSegProcessor(tokenizer=A_ , image_processor=A_ )
__snake_case = '''lower newer'''
__snake_case = processor(text=A_ )
__snake_case = tokenizer(A_ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def lowercase ( self : List[Any] ) -> str:
__snake_case = self.get_image_processor()
__snake_case = self.get_tokenizer()
__snake_case = CLIPSegProcessor(tokenizer=A_ , image_processor=A_ )
__snake_case = '''lower newer'''
__snake_case = self.prepare_image_inputs()
__snake_case = processor(text=A_ , images=A_ )
self.assertListEqual(list(inputs.keys() ) , ['''input_ids''', '''attention_mask''', '''pixel_values'''] )
# test if it raises when no input is passed
with pytest.raises(A_ ):
processor()
def lowercase ( self : Union[str, Any] ) -> Any:
__snake_case = self.get_image_processor()
__snake_case = self.get_tokenizer()
__snake_case = CLIPSegProcessor(tokenizer=A_ , image_processor=A_ )
__snake_case = self.prepare_image_inputs()
__snake_case = self.prepare_image_inputs()
__snake_case = processor(images=A_ , visual_prompt=A_ )
self.assertListEqual(list(inputs.keys() ) , ['''pixel_values''', '''conditional_pixel_values'''] )
# test if it raises when no input is passed
with pytest.raises(A_ ):
processor()
def lowercase ( self : Optional[int] ) -> Dict:
__snake_case = self.get_image_processor()
__snake_case = self.get_tokenizer()
__snake_case = CLIPSegProcessor(tokenizer=A_ , image_processor=A_ )
__snake_case = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
__snake_case = processor.batch_decode(A_ )
__snake_case = tokenizer.batch_decode(A_ )
self.assertListEqual(A_ , A_ )
| 93
| 0
|
"""simple docstring"""
from __future__ import annotations
def A_ ( __lowercase , __lowercase = None , __lowercase = None ):
if start is None:
UpperCamelCase_ : Optional[int] =0
if end is None:
UpperCamelCase_ : Tuple =len(_SCREAMING_SNAKE_CASE ) - 1
if start >= end:
return
UpperCamelCase_ : Union[str, Any] =(start + end) // 2
slowsort(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
slowsort(_SCREAMING_SNAKE_CASE , mid + 1 , _SCREAMING_SNAKE_CASE )
if sequence[end] < sequence[mid]:
UpperCamelCase_ , UpperCamelCase_ : Any =sequence[mid], sequence[end]
slowsort(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , end - 1 )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 357
|
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper
import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname
import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary
from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging
logging.set_verbosity_warning()
snake_case__ : Tuple = 2
# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
snake_case__ : List[Any] = {
# fairseq:
"""wmt19-ru-en""": {"""length_penalty""": 1.1},
"""wmt19-en-ru""": {"""length_penalty""": 1.1_5},
"""wmt19-en-de""": {"""length_penalty""": 1.0},
"""wmt19-de-en""": {"""length_penalty""": 1.1},
# allenai:
"""wmt16-en-de-dist-12-1""": {"""length_penalty""": 0.6},
"""wmt16-en-de-dist-6-1""": {"""length_penalty""": 0.6},
"""wmt16-en-de-12-1""": {"""length_penalty""": 0.8},
"""wmt19-de-en-6-6-base""": {"""length_penalty""": 0.6},
"""wmt19-de-en-6-6-big""": {"""length_penalty""": 0.6},
}
# this remaps the different models to their organization names
snake_case__ : Optional[int] = {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
snake_case__ : int = """facebook"""
for m in [
"wmt16-en-de-dist-12-1",
"wmt16-en-de-dist-6-1",
"wmt16-en-de-12-1",
"wmt19-de-en-6-6-base",
"wmt19-de-en-6-6-big",
]:
snake_case__ : Union[str, Any] = """allenai"""
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
# (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
# e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
__lowercase = dict((re.sub(R"@@$" , "" , _SCREAMING_SNAKE_CASE ), v) if k.endswith("@@" ) else (re.sub(R"$" , "</w>" , _SCREAMING_SNAKE_CASE ), v) for k, v in d.items() )
__lowercase = "<s> <pad> </s> <unk>".split()
# restore the special tokens
for k in keep_keys:
del da[F"""{k}</w>"""]
__lowercase = d[k] # restore
return da
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
# prep
assert os.path.exists(_SCREAMING_SNAKE_CASE )
os.makedirs(_SCREAMING_SNAKE_CASE , exist_ok=_SCREAMING_SNAKE_CASE )
print(F"""Writing results to {pytorch_dump_folder_path}""" )
# handle various types of models
__lowercase = basename(_SCREAMING_SNAKE_CASE )
__lowercase = dirname(_SCREAMING_SNAKE_CASE )
__lowercase = fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
__lowercase = cls.hub_models()
__lowercase = {"bpe": "fastbpe", "tokenizer": "moses"}
__lowercase = "."
# note: since the model dump is old, fairseq has upgraded its model some
# time later, and it does a whole lot of rewrites and splits on the saved
# weights, therefore we can't use torch.load() directly on the model file.
# see: upgrade_state_dict(state_dict) in fairseq_model.py
print(F"""using checkpoint {checkpoint_file}""" )
__lowercase = hub_utils.from_pretrained(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , archive_map=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
__lowercase = vars(chkpt["args"]["model"] )
__lowercase = args["source_lang"]
__lowercase = args["target_lang"]
__lowercase = dirname(_SCREAMING_SNAKE_CASE )
__lowercase = basename(_SCREAMING_SNAKE_CASE )
# dicts
__lowercase = os.path.join(_SCREAMING_SNAKE_CASE , F"""dict.{src_lang}.txt""" )
__lowercase = os.path.join(_SCREAMING_SNAKE_CASE , F"""dict.{tgt_lang}.txt""" )
__lowercase = Dictionary.load(_SCREAMING_SNAKE_CASE )
__lowercase = rewrite_dict_keys(src_dict.indices )
__lowercase = len(_SCREAMING_SNAKE_CASE )
__lowercase = os.path.join(_SCREAMING_SNAKE_CASE , "vocab-src.json" )
print(F"""Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records""" )
with open(_SCREAMING_SNAKE_CASE , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(_SCREAMING_SNAKE_CASE , ensure_ascii=_SCREAMING_SNAKE_CASE , indent=_SCREAMING_SNAKE_CASE ) )
# detect whether this is a do_lower_case situation, which can be derived by checking whether we
# have at least one uppercase letter in the source vocab
__lowercase = True
for k in src_vocab.keys():
