code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
SCREAMING_SNAKE_CASE_ = importlib.util.find_spec("""s3fs""") is not None
if _has_safs:
from .safilesystem import SaFileSystem # noqa: F401
SCREAMING_SNAKE_CASE_ = [
compression.BzaFileSystem,
compression.GzipFileSystem,
compression.LzaFileSystem,
compression.XzFileSystem,
compression.ZstdFileSystem,
]
# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
warnings.warn(F'''A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.''')
fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> str:
'''simple docstring'''
if "://" in dataset_path:
SCREAMING_SNAKE_CASE = dataset_path.split("""://""" )[1]
return dataset_path
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> bool:
'''simple docstring'''
if fs is not None and fs.protocol != "file":
return True
else:
return False
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = not is_remote_filesystem(_SCREAMING_SNAKE_CASE )
if is_local:
# LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
shutil.move(fs._strip_protocol(_SCREAMING_SNAKE_CASE ) , fs._strip_protocol(_SCREAMING_SNAKE_CASE ) )
else:
fs.mv(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , recursive=_SCREAMING_SNAKE_CASE )
def __lowercase ( ) -> None:
'''simple docstring'''
if hasattr(fsspec.asyn , """reset_lock""" ):
# for future fsspec>2022.05.0
fsspec.asyn.reset_lock()
else:
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = threading.Lock()
| 296 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_torch_available():
import torch
if is_datasets_available():
from datasets import load_dataset
SCREAMING_SNAKE_CASE_ = random.Random()
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=1.0 , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None ) -> Tuple:
'''simple docstring'''
if rng is None:
SCREAMING_SNAKE_CASE = global_rng
SCREAMING_SNAKE_CASE = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : List[Any] ,lowerCamelCase__ : str ,lowerCamelCase__ : Optional[int]=7 ,lowerCamelCase__ : Optional[Any]=400 ,lowerCamelCase__ : List[str]=2000 ,lowerCamelCase__ : List[str]=2048 ,lowerCamelCase__ : Any=128 ,lowerCamelCase__ : List[str]=1 ,lowerCamelCase__ : str=512 ,lowerCamelCase__ : Optional[Any]=30 ,lowerCamelCase__ : Tuple=44100 ,) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE = parent
SCREAMING_SNAKE_CASE = batch_size
SCREAMING_SNAKE_CASE = min_seq_length
SCREAMING_SNAKE_CASE = max_seq_length
SCREAMING_SNAKE_CASE = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
SCREAMING_SNAKE_CASE = spectrogram_length
SCREAMING_SNAKE_CASE = feature_size
SCREAMING_SNAKE_CASE = num_audio_channels
SCREAMING_SNAKE_CASE = hop_length
SCREAMING_SNAKE_CASE = chunk_length
SCREAMING_SNAKE_CASE = sampling_rate
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ) -> Optional[int]:
'''simple docstring'''
return {
"spectrogram_length": self.spectrogram_length,
"feature_size": self.feature_size,
"num_audio_channels": self.num_audio_channels,
"hop_length": self.hop_length,
"chunk_length": self.chunk_length,
"sampling_rate": self.sampling_rate,
}
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ,lowerCamelCase__ : Tuple=False ,lowerCamelCase__ : Union[str, Any]=False ) -> str:
'''simple docstring'''
def _flatten(lowerCamelCase__ : List[Any] ):
return list(itertools.chain(*lowerCamelCase__ ) )
if equal_length:
SCREAMING_SNAKE_CASE = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
SCREAMING_SNAKE_CASE = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length ,self.max_seq_length ,self.seq_length_diff )
]
if numpify:
SCREAMING_SNAKE_CASE = [np.asarray(lowerCamelCase__ ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class UpperCamelCase__ ( lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
__snake_case : List[Any] = TvltFeatureExtractor
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = TvltFeatureExtractionTester(self )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(lowerCamelCase__ ,"""spectrogram_length""" ) )
self.assertTrue(hasattr(lowerCamelCase__ ,"""feature_size""" ) )
self.assertTrue(hasattr(lowerCamelCase__ ,"""num_audio_channels""" ) )
self.assertTrue(hasattr(lowerCamelCase__ ,"""hop_length""" ) )
self.assertTrue(hasattr(lowerCamelCase__ ,"""chunk_length""" ) )
self.assertTrue(hasattr(lowerCamelCase__ ,"""sampling_rate""" ) )
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE = feat_extract_first.save_pretrained(lowerCamelCase__ )[0]
check_json_file_has_correct_format(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self.feature_extraction_class.from_pretrained(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = feat_extract_first.to_dict()
SCREAMING_SNAKE_CASE = feat_extract_second.to_dict()
SCREAMING_SNAKE_CASE = dict_first.pop("""mel_filters""" )
SCREAMING_SNAKE_CASE = dict_second.pop("""mel_filters""" )
self.assertTrue(np.allclose(lowerCamelCase__ ,lowerCamelCase__ ) )
self.assertEqual(lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE = os.path.join(lowerCamelCase__ ,"""feat_extract.json""" )
feat_extract_first.to_json_file(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self.feature_extraction_class.from_json_file(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = feat_extract_first.to_dict()
SCREAMING_SNAKE_CASE = feat_extract_second.to_dict()
SCREAMING_SNAKE_CASE = dict_first.pop("""mel_filters""" )
SCREAMING_SNAKE_CASE = dict_second.pop("""mel_filters""" )
self.assertTrue(np.allclose(lowerCamelCase__ ,lowerCamelCase__ ) )
self.assertEqual(lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.feature_extraction_class(**self.feat_extract_dict )
# create three inputs of length 800, 1000, and 1200
SCREAMING_SNAKE_CASE = [floats_list((1, x) )[0] for x in range(800 ,1400 ,200 )]
SCREAMING_SNAKE_CASE = [np.asarray(lowerCamelCase__ ) for speech_input in speech_inputs]
# Test not batched input
SCREAMING_SNAKE_CASE = feature_extractor(np_speech_inputs[0] ,return_tensors="""np""" ,sampling_rate=44100 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test batched
SCREAMING_SNAKE_CASE = feature_extractor(lowerCamelCase__ ,return_tensors="""np""" ,sampling_rate=44100 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test audio masking
SCREAMING_SNAKE_CASE = feature_extractor(
lowerCamelCase__ ,return_tensors="""np""" ,sampling_rate=44100 ,mask_audio=lowerCamelCase__ ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test 2-D numpy arrays are batched.
SCREAMING_SNAKE_CASE = [floats_list((1, x) )[0] for x in (800, 800, 800)]
SCREAMING_SNAKE_CASE = np.asarray(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = feature_extractor(lowerCamelCase__ ,return_tensors="""np""" ,sampling_rate=44100 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
def SCREAMING_SNAKE_CASE__ ( self : Any ,lowerCamelCase__ : str ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = load_dataset("""hf-internal-testing/librispeech_asr_dummy""" ,"""clean""" ,split="""validation""" )
# automatic decoding with librispeech
SCREAMING_SNAKE_CASE = ds.sort("""id""" ).select(range(lowerCamelCase__ ) )[:num_samples]["""audio"""]
return [x["array"] for x in speech_samples]
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self._load_datasamples(1 )
SCREAMING_SNAKE_CASE = TvltFeatureExtractor()
SCREAMING_SNAKE_CASE = feature_extractor(lowerCamelCase__ ,return_tensors="""pt""" ).audio_values
self.assertEquals(audio_values.shape ,(1, 1, 192, 128) )
SCREAMING_SNAKE_CASE = torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]] )
self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2] ,lowerCamelCase__ ,atol=1e-4 ) )
| 296 | 1 |
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( __SCREAMING_SNAKE_CASE ):
snake_case__ : Tuple = ["input_ids", "attention_mask"]
def __init__( self : Tuple , SCREAMING_SNAKE_CASE__ : str="</s>" , SCREAMING_SNAKE_CASE__ : List[Any]="<unk>" , SCREAMING_SNAKE_CASE__ : Optional[int]="<pad>" , SCREAMING_SNAKE_CASE__ : str=1_2_5 , SCREAMING_SNAKE_CASE__ : Union[str, Any]=None , **SCREAMING_SNAKE_CASE__ : Dict , ) -> str:
# Add extra_ids to the special token list
if extra_ids > 0 and additional_special_tokens is None:
a_ : Optional[int] = [F"""<extra_id_{i}>""" for i in range(_snake_case )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra_id special tokens
a_ : List[Any] = len(set(filter(lambda SCREAMING_SNAKE_CASE__ : bool('extra_id' in str(_snake_case ) ) , _snake_case ) ) )
if extra_tokens != extra_ids:
raise ValueError(
F"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
' provided to ByT5Tokenizer. In this case the additional_special_tokens must include the'
' extra_ids tokens' )
a_ : str = AddedToken(_snake_case , lstrip=_snake_case , rstrip=_snake_case ) if isinstance(_snake_case , _snake_case ) else pad_token
a_ : Tuple = AddedToken(_snake_case , lstrip=_snake_case , rstrip=_snake_case ) if isinstance(_snake_case , _snake_case ) else eos_token
a_ : Union[str, Any] = AddedToken(_snake_case , lstrip=_snake_case , rstrip=_snake_case ) if isinstance(_snake_case , _snake_case ) else unk_token
super().__init__(
eos_token=_snake_case , unk_token=_snake_case , pad_token=_snake_case , extra_ids=_snake_case , additional_special_tokens=_snake_case , **_snake_case , )
a_ : Union[str, Any] = extra_ids
a_ : Dict = 2**8 # utf is 8 bits
# define special tokens dict
a_ : Dict[int, str] = {
self.pad_token: 0,
self.eos_token: 1,
self.unk_token: 2,
}
a_ : Any = len(self.special_tokens_encoder )
a_ : List[str] = len(_snake_case )
for i, token in enumerate(_snake_case ):
a_ : List[str] = self.vocab_size + i - n
a_ : Dict[str, int] = {v: k for k, v in self.special_tokens_encoder.items()}
@property
def SCREAMING_SNAKE_CASE ( self : Dict ) -> List[str]:
return self._utf_vocab_size + self._num_special_tokens + self._extra_ids
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Dict = None , SCREAMING_SNAKE_CASE__ : Tuple = False ) -> Optional[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_snake_case , token_ids_a=_snake_case , already_has_special_tokens=_snake_case )
# normal case: some special tokens
if token_ids_a is None:
return ([0] * len(_snake_case )) + [1]
return ([0] * len(_snake_case )) + [1] + ([0] * len(_snake_case )) + [1]
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : List[Any] ) -> Tuple:
if len(_snake_case ) > 0 and token_ids[-1] == self.eos_token_id:
warnings.warn(
F"""This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"""
' eos tokens being added.' )
return token_ids
else:
return token_ids + [self.eos_token_id]
def SCREAMING_SNAKE_CASE ( self : List[Any] , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Union[str, Any] = None ) -> Any:
a_ : List[Any] = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos ) * [0]
return len(token_ids_a + eos + token_ids_a + eos ) * [0]
def SCREAMING_SNAKE_CASE ( self : str , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : List[Any] = None ) -> str:
a_ : List[Any] = self._add_eos_if_not_present(_snake_case )
if token_ids_a is None:
return token_ids_a
else:
a_ : Tuple = self._add_eos_if_not_present(_snake_case )
return token_ids_a + token_ids_a
def SCREAMING_SNAKE_CASE ( self : List[str] , SCREAMING_SNAKE_CASE__ : int ) -> Dict:
a_ : List[Any] = [chr(_snake_case ) for i in text.encode('utf-8' )]
return tokens
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Optional[int] ) -> str:
if token in self.special_tokens_encoder:
a_ : Optional[Any] = self.special_tokens_encoder[token]
elif token in self.added_tokens_encoder:
a_ : str = self.added_tokens_encoder[token]
elif len(_snake_case ) != 1:
a_ : Optional[int] = self.unk_token_id
else:
a_ : Union[str, Any] = ord(_snake_case ) + self._num_special_tokens
return token_id
def SCREAMING_SNAKE_CASE ( self : Optional[int] , SCREAMING_SNAKE_CASE__ : Optional[int] ) -> Optional[int]:
if index in self.special_tokens_decoder:
a_ : Tuple = self.special_tokens_decoder[index]
else:
a_ : int = chr(index - self._num_special_tokens )
return token
def SCREAMING_SNAKE_CASE ( self : int , SCREAMING_SNAKE_CASE__ : Tuple ) -> Any:
a_ : Optional[int] = b""
for token in tokens:
if token in self.special_tokens_decoder:
a_ : Union[str, Any] = self.special_tokens_decoder[token].encode('utf-8' )
elif token in self.added_tokens_decoder:
a_ : Dict = self.special_tokens_decoder[token].encode('utf-8' )
elif token in self.special_tokens_encoder:
a_ : Optional[int] = token.encode('utf-8' )
elif token in self.added_tokens_encoder:
a_ : Any = token.encode('utf-8' )
else:
a_ : Optional[Any] = bytes([ord(_snake_case )] )
bstring += tok_string
a_ : Tuple = bstring.decode('utf-8' , errors='ignore' )
return string
def SCREAMING_SNAKE_CASE ( self : List[str] , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Union[str, Any] = None ) -> str:
return ()
| 351 |
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
from ...utils.backbone_utils import BackboneMixin
from .configuration_upernet import UperNetConfig
UpperCAmelCase_ : Dict = [
'openmmlab/upernet-convnext-tiny',
# See all UperNet models at https://huggingface.co/models?filter=upernet
]
# General docstring
UpperCAmelCase_ : Dict = 'UperNetConfig'
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
def __init__( self : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : Union[int, Tuple[int, int]] , SCREAMING_SNAKE_CASE__ : Union[int, Tuple[int, int], str] = 0 , SCREAMING_SNAKE_CASE__ : bool = False , SCREAMING_SNAKE_CASE__ : Union[int, Tuple[int, int]] = 1 , ) -> None:
super().__init__()
a_ : Dict = nn.Convad(
in_channels=SCREAMING_SNAKE_CASE__ , out_channels=SCREAMING_SNAKE_CASE__ , kernel_size=SCREAMING_SNAKE_CASE__ , padding=SCREAMING_SNAKE_CASE__ , bias=SCREAMING_SNAKE_CASE__ , dilation=SCREAMING_SNAKE_CASE__ , )
a_ : List[str] = nn.BatchNormad(SCREAMING_SNAKE_CASE__ )
a_ : Tuple = nn.ReLU()
def SCREAMING_SNAKE_CASE ( self : Optional[int] , SCREAMING_SNAKE_CASE__ : torch.Tensor ) -> torch.Tensor:
a_ : int = self.conv(SCREAMING_SNAKE_CASE__ )
a_ : Optional[int] = self.batch_norm(SCREAMING_SNAKE_CASE__ )
a_ : List[Any] = self.activation(SCREAMING_SNAKE_CASE__ )
return output
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
def __init__( self : str , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ) -> None:
super().__init__()
a_ : str = [
nn.AdaptiveAvgPoolad(SCREAMING_SNAKE_CASE__ ),
UperNetConvModule(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , kernel_size=1 ),
]
for i, layer in enumerate(self.layers ):
self.add_module(str(SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ )
def SCREAMING_SNAKE_CASE ( self : Any , SCREAMING_SNAKE_CASE__ : torch.Tensor ) -> torch.Tensor:
a_ : Optional[int] = input
for layer in self.layers:
a_ : int = layer(SCREAMING_SNAKE_CASE__ )
return hidden_state
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
def __init__( self : Tuple , SCREAMING_SNAKE_CASE__ : Tuple[int, ...] , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : bool ) -> None:
super().__init__()
a_ : Tuple = pool_scales
a_ : List[Any] = align_corners
a_ : Tuple = in_channels
a_ : Optional[Any] = channels
a_ : Tuple = []
for i, pool_scale in enumerate(SCREAMING_SNAKE_CASE__ ):
a_ : Optional[int] = UperNetPyramidPoolingBlock(pool_scale=SCREAMING_SNAKE_CASE__ , in_channels=SCREAMING_SNAKE_CASE__ , channels=SCREAMING_SNAKE_CASE__ )
self.blocks.append(SCREAMING_SNAKE_CASE__ )
self.add_module(str(SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ )
def SCREAMING_SNAKE_CASE ( self : Any , SCREAMING_SNAKE_CASE__ : torch.Tensor ) -> List[torch.Tensor]:
a_ : List[Any] = []
for ppm in self.blocks:
a_ : List[Any] = ppm(SCREAMING_SNAKE_CASE__ )
a_ : str = nn.functional.interpolate(
SCREAMING_SNAKE_CASE__ , size=x.size()[2:] , mode='bilinear' , align_corners=self.align_corners )
ppm_outs.append(SCREAMING_SNAKE_CASE__ )
return ppm_outs
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
def __init__( self : Tuple , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : str ) -> Tuple:
super().__init__()
a_ : List[str] = config
a_ : Optional[int] = config.pool_scales # e.g. (1, 2, 3, 6)
a_ : int = in_channels
a_ : Dict = config.hidden_size
a_ : Optional[Any] = False
a_ : Tuple = nn.Convad(self.channels , config.num_labels , kernel_size=1 )
# PSP Module
a_ : int = UperNetPyramidPoolingModule(
self.pool_scales , self.in_channels[-1] , self.channels , align_corners=self.align_corners , )
a_ : int = UperNetConvModule(
self.in_channels[-1] + len(self.pool_scales ) * self.channels , self.channels , kernel_size=3 , padding=1 , )
# FPN Module
a_ : str = nn.ModuleList()
a_ : Dict = nn.ModuleList()
for in_channels in self.in_channels[:-1]: # skip the top layer
a_ : str = UperNetConvModule(SCREAMING_SNAKE_CASE__ , self.channels , kernel_size=1 )
a_ : Union[str, Any] = UperNetConvModule(self.channels , self.channels , kernel_size=3 , padding=1 )
self.lateral_convs.append(SCREAMING_SNAKE_CASE__ )
self.fpn_convs.append(SCREAMING_SNAKE_CASE__ )
a_ : Optional[int] = UperNetConvModule(
len(self.in_channels ) * self.channels , self.channels , kernel_size=3 , padding=1 , )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> str:
self.apply(self._init_weights )
def SCREAMING_SNAKE_CASE ( self : List[str] , SCREAMING_SNAKE_CASE__ : Dict ) -> str:
if isinstance(SCREAMING_SNAKE_CASE__ , nn.Convad ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : Tuple ) -> Dict:
a_ : Dict = inputs[-1]
a_ : str = [x]
psp_outs.extend(self.psp_modules(SCREAMING_SNAKE_CASE__ ) )
a_ : List[Any] = torch.cat(SCREAMING_SNAKE_CASE__ , dim=1 )
a_ : Dict = self.bottleneck(SCREAMING_SNAKE_CASE__ )
return output
def SCREAMING_SNAKE_CASE ( self : str , SCREAMING_SNAKE_CASE__ : torch.Tensor ) -> torch.Tensor:
# build laterals
a_ : List[str] = [lateral_conv(encoder_hidden_states[i] ) for i, lateral_conv in enumerate(self.lateral_convs )]
laterals.append(self.psp_forward(SCREAMING_SNAKE_CASE__ ) )
# build top-down path
a_ : List[str] = len(SCREAMING_SNAKE_CASE__ )
for i in range(used_backbone_levels - 1 , 0 , -1 ):
a_ : Optional[Any] = laterals[i - 1].shape[2:]
a_ : List[Any] = laterals[i - 1] + nn.functional.interpolate(
laterals[i] , size=SCREAMING_SNAKE_CASE__ , mode='bilinear' , align_corners=self.align_corners )
# build outputs
a_ : List[Any] = [self.fpn_convs[i](laterals[i] ) for i in range(used_backbone_levels - 1 )]
# append psp feature
fpn_outs.append(laterals[-1] )
for i in range(used_backbone_levels - 1 , 0 , -1 ):
a_ : int = nn.functional.interpolate(
fpn_outs[i] , size=fpn_outs[0].shape[2:] , mode='bilinear' , align_corners=self.align_corners )
a_ : Optional[int] = torch.cat(SCREAMING_SNAKE_CASE__ , dim=1 )
a_ : Tuple = self.fpn_bottleneck(SCREAMING_SNAKE_CASE__ )
a_ : Dict = self.classifier(SCREAMING_SNAKE_CASE__ )
return output
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
def __init__( self : int , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : int = 2 , SCREAMING_SNAKE_CASE__ : int = 3 , SCREAMING_SNAKE_CASE__ : Union[int, Tuple[int, int]] = 1 ) -> None:
super().__init__()
a_ : Optional[int] = config
a_ : str = config.auxiliary_in_channels
a_ : Optional[int] = config.auxiliary_channels
a_ : List[str] = config.auxiliary_num_convs
a_ : Dict = config.auxiliary_concat_input
a_ : int = in_index
a_ : List[Any] = (kernel_size // 2) * dilation
a_ : List[Any] = []
convs.append(
UperNetConvModule(
self.in_channels , self.channels , kernel_size=SCREAMING_SNAKE_CASE__ , padding=SCREAMING_SNAKE_CASE__ , dilation=SCREAMING_SNAKE_CASE__ ) )
for i in range(self.num_convs - 1 ):
convs.append(
UperNetConvModule(
self.channels , self.channels , kernel_size=SCREAMING_SNAKE_CASE__ , padding=SCREAMING_SNAKE_CASE__ , dilation=SCREAMING_SNAKE_CASE__ ) )
if self.num_convs == 0:
a_ : Optional[int] = nn.Identity()
else:
a_ : int = nn.Sequential(*SCREAMING_SNAKE_CASE__ )
if self.concat_input:
a_ : Union[str, Any] = UperNetConvModule(
self.in_channels + self.channels , self.channels , kernel_size=SCREAMING_SNAKE_CASE__ , padding=kernel_size // 2 )
a_ : List[str] = nn.Convad(self.channels , config.num_labels , kernel_size=1 )
def SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> List[str]:
self.apply(self._init_weights )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Any ) -> List[str]:
if isinstance(SCREAMING_SNAKE_CASE__ , nn.Convad ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] , SCREAMING_SNAKE_CASE__ : torch.Tensor ) -> torch.Tensor:
# just take the relevant feature maps
a_ : Optional[int] = encoder_hidden_states[self.in_index]
a_ : int = self.convs(SCREAMING_SNAKE_CASE__ )
if self.concat_input:
a_ : Optional[Any] = self.conv_cat(torch.cat([hidden_states, output] , dim=1 ) )
a_ : Dict = self.classifier(SCREAMING_SNAKE_CASE__ )
return output
class SCREAMING_SNAKE_CASE__ ( lowercase__ ):
snake_case__ : Any = UperNetConfig
snake_case__ : Optional[int] = '''pixel_values'''
snake_case__ : Optional[int] = True
def SCREAMING_SNAKE_CASE ( self : List[str] , SCREAMING_SNAKE_CASE__ : Optional[Any] ) -> str:
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
module.backbone.init_weights()
module.decode_head.init_weights()
module.auxiliary_head.init_weights()
def SCREAMING_SNAKE_CASE ( self : Dict ) -> Dict:
self.backbone.init_weights()
self.decode_head.init_weights()
self.auxiliary_head.init_weights()
def SCREAMING_SNAKE_CASE ( self : Optional[int] , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : int=False ) -> Any:
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
a_ : Dict = value
UpperCAmelCase_ : int = R'\n Parameters:\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use\n it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n config ([`UperNetConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'
UpperCAmelCase_ : Tuple = R'\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using\n [`AutoImageProcessor`]. See [`SegformerImageProcessor.__call__`] for details.\n output_attentions (`bool`, *optional*):\n Whether or not to return the attentions tensors of all attention layers in case the backbone has them. See\n `attentions` under returned tensors for more detail.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers of the backbone. See `hidden_states` under\n returned tensors for more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
'''UperNet framework leveraging any vision backbone e.g. for ADE20k, CityScapes.''' , lowercase__ , )
class SCREAMING_SNAKE_CASE__ ( lowercase__ ):
def __init__( self : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Optional[int] ) -> str:
super().__init__(SCREAMING_SNAKE_CASE__ )
a_ : List[str] = AutoBackbone.from_config(config.backbone_config )
# Semantic segmentation head(s)
a_ : List[Any] = UperNetHead(SCREAMING_SNAKE_CASE__ , in_channels=self.backbone.channels )
a_ : Dict = UperNetFCNHead(SCREAMING_SNAKE_CASE__ ) if config.use_auxiliary_head else None
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(UPERNET_INPUTS_DOCSTRING.format('batch_size, sequence_length' ) )
@replace_return_docstrings(output_type=SCREAMING_SNAKE_CASE__ , config_class=_CONFIG_FOR_DOC )
def SCREAMING_SNAKE_CASE ( self : List[Any] , SCREAMING_SNAKE_CASE__ : Optional[torch.Tensor] = None , SCREAMING_SNAKE_CASE__ : Optional[bool] = None , SCREAMING_SNAKE_CASE__ : Optional[bool] = None , SCREAMING_SNAKE_CASE__ : Optional[torch.Tensor] = None , SCREAMING_SNAKE_CASE__ : Optional[bool] = None , ) -> Union[tuple, SemanticSegmenterOutput]:
a_ : Optional[Any] = return_dict if return_dict is not None else self.config.use_return_dict
a_ : Tuple = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
a_ : int = output_attentions if output_attentions is not None else self.config.output_attentions
a_ : str = self.backbone.forward_with_filtered_kwargs(
SCREAMING_SNAKE_CASE__ , output_hidden_states=SCREAMING_SNAKE_CASE__ , output_attentions=SCREAMING_SNAKE_CASE__ )
a_ : Optional[int] = outputs.feature_maps
a_ : Tuple = self.decode_head(SCREAMING_SNAKE_CASE__ )
a_ : List[Any] = nn.functional.interpolate(SCREAMING_SNAKE_CASE__ , size=pixel_values.shape[2:] , mode='bilinear' , align_corners=SCREAMING_SNAKE_CASE__ )
a_ : Union[str, Any] = None
if self.auxiliary_head is not None:
a_ : Any = self.auxiliary_head(SCREAMING_SNAKE_CASE__ )
a_ : int = nn.functional.interpolate(
SCREAMING_SNAKE_CASE__ , size=pixel_values.shape[2:] , mode='bilinear' , align_corners=SCREAMING_SNAKE_CASE__ )
a_ : List[Any] = None
if labels is not None:
if self.config.num_labels == 1:
raise ValueError('The number of labels should be greater than one' )
else:
# compute weighted loss
a_ : List[str] = CrossEntropyLoss(ignore_index=self.config.loss_ignore_index )
a_ : Optional[Any] = loss_fct(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
a_ : str = loss_fct(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
a_ : str = main_loss + self.config.auxiliary_loss_weight * auxiliary_loss
if not return_dict:
if output_hidden_states:
a_ : Optional[Any] = (logits,) + outputs[1:]
else:
a_ : List[str] = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SemanticSegmenterOutput(
loss=SCREAMING_SNAKE_CASE__ , logits=SCREAMING_SNAKE_CASE__ , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
| 120 | 0 |
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS
logging.basicConfig(level=logging.DEBUG)
snake_case_ : str = logging.getLogger()
def A (__A : Path , __A : list ) -> Tuple:
"""simple docstring"""
UpperCAmelCase_ = '''\n'''.join(__A )
Path(__A ).open('''w''' ).writelines(__A )
snake_case_ : Union[str, Any] = "patrickvonplaten/t5-tiny-random"
snake_case_ : int = "sshleifer/bart-tiny-random"
snake_case_ : List[str] = "sshleifer/tiny-mbart"
snake_case_ : int = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL) # remove noisy download output from tracebacks
class __snake_case ( a ):
def lowerCamelCase ( self : List[str] , _snake_case : int):
"""simple docstring"""
UpperCAmelCase_ = Path(self.get_auto_remove_tmp_dir()) / '''utest_input.source'''
UpperCAmelCase_ = input_file_name.parent / '''utest_output.txt'''
assert not output_file_name.exists()
UpperCAmelCase_ = [''' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.''']