if not k.islower():
__lowercase = False
break
__lowercase = Dictionary.load(_SCREAMING_SNAKE_CASE )
__lowercase = rewrite_dict_keys(tgt_dict.indices )
__lowercase = len(_SCREAMING_SNAKE_CASE )
__lowercase = os.path.join(_SCREAMING_SNAKE_CASE , "vocab-tgt.json" )
print(F"""Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records""" )
with open(_SCREAMING_SNAKE_CASE , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(_SCREAMING_SNAKE_CASE , ensure_ascii=_SCREAMING_SNAKE_CASE , indent=_SCREAMING_SNAKE_CASE ) )
# merges_file (bpecodes)
__lowercase = os.path.join(_SCREAMING_SNAKE_CASE , VOCAB_FILES_NAMES["merges_file"] )
for fn in ["bpecodes", "code"]: # older fairseq called the merges file "code"
__lowercase = os.path.join(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if os.path.exists(_SCREAMING_SNAKE_CASE ):
break
with open(_SCREAMING_SNAKE_CASE , encoding="utf-8" ) as fin:
__lowercase = fin.read()
__lowercase = re.sub(R" \d+$" , "" , _SCREAMING_SNAKE_CASE , 0 , re.M ) # remove frequency number
print(F"""Generating {merges_file}""" )
with open(_SCREAMING_SNAKE_CASE , "w" , encoding="utf-8" ) as fout:
fout.write(_SCREAMING_SNAKE_CASE )
# model config
__lowercase = os.path.join(_SCREAMING_SNAKE_CASE , "config.json" )
# validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
# may have to modify the tokenizer if a different type is used by a future model
assert args["bpe"] == "fastbpe", F"""need to extend tokenizer to support bpe={args['bpe']}"""
assert args["tokenizer"] == "moses", F"""need to extend tokenizer to support bpe={args['tokenizer']}"""
__lowercase = {
"architectures": ["FSMTForConditionalGeneration"],
"model_type": "fsmt",
"activation_dropout": args["activation_dropout"],
"activation_function": "relu",
"attention_dropout": args["attention_dropout"],
"d_model": args["decoder_embed_dim"],
"dropout": args["dropout"],
"init_std": 0.0_2,
"max_position_embeddings": args["max_source_positions"],
"num_hidden_layers": args["encoder_layers"],
"src_vocab_size": src_vocab_size,
"tgt_vocab_size": tgt_vocab_size,
"langs": [src_lang, tgt_lang],
"encoder_attention_heads": args["encoder_attention_heads"],
"encoder_ffn_dim": args["encoder_ffn_embed_dim"],
"encoder_layerdrop": args["encoder_layerdrop"],
"encoder_layers": args["encoder_layers"],
"decoder_attention_heads": args["decoder_attention_heads"],
"decoder_ffn_dim": args["decoder_ffn_embed_dim"],
"decoder_layerdrop": args["decoder_layerdrop"],
"decoder_layers": args["decoder_layers"],
"bos_token_id": 0,
"pad_token_id": 1,
"eos_token_id": 2,
"is_encoder_decoder": True,
"scale_embedding": not args["no_scale_embedding"],
"tie_word_embeddings": args["share_all_embeddings"],
}
# good hparam defaults to start with
__lowercase = 5
__lowercase = False
if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
__lowercase = best_score_hparams[model_dir]["length_penalty"]
else:
__lowercase = 1.0
print(F"""Generating {fsmt_model_config_file}""" )
with open(_SCREAMING_SNAKE_CASE , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(_SCREAMING_SNAKE_CASE , ensure_ascii=_SCREAMING_SNAKE_CASE , indent=_SCREAMING_SNAKE_CASE ) )
# tokenizer config
__lowercase = os.path.join(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__lowercase = {
"langs": [src_lang, tgt_lang],
"model_max_length": 1_0_2_4,
"do_lower_case": do_lower_case,
}
print(F"""Generating {fsmt_tokenizer_config_file}""" )
with open(_SCREAMING_SNAKE_CASE , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(_SCREAMING_SNAKE_CASE , ensure_ascii=_SCREAMING_SNAKE_CASE , indent=_SCREAMING_SNAKE_CASE ) )
# model
__lowercase = chkpt["models"][0]
__lowercase = model.state_dict()
# rename keys to start with 'model.'
__lowercase = OrderedDict(("model." + k, v) for k, v in model_state_dict.items() )
# remove unneeded keys
__lowercase = [
"model.model",
"model.encoder.version",
"model.decoder.version",
"model.encoder_embed_tokens.weight",
"model.decoder_embed_tokens.weight",
"model.encoder.embed_positions._float_tensor",
"model.decoder.embed_positions._float_tensor",
]
for k in ignore_keys:
model_state_dict.pop(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__lowercase = FSMTConfig.from_pretrained(_SCREAMING_SNAKE_CASE )
__lowercase = FSMTForConditionalGeneration(_SCREAMING_SNAKE_CASE )
# check that it loads ok
model_new.load_state_dict(_SCREAMING_SNAKE_CASE , strict=_SCREAMING_SNAKE_CASE )
# save
__lowercase = os.path.join(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
print(F"""Generating {pytorch_weights_dump_path}""" )
torch.save(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
print("Conversion is done!" )
print("\nLast step is to upload the files to s3" )
print(F"""cd {data_root}""" )
print(F"""transformers-cli upload {model_dir}""" )
if __name__ == "__main__":
snake_case__ : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--fsmt_checkpoint_path""",
default=None,
type=str,
required=True,
help=(
"""Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"""
""" bpecodes, etc."""
),
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
snake_case__ : Union[str, Any] = parser.parse_args()
convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)
| 402
| 0
|
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline # noqa: F401
deprecate(
"stable diffusion controlnet",
"0.22.0",
"Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.",
standard_warn=False,
stacklevel=3,
)
| 719
|
from collections.abc import Callable
def A ( lowercase , lowercase , lowercase ) -> float:
'''simple docstring'''
UpperCamelCase = a
UpperCamelCase = b
if function(lowercase ) == 0: # one of the a or b is a root for the function
return a
elif function(lowercase ) == 0:
return b
elif (
function(lowercase ) * function(lowercase ) > 0
): # if none of these are root and they are both positive or negative,
# then this algorithm can't find the root
raise ValueError('could not find root in given interval.' )
else:
UpperCamelCase = start + (end - start) / 2.0
while abs(start - mid ) > 10**-7: # until precisely equals to 10^-7
if function(lowercase ) == 0:
return mid
elif function(lowercase ) * function(lowercase ) < 0:
UpperCamelCase = mid
else:
UpperCamelCase = mid
UpperCamelCase = start + (end - start) / 2.0
return mid
def A ( lowercase ) -> float:
'''simple docstring'''
return x**3 - 2 * x - 5
if __name__ == "__main__":
print(bisection(f, 1, 1_000))
import doctest
doctest.testmod()
| 3
| 0
|
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
UpperCamelCase_ = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}
UpperCamelCase_ = {
"""vocab_file""": {
"""unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt""",
},
"""tokenizer_file""": {
"""unc-nlp/lxmert-base-uncased""": (
"""https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json"""
),
},
}
UpperCamelCase_ = {
"""unc-nlp/lxmert-base-uncased""": 512,
}
UpperCamelCase_ = {
"""unc-nlp/lxmert-base-uncased""": {"""do_lower_case""": True},
}
class __SCREAMING_SNAKE_CASE ( lowercase__ ):
lowerCamelCase_ = VOCAB_FILES_NAMES
lowerCamelCase_ = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase_ = PRETRAINED_INIT_CONFIGURATION
lowerCamelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase_ = LxmertTokenizer
def __init__( self : str , UpperCAmelCase__ : str=None , UpperCAmelCase__ : List[Any]=None , UpperCAmelCase__ : List[Any]=True , UpperCAmelCase__ : int="[UNK]" , UpperCAmelCase__ : Tuple="[SEP]" , UpperCAmelCase__ : str="[PAD]" , UpperCAmelCase__ : Optional[Any]="[CLS]" , UpperCAmelCase__ : Tuple="[MASK]" , UpperCAmelCase__ : Tuple=True , UpperCAmelCase__ : int=None , **UpperCAmelCase__ : Optional[Any] , ):
'''simple docstring'''
super().__init__(
UpperCAmelCase__ , tokenizer_file=UpperCAmelCase__ , do_lower_case=UpperCAmelCase__ , unk_token=UpperCAmelCase__ , sep_token=UpperCAmelCase__ , pad_token=UpperCAmelCase__ , cls_token=UpperCAmelCase__ , mask_token=UpperCAmelCase__ , tokenize_chinese_chars=UpperCAmelCase__ , strip_accents=UpperCAmelCase__ , **UpperCAmelCase__ , )
lowercase : Union[str, Any] =json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''' , UpperCAmelCase__ ) != do_lower_case
or normalizer_state.get('''strip_accents''' , UpperCAmelCase__ ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , UpperCAmelCase__ ) != tokenize_chinese_chars
):
lowercase : Union[str, Any] =getattr(UpperCAmelCase__ , normalizer_state.pop('''type''' ) )
lowercase : int =do_lower_case
lowercase : Tuple =strip_accents
lowercase : Any =tokenize_chinese_chars
lowercase : Union[str, Any] =normalizer_class(**UpperCAmelCase__ )
lowercase : int =do_lower_case
def lowerCamelCase_ ( self : List[Any] , UpperCAmelCase__ : Any , UpperCAmelCase__ : int=None ):
'''simple docstring'''
lowercase : Union[str, Any] =[self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def lowerCamelCase_ ( self : Dict , UpperCAmelCase__ : List[int] , UpperCAmelCase__ : Optional[List[int]] = None ):
'''simple docstring'''
lowercase : List[Any] =[self.sep_token_id]
lowercase : List[str] =[self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def lowerCamelCase_ ( self : str , UpperCAmelCase__ : str , UpperCAmelCase__ : Optional[str] = None ):
'''simple docstring'''
lowercase : List[Any] =self._tokenizer.model.save(UpperCAmelCase__ , name=UpperCAmelCase__ )
return tuple(UpperCAmelCase__ )
| 92
|
from importlib import import_module
from .logging import get_logger
_lowerCAmelCase: str = get_logger(__name__)
class lowercase_ :
def __init__( self , lowercase_ , lowercase_=None) -> Tuple:
a__ =attrs or []
if module is not None:
for key in module.__dict__:
if key in attrs or not key.startswith('__'):
setattr(self , lowercase_ , getattr(lowercase_ , lowercase_))
a__ =module._original_module if isinstance(lowercase_ , _PatchedModuleObj) else module
class lowercase_ :
snake_case =[]
def __init__( self , lowercase_ , lowercase_ , lowercase_ , lowercase_=None) -> List[str]:
a__ =obj
a__ =target
a__ =new
a__ =target.split('.')[0]
a__ ={}
a__ =attrs or []
def __enter__( self) -> Optional[int]:
*a__ , a__ =self.target.split('.')
# Patch modules:
# it's used to patch attributes of submodules like "os.path.join";
# in this case we need to patch "os" and "os.path"
for i in range(len(lowercase_)):
try:
a__ =import_module('.'.join(submodules[: i + 1]))
except ModuleNotFoundError:
continue
# We iterate over all the globals in self.obj in case we find "os" or "os.path"
for attr in self.obj.__dir__():
a__ =getattr(self.obj , lowercase_)
# We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
# This allows to patch renamed modules like "from os import path as ospath".
if obj_attr is submodule or (
(isinstance(lowercase_ , _PatchedModuleObj) and obj_attr._original_module is submodule)
):
a__ =obj_attr
# patch at top level
setattr(self.obj , lowercase_ , _PatchedModuleObj(lowercase_ , attrs=self.attrs))
a__ =getattr(self.obj , lowercase_)
# construct lower levels patches
for key in submodules[i + 1 :]:
setattr(lowercase_ , lowercase_ , _PatchedModuleObj(getattr(lowercase_ , lowercase_ , lowercase_) , attrs=self.attrs))
a__ =getattr(lowercase_ , lowercase_)
# finally set the target attribute
setattr(lowercase_ , lowercase_ , self.new)
# Patch attribute itself:
# it's used for builtins like "open",
# and also to patch "os.path.join" we may also need to patch "join"
# itself if it was imported as "from os.path import join".
if submodules: # if it's an attribute of a submodule like "os.path.join"
try:
a__ =getattr(import_module('.'.join(lowercase_)) , lowercase_)
except (AttributeError, ModuleNotFoundError):
return
# We iterate over all the globals in self.obj in case we find "os.path.join"
for attr in self.obj.__dir__():
# We don't check for the name of the global, but rather if its value *is* "os.path.join".
# This allows to patch renamed attributes like "from os.path import join as pjoin".
if getattr(self.obj , lowercase_) is attr_value:
a__ =getattr(self.obj , lowercase_)
setattr(self.obj , lowercase_ , self.new)
elif target_attr in globals()["__builtins__"]: # if it'a s builtin like "open"
a__ =globals()['__builtins__'][target_attr]
setattr(self.obj , lowercase_ , self.new)
else:
raise RuntimeError(F"""Tried to patch attribute {target_attr} instead of a submodule.""")