_dump_articles(_snake_case , _snake_case)
UpperCAmelCase_ = str(Path(self.get_auto_remove_tmp_dir()) / '''scores.json''')
UpperCAmelCase_ = '''translation_en_to_de''' if model == T5_TINY else '''summarization'''
UpperCAmelCase_ = F"""
run_eval_search.py
{model}
{input_file_name}
{output_file_name}
--score_path {score_path}
--task {task}
--num_beams 2
--length_penalty 2.0
""".split()
with patch.object(_snake_case , '''argv''' , _snake_case):
run_generate()
assert Path(_snake_case).exists()
# os.remove(Path(output_file_name))
def lowerCamelCase ( self : Tuple):
"""simple docstring"""
self.run_eval_tester(_snake_case)
@parameterized.expand([BART_TINY, MBART_TINY])
@slow
def lowerCamelCase ( self : List[Any] , _snake_case : Tuple):
"""simple docstring"""
self.run_eval_tester(_snake_case)
@parameterized.expand([T5_TINY, MBART_TINY])
@slow
def lowerCamelCase ( self : str , _snake_case : List[Any]):
"""simple docstring"""
UpperCAmelCase_ = Path(self.get_auto_remove_tmp_dir()) / '''utest_input.source'''
UpperCAmelCase_ = input_file_name.parent / '''utest_output.txt'''
assert not output_file_name.exists()
UpperCAmelCase_ = {
'''en''': ['''Machine learning is great, isn\'t it?''', '''I like to eat bananas''', '''Tomorrow is another great day!'''],
'''de''': [
'''Maschinelles Lernen ist großartig, oder?''',
'''Ich esse gerne Bananen''',
'''Morgen ist wieder ein toller Tag!''',
],
}
UpperCAmelCase_ = Path(self.get_auto_remove_tmp_dir())
UpperCAmelCase_ = str(tmp_dir / '''scores.json''')
UpperCAmelCase_ = str(tmp_dir / '''val.target''')
_dump_articles(_snake_case , text['''en'''])
_dump_articles(_snake_case , text['''de'''])
UpperCAmelCase_ = '''translation_en_to_de''' if model == T5_TINY else '''summarization'''
UpperCAmelCase_ = F"""
run_eval_search.py
{model}
{str(_snake_case)}
{str(_snake_case)}
--score_path {score_path}
--reference_path {reference_path}
--task {task}
""".split()
testargs.extend(['''--search''', '''num_beams=1:2 length_penalty=0.9:1.0'''])
with patch.object(_snake_case , '''argv''' , _snake_case):
with CaptureStdout() as cs:
run_search()
UpperCAmelCase_ = [''' num_beams | length_penalty''', model, '''Best score args''']
UpperCAmelCase_ = ['''Info''']
if "translation" in task:
expected_strings.append('''bleu''')
else:
expected_strings.extend(_snake_case)
for w in expected_strings:
assert w in cs.out
for w in un_expected_strings:
assert w not in cs.out
assert Path(_snake_case).exists()
os.remove(Path(_snake_case))
| 51 |
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class __snake_case :
pass
| 51 | 1 |
"""simple docstring"""
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
FlaxCrossAttnDownBlockaD,
FlaxCrossAttnUpBlockaD,
FlaxDownBlockaD,
FlaxUNetMidBlockaDCrossAttn,
FlaxUpBlockaD,
)
@flax.struct.dataclass
class lowerCAmelCase_ ( lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase : jnp.ndarray
@flax_register_to_config
class lowerCAmelCase_ ( nn.Module , lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase : int = 32
_lowerCAmelCase : int = 4
_lowerCAmelCase : int = 4
_lowerCAmelCase : Tuple[str] = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
)
_lowerCAmelCase : Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
_lowerCAmelCase : Union[bool, Tuple[bool]] = False
_lowerCAmelCase : Tuple[int] = (320, 640, 1280, 1280)
_lowerCAmelCase : int = 2
_lowerCAmelCase : Union[int, Tuple[int]] = 8
_lowerCAmelCase : Optional[Union[int, Tuple[int]]] = None
_lowerCAmelCase : int = 1280
_lowerCAmelCase : float = 0.0
_lowerCAmelCase : bool = False
_lowerCAmelCase : jnp.dtype = jnp.floataa
_lowerCAmelCase : bool = True
_lowerCAmelCase : int = 0
_lowerCAmelCase : bool = False
def snake_case ( self , lowerCAmelCase ):
"""simple docstring"""
snake_case = (1, self.in_channels, self.sample_size, self.sample_size)
snake_case = jnp.zeros(lowerCAmelCase , dtype=jnp.floataa )
snake_case = jnp.ones((1,) , dtype=jnp.intaa )
snake_case = jnp.zeros((1, 1, self.cross_attention_dim) , dtype=jnp.floataa )
snake_case ,snake_case = jax.random.split(lowerCAmelCase )
snake_case = {'params': params_rng, 'dropout': dropout_rng}
return self.init(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )["params"]
def snake_case ( self ):
"""simple docstring"""
snake_case = self.block_out_channels
snake_case = 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.
snake_case = self.num_attention_heads or self.attention_head_dim
# input
snake_case = nn.Conv(
block_out_channels[0] , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
# time
snake_case = FlaxTimesteps(
block_out_channels[0] , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.config.freq_shift )
snake_case = FlaxTimestepEmbedding(lowerCAmelCase , dtype=self.dtype )
snake_case = self.only_cross_attention
if isinstance(lowerCAmelCase , lowerCAmelCase ):
snake_case = (only_cross_attention,) * len(self.down_block_types )
if isinstance(lowerCAmelCase , lowerCAmelCase ):
snake_case = (num_attention_heads,) * len(self.down_block_types )
# down
snake_case = []
snake_case = block_out_channels[0]
for i, down_block_type in enumerate(self.down_block_types ):
snake_case = output_channel
snake_case = block_out_channels[i]
snake_case = i == len(lowerCAmelCase ) - 1
if down_block_type == "CrossAttnDownBlock2D":
snake_case = 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] , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
else:
snake_case = 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 )
snake_case = down_blocks
# mid
snake_case = 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
snake_case = []
snake_case = list(reversed(lowerCAmelCase ) )
snake_case = list(reversed(lowerCAmelCase ) )
snake_case = list(reversed(lowerCAmelCase ) )
snake_case = reversed_block_out_channels[0]
for i, up_block_type in enumerate(self.up_block_types ):
snake_case = output_channel
snake_case = reversed_block_out_channels[i]
snake_case = reversed_block_out_channels[min(i + 1 , len(lowerCAmelCase ) - 1 )]
snake_case = i == len(lowerCAmelCase ) - 1
if up_block_type == "CrossAttnUpBlock2D":
snake_case = FlaxCrossAttnUpBlockaD(
in_channels=lowerCAmelCase , out_channels=lowerCAmelCase , prev_output_channel=lowerCAmelCase , 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:
snake_case = FlaxUpBlockaD(
in_channels=lowerCAmelCase , out_channels=lowerCAmelCase , prev_output_channel=lowerCAmelCase , num_layers=self.layers_per_block + 1 , add_upsample=not is_final_block , dropout=self.dropout , dtype=self.dtype , )
up_blocks.append(lowerCAmelCase )
snake_case = output_channel
snake_case = up_blocks
# out
snake_case = nn.GroupNorm(num_groups=32 , epsilon=1E-5 )
snake_case = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase=None , lowerCAmelCase=None , lowerCAmelCase = True , lowerCAmelCase = False , ):
"""simple docstring"""
if not isinstance(lowerCAmelCase , jnp.ndarray ):
snake_case = jnp.array([timesteps] , dtype=jnp.intaa )
elif isinstance(lowerCAmelCase , jnp.ndarray ) and len(timesteps.shape ) == 0:
snake_case = timesteps.astype(dtype=jnp.floataa )
snake_case = jnp.expand_dims(lowerCAmelCase , 0 )
snake_case = self.time_proj(lowerCAmelCase )
snake_case = self.time_embedding(lowerCAmelCase )
# 2. pre-process
snake_case = jnp.transpose(lowerCAmelCase , (0, 2, 3, 1) )
snake_case = self.conv_in(lowerCAmelCase )
# 3. down
snake_case = (sample,)
for down_block in self.down_blocks:
if isinstance(lowerCAmelCase , lowerCAmelCase ):
snake_case ,snake_case = down_block(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , deterministic=not train )
else:
snake_case ,snake_case = down_block(lowerCAmelCase , lowerCAmelCase , deterministic=not train )
down_block_res_samples += res_samples
if down_block_additional_residuals is not None:
snake_case = ()
for down_block_res_sample, down_block_additional_residual in zip(
lowerCAmelCase , lowerCAmelCase ):
down_block_res_sample += down_block_additional_residual
new_down_block_res_samples += (down_block_res_sample,)
snake_case = new_down_block_res_samples
# 4. mid
snake_case = self.mid_block(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , 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:
snake_case = down_block_res_samples[-(self.layers_per_block + 1) :]
snake_case = down_block_res_samples[: -(self.layers_per_block + 1)]
if isinstance(lowerCAmelCase , lowerCAmelCase ):
snake_case = up_block(
lowerCAmelCase , temb=lowerCAmelCase , encoder_hidden_states=lowerCAmelCase , res_hidden_states_tuple=lowerCAmelCase , deterministic=not train , )
else:
snake_case = up_block(lowerCAmelCase , temb=lowerCAmelCase , res_hidden_states_tuple=lowerCAmelCase , deterministic=not train )
# 6. post-process
snake_case = self.conv_norm_out(lowerCAmelCase )
snake_case = nn.silu(lowerCAmelCase )
snake_case = self.conv_out(lowerCAmelCase )
snake_case = jnp.transpose(lowerCAmelCase , (0, 3, 1, 2) )
if not return_dict:
return (sample,)
return FlaxUNetaDConditionOutput(sample=lowerCAmelCase )
| 149 | """simple docstring"""
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def lowerCAmelCase__ ( _UpperCamelCase : int = 8 ) -> str:
"""simple docstring"""
snake_case = ascii_letters + digits + punctuation
return "".join(secrets.choice(_UpperCamelCase ) for _ in range(_UpperCamelCase ) )
def lowerCAmelCase__ ( _UpperCamelCase : str , _UpperCamelCase : int ) -> str:
"""simple docstring"""
i -= len(_UpperCamelCase )
snake_case = i // 3
snake_case = i % 3
# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
# random_number(digits, i / 3) + random_characters(punctuation, i / 3)
snake_case = (
chars_incl
+ random(_UpperCamelCase , quotient + remainder )
+ random(_UpperCamelCase , _UpperCamelCase )
+ random(_UpperCamelCase , _UpperCamelCase )
)
snake_case = list(_UpperCamelCase )
shuffle(_UpperCamelCase )
return "".join(_UpperCamelCase )
# random is a generalised function for letters, characters and numbers
def lowerCAmelCase__ ( _UpperCamelCase : str , _UpperCamelCase : int ) -> str:
"""simple docstring"""
return "".join(secrets.choice(_UpperCamelCase ) for _ in range(_UpperCamelCase ) )
def lowerCAmelCase__ ( _UpperCamelCase : Optional[Any] , _UpperCamelCase : Optional[Any] ) -> List[Any]:
"""simple docstring"""
pass # Put your code here...