def __exit__( self , *lowercase_) -> str:
for attr in list(self.original):
setattr(self.obj , lowercase_ , self.original.pop(lowercase_))
def __UpperCamelCase ( self) -> Any:
self.__enter__()
self._active_patches.append(self)
def __UpperCamelCase ( self) -> Union[str, Any]:
try:
self._active_patches.remove(self)
except ValueError:
# If the patch hasn't been started this will fail
return None
return self.__exit__()
| 20
| 0
|
'''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE__ ( self ) -> Union[str, Any]:
'''simple docstring'''
UpperCamelCase : Optional[Any] = [
"safety_checker/pytorch_model.bin",
"safety_checker/model.safetensors",
"vae/diffusion_pytorch_model.bin",
"vae/diffusion_pytorch_model.safetensors",
"text_encoder/pytorch_model.bin",
"text_encoder/model.safetensors",
"unet/diffusion_pytorch_model.bin",
"unet/diffusion_pytorch_model.safetensors",
]
self.assertTrue(is_safetensors_compatible(lowerCamelCase ) )
def SCREAMING_SNAKE_CASE__ ( self ) -> Any:
'''simple docstring'''
UpperCamelCase : List[Any] = [
"unet/diffusion_pytorch_model.bin",
"unet/diffusion_pytorch_model.safetensors",
]
self.assertTrue(is_safetensors_compatible(lowerCamelCase ) )
def SCREAMING_SNAKE_CASE__ ( self ) -> Any:
'''simple docstring'''
UpperCamelCase : int = [
"safety_checker/pytorch_model.bin",
"safety_checker/model.safetensors",
"vae/diffusion_pytorch_model.bin",
"vae/diffusion_pytorch_model.safetensors",
"text_encoder/pytorch_model.bin",
"text_encoder/model.safetensors",
"unet/diffusion_pytorch_model.bin",
# Removed: 'unet/diffusion_pytorch_model.safetensors',
]
self.assertFalse(is_safetensors_compatible(lowerCamelCase ) )
def SCREAMING_SNAKE_CASE__ ( self ) -> Optional[int]:
'''simple docstring'''
UpperCamelCase : List[Any] = [
"text_encoder/pytorch_model.bin",
"text_encoder/model.safetensors",
]
self.assertTrue(is_safetensors_compatible(lowerCamelCase ) )
def SCREAMING_SNAKE_CASE__ ( self ) -> Any:
'''simple docstring'''
UpperCamelCase : List[str] = [
"safety_checker/pytorch_model.bin",
"safety_checker/model.safetensors",
"vae/diffusion_pytorch_model.bin",
"vae/diffusion_pytorch_model.safetensors",
"text_encoder/pytorch_model.bin",
# Removed: 'text_encoder/model.safetensors',
"unet/diffusion_pytorch_model.bin",
"unet/diffusion_pytorch_model.safetensors",
]
self.assertFalse(is_safetensors_compatible(lowerCamelCase ) )
def SCREAMING_SNAKE_CASE__ ( self ) -> Dict:
'''simple docstring'''
UpperCamelCase : int = [
"safety_checker/pytorch_model.fp16.bin",
"safety_checker/model.fp16.safetensors",
"vae/diffusion_pytorch_model.fp16.bin",
"vae/diffusion_pytorch_model.fp16.safetensors",
"text_encoder/pytorch_model.fp16.bin",
"text_encoder/model.fp16.safetensors",
"unet/diffusion_pytorch_model.fp16.bin",
"unet/diffusion_pytorch_model.fp16.safetensors",
]
UpperCamelCase : Union[str, Any] = "fp16"
self.assertTrue(is_safetensors_compatible(lowerCamelCase , variant=lowerCamelCase ) )
def SCREAMING_SNAKE_CASE__ ( self ) -> Optional[int]:
'''simple docstring'''
UpperCamelCase : List[str] = [
"unet/diffusion_pytorch_model.fp16.bin",
"unet/diffusion_pytorch_model.fp16.safetensors",
]
UpperCamelCase : List[Any] = "fp16"
self.assertTrue(is_safetensors_compatible(lowerCamelCase , variant=lowerCamelCase ) )
def SCREAMING_SNAKE_CASE__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCamelCase : Optional[Any] = [
"unet/diffusion_pytorch_model.bin",
"unet/diffusion_pytorch_model.safetensors",
]
UpperCamelCase : Optional[int] = "fp16"
self.assertTrue(is_safetensors_compatible(lowerCamelCase , variant=lowerCamelCase ) )
def SCREAMING_SNAKE_CASE__ ( self ) -> Tuple:
'''simple docstring'''
UpperCamelCase : List[Any] = [
"safety_checker/pytorch_model.fp16.bin",
"safety_checker/model.fp16.safetensors",
"vae/diffusion_pytorch_model.fp16.bin",
"vae/diffusion_pytorch_model.fp16.safetensors",
"text_encoder/pytorch_model.fp16.bin",
"text_encoder/model.fp16.safetensors",
"unet/diffusion_pytorch_model.fp16.bin",
# Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
]
UpperCamelCase : int = "fp16"
self.assertFalse(is_safetensors_compatible(lowerCamelCase , variant=lowerCamelCase ) )
def SCREAMING_SNAKE_CASE__ ( self ) -> Any:
'''simple docstring'''
UpperCamelCase : Optional[int] = [
"text_encoder/pytorch_model.fp16.bin",
"text_encoder/model.fp16.safetensors",
]
UpperCamelCase : Tuple = "fp16"
self.assertTrue(is_safetensors_compatible(lowerCamelCase , variant=lowerCamelCase ) )
def SCREAMING_SNAKE_CASE__ ( self ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase : Optional[int] = [
"text_encoder/pytorch_model.bin",
"text_encoder/model.safetensors",
]
UpperCamelCase : str = "fp16"
self.assertTrue(is_safetensors_compatible(lowerCamelCase , variant=lowerCamelCase ) )
def SCREAMING_SNAKE_CASE__ ( self ) -> Dict:
'''simple docstring'''
UpperCamelCase : int = [
"safety_checker/pytorch_model.fp16.bin",
"safety_checker/model.fp16.safetensors",
"vae/diffusion_pytorch_model.fp16.bin",
"vae/diffusion_pytorch_model.fp16.safetensors",
"text_encoder/pytorch_model.fp16.bin",
# 'text_encoder/model.fp16.safetensors',
"unet/diffusion_pytorch_model.fp16.bin",
"unet/diffusion_pytorch_model.fp16.safetensors",
]
UpperCamelCase : List[str] = "fp16"
self.assertFalse(is_safetensors_compatible(lowerCamelCase , variant=lowerCamelCase ) )
| 435
|
'''simple docstring'''
import json
import os
import tempfile
from unittest.mock import patch
import torch
from torch.utils.data import DataLoader, TensorDataset
from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState, PartialState
from accelerate.test_utils import require_bnb, require_multi_gpu, slow
from accelerate.test_utils.testing import AccelerateTestCase, require_cuda
from accelerate.utils import patch_environment
def A__ ( ):
'''simple docstring'''
UpperCamelCase : Tuple = torch.nn.Linear(2 , 4)
UpperCamelCase : Optional[Any] = torch.optim.AdamW(model.parameters() , lr=1.0)
UpperCamelCase : List[Any] = torch.optim.lr_scheduler.OneCycleLR(A , max_lr=0.01 , steps_per_epoch=2 , epochs=1)
UpperCamelCase : List[Any] = DataLoader(TensorDataset(torch.tensor([1, 2, 3])))
UpperCamelCase : List[str] = DataLoader(TensorDataset(torch.tensor([4, 5, 6])))
return model, optimizer, scheduler, train_dl, valid_dl
def A__ ( A : int):
'''simple docstring'''
return (model.weight.abs().sum() + model.bias.abs().sum()).item()
def A__ ( A : int):
'''simple docstring'''
UpperCamelCase : Optional[int] = torch.nn.Linear(*tuple(model.weight.T.shape)).state_dict()
model.load_state_dict(A)
class UpperCAmelCase_ ( lowerCamelCase_ ):
"""simple docstring"""
@require_cuda
def SCREAMING_SNAKE_CASE__ ( self ) -> Optional[int]:
'''simple docstring'''
UpperCamelCase : Tuple = Accelerator()
assert PartialState._shared_state["_cpu"] is False
assert PartialState._shared_state["device"].type == "cuda"
with self.assertRaises(lowerCamelCase ):
UpperCamelCase : int = Accelerator(cpu=lowerCamelCase )
def SCREAMING_SNAKE_CASE__ ( self ) -> int:
'''simple docstring'''
UpperCamelCase : Dict = Accelerator()
UpperCamelCase : Any = GradientState()
assert state.num_steps == 1
UpperCamelCase : List[Any] = 4
assert state.num_steps == 4
assert state.sync_gradients is True
UpperCamelCase : Optional[int] = False
assert state.sync_gradients is False
GradientState._reset_state()
def SCREAMING_SNAKE_CASE__ ( self ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase : Optional[int] = Accelerator()
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase : Optional[Any] = create_components()
(
(
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) ,
) : Any = accelerator.prepare(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
self.assertTrue(prepared_model in accelerator._models )
self.assertTrue(prepared_optimizer in accelerator._optimizers )
self.assertTrue(prepared_scheduler in accelerator._schedulers )
self.assertTrue(prepared_train_dl in accelerator._dataloaders )
self.assertTrue(prepared_valid_dl in accelerator._dataloaders )
def SCREAMING_SNAKE_CASE__ ( self ) -> str:
'''simple docstring'''
UpperCamelCase : Tuple = Accelerator()
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase : Any = create_components()
accelerator.prepare(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
accelerator.free_memory()
self.assertTrue(len(accelerator._models ) == 0 )
self.assertTrue(len(accelerator._optimizers ) == 0 )
self.assertTrue(len(accelerator._schedulers ) == 0 )
self.assertTrue(len(accelerator._dataloaders ) == 0 )
def SCREAMING_SNAKE_CASE__ ( self ) -> Any:
'''simple docstring'''
PartialState._reset_state()
# Mock torch.cuda.set_device to avoid an exception as the device doesn't exist
def noop(*lowerCamelCase , **lowerCamelCase ):
pass
with patch("torch.cuda.set_device" , lowerCamelCase ), patch_environment(ACCELERATE_TORCH_DEVICE="cuda:64" ):
UpperCamelCase : Union[str, Any] = Accelerator()
self.assertEqual(str(accelerator.state.device ) , "cuda:64" )
def SCREAMING_SNAKE_CASE__ ( self ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase : int = Accelerator()
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase : Optional[int] = create_components()
accelerator.prepare(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
UpperCamelCase : str = get_signature(lowerCamelCase )
with tempfile.TemporaryDirectory() as tmpdirname:
accelerator.save_state(lowerCamelCase )
# make sure random weights don't match
load_random_weights(lowerCamelCase )
self.assertTrue(abs(model_signature - get_signature(lowerCamelCase ) ) > 1e-3 )
# make sure loaded weights match
accelerator.load_state(lowerCamelCase )
self.assertTrue(abs(model_signature - get_signature(lowerCamelCase ) ) < 1e-3 )
def SCREAMING_SNAKE_CASE__ ( self ) -> Optional[int]:
'''simple docstring'''
UpperCamelCase : Optional[int] = Accelerator()
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase : int = create_components()
accelerator.prepare(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
UpperCamelCase : List[Any] = get_signature(lowerCamelCase )
# saving hook
def save_config(lowerCamelCase , lowerCamelCase , lowerCamelCase ):
UpperCamelCase : str = {"class_name": models[0].__class__.__name__}
with open(os.path.join(lowerCamelCase , "data.json" ) , "w" ) as f:
json.dump(lowerCamelCase , lowerCamelCase )
# loading hook
def load_config(lowerCamelCase , lowerCamelCase ):
with open(os.path.join(lowerCamelCase , "data.json" ) , "r" ) as f:
UpperCamelCase : Optional[int] = json.load(lowerCamelCase )
UpperCamelCase : int = config["class_name"]
UpperCamelCase : Dict = accelerator.register_save_state_pre_hook(lowerCamelCase )
UpperCamelCase : Union[str, Any] = accelerator.register_load_state_pre_hook(lowerCamelCase )
with tempfile.TemporaryDirectory() as tmpdirname:
accelerator.save_state(lowerCamelCase )
# make sure random weights don't match with hooks
load_random_weights(lowerCamelCase )
self.assertTrue(abs(model_signature - get_signature(lowerCamelCase ) ) > 1e-3 )
# random class name to verify correct one is loaded
UpperCamelCase : Union[str, Any] = "random"
# make sure loaded weights match with hooks
accelerator.load_state(lowerCamelCase )
self.assertTrue(abs(model_signature - get_signature(lowerCamelCase ) ) < 1e-3 )
# mode.class_name is loaded from config
self.assertTrue(model.class_name == model.__class__.__name__ )
# remove hooks
save_hook.remove()
load_hook.remove()
with tempfile.TemporaryDirectory() as tmpdirname:
accelerator.save_state(lowerCamelCase )
# make sure random weights don't match with hooks removed
load_random_weights(lowerCamelCase )
self.assertTrue(abs(model_signature - get_signature(lowerCamelCase ) ) > 1e-3 )
# random class name to verify correct one is loaded
UpperCamelCase : Any = "random"
# make sure loaded weights match with hooks removed
accelerator.load_state(lowerCamelCase )
self.assertTrue(abs(model_signature - get_signature(lowerCamelCase ) ) < 1e-3 )
# mode.class_name is NOT loaded from config
self.assertTrue(model.class_name != model.__class__.__name__ )
def SCREAMING_SNAKE_CASE__ ( self ) -> Any:
'''simple docstring'''
UpperCamelCase : Optional[int] = Accelerator()
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase : List[str] = create_components()
UpperCamelCase : int = None
# This should work
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase : List[str] = accelerator.prepare(
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
self.assertTrue(dummy_obj is None )
def SCREAMING_SNAKE_CASE__ ( self ) -> Union[str, Any]:
'''simple docstring'''
UpperCamelCase : List[str] = Accelerator()
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase : List[Any] = create_components()
UpperCamelCase : Union[str, Any] = [1, 2, 3]
# This should work
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase : Tuple = accelerator.prepare(
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
self.assertEqual(
getattr(lowerCamelCase , "_is_accelerate_prepared" , lowerCamelCase ) , lowerCamelCase , "Dummy object should have `_is_accelerate_prepared` set to `True`" , )
self.assertEqual(
getattr(lowerCamelCase , "_is_accelerate_prepared" , lowerCamelCase ) , lowerCamelCase , "Model is missing `_is_accelerator_prepared` or is set to `False`" , )
self.assertEqual(
getattr(lowerCamelCase , "_is_accelerate_prepared" , lowerCamelCase ) , lowerCamelCase , "Optimizer is missing `_is_accelerator_prepared` or is set to `False`" , )
self.assertEqual(
getattr(lowerCamelCase , "_is_accelerate_prepared" , lowerCamelCase ) , lowerCamelCase , "Scheduler is missing `_is_accelerator_prepared` or is set to `False`" , )
self.assertEqual(
getattr(lowerCamelCase , "_is_accelerate_prepared" , lowerCamelCase ) , lowerCamelCase , "Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`" , )
self.assertEqual(
getattr(lowerCamelCase , "_is_accelerate_prepared" , lowerCamelCase ) , lowerCamelCase , "Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`" , )
@slow
@require_bnb
def SCREAMING_SNAKE_CASE__ ( self ) -> Optional[int]:
'''simple docstring'''
from transformers import AutoModelForCausalLM
UpperCamelCase : Dict = AutoModelForCausalLM.from_pretrained(
"EleutherAI/gpt-neo-125m" , load_in_abit=lowerCamelCase , device_map={"": 0} , )
UpperCamelCase : str = Accelerator()
# This should work
UpperCamelCase : Any = accelerator.prepare(lowerCamelCase )