def lowerCAmelCase__ ( _UpperCamelCase : Union[str, Any] , _UpperCamelCase : Optional[Any] ) -> Any:
"""simple docstring"""
pass # Put your code here...
def lowerCAmelCase__ ( _UpperCamelCase : Dict , _UpperCamelCase : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
pass # Put your code here...
def lowerCAmelCase__ ( _UpperCamelCase : str , _UpperCamelCase : int = 8 ) -> bool:
"""simple docstring"""
if len(_UpperCamelCase ) < min_length:
# Your Password must be at least 8 characters long
return False
snake_case = any(char in ascii_uppercase for char in password )
snake_case = any(char in ascii_lowercase for char in password )
snake_case = any(char in digits for char in password )
snake_case = any(char in punctuation for char in password )
return upper and lower and num and spec_char
# Passwords should contain UPPERCASE, lowerase
# numbers, and special characters
def lowerCAmelCase__ ( ) -> Any:
"""simple docstring"""
snake_case = int(input('Please indicate the max length of your password: ' ).strip() )
snake_case = input(
'Please indicate the characters that must be in your password: ' ).strip()
print('Password generated:' , password_generator(_UpperCamelCase ) )
print(
'Alternative Password generated:' , alternative_password_generator(_UpperCamelCase , _UpperCamelCase ) , )
print('[If you are thinking of using this passsword, You better save it.]' )
if __name__ == "__main__":
main()
| 149 | 1 |
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
SCREAMING_SNAKE_CASE : Optional[Any] = 1E-4
if is_torch_available():
import torch
from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder
@require_torch
class _lowerCamelCase:
def __init__( self, lowerCamelCase, lowerCamelCase=16, lowerCamelCase=13, lowerCamelCase=7, lowerCamelCase=14, lowerCamelCase=10, lowerCamelCase=19, lowerCamelCase=5, lowerCamelCase=4, lowerCamelCase=True, lowerCamelCase=16, lowerCamelCase=2, lowerCamelCase=4, lowerCamelCase=4, lowerCamelCase="gelu", lowerCamelCase=0.1, lowerCamelCase=0.1, lowerCamelCase=[1, 2, 3, 4, 5], lowerCamelCase=25, lowerCamelCase=5, ) -> List[str]:
"""simple docstring"""
_lowercase : Optional[Any] = d_model
_lowercase : Optional[int] = parent
_lowercase : Tuple = batch_size
_lowercase : Optional[int] = prediction_length
_lowercase : Optional[Any] = context_length
_lowercase : Optional[int] = cardinality
_lowercase : Union[str, Any] = num_time_features
_lowercase : Union[str, Any] = lags_sequence
_lowercase : Dict = embedding_dimension
_lowercase : Dict = is_training
_lowercase : List[Any] = hidden_size
_lowercase : List[str] = num_hidden_layers
_lowercase : Any = num_attention_heads
_lowercase : List[Any] = intermediate_size
_lowercase : Optional[Any] = hidden_act
_lowercase : str = hidden_dropout_prob
_lowercase : str = attention_probs_dropout_prob
_lowercase : Union[str, Any] = context_length
_lowercase : int = prediction_length + label_length
_lowercase : Any = label_length
_lowercase : str = moving_average
_lowercase : int = autocorrelation_factor
def UpperCamelCase ( self) -> List[Any]:
"""simple docstring"""
return AutoformerConfig(
d_model=self.d_model, encoder_layers=self.num_hidden_layers, decoder_layers=self.num_hidden_layers, encoder_attention_heads=self.num_attention_heads, decoder_attention_heads=self.num_attention_heads, encoder_ffn_dim=self.intermediate_size, decoder_ffn_dim=self.intermediate_size, dropout=self.hidden_dropout_prob, attention_dropout=self.attention_probs_dropout_prob, prediction_length=self.prediction_length, context_length=self.context_length, label_length=self.label_length, lags_sequence=self.lags_sequence, num_time_features=self.num_time_features, num_static_categorical_features=1, cardinality=[self.cardinality], embedding_dimension=[self.embedding_dimension], moving_average=self.moving_average, )
def UpperCamelCase ( self, lowerCamelCase) -> List[Any]:
"""simple docstring"""
_lowercase : List[Any] = config.context_length + max(config.lags_sequence)
_lowercase : Tuple = ids_tensor([self.batch_size, 1], config.cardinality[0])
_lowercase : Optional[Any] = floats_tensor([self.batch_size, _past_length, config.num_time_features])
_lowercase : str = floats_tensor([self.batch_size, _past_length])
_lowercase : Optional[Any] = floats_tensor([self.batch_size, _past_length]) > 0.5
# decoder inputs
_lowercase : str = floats_tensor([self.batch_size, config.prediction_length, config.num_time_features])
_lowercase : Dict = floats_tensor([self.batch_size, config.prediction_length])
_lowercase : Dict = {
"""past_values""": past_values,
"""static_categorical_features""": static_categorical_features,
"""past_time_features""": past_time_features,
"""past_observed_mask""": past_observed_mask,
"""future_time_features""": future_time_features,
"""future_values""": future_values,
}
return inputs_dict
def UpperCamelCase ( self) -> Any:
"""simple docstring"""
_lowercase : Tuple = self.get_config()
_lowercase : List[str] = self.prepare_autoformer_inputs_dict(_SCREAMING_SNAKE_CASE)
return config, inputs_dict
def UpperCamelCase ( self) -> List[str]:
"""simple docstring"""
_lowercase : int = self.prepare_config_and_inputs()
return config, inputs_dict
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase) -> List[str]:
"""simple docstring"""
_lowercase : List[Any] = AutoformerModel(config=_SCREAMING_SNAKE_CASE).to(_SCREAMING_SNAKE_CASE).eval()
_lowercase : List[str] = model(**_SCREAMING_SNAKE_CASE)
_lowercase : List[str] = outputs.encoder_last_hidden_state
_lowercase : Optional[Any] = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
_lowercase : Any = model.get_encoder()
encoder.save_pretrained(_SCREAMING_SNAKE_CASE)
_lowercase : Optional[int] = AutoformerEncoder.from_pretrained(_SCREAMING_SNAKE_CASE).to(_SCREAMING_SNAKE_CASE)
_lowercase : Union[str, Any] = model.create_network_inputs(**_SCREAMING_SNAKE_CASE)
_lowercase : Tuple = model.decomposition_layer(transformer_inputs[:, : config.context_length, ...])
_lowercase : int = torch.cat(
(transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...]), dim=-1, )
_lowercase : Optional[Any] = encoder(inputs_embeds=_SCREAMING_SNAKE_CASE)[0]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3)
_lowercase : str = (
torch.mean(transformer_inputs[:, : config.context_length, ...], dim=1)
.unsqueeze(1)
.repeat(1, config.prediction_length, 1)
)
_lowercase : Optional[int] = torch.zeros(
[transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]], device=enc_input.device, )
_lowercase : str = torch.cat(
(
torch.cat((seasonal_input[:, -config.label_length :, ...], zeros), dim=1),
feature[:, config.context_length - config.label_length :, ...],
), dim=-1, )
_lowercase : Optional[int] = torch.cat(
(
torch.cat((trend_input[:, -config.label_length :, ...], mean), dim=1),
feature[:, config.context_length - config.label_length :, ...],
), dim=-1, )
with tempfile.TemporaryDirectory() as tmpdirname:
_lowercase : str = model.get_decoder()
decoder.save_pretrained(_SCREAMING_SNAKE_CASE)
_lowercase : Optional[Any] = AutoformerDecoder.from_pretrained(_SCREAMING_SNAKE_CASE).to(_SCREAMING_SNAKE_CASE)
_lowercase : Dict = decoder(
trend=_SCREAMING_SNAKE_CASE, inputs_embeds=_SCREAMING_SNAKE_CASE, encoder_hidden_states=_SCREAMING_SNAKE_CASE, )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3)
@require_torch
class _lowerCamelCase( UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase ):
lowercase_ : Any = (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
lowercase_ : Any = (AutoformerForPrediction,) if is_torch_available() else ()
lowercase_ : int = {'feature-extraction': AutoformerModel} if is_torch_available() else {}
lowercase_ : List[Any] = False
lowercase_ : Tuple = False
lowercase_ : Any = False
lowercase_ : Union[str, Any] = False
lowercase_ : Optional[Any] = False
lowercase_ : Union[str, Any] = False
def UpperCamelCase ( self) -> Optional[int]:
"""simple docstring"""
_lowercase : int = AutoformerModelTester(self)
_lowercase : Tuple = ConfigTester(self, config_class=_SCREAMING_SNAKE_CASE, has_text_modality=_SCREAMING_SNAKE_CASE)
def UpperCamelCase ( self) -> int:
"""simple docstring"""
self.config_tester.run_common_tests()
def UpperCamelCase ( self) -> Any:
"""simple docstring"""
_lowercase : Tuple = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
_lowercase : int = model_class(_SCREAMING_SNAKE_CASE)
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_SCREAMING_SNAKE_CASE)
_lowercase : Union[str, Any] = model_class.from_pretrained(_SCREAMING_SNAKE_CASE, output_loading_info=_SCREAMING_SNAKE_CASE)
self.assertEqual(info['missing_keys'], [])
def UpperCamelCase ( self) -> Any:
"""simple docstring"""
_lowercase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*_SCREAMING_SNAKE_CASE)
@unittest.skip(reason='Model has no tokens embeddings')
def UpperCamelCase ( self) -> Union[str, Any]:
"""simple docstring"""
pass
def UpperCamelCase ( self) -> int:
"""simple docstring"""
_lowercase : Union[str, Any] = inspect.signature(getattr(_SCREAMING_SNAKE_CASE, 'forward'))
# The main input is the name of the argument after `self`
_lowercase : Any = list(model_signature.parameters.keys())[1]
self.assertEqual(AutoformerModel.main_input_name, _SCREAMING_SNAKE_CASE)
def UpperCamelCase ( self) -> Union[str, Any]:
"""simple docstring"""
_lowercase : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowercase : int = model_class(_SCREAMING_SNAKE_CASE)
_lowercase : int = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_lowercase : Any = [*signature.parameters.keys()]
_lowercase : Optional[int] = [
"""past_values""",
"""past_time_features""",
"""past_observed_mask""",
"""static_categorical_features""",
"""static_real_features""",
"""future_values""",
"""future_time_features""",
]
if model.__class__.__name__ in ["AutoformerForPrediction"]:
expected_arg_names.append('future_observed_mask')
expected_arg_names.extend(
[
'decoder_attention_mask',
'head_mask',
'decoder_head_mask',
'cross_attn_head_mask',
'encoder_outputs',
'past_key_values',
'output_hidden_states',
'output_attentions',
'use_cache',
'return_dict',
])
self.assertListEqual(arg_names[: len(_SCREAMING_SNAKE_CASE)], _SCREAMING_SNAKE_CASE)
def UpperCamelCase ( self) -> Union[str, Any]:
"""simple docstring"""
_lowercase : Any = self.model_tester.prepare_config_and_inputs_for_common()
_lowercase : List[Any] = True
_lowercase : List[str] = getattr(self.model_tester, 'seq_length', _SCREAMING_SNAKE_CASE)
_lowercase : Any = getattr(self.model_tester, 'decoder_seq_length', _SCREAMING_SNAKE_CASE)
_lowercase : Union[str, Any] = getattr(self.model_tester, 'encoder_seq_length', _SCREAMING_SNAKE_CASE)
_lowercase : Tuple = getattr(self.model_tester, 'd_model', _SCREAMING_SNAKE_CASE)
_lowercase : Optional[Any] = getattr(self.model_tester, 'num_attention_heads', _SCREAMING_SNAKE_CASE)
_lowercase : Optional[int] = d_model // num_attention_heads
for model_class in self.all_model_classes:
_lowercase : Any = True
_lowercase : List[Any] = False
_lowercase : Union[str, Any] = True
_lowercase : List[Any] = model_class(_SCREAMING_SNAKE_CASE)
model.to(_SCREAMING_SNAKE_CASE)
model.eval()
with torch.no_grad():
_lowercase : List[str] = model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE))
_lowercase : Optional[int] = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(_SCREAMING_SNAKE_CASE), self.model_tester.num_hidden_layers)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
_lowercase : str = True
_lowercase : Tuple = model_class(_SCREAMING_SNAKE_CASE)
model.to(_SCREAMING_SNAKE_CASE)
model.eval()
with torch.no_grad():
_lowercase : Optional[int] = model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE))
_lowercase : Any = outputs.encoder_attentions
self.assertEqual(len(_SCREAMING_SNAKE_CASE), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, encoder_seq_length, dim], )
_lowercase : Optional[Any] = len(_SCREAMING_SNAKE_CASE)
_lowercase : int = 7
if "last_hidden_state" in outputs:
correct_outlen += 1
if "trend" in outputs:
correct_outlen += 1
if "past_key_values" in outputs:
correct_outlen += 1 # past_key_values have been returned
if "loss" in outputs:
correct_outlen += 1
if "params" in outputs:
correct_outlen += 1
self.assertEqual(_SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE)
# decoder attentions
_lowercase : str = outputs.decoder_attentions
self.assertIsInstance(_SCREAMING_SNAKE_CASE, (list, tuple))
self.assertEqual(len(_SCREAMING_SNAKE_CASE), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(decoder_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, decoder_seq_length, dim], )
# cross attentions
_lowercase : Dict = outputs.cross_attentions
self.assertIsInstance(_SCREAMING_SNAKE_CASE, (list, tuple))
self.assertEqual(len(_SCREAMING_SNAKE_CASE), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(cross_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, decoder_seq_length, dim], )
# Check attention is always last and order is fine
_lowercase : int = True
_lowercase : Optional[Any] = True
_lowercase : List[Any] = model_class(_SCREAMING_SNAKE_CASE)
model.to(_SCREAMING_SNAKE_CASE)
model.eval()
with torch.no_grad():
_lowercase : Dict = model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE))
self.assertEqual(out_len + 2, len(_SCREAMING_SNAKE_CASE))
_lowercase : Tuple = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(_SCREAMING_SNAKE_CASE), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(self_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, encoder_seq_length, dim], )
@is_flaky()
def UpperCamelCase ( self) -> List[Any]:
"""simple docstring"""
super().test_retain_grad_hidden_states_attentions()
def UpperCamelCase_( lowerCamelCase_="train-batch.pt" ) -> int:
_lowercase : List[str] = hf_hub_download(repo_id='hf-internal-testing/tourism-monthly-batch' , filename=lowerCamelCase_ , repo_type='dataset' )
_lowercase : List[str] = torch.load(lowerCamelCase_ , map_location=lowerCamelCase_ )
return batch
@require_torch
@slow
class _lowerCamelCase( unittest.TestCase ):
def UpperCamelCase ( self) -> List[str]:
"""simple docstring"""
_lowercase : str = AutoformerModel.from_pretrained('huggingface/autoformer-tourism-monthly').to(_SCREAMING_SNAKE_CASE)
_lowercase : Union[str, Any] = prepare_batch()
with torch.no_grad():
_lowercase : Any = model(
past_values=batch['past_values'], past_time_features=batch['past_time_features'], past_observed_mask=batch['past_observed_mask'], static_categorical_features=batch['static_categorical_features'], future_values=batch['future_values'], future_time_features=batch['future_time_features'], )[0]
_lowercase : Union[str, Any] = torch.Size(
(64, model.config.prediction_length + model.config.label_length, model.config.feature_size))
self.assertEqual(output.shape, _SCREAMING_SNAKE_CASE)
_lowercase : str = torch.tensor(
[[0.3_5_9_3, -1.3_3_9_8, 0.6_3_3_0], [0.2_2_7_9, 1.5_3_9_6, -0.1_7_9_2], [0.0_4_5_0, 1.3_2_2_5, -0.2_3_3_5]], device=_SCREAMING_SNAKE_CASE)
self.assertTrue(torch.allclose(output[0, :3, :3], _SCREAMING_SNAKE_CASE, atol=_SCREAMING_SNAKE_CASE))
def UpperCamelCase ( self) -> Any:
"""simple docstring"""
_lowercase : Any = AutoformerForPrediction.from_pretrained('huggingface/autoformer-tourism-monthly').to(_SCREAMING_SNAKE_CASE)
_lowercase : Dict = prepare_batch('val-batch.pt')
with torch.no_grad():
_lowercase : Dict = model(
past_values=batch['past_values'], past_time_features=batch['past_time_features'], past_observed_mask=batch['past_observed_mask'], static_categorical_features=batch['static_categorical_features'], ).encoder_last_hidden_state
_lowercase : List[str] = torch.Size((64, model.config.context_length, model.config.d_model))
self.assertEqual(output.shape, _SCREAMING_SNAKE_CASE)
_lowercase : Union[str, Any] = torch.tensor(
[[-0.0_7_3_4, -0.9_0_3_6, 0.8_3_5_8], [4.7_1_8_6, 2.4_1_1_3, 1.9_5_8_1], [1.7_9_5_3, 2.3_5_5_8, 1.2_9_7_0]], device=_SCREAMING_SNAKE_CASE)
self.assertTrue(torch.allclose(output[0, :3, :3], _SCREAMING_SNAKE_CASE, atol=_SCREAMING_SNAKE_CASE))
def UpperCamelCase ( self) -> Tuple:
"""simple docstring"""
_lowercase : str = AutoformerForPrediction.from_pretrained('huggingface/autoformer-tourism-monthly').to(_SCREAMING_SNAKE_CASE)
_lowercase : Optional[Any] = prepare_batch('val-batch.pt')
with torch.no_grad():
_lowercase : Union[str, Any] = model.generate(
static_categorical_features=batch['static_categorical_features'], past_time_features=batch['past_time_features'], past_values=batch['past_values'], future_time_features=batch['future_time_features'], past_observed_mask=batch['past_observed_mask'], )
_lowercase : List[Any] = torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length))
self.assertEqual(outputs.sequences.shape, _SCREAMING_SNAKE_CASE)
_lowercase : Tuple = torch.tensor([3_1_3_0.6_7_6_3, 4_0_5_6.5_2_9_3, 7_0_5_3.0_7_8_6], device=_SCREAMING_SNAKE_CASE)
_lowercase : Dict = outputs.sequences.mean(dim=1)
self.assertTrue(torch.allclose(mean_prediction[0, -3:], _SCREAMING_SNAKE_CASE, rtol=1E-1))
| 21 |
"""simple docstring"""
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def _snake_case ( *UpperCamelCase : str , UpperCamelCase : Optional[Union[Dict, Any]] = None , UpperCamelCase : Tuple=True , UpperCamelCase : Optional[int]=2 ):
from .. import __version__
UpperCAmelCase : Tuple = take_from
UpperCAmelCase : Optional[Any] = ()
if not isinstance(args[0] , UpperCamelCase ):
UpperCAmelCase : List[str] = (args,)
for attribute, version_name, message in args:
if version.parse(version.parse(UpperCamelCase ).base_version ) >= version.parse(UpperCamelCase ):
raise ValueError(
F"The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"
F" version {__version__} is >= {version_name}" )
UpperCAmelCase : Optional[int] = None
if isinstance(UpperCamelCase , UpperCamelCase ) and attribute in deprecated_kwargs:
values += (deprecated_kwargs.pop(UpperCamelCase ),)
UpperCAmelCase : List[str] = F"The `{attribute}` argument is deprecated and will be removed in version {version_name}."
elif hasattr(UpperCamelCase , UpperCamelCase ):
values += (getattr(UpperCamelCase , UpperCamelCase ),)
UpperCAmelCase : List[Any] = F"The `{attribute}` attribute is deprecated and will be removed in version {version_name}."
elif deprecated_kwargs is None:
UpperCAmelCase : int = F"`{attribute}` is deprecated and will be removed in version {version_name}."
if warning is not None:
UpperCAmelCase : Optional[Any] = warning + """ """ if standard_warn else """"""
warnings.warn(warning + message , UpperCamelCase , stacklevel=UpperCamelCase )
if isinstance(UpperCamelCase , UpperCamelCase ) and len(UpperCamelCase ) > 0:
UpperCAmelCase : Optional[int] = inspect.getouterframes(inspect.currentframe() )[1]
UpperCAmelCase : Union[str, Any] = call_frame.filename
UpperCAmelCase : List[Any] = call_frame.lineno
UpperCAmelCase : List[str] = call_frame.function
UpperCAmelCase , UpperCAmelCase : Optional[int] = next(iter(deprecated_kwargs.items() ) )
raise TypeError(F"{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`" )
if len(UpperCamelCase ) == 0:
return
elif len(UpperCamelCase ) == 1:
return values[0]
return values
| 109 | 0 |
"""simple docstring"""
import gc
import unittest
from transformers import CTRLConfig, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
CTRLForSequenceClassification,
CTRLLMHeadModel,
CTRLModel,
)
class _UpperCAmelCase :
"""simple docstring"""
def __init__( self : Any , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Tuple=1_4 , lowerCAmelCase_ : List[str]=7 , lowerCAmelCase_ : int=True , lowerCAmelCase_ : Union[str, Any]=True , lowerCAmelCase_ : List[Any]=True , lowerCAmelCase_ : Tuple=True , lowerCAmelCase_ : int=True , lowerCAmelCase_ : Optional[Any]=9_9 , lowerCAmelCase_ : Union[str, Any]=3_2 , lowerCAmelCase_ : List[Any]=5 , lowerCAmelCase_ : int=4 , lowerCAmelCase_ : List[str]=3_7 , lowerCAmelCase_ : Tuple="gelu" , lowerCAmelCase_ : Optional[Any]=0.1 , lowerCAmelCase_ : Union[str, Any]=0.1 , lowerCAmelCase_ : Tuple=5_1_2 , lowerCAmelCase_ : str=1_6 , lowerCAmelCase_ : Optional[Any]=2 , lowerCAmelCase_ : int=0.02 , lowerCAmelCase_ : str=3 , lowerCAmelCase_ : List[str]=4 , lowerCAmelCase_ : Optional[Any]=None , ) -> Any:
__lowerCAmelCase = parent
__lowerCAmelCase = batch_size
__lowerCAmelCase = seq_length
__lowerCAmelCase = is_training
__lowerCAmelCase = use_token_type_ids
__lowerCAmelCase = use_input_mask
__lowerCAmelCase = use_labels
__lowerCAmelCase = use_mc_token_ids
__lowerCAmelCase = vocab_size
__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 = max_position_embeddings
__lowerCAmelCase = type_vocab_size
__lowerCAmelCase = type_sequence_label_size
__lowerCAmelCase = initializer_range
__lowerCAmelCase = num_labels
__lowerCAmelCase = num_choices
__lowerCAmelCase = scope
__lowerCAmelCase = self.vocab_size - 1
def lowercase ( self : str ) -> List[str]:
__lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCAmelCase = None
if self.use_input_mask:
__lowerCAmelCase = random_attention_mask([self.batch_size, self.seq_length] )
__lowerCAmelCase = None
if self.use_token_type_ids:
__lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__lowerCAmelCase = None
if self.use_mc_token_ids:
__lowerCAmelCase = ids_tensor([self.batch_size, self.num_choices] , self.seq_length )
__lowerCAmelCase = None
__lowerCAmelCase = None
__lowerCAmelCase = None
if self.use_labels:
__lowerCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__lowerCAmelCase = ids_tensor([self.batch_size] , self.num_choices )
__lowerCAmelCase = self.get_config()
__lowerCAmelCase = ids_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
input_mask,
head_mask,
token_type_ids,
mc_token_ids,
sequence_labels,
token_labels,
choice_labels,
)
def lowercase ( self : Optional[Any] ) -> Tuple:
return CTRLConfig(
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 , )
def lowercase ( self : str , lowerCAmelCase_ : Any , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Tuple , *lowerCAmelCase_ : Optional[int] ) -> Dict:
__lowerCAmelCase = CTRLModel(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
model(SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , head_mask=SCREAMING_SNAKE_CASE_ )
model(SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(len(result.past_key_values ) , config.n_layer )
def lowercase ( self : Any , lowerCAmelCase_ : Any , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Dict , lowerCAmelCase_ : Optional[int] , *lowerCAmelCase_ : str ) -> Any:
__lowerCAmelCase = CTRLLMHeadModel(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
__lowerCAmelCase = model(SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowercase ( self : Optional[int] ) -> Optional[int]:
__lowerCAmelCase = self.prepare_config_and_inputs()
(
__lowerCAmelCase
) = config_and_inputs
__lowerCAmelCase = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """head_mask""": head_mask}
return config, inputs_dict
def lowercase ( self : Union[str, Any] , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : int , lowerCAmelCase_ : str , lowerCAmelCase_ : str , *lowerCAmelCase_ : int ) -> Optional[int]:
__lowerCAmelCase = self.num_labels
__lowerCAmelCase = CTRLForSequenceClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
__lowerCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCAmelCase = model(SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
@require_torch
class _UpperCAmelCase ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
a_ = (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
a_ = (CTRLLMHeadModel,) if is_torch_available() else ()
a_ = (
{
'feature-extraction': CTRLModel,
'text-classification': CTRLForSequenceClassification,
'text-generation': CTRLLMHeadModel,
'zero-shot': CTRLForSequenceClassification,
}
if is_torch_available()
else {}
)
a_ = True
a_ = False
a_ = False
def lowercase ( self : Union[str, Any] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Dict , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Optional[int] ) -> Tuple:
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `CTRLConfig` 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 lowercase ( self : int ) -> List[str]:
__lowerCAmelCase = CTRLModelTester(self )
__lowerCAmelCase = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , n_embd=3_7 )
def lowercase ( self : Optional[Any] ) -> Dict:
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
torch.cuda.empty_cache()
def lowercase ( self : List[Any] ) -> List[str]:
self.config_tester.run_common_tests()
def lowercase ( self : str ) -> Optional[int]:
__lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_ctrl_model(*SCREAMING_SNAKE_CASE_ )
def lowercase ( self : Tuple ) -> Any:
__lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*SCREAMING_SNAKE_CASE_ )
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def lowercase ( self : List[str] ) -> Optional[Any]:
pass
@slow
def lowercase ( self : str ) -> Any:
for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCAmelCase = CTRLModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
@unittest.skip('The model doesn\'t support left padding' ) # and it's not used enough to be worth fixing :)
def lowercase ( self : Tuple ) -> int:
pass
@require_torch
class _UpperCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowercase ( self : Union[str, Any] ) -> Optional[Any]:
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
torch.cuda.empty_cache()
@slow
def lowercase ( self : List[Any] ) -> Tuple:
__lowerCAmelCase = CTRLLMHeadModel.from_pretrained('ctrl' )
model.to(SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = torch.tensor(
[[1_1_8_5_9, 0, 1_6_1_1, 8]] , dtype=torch.long , device=SCREAMING_SNAKE_CASE_ ) # Legal the president is
__lowerCAmelCase = [
1_1_8_5_9,
0,
1_6_1_1,
8,
5,
1_5_0,
2_6_4_4_9,
2,
1_9,
3_4_8,
4_6_9,
3,
2_5_9_5,
4_8,
2_0_7_4_0,
2_4_6_5_3_3,
2_4_6_5_3_3,
1_9,
3_0,
5,
] # Legal the president is a good guy and I don't want to lose my job. \n \n I have a
__lowerCAmelCase = model.generate(SCREAMING_SNAKE_CASE_ , do_sample=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(output_ids[0].tolist() , SCREAMING_SNAKE_CASE_ )
| 355 |
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 _UpperCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowercase ( self : List[str] ) -> int:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def lowercase ( self : Union[str, Any] ) -> Any:
torch.manual_seed(0 )
__lowerCAmelCase = UNetaDModel(
sample_size=(3_2, 6_4) , in_channels=1 , out_channels=1 , layers_per_block=2 , block_out_channels=(1_2_8, 1_2_8) , down_block_types=('AttnDownBlock2D', 'DownBlock2D') , up_block_types=('UpBlock2D', 'AttnUpBlock2D') , )
return model
@property
def lowercase ( self : Optional[Any] ) -> List[Any]:
torch.manual_seed(0 )
__lowerCAmelCase = UNetaDConditionModel(
sample_size=(6_4, 3_2) , in_channels=1 , out_channels=1 , layers_per_block=2 , block_out_channels=(1_2_8, 1_2_8) , down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D') , up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D') , cross_attention_dim=1_0 , )
return model
@property
def lowercase ( self : Dict ) -> Optional[Any]:
torch.manual_seed(0 )
__lowerCAmelCase = AutoencoderKL(
sample_size=(1_2_8, 6_4) , in_channels=1 , out_channels=1 , latent_channels=1 , layers_per_block=2 , block_out_channels=(1_2_8, 1_2_8) , down_block_types=('DownEncoderBlock2D', 'DownEncoderBlock2D') , up_block_types=('UpDecoderBlock2D', 'UpDecoderBlock2D') , )
__lowerCAmelCase = UNetaDModel(
sample_size=(6_4, 3_2) , in_channels=1 , out_channels=1 , layers_per_block=2 , block_out_channels=(1_2_8, 1_2_8) , down_block_types=('AttnDownBlock2D', 'DownBlock2D') , up_block_types=('UpBlock2D', 'AttnUpBlock2D') , )
return vqvae, unet
@slow
def lowercase ( self : Dict ) -> Optional[Any]:
__lowerCAmelCase = 'cpu' # ensure determinism for the device-dependent torch.Generator
__lowerCAmelCase = Mel(
x_res=self.dummy_unet.config.sample_size[1] , y_res=self.dummy_unet.config.sample_size[0] , )
__lowerCAmelCase = DDPMScheduler()
__lowerCAmelCase = AudioDiffusionPipeline(vqvae=lowerCAmelCase_ , unet=self.dummy_unet , mel=lowerCAmelCase_ , scheduler=lowerCAmelCase_ )
__lowerCAmelCase = pipe.to(lowerCAmelCase_ )
pipe.set_progress_bar_config(disable=lowerCAmelCase_ )
__lowerCAmelCase = torch.Generator(device=lowerCAmelCase_ ).manual_seed(4_2 )
__lowerCAmelCase = pipe(generator=lowerCAmelCase_ , steps=4 )
__lowerCAmelCase = output.audios[0]
__lowerCAmelCase = output.images[0]
__lowerCAmelCase = torch.Generator(device=lowerCAmelCase_ ).manual_seed(4_2 )
__lowerCAmelCase = pipe(generator=lowerCAmelCase_ , steps=4 , return_dict=lowerCAmelCase_ )
__lowerCAmelCase = 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]
)
__lowerCAmelCase = np.frombuffer(image.tobytes() , dtype='uint8' )[:1_0]
__lowerCAmelCase = np.frombuffer(image_from_tuple.tobytes() , dtype='uint8' )[:1_0]
__lowerCAmelCase = np.array([6_9, 2_5_5, 2_5_5, 2_5_5, 0, 0, 7_7, 1_8_1, 1_2, 1_2_7] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() == 0
__lowerCAmelCase = 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] , )
__lowerCAmelCase = DDIMScheduler()
__lowerCAmelCase = self.dummy_vqvae_and_unet
__lowerCAmelCase = AudioDiffusionPipeline(
vqvae=self.dummy_vqvae_and_unet[0] , unet=dummy_vqvae_and_unet[1] , mel=lowerCAmelCase_ , scheduler=lowerCAmelCase_ )
__lowerCAmelCase = pipe.to(lowerCAmelCase_ )
pipe.set_progress_bar_config(disable=lowerCAmelCase_ )
np.random.seed(0 )
__lowerCAmelCase = np.random.uniform(-1 , 1 , ((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,) )
__lowerCAmelCase = torch.Generator(device=lowerCAmelCase_ ).manual_seed(4_2 )
__lowerCAmelCase = pipe(raw_audio=lowerCAmelCase_ , generator=lowerCAmelCase_ , start_step=5 , steps=1_0 )
__lowerCAmelCase = 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]
)
__lowerCAmelCase = np.frombuffer(image.tobytes() , dtype='uint8' )[:1_0]
__lowerCAmelCase = np.array([1_2_0, 1_1_7, 1_1_0, 1_0_9, 1_3_8, 1_6_7, 1_3_8, 1_4_8, 1_3_2, 1_2_1] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
__lowerCAmelCase = self.dummy_unet_condition
__lowerCAmelCase = AudioDiffusionPipeline(
vqvae=self.dummy_vqvae_and_unet[0] , unet=lowerCAmelCase_ , mel=lowerCAmelCase_ , scheduler=lowerCAmelCase_ )
__lowerCAmelCase = pipe.to(lowerCAmelCase_ )
pipe.set_progress_bar_config(disable=lowerCAmelCase_ )
np.random.seed(0 )
__lowerCAmelCase = torch.rand((1, 1, 1_0) )
__lowerCAmelCase = pipe(generator=lowerCAmelCase_ , encoding=lowerCAmelCase_ )
__lowerCAmelCase = output.images[0]
__lowerCAmelCase = np.frombuffer(image.tobytes() , dtype='uint8' )[:1_0]
__lowerCAmelCase = np.array([1_0_7, 1_0_3, 1_2_0, 1_2_7, 1_4_2, 1_2_2, 1_1_3, 1_2_2, 9_7, 1_1_1] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
@slow
@require_torch_gpu
class _UpperCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowercase ( self : List[Any] ) -> Optional[int]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowercase ( self : Union[str, Any] ) -> Optional[int]:
__lowerCAmelCase = torch_device
__lowerCAmelCase = DiffusionPipeline.from_pretrained('teticio/audio-diffusion-ddim-256' )
__lowerCAmelCase = pipe.to(lowerCAmelCase_ )
pipe.set_progress_bar_config(disable=lowerCAmelCase_ )
__lowerCAmelCase = torch.Generator(device=lowerCAmelCase_ ).manual_seed(4_2 )
__lowerCAmelCase = pipe(generator=lowerCAmelCase_ )
__lowerCAmelCase = output.audios[0]
__lowerCAmelCase = 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]
__lowerCAmelCase = np.frombuffer(image.tobytes() , dtype='uint8' )[:1_0]
__lowerCAmelCase = np.array([1_5_1, 1_6_7, 1_5_4, 1_4_4, 1_2_2, 1_3_4, 1_2_1, 1_0_5, 7_0, 2_6] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
| 207 | 0 |
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE_ ( snake_case__ , snake_case__ , snake_case__ , snake_case__="attention" ) -> str:
lowerCAmelCase = params[f"{prefix}/layers_{i}/{layer_name}/key/kernel"]
lowerCAmelCase = params[f"{prefix}/layers_{i}/{layer_name}/out/kernel"]
lowerCAmelCase = params[f"{prefix}/layers_{i}/{layer_name}/query/kernel"]
lowerCAmelCase = params[f"{prefix}/layers_{i}/{layer_name}/value/kernel"]
return k, o, q, v
def SCREAMING_SNAKE_CASE_ ( snake_case__ , snake_case__ , snake_case__ , snake_case__=False ) -> Optional[Any]:
if split_mlp_wi:
lowerCAmelCase = params[f"{prefix}/layers_{i}/mlp/wi_0/kernel"]
lowerCAmelCase = params[f"{prefix}/layers_{i}/mlp/wi_1/kernel"]
lowerCAmelCase = (wi_a, wi_a)
else:
lowerCAmelCase = params[f"{prefix}/layers_{i}/mlp/wi/kernel"]
lowerCAmelCase = params[f"{prefix}/layers_{i}/mlp/wo/kernel"]
return wi, wo
def SCREAMING_SNAKE_CASE_ ( snake_case__ , snake_case__ , snake_case__ , snake_case__ ) -> Tuple:
return params[f"{prefix}/layers_{i}/{layer_name}/scale"]
def SCREAMING_SNAKE_CASE_ ( snake_case__ , *, snake_case__ , snake_case__ ) -> Optional[Any]:
lowerCAmelCase = traverse_util.flatten_dict(variables['''target'''] )
lowerCAmelCase = {'''/'''.join(snake_case__ ): v for k, v in old.items()}
# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
lowerCAmelCase = '''encoder/layers_0/mlp/wi_0/kernel''' in old
print('''Split MLP:''' , snake_case__ )
lowerCAmelCase = collections.OrderedDict()
# Shared embeddings.
lowerCAmelCase = old['''token_embedder/embedding''']
# Encoder.
for i in range(snake_case__ ):
# Block i, layer 0 (Self Attention).
lowerCAmelCase = tax_layer_norm_lookup(snake_case__ , snake_case__ , '''encoder''' , '''pre_attention_layer_norm''' )
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = tax_attention_lookup(snake_case__ , snake_case__ , '''encoder''' , '''attention''' )
lowerCAmelCase = layer_norm
lowerCAmelCase = k.T
lowerCAmelCase = o.T
lowerCAmelCase = q.T
lowerCAmelCase = v.T
# Block i, layer 1 (MLP).
lowerCAmelCase = tax_layer_norm_lookup(snake_case__ , snake_case__ , '''encoder''' , '''pre_mlp_layer_norm''' )
lowerCAmelCase , lowerCAmelCase = tax_mlp_lookup(snake_case__ , snake_case__ , '''encoder''' , snake_case__ )
lowerCAmelCase = layer_norm
if split_mlp_wi:
lowerCAmelCase = wi[0].T
lowerCAmelCase = wi[1].T
else:
lowerCAmelCase = wi.T
lowerCAmelCase = wo.T
lowerCAmelCase = old[
'''encoder/relpos_bias/rel_embedding'''
].T
lowerCAmelCase = old['''encoder/encoder_norm/scale''']
if not is_encoder_only:
# Decoder.
for i in range(snake_case__ ):
# Block i, layer 0 (Self Attention).
lowerCAmelCase = tax_layer_norm_lookup(snake_case__ , snake_case__ , '''decoder''' , '''pre_self_attention_layer_norm''' )
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = tax_attention_lookup(snake_case__ , snake_case__ , '''decoder''' , '''self_attention''' )
lowerCAmelCase = layer_norm
lowerCAmelCase = k.T
lowerCAmelCase = o.T
lowerCAmelCase = q.T
lowerCAmelCase = v.T
# Block i, layer 1 (Cross Attention).
lowerCAmelCase = tax_layer_norm_lookup(snake_case__ , snake_case__ , '''decoder''' , '''pre_cross_attention_layer_norm''' )
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = tax_attention_lookup(snake_case__ , snake_case__ , '''decoder''' , '''encoder_decoder_attention''' )
lowerCAmelCase = layer_norm
lowerCAmelCase = k.T
lowerCAmelCase = o.T
lowerCAmelCase = q.T
lowerCAmelCase = v.T
# Block i, layer 2 (MLP).
lowerCAmelCase = tax_layer_norm_lookup(snake_case__ , snake_case__ , '''decoder''' , '''pre_mlp_layer_norm''' )
lowerCAmelCase , lowerCAmelCase = tax_mlp_lookup(snake_case__ , snake_case__ , '''decoder''' , snake_case__ )
lowerCAmelCase = layer_norm
if split_mlp_wi:
lowerCAmelCase = wi[0].T
lowerCAmelCase = wi[1].T
else:
lowerCAmelCase = wi.T
lowerCAmelCase = wo.T
lowerCAmelCase = old['''decoder/decoder_norm/scale''']
lowerCAmelCase = old[
'''decoder/relpos_bias/rel_embedding'''
].T
# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
if "decoder/logits_dense/kernel" in old:
lowerCAmelCase = old['''decoder/logits_dense/kernel'''].T
return new
def SCREAMING_SNAKE_CASE_ ( snake_case__ , snake_case__ ) -> Union[str, Any]:
lowerCAmelCase = collections.OrderedDict([(k, torch.from_numpy(v.copy() )) for (k, v) in converted_params.items()] )
# Add what is missing.
if "encoder.embed_tokens.weight" not in state_dict:
lowerCAmelCase = state_dict['''shared.weight''']
if not is_encoder_only:
if "decoder.embed_tokens.weight" not in state_dict:
lowerCAmelCase = state_dict['''shared.weight''']
if "lm_head.weight" not in state_dict: # For old 1.0 models.