@slow
@require_bnb
def SCREAMING_SNAKE_CASE__ ( self ) -> str:
'''simple docstring'''
from transformers import AutoModelForCausalLM
UpperCamelCase : Optional[Any] = Accelerator()
with init_empty_weights():
UpperCamelCase : Optional[Any] = AutoModelForCausalLM.from_pretrained(
"EleutherAI/gpt-neo-125m" , )
model.tie_weights()
UpperCamelCase : Optional[int] = infer_auto_device_map(lowerCamelCase )
UpperCamelCase : Dict = "cpu"
UpperCamelCase : int = AutoModelForCausalLM.from_pretrained(
"EleutherAI/gpt-neo-125m" , device_map=lowerCamelCase , load_in_abit=lowerCamelCase , llm_inta_enable_fpaa_cpu_offload=lowerCamelCase )
# This should not work and get value error
with self.assertRaises(lowerCamelCase ):
UpperCamelCase : Dict = accelerator.prepare(lowerCamelCase )
@slow
@require_bnb
@require_multi_gpu
def SCREAMING_SNAKE_CASE__ ( self ) -> Any:
'''simple docstring'''
from transformers import AutoModelForCausalLM
UpperCamelCase : Dict = {"distributed_type": DistributedType.MULTI_GPU}
with init_empty_weights():
UpperCamelCase : Dict = AutoModelForCausalLM.from_pretrained(
"EleutherAI/gpt-neo-125m" , )
model.tie_weights()
UpperCamelCase : List[Any] = infer_auto_device_map(lowerCamelCase )
UpperCamelCase : Any = 1
UpperCamelCase : Optional[int] = AutoModelForCausalLM.from_pretrained(
"EleutherAI/gpt-neo-125m" , load_in_abit=lowerCamelCase , device_map=lowerCamelCase , )
UpperCamelCase : Optional[int] = Accelerator()
# This should not work and get value error
with self.assertRaises(lowerCamelCase ):
UpperCamelCase : Dict = accelerator.prepare(lowerCamelCase )
PartialState._reset_state()
@slow
@require_bnb
@require_multi_gpu
def SCREAMING_SNAKE_CASE__ ( self ) -> List[str]:
'''simple docstring'''
from transformers import AutoModelForCausalLM
with init_empty_weights():
UpperCamelCase : str = AutoModelForCausalLM.from_pretrained(
"EleutherAI/gpt-neo-125m" , )
UpperCamelCase : Union[str, Any] = infer_auto_device_map(lowerCamelCase )
UpperCamelCase : Tuple = 1
UpperCamelCase : List[str] = AutoModelForCausalLM.from_pretrained(
"EleutherAI/gpt-neo-125m" , load_in_abit=lowerCamelCase , device_map=lowerCamelCase , )
UpperCamelCase : Tuple = Accelerator()
# This should work
UpperCamelCase : Optional[Any] = accelerator.prepare(lowerCamelCase )
@require_cuda
def SCREAMING_SNAKE_CASE__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCamelCase : int = torch.nn.Linear(10 , 10 )
UpperCamelCase : Dict = torch.optim.SGD(model.parameters() , lr=0.01 )
UpperCamelCase : Optional[Any] = Accelerator(cpu=lowerCamelCase )
UpperCamelCase : Any = accelerator.prepare(lowerCamelCase )
| 435
| 1
|
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
def A__ ( A__ ) -> Tuple:
'''simple docstring'''
_UpperCAmelCase = MobileViTConfig()
# size of the architecture
if "mobilevit_s" in mobilevit_name:
_UpperCAmelCase = [144, 192, 240]
_UpperCAmelCase = [16, 32, 64, 96, 128, 160, 640]
elif "mobilevit_xs" in mobilevit_name:
_UpperCAmelCase = [96, 120, 144]
_UpperCAmelCase = [16, 32, 48, 64, 80, 96, 384]
elif "mobilevit_xxs" in mobilevit_name:
_UpperCAmelCase = [64, 80, 96]
_UpperCAmelCase = [16, 16, 24, 48, 64, 80, 320]
_UpperCAmelCase = 0.05
_UpperCAmelCase = 2.0
if mobilevit_name.startswith("deeplabv3_" ):
_UpperCAmelCase = 512
_UpperCAmelCase = 16
_UpperCAmelCase = 21
_UpperCAmelCase = 'pascal-voc-id2label.json'
else:
_UpperCAmelCase = 1000
_UpperCAmelCase = 'imagenet-1k-id2label.json'
_UpperCAmelCase = 'huggingface/label-files'
_UpperCAmelCase = json.load(open(hf_hub_download(_UpperCAmelCase , _UpperCAmelCase , repo_type="dataset" ) , "r" ) )
_UpperCAmelCase = {int(_UpperCAmelCase ): v for k, v in idalabel.items()}
_UpperCAmelCase = idalabel
_UpperCAmelCase = {v: k for k, v in idalabel.items()}
return config
def A__ ( A__ , A__=False ) -> Any:
'''simple docstring'''
for i in range(1 , 6 ):
if F"""layer_{i}.""" in name:
_UpperCAmelCase = name.replace(F"""layer_{i}.""" , F"""encoder.layer.{i - 1}.""" )
if "conv_1." in name:
_UpperCAmelCase = name.replace("conv_1." , "conv_stem." )
if ".block." in name:
_UpperCAmelCase = name.replace(".block." , "." )
if "exp_1x1" in name:
_UpperCAmelCase = name.replace("exp_1x1" , "expand_1x1" )
if "red_1x1" in name:
_UpperCAmelCase = name.replace("red_1x1" , "reduce_1x1" )
if ".local_rep.conv_3x3." in name:
_UpperCAmelCase = name.replace(".local_rep.conv_3x3." , ".conv_kxk." )
if ".local_rep.conv_1x1." in name:
_UpperCAmelCase = name.replace(".local_rep.conv_1x1." , ".conv_1x1." )
if ".norm." in name:
_UpperCAmelCase = name.replace(".norm." , ".normalization." )
if ".conv." in name:
_UpperCAmelCase = name.replace(".conv." , ".convolution." )
if ".conv_proj." in name:
_UpperCAmelCase = name.replace(".conv_proj." , ".conv_projection." )
for i in range(0 , 2 ):
for j in range(0 , 4 ):
if F""".{i}.{j}.""" in name:
_UpperCAmelCase = name.replace(F""".{i}.{j}.""" , F""".{i}.layer.{j}.""" )
for i in range(2 , 6 ):
for j in range(0 , 4 ):
if F""".{i}.{j}.""" in name:
_UpperCAmelCase = name.replace(F""".{i}.{j}.""" , F""".{i}.""" )
if "expand_1x1" in name:
_UpperCAmelCase = name.replace("expand_1x1" , "downsampling_layer.expand_1x1" )
if "conv_3x3" in name:
_UpperCAmelCase = name.replace("conv_3x3" , "downsampling_layer.conv_3x3" )
if "reduce_1x1" in name:
_UpperCAmelCase = name.replace("reduce_1x1" , "downsampling_layer.reduce_1x1" )
for i in range(2 , 5 ):
if F""".global_rep.{i}.weight""" in name:
_UpperCAmelCase = name.replace(F""".global_rep.{i}.weight""" , ".layernorm.weight" )
if F""".global_rep.{i}.bias""" in name:
_UpperCAmelCase = name.replace(F""".global_rep.{i}.bias""" , ".layernorm.bias" )
if ".global_rep." in name:
_UpperCAmelCase = name.replace(".global_rep." , ".transformer." )
if ".pre_norm_mha.0." in name:
_UpperCAmelCase = name.replace(".pre_norm_mha.0." , ".layernorm_before." )
if ".pre_norm_mha.1.out_proj." in name:
_UpperCAmelCase = name.replace(".pre_norm_mha.1.out_proj." , ".attention.output.dense." )
if ".pre_norm_ffn.0." in name:
_UpperCAmelCase = name.replace(".pre_norm_ffn.0." , ".layernorm_after." )
if ".pre_norm_ffn.1." in name:
_UpperCAmelCase = name.replace(".pre_norm_ffn.1." , ".intermediate.dense." )
if ".pre_norm_ffn.4." in name:
_UpperCAmelCase = name.replace(".pre_norm_ffn.4." , ".output.dense." )
if ".transformer." in name:
_UpperCAmelCase = name.replace(".transformer." , ".transformer.layer." )
if ".aspp_layer." in name:
_UpperCAmelCase = name.replace(".aspp_layer." , "." )
if ".aspp_pool." in name:
_UpperCAmelCase = name.replace(".aspp_pool." , "." )
if "seg_head." in name:
_UpperCAmelCase = name.replace("seg_head." , "segmentation_head." )
if "segmentation_head.classifier.classifier." in name:
_UpperCAmelCase = name.replace("segmentation_head.classifier.classifier." , "segmentation_head.classifier." )
if "classifier.fc." in name:
_UpperCAmelCase = name.replace("classifier.fc." , "classifier." )
elif (not base_model) and ("segmentation_head." not in name):
_UpperCAmelCase = 'mobilevit.' + name
return name
def A__ ( A__ , A__ , A__=False ) -> str:
'''simple docstring'''
if base_model:
_UpperCAmelCase = ''
else:
_UpperCAmelCase = 'mobilevit.'