print('''Using shared word embeddings as lm_head.''' )
lowerCAmelCase = state_dict['''shared.weight''']
return state_dict
def SCREAMING_SNAKE_CASE_ ( snake_case__ , snake_case__ , snake_case__ , snake_case__ ) -> Union[str, Any]:
lowerCAmelCase = checkpoints.load_tax_checkpoint(snake_case__ )
lowerCAmelCase = convert_tax_to_pytorch(snake_case__ , num_layers=config.num_layers , is_encoder_only=snake_case__ )
lowerCAmelCase = make_state_dict(snake_case__ , snake_case__ )
model.load_state_dict(snake_case__ , strict=snake_case__ )
def SCREAMING_SNAKE_CASE_ ( snake_case__ , snake_case__ , snake_case__ , snake_case__ = False ) -> int:
lowerCAmelCase = TaConfig.from_json_file(snake_case__ )
print(f"Building PyTorch model from configuration: {config}" )
# Non-v1.1 checkpoints could also use T5Model, but this works for all.
# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
if is_encoder_only:
lowerCAmelCase = TaEncoderModel(snake_case__ )
else:
lowerCAmelCase = TaForConditionalGeneration(snake_case__ )
# Load weights from tf checkpoint
load_tax_weights_in_ta(snake_case__ , snake_case__ , snake_case__ , snake_case__ )
# Save pytorch-model
print(f"Save PyTorch model to {pytorch_dump_path}" )
model.save_pretrained(snake_case__ )
# Verify that we can load the checkpoint.
model.from_pretrained(snake_case__ )
print('''Done''' )
if __name__ == "__main__":
lowercase__ : Dict = argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
# Required parameters
parser.add_argument(
'''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
)
lowercase__ : Optional[Any] = parser.parse_args()
convert_tax_checkpoint_to_pytorch(
args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
)
| 338 | import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
lowercase__ : Dict = np.linspace(start=0, stop=7_5, num=7_5, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(), gbellmf(), gaussmf(), etc).
lowercase__ : Optional[int] = [0, 2_5, 5_0]
lowercase__ : Union[str, Any] = [2_5, 5_0, 7_5]
lowercase__ : int = fuzz.membership.trimf(X, abca)
lowercase__ : Tuple = fuzz.membership.trimf(X, abca)
# Compute the different operations using inbuilt functions.
lowercase__ : List[str] = np.ones(7_5)
lowercase__ : Any = np.zeros((7_5,))
# 1. Union = max(µA(x), µB(x))
lowercase__ : Union[str, Any] = fuzz.fuzzy_or(X, young, X, middle_aged)[1]
# 2. Intersection = min(µA(x), µB(x))
lowercase__ : int = fuzz.fuzzy_and(X, young, X, middle_aged)[1]
# 3. Complement (A) = (1- min(µA(x))
lowercase__ : Union[str, Any] = fuzz.fuzzy_not(young)
# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
lowercase__ : Optional[int] = fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
lowercase__ : Any = young + middle_aged - (young * middle_aged)
# 6. Algebraic Product = (µA(x) * µB(x))
lowercase__ : str = young * middle_aged
# 7. Bounded Sum = min[1,(µA(x), µB(x))]
lowercase__ : Tuple = fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
# 8. Bounded difference = min[0,(µA(x), µB(x))]
lowercase__ : Tuple = 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, 1_0)
plt.plot(X, bdd_difference)
plt.title('''bdd_difference''')
plt.grid(True)
plt.subplots_adjust(hspace=0.5)
plt.show()
| 338 | 1 |
'''simple docstring'''
def UpperCAmelCase_ (__a : str = 1_0_0_0 ):
"""simple docstring"""
_a : Optional[Any] = 1, 1
_a : Any = []
for i in range(1 , n + 1 ):
_a : Union[str, Any] = prev_numerator + 2 * prev_denominator
_a : List[Any] = prev_numerator + prev_denominator
if len(str(_UpperCAmelCase ) ) > len(str(_UpperCAmelCase ) ):
result.append(_UpperCAmelCase )
_a : Union[str, Any] = numerator
_a : Union[str, Any] = denominator
return len(_UpperCAmelCase )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 364 |
'''simple docstring'''
from collections.abc import Generator
from math import sin
def UpperCAmelCase_ (__a : bytes ):
"""simple docstring"""
if len(__a ) != 3_2:
raise ValueError('Input must be of length 32' )
_a : Any = b''
for i in [3, 2, 1, 0]:
little_endian += string_aa[8 * i : 8 * i + 8]
return little_endian
def UpperCAmelCase_ (__a : int ):
"""simple docstring"""
if i < 0:
raise ValueError('Input must be non-negative' )
_a : List[str] = format(__a , '08x' )[-8:]
_a : str = b''
for i in [3, 2, 1, 0]:
little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode('utf-8' )
return little_endian_hex
def UpperCAmelCase_ (__a : bytes ):
"""simple docstring"""
_a : List[Any] = b''
for char in message:
bit_string += format(__a , '08b' ).encode('utf-8' )
_a : int = format(len(__a ) , '064b' ).encode('utf-8' )
# Pad bit_string to a multiple of 512 chars
bit_string += b"1"
while len(__a ) % 5_1_2 != 4_4_8:
bit_string += b"0"
bit_string += to_little_endian(start_len[3_2:] ) + to_little_endian(start_len[:3_2] )
return bit_string
def UpperCAmelCase_ (__a : bytes ):
"""simple docstring"""
if len(__a ) % 5_1_2 != 0:
raise ValueError('Input must have length that\'s a multiple of 512' )
for pos in range(0 , len(__a ) , 5_1_2 ):
_a : List[Any] = bit_string[pos : pos + 5_1_2]
_a : str = []
for i in range(0 , 5_1_2 , 3_2 ):
block_words.append(int(to_little_endian(block[i : i + 3_2] ) , 2 ) )
yield block_words
def UpperCAmelCase_ (__a : int ):
"""simple docstring"""
if i < 0:
raise ValueError('Input must be non-negative' )
_a : List[str] = format(__a , '032b' )
_a : int = ''
for c in i_str:
new_str += "1" if c == "0" else "0"
return int(__a , 2 )
def UpperCAmelCase_ (__a : int , __a : int ):
"""simple docstring"""
return (a + b) % 2**3_2
def UpperCAmelCase_ (__a : int , __a : int ):
"""simple docstring"""
if i < 0:
raise ValueError('Input must be non-negative' )
if shift < 0:
raise ValueError('Shift must be non-negative' )
return ((i << shift) ^ (i >> (3_2 - shift))) % 2**3_2
def UpperCAmelCase_ (__a : bytes ):
"""simple docstring"""
_a : str = preprocess(__a )
_a : Optional[int] = [int(2**3_2 * abs(sin(i + 1 ) ) ) for i in range(6_4 )]
# Starting states
_a : int = 0x67_45_23_01
_a : Union[str, Any] = 0xEF_CD_AB_89
_a : str = 0x98_BA_DC_FE
_a : List[Any] = 0x10_32_54_76
_a : Optional[int] = [
7,
1_2,
1_7,
2_2,
7,
1_2,
1_7,
2_2,
7,
1_2,
1_7,
2_2,
7,
1_2,
1_7,
2_2,
5,
9,
1_4,
2_0,
5,
9,
1_4,
2_0,
5,
9,
1_4,
2_0,
5,
9,
1_4,
2_0,
4,
1_1,
1_6,
2_3,
4,
1_1,
1_6,
2_3,
4,
1_1,
1_6,
2_3,
4,
1_1,
1_6,
2_3,
6,
1_0,
1_5,
2_1,
6,
1_0,
1_5,
2_1,
6,
1_0,
1_5,
2_1,
6,
1_0,
1_5,
2_1,
]
# Process bit string in chunks, each with 16 32-char words
for block_words in get_block_words(__a ):
_a : Union[str, Any] = aa
_a : List[Any] = ba
_a : List[Any] = ca
_a : Dict = da
# Hash current chunk
for i in range(6_4 ):
if i <= 1_5:
# f = (b & c) | (not_32(b) & d) # Alternate definition for f
_a : Optional[int] = d ^ (b & (c ^ d))
_a : Optional[Any] = i
elif i <= 3_1:
# f = (d & b) | (not_32(d) & c) # Alternate definition for f
_a : Optional[Any] = c ^ (d & (b ^ c))
_a : Dict = (5 * i + 1) % 1_6
elif i <= 4_7:
_a : Optional[Any] = b ^ c ^ d
_a : Dict = (3 * i + 5) % 1_6
else:
_a : int = c ^ (b | not_aa(__a ))
_a : List[str] = (7 * i) % 1_6
_a : Optional[int] = (f + a + added_consts[i] + block_words[g]) % 2**3_2
_a : Union[str, Any] = d
_a : Tuple = c
_a : Optional[int] = b
_a : Union[str, Any] = sum_aa(__a , left_rotate_aa(__a , shift_amounts[i] ) )
# Add hashed chunk to running total
_a : Any = sum_aa(__a , __a )
_a : Dict = sum_aa(__a , __a )
_a : Union[str, Any] = sum_aa(__a , __a )
_a : str = sum_aa(__a , __a )
_a : Optional[Any] = reformat_hex(__a ) + reformat_hex(__a ) + reformat_hex(__a ) + reformat_hex(__a )
return digest
if __name__ == "__main__":
import doctest
doctest.testmod()
| 5 | 0 |
"""simple docstring"""
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def lowercase ( lowerCAmelCase__ : List[Any] ) -> Any:
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
@staticmethod
def __UpperCAmelCase ( _a ):
__a = parser.add_parser('''download''' )
download_parser.add_argument(
'''--cache-dir''' , type=_a , default=_a , help='''Path to location to store the models''' )
download_parser.add_argument(
'''--force''' , action='''store_true''' , help='''Force the model to be download even if already in cache-dir''' )
download_parser.add_argument(
'''--trust-remote-code''' , action='''store_true''' , help='''Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you\'ve reviewed the code as it will execute on your local machine''' , )
download_parser.add_argument('''model''' , type=_a , help='''Name of the model to download''' )
download_parser.set_defaults(func=_a )
def __init__( self , _a , _a , _a , _a ):
__a = model
__a = cache
__a = force
__a = trust_remote_code
def __UpperCAmelCase ( self ):
from ..models.auto import AutoModel, AutoTokenizer
AutoModel.from_pretrained(
self._model , cache_dir=self._cache , force_download=self._force , trust_remote_code=self._trust_remote_code )
AutoTokenizer.from_pretrained(
self._model , cache_dir=self._cache , force_download=self._force , trust_remote_code=self._trust_remote_code )
| 45 |
"""simple docstring"""
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class __lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def __UpperCAmelCase ( self ):
__a = inspect.getfile(accelerate.test_utils )
__a = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_script.py'''] )
__a = os.path.sep.join(
mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_distributed_data_loop.py'''] )
__a = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_ops.py'''] )
@require_multi_gpu
def __UpperCAmelCase ( self ):
print(f'''Found {torch.cuda.device_count()} devices.''' )
__a = ['''torchrun''', f'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(_a , env=os.environ.copy() )
@require_multi_gpu
def __UpperCAmelCase ( self ):
print(f'''Found {torch.cuda.device_count()} devices.''' )
__a = ['''torchrun''', f'''--nproc_per_node={torch.cuda.device_count()}''', self.operation_file_path]
print(f'''Command: {cmd}''' )
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(_a , env=os.environ.copy() )
@require_multi_gpu
def __UpperCAmelCase ( self ):
__a = ['''torchrun''', f'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__ )]
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(_a , env=os.environ.copy() )
@require_multi_gpu
def __UpperCAmelCase ( self ):
print(f'''Found {torch.cuda.device_count()} devices, using 2 devices only''' )
__a = ['''torchrun''', f'''--nproc_per_node={torch.cuda.device_count()}''', self.data_loop_file_path]
with patch_environment(omp_num_threads=1 , cuda_visible_devices='''0,1''' ):
execute_subprocess_async(_a , env=os.environ.copy() )
if __name__ == "__main__":
lowercase_ = Accelerator()
lowercase_ = (accelerator.state.process_index + 2, 1_0)
lowercase_ = torch.randint(0, 1_0, shape).to(accelerator.device)
lowercase_ = ""
lowercase_ = accelerator.pad_across_processes(tensor)
if tensora.shape[0] != accelerator.state.num_processes + 1:
error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
error_msg += "Tensors have different values."
if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
error_msg += "Padding was not done with the right value (0)."
lowercase_ = accelerator.pad_across_processes(tensor, pad_first=True)
if tensora.shape[0] != accelerator.state.num_processes + 1:
error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
lowercase_ = accelerator.state.num_processes - accelerator.state.process_index - 1
if not torch.equal(tensora[index:], tensor):
error_msg += "Tensors have different values."
if not torch.all(tensora[:index] == 0):
error_msg += "Padding was not done with the right value (0)."