for key in orig_state_dict.copy().keys():
_UpperCAmelCase = orig_state_dict.pop(_UpperCAmelCase )
if key[:8] == "encoder.":
_UpperCAmelCase = key[8:]
if "qkv" in key:
_UpperCAmelCase = key.split("." )
_UpperCAmelCase = int(key_split[0][6:] ) - 1
_UpperCAmelCase = int(key_split[3] )
_UpperCAmelCase = model.get_submodule(F"""{model_prefix}encoder.layer.{layer_num}""" )
_UpperCAmelCase = layer.transformer.layer[transformer_num].attention.attention.all_head_size
_UpperCAmelCase = (
F"""{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."""
)
if "weight" in key:
_UpperCAmelCase = val[:dim, :]
_UpperCAmelCase = val[dim : dim * 2, :]
_UpperCAmelCase = val[-dim:, :]
else:
_UpperCAmelCase = val[:dim]
_UpperCAmelCase = val[dim : dim * 2]
_UpperCAmelCase = val[-dim:]
else:
_UpperCAmelCase = val
return orig_state_dict
def A__ ( ) -> List[Any]:
'''simple docstring'''
_UpperCAmelCase = 'http://images.cocodataset.org/val2017/000000039769.jpg'
_UpperCAmelCase = Image.open(requests.get(_UpperCAmelCase , stream=_UpperCAmelCase ).raw )
return im
@torch.no_grad()
def A__ ( A__ , A__ , A__ , A__=False ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase = get_mobilevit_config(_UpperCAmelCase )
# load original state_dict
_UpperCAmelCase = torch.load(_UpperCAmelCase , map_location="cpu" )
# load 🤗 model
if mobilevit_name.startswith("deeplabv3_" ):
_UpperCAmelCase = MobileViTForSemanticSegmentation(_UpperCAmelCase ).eval()
else:
_UpperCAmelCase = MobileViTForImageClassification(_UpperCAmelCase ).eval()
_UpperCAmelCase = convert_state_dict(_UpperCAmelCase , _UpperCAmelCase )
model.load_state_dict(_UpperCAmelCase )
# Check outputs on an image, prepared by MobileViTImageProcessor
_UpperCAmelCase = MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32 )
_UpperCAmelCase = image_processor(images=prepare_img() , return_tensors="pt" )
_UpperCAmelCase = model(**_UpperCAmelCase )
_UpperCAmelCase = outputs.logits
if mobilevit_name.startswith("deeplabv3_" ):
assert logits.shape == (1, 21, 32, 32)
if mobilevit_name == "deeplabv3_mobilevit_s":
_UpperCAmelCase = torch.tensor(
[
[[6.2065, 6.1292, 6.2070], [6.1079, 6.1254, 6.1747], [6.0042, 6.1071, 6.1034]],
[[-6.9253, -6.8653, -7.0398], [-7.3218, -7.3983, -7.3670], [-7.1961, -7.2482, -7.1569]],
[[-4.4723, -4.4348, -4.3769], [-5.3629, -5.4632, -5.4598], [-5.1587, -5.3402, -5.5059]],
] )
elif mobilevit_name == "deeplabv3_mobilevit_xs":
_UpperCAmelCase = torch.tensor(
[
[[5.4449, 5.5733, 5.6314], [5.1815, 5.3930, 5.5963], [5.1656, 5.4333, 5.4853]],
[[-9.4423, -9.7766, -9.6714], [-9.1581, -9.5720, -9.5519], [-9.1006, -9.6458, -9.5703]],
[[-7.7721, -7.3716, -7.1583], [-8.4599, -8.0624, -7.7944], [-8.4172, -7.8366, -7.5025]],
] )
elif mobilevit_name == "deeplabv3_mobilevit_xxs":
_UpperCAmelCase = torch.tensor(
[
[[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
[[-10.5536, -10.2332, -10.2924], [-10.2336, -9.8624, -9.5964], [-10.8840, -10.8158, -10.6659]],
[[-3.4938, -3.0631, -2.8620], [-3.4205, -2.8135, -2.6875], [-3.4179, -2.7945, -2.8750]],
] )
else:
raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}""" )
assert torch.allclose(logits[0, :3, :3, :3] , _UpperCAmelCase , atol=1E-4 )
else:
assert logits.shape == (1, 1000)
if mobilevit_name == "mobilevit_s":
_UpperCAmelCase = torch.tensor([-0.9866, 0.2392, -1.1241] )
elif mobilevit_name == "mobilevit_xs":
_UpperCAmelCase = torch.tensor([-2.4761, -0.9399, -1.9587] )
elif mobilevit_name == "mobilevit_xxs":
_UpperCAmelCase = torch.tensor([-1.9364, -1.2327, -0.4653] )
else:
raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}""" )
assert torch.allclose(logits[0, :3] , _UpperCAmelCase , atol=1E-4 )
Path(_UpperCAmelCase ).mkdir(exist_ok=_UpperCAmelCase )
print(F"""Saving model {mobilevit_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(_UpperCAmelCase )
print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(_UpperCAmelCase )
if push_to_hub:
_UpperCAmelCase = {
'mobilevit_s': 'mobilevit-small',
'mobilevit_xs': 'mobilevit-x-small',
'mobilevit_xxs': 'mobilevit-xx-small',
'deeplabv3_mobilevit_s': 'deeplabv3-mobilevit-small',
'deeplabv3_mobilevit_xs': 'deeplabv3-mobilevit-x-small',
'deeplabv3_mobilevit_xxs': 'deeplabv3-mobilevit-xx-small',
}
print("Pushing to the hub..." )
_UpperCAmelCase = model_mapping[mobilevit_name]
image_processor.push_to_hub(_UpperCAmelCase , organization="apple" )
model.push_to_hub(_UpperCAmelCase , organization="apple" )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--mobilevit_name''',
default='''mobilevit_s''',
type=str,
help=(
'''Name of the MobileViT model you\'d like to convert. Should be one of \'mobilevit_s\', \'mobilevit_xs\','''
''' \'mobilevit_xxs\', \'deeplabv3_mobilevit_s\', \'deeplabv3_mobilevit_xs\', \'deeplabv3_mobilevit_xxs\'.'''
),
)
parser.add_argument(
'''--checkpoint_path''', required=True, type=str, help='''Path to the original state dict (.pt file).'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument(
'''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
)
SCREAMING_SNAKE_CASE_ = parser.parse_args()
convert_movilevit_checkpoint(
args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 426
|
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class snake_case__ ( SCREAMING_SNAKE_CASE_ ):
A__ = ['''image_processor''', '''tokenizer''']
A__ = '''CLIPImageProcessor'''
A__ = ('''CLIPTokenizer''', '''CLIPTokenizerFast''')
def __init__( self : Any , __a : str=None , __a : List[Any]=None , **__a : List[str] ) -> Optional[Any]:
'''simple docstring'''
__snake_case : Optional[Any] = None
if "feature_extractor" in kwargs:
warnings.warn(
'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
' instead.' , __a , )
__snake_case : List[str] = kwargs.pop('feature_extractor' )
__snake_case : List[Any] = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('You need to specify an `image_processor`.' )
if tokenizer is None:
raise ValueError('You need to specify a `tokenizer`.' )
super().__init__(__a , __a )
def __call__( self : List[Any] , __a : Optional[int]=None , __a : Optional[int]=None , __a : Union[str, Any]=None , **__a : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
if text is None and images is None:
raise ValueError('You have to specify either text or images. Both cannot be none.' )
if text is not None:
__snake_case : Any = self.tokenizer(__a , return_tensors=__a , **__a )
if images is not None:
__snake_case : str = self.image_processor(__a , return_tensors=__a , **__a )
if text is not None and images is not None:
__snake_case : Optional[Any] = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**__a ) , tensor_type=__a )
def A_ ( self : List[Any] , *__a : Dict , **__a : Dict ) -> Union[str, Any]:
'''simple docstring'''
return self.tokenizer.batch_decode(*__a , **__a )
def A_ ( self : str , *__a : Tuple , **__a : List[str] ) -> int:
'''simple docstring'''
return self.tokenizer.decode(*__a , **__a )
@property
def A_ ( self : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
__snake_case : Tuple = self.tokenizer.model_input_names
__snake_case : Tuple = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def A_ ( self : Any ) -> Any:
'''simple docstring'''
warnings.warn(
'`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' , __a , )
return self.image_processor_class
@property
def A_ ( self : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
warnings.warn(
'`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , __a , )
return self.image_processor
| 286
| 0
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
"weiweishi/roc-bert-base-zh": "https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json",
}
class __a ( __lowerCamelCase ):
"""simple docstring"""
_A : Optional[Any] = "roc_bert"
def __init__( self : Any ,_UpperCamelCase : str=3_0_5_2_2 ,_UpperCamelCase : Optional[int]=7_6_8 ,_UpperCamelCase : List[Any]=1_2 ,_UpperCamelCase : List[str]=1_2 ,_UpperCamelCase : Tuple=3_0_7_2 ,_UpperCamelCase : Optional[Any]="gelu" ,_UpperCamelCase : int=0.1 ,_UpperCamelCase : Optional[Any]=0.1 ,_UpperCamelCase : List[Any]=5_1_2 ,_UpperCamelCase : Union[str, Any]=2 ,_UpperCamelCase : List[Any]=0.02 ,_UpperCamelCase : List[Any]=1e-12 ,_UpperCamelCase : Optional[Any]=True ,_UpperCamelCase : Optional[Any]=0 ,_UpperCamelCase : List[str]="absolute" ,_UpperCamelCase : Dict=None ,_UpperCamelCase : Optional[Any]=True ,_UpperCamelCase : str=True ,_UpperCamelCase : Union[str, Any]=7_6_8 ,_UpperCamelCase : int=9_1_0 ,_UpperCamelCase : Any=5_1_2 ,_UpperCamelCase : Optional[Any]=2_4_8_5_8 ,_UpperCamelCase : List[str]=True ,**_UpperCamelCase : Dict ,) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ =vocab_size
SCREAMING_SNAKE_CASE__ =max_position_embeddings
SCREAMING_SNAKE_CASE__ =hidden_size
SCREAMING_SNAKE_CASE__ =num_hidden_layers
SCREAMING_SNAKE_CASE__ =num_attention_heads
SCREAMING_SNAKE_CASE__ =intermediate_size
SCREAMING_SNAKE_CASE__ =hidden_act
SCREAMING_SNAKE_CASE__ =hidden_dropout_prob
SCREAMING_SNAKE_CASE__ =attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ =initializer_range
SCREAMING_SNAKE_CASE__ =type_vocab_size
SCREAMING_SNAKE_CASE__ =layer_norm_eps
SCREAMING_SNAKE_CASE__ =use_cache
SCREAMING_SNAKE_CASE__ =enable_pronunciation
SCREAMING_SNAKE_CASE__ =enable_shape
SCREAMING_SNAKE_CASE__ =pronunciation_embed_dim
SCREAMING_SNAKE_CASE__ =pronunciation_vocab_size
SCREAMING_SNAKE_CASE__ =shape_embed_dim
SCREAMING_SNAKE_CASE__ =shape_vocab_size
SCREAMING_SNAKE_CASE__ =concat_input
SCREAMING_SNAKE_CASE__ =position_embedding_type
SCREAMING_SNAKE_CASE__ =classifier_dropout
super().__init__(pad_token_id=_UpperCamelCase ,**_UpperCamelCase )
| 588
|
from __future__ import annotations
def UpperCAmelCase_ ( __UpperCamelCase, __UpperCamelCase, __UpperCamelCase, __UpperCamelCase ): # noqa: E741
while r - l > 1:
SCREAMING_SNAKE_CASE__ =(l + r) // 2
if v[m] >= key:
SCREAMING_SNAKE_CASE__ =m
else:
SCREAMING_SNAKE_CASE__ =m # noqa: E741
return r
def UpperCAmelCase_ ( __UpperCamelCase ):
if len(__UpperCamelCase ) == 0:
return 0
SCREAMING_SNAKE_CASE__ =[0] * len(__UpperCamelCase )
SCREAMING_SNAKE_CASE__ =1
SCREAMING_SNAKE_CASE__ =v[0]
for i in range(1, len(__UpperCamelCase ) ):
if v[i] < tail[0]:
SCREAMING_SNAKE_CASE__ =v[i]
elif v[i] > tail[length - 1]:
SCREAMING_SNAKE_CASE__ =v[i]
length += 1
else:
SCREAMING_SNAKE_CASE__ =v[i]
return length
if __name__ == "__main__":
import doctest
doctest.testmod()
| 588
| 1
|
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