# Raise error at the end to make sure we don't stop at the first failure.
if len(error_msg) > 0:
raise ValueError(error_msg)
| 45 | 1 |
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
from transformers.models.ta.modeling_flax_ta import shift_tokens_right
@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class a ( unittest.TestCase ):
@slow
def _UpperCAmelCase ( self ):
'''simple docstring'''
_UpperCAmelCase : Dict = FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small" )
_UpperCAmelCase : Dict = AutoTokenizer.from_pretrained("google/mt5-small" )
_UpperCAmelCase : List[Any] = tokenizer("Hello there" , return_tensors="np" ).input_ids
_UpperCAmelCase : int = tokenizer("Hi I am" , return_tensors="np" ).input_ids
_UpperCAmelCase : List[Any] = shift_tokens_right(A_ , model.config.pad_token_id , model.config.decoder_start_token_id )
_UpperCAmelCase : List[str] = model(A_ , decoder_input_ids=A_ ).logits
_UpperCAmelCase : Optional[Any] = optax.softmax_cross_entropy(A_ , onehot(A_ , logits.shape[-1] ) ).mean()
_UpperCAmelCase : str = -(labels.shape[-1] * loss.item())
_UpperCAmelCase : str = -84.91_27
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1e-4 )
| 189 |
from __future__ import annotations
from random import choice
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: Any ) -> Optional[int]:
return choice(lowerCAmelCase )
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: list[int] , lowerCAmelCase: int ) -> int:
_UpperCAmelCase : List[Any] = random_pivot(lowerCAmelCase )
# partition based on pivot
# linear time
_UpperCAmelCase : List[str] = [e for e in lst if e < pivot]
_UpperCAmelCase : Any = [e for e in lst if e > pivot]
# if we get lucky, pivot might be the element we want.
# we can easily see this:
# small (elements smaller than k)
# + pivot (kth element)
# + big (elements larger than k)
if len(lowerCAmelCase ) == k - 1:
return pivot
# pivot is in elements bigger than k
elif len(lowerCAmelCase ) < k - 1:
return kth_number(lowerCAmelCase , k - len(lowerCAmelCase ) - 1 )
# pivot is in elements smaller than k
else:
return kth_number(lowerCAmelCase , lowerCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 189 | 1 |
# 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
UpperCAmelCase_ = get_logger(__name__)
class lowerCamelCase__:
UpperCAmelCase__ : List[Any] = 'dummy_data'
UpperCAmelCase__ : str = 'datasets'
UpperCAmelCase__ : Tuple = False
def __init__( self: Optional[Any] , UpperCamelCase_: str , UpperCamelCase_: str , UpperCamelCase_: Union[Version, str] , UpperCamelCase_: Optional[str] = None , UpperCamelCase_: bool = False , UpperCamelCase_: bool = True , UpperCamelCase_: Optional[List[Callable]] = None , ):
__lowerCamelCase = 0
__lowerCamelCase = dataset_name
__lowerCamelCase = cache_dir
__lowerCamelCase = use_local_dummy_data
__lowerCamelCase = config
# download_callbacks take a single url as input
__lowerCamelCase = 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 = load_existing_dummy_data
# TODO(PVP, QL) might need to make this more general
__lowerCamelCase = str(UpperCamelCase_ )
# to be downloaded
__lowerCamelCase = None
__lowerCamelCase = None
@property
def lowerCAmelCase__ ( self: List[Any] ):
if self._dummy_file is None:
__lowerCamelCase = self.download_dummy_data()
return self._dummy_file
@property
def lowerCAmelCase__ ( self: str ):
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 lowerCAmelCase__ ( self: Optional[Any] ):
return os.path.join(self.dummy_data_folder , """dummy_data.zip""" )
def lowerCAmelCase__ ( self: Any ):
__lowerCamelCase = (
self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
)
__lowerCamelCase = cached_path(
UpperCamelCase_ , cache_dir=self.cache_dir , extract_compressed_file=UpperCamelCase_ , force_extract=UpperCamelCase_ )
return os.path.join(UpperCamelCase_ , self.dummy_file_name )
@property
def lowerCAmelCase__ ( self: Optional[Any] ):
return os.path.join(self.datasets_scripts_dir , self.dataset_name , self.dummy_zip_file )
@property
def lowerCAmelCase__ ( self: Tuple ):
if self._bucket_url is None:
__lowerCamelCase = hf_github_url(self.dataset_name , self.dummy_zip_file.replace(os.sep , """/""" ) )
return self._bucket_url
@property
def lowerCAmelCase__ ( self: str ):
# 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 lowerCAmelCase__ ( self: List[str] , UpperCamelCase_: Dict , *UpperCamelCase_: str ):
if self.load_existing_dummy_data:
# dummy data is downloaded and tested
__lowerCamelCase = self.dummy_file
else:
# dummy data cannot be downloaded and only the path to dummy file is returned
__lowerCamelCase = self.dummy_file_name
# special case when data_url is a dict
if isinstance(UpperCamelCase_ , UpperCamelCase_ ):
return self.create_dummy_data_dict(UpperCamelCase_ , UpperCamelCase_ )
elif isinstance(UpperCamelCase_ , (list, tuple) ):
return self.create_dummy_data_list(UpperCamelCase_ , UpperCamelCase_ )
else:
return self.create_dummy_data_single(UpperCamelCase_ , UpperCamelCase_ )
def lowerCAmelCase__ ( self: Any , UpperCamelCase_: Optional[Any] , *UpperCamelCase_: str ):
return self.download_and_extract(UpperCamelCase_ )
def lowerCAmelCase__ ( self: List[Any] , UpperCamelCase_: Tuple , UpperCamelCase_: str ):
return self.download_and_extract(UpperCamelCase_ )
def lowerCAmelCase__ ( self: str , UpperCamelCase_: int , *UpperCamelCase_: List[str] , **UpperCamelCase_: str ):
return path
def lowerCAmelCase__ ( self: Dict ):
return {}
def lowerCAmelCase__ ( self: str , UpperCamelCase_: List[Any] , UpperCamelCase_: Union[str, Any] ):
__lowerCamelCase = {}
for key, single_urls in data_url.items():
for download_callback in self.download_callbacks:
if isinstance(UpperCamelCase_ , UpperCamelCase_ ):
for single_url in single_urls:
download_callback(UpperCamelCase_ )
else:
__lowerCamelCase = single_urls
download_callback(UpperCamelCase_ )
# 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(UpperCamelCase_ , UpperCamelCase_ ):
__lowerCamelCase = [os.path.join(UpperCamelCase_ , urllib.parse.quote_plus(Path(UpperCamelCase_ ).name ) ) for x in single_urls]
else:
__lowerCamelCase = single_urls
__lowerCamelCase = os.path.join(UpperCamelCase_ , urllib.parse.quote_plus(Path(UpperCamelCase_ ).name ) )
__lowerCamelCase = value
# make sure that values are unique
if all(isinstance(UpperCamelCase_ , UpperCamelCase_ ) 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 = {key: value + key for key, value in dummy_data_dict.items()}
return dummy_data_dict
def lowerCAmelCase__ ( self: Optional[Any] , UpperCamelCase_: Optional[Any] , UpperCamelCase_: Optional[int] ):
__lowerCamelCase = []
# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
__lowerCamelCase = all(bool(re.findall("""[0-9]{3,}-of-[0-9]{3,}""" , UpperCamelCase_ ) ) for url in data_url )
__lowerCamelCase = 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 = [data_url[0]] * len(UpperCamelCase_ )
for single_url in data_url:
for download_callback in self.download_callbacks:
download_callback(UpperCamelCase_ )
# 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 = os.path.join(UpperCamelCase_ , urllib.parse.quote_plus(single_url.split("""/""" )[-1] ) )
dummy_data_list.append(UpperCamelCase_ )
return dummy_data_list
def lowerCAmelCase__ ( self: Tuple , UpperCamelCase_: Optional[int] , UpperCamelCase_: Optional[Any] ):
for download_callback in self.download_callbacks:
download_callback(UpperCamelCase_ )
# 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 = os.path.join(UpperCamelCase_ , urllib.parse.quote_plus(data_url.split("""/""" )[-1] ) )
if os.path.exists(UpperCamelCase_ ) 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 lowerCAmelCase__ ( self: Optional[Any] ):
pass
def lowerCAmelCase__ ( self: List[Any] ):
pass
def lowerCAmelCase__ ( self: Any , UpperCamelCase_: Dict ):
def _iter_archive_members(UpperCamelCase_: Any ):
# this preserves the order of the members inside the ZIP archive
__lowerCamelCase = Path(self.dummy_file ).parent
__lowerCamelCase = path.relative_to(UpperCamelCase_ )
with ZipFile(self.local_path_to_dummy_data ) as zip_file:
__lowerCamelCase = zip_file.namelist()
for member in members:
if member.startswith(relative_path.as_posix() ):
yield dummy_parent_path.joinpath(UpperCamelCase_ )
__lowerCamelCase = Path(UpperCamelCase_ )
__lowerCamelCase = _iter_archive_members(UpperCamelCase_ ) 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(UpperCamelCase_ ).as_posix(), file_path.open("""rb""" )
def lowerCAmelCase__ ( self: List[str] , UpperCamelCase_: Dict ):
if not isinstance(UpperCamelCase_ , UpperCamelCase_ ):
__lowerCamelCase = [paths]
for path in paths:
if os.path.isfile(UpperCamelCase_ ):
if os.path.basename(UpperCamelCase_ ).startswith((""".""", """__""") ):
return
yield path
else:
for dirpath, dirnames, filenames in os.walk(UpperCamelCase_ ):
if os.path.basename(UpperCamelCase_ ).startswith((""".""", """__""") ):
continue
dirnames.sort()
for filename in sorted(UpperCamelCase_ ):
if filename.startswith((""".""", """__""") ):
continue
yield os.path.join(UpperCamelCase_ , UpperCamelCase_ )
| 12 |
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
import onnxruntime as ort
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
'tensor(bool)': np.bool_,
'tensor(int8)': np.inta,
'tensor(uint8)': np.uinta,
'tensor(int16)': np.intaa,
'tensor(uint16)': np.uintaa,
'tensor(int32)': np.intaa,
'tensor(uint32)': np.uintaa,
'tensor(int64)': np.intaa,
'tensor(uint64)': np.uintaa,
'tensor(float16)': np.floataa,
'tensor(float)': np.floataa,
'tensor(double)': np.floataa,
}
class lowerCamelCase__:
def __init__( self: str , UpperCamelCase_: List[Any]=None , **UpperCamelCase_: str ):
logger.info("""`diffusers.OnnxRuntimeModel` is experimental and might change in the future.""" )
__lowerCamelCase = model
__lowerCamelCase = kwargs.get("""model_save_dir""" , UpperCamelCase_ )
__lowerCamelCase = kwargs.get("""latest_model_name""" , UpperCamelCase_ )
def __call__( self: Dict , **UpperCamelCase_: Any ):
__lowerCamelCase = {k: np.array(UpperCamelCase_ ) for k, v in kwargs.items()}
return self.model.run(UpperCamelCase_ , UpperCamelCase_ )
@staticmethod
def lowerCAmelCase__ ( UpperCamelCase_: Union[str, Path] , UpperCamelCase_: Tuple=None , UpperCamelCase_: Tuple=None ):
if provider is None:
logger.info("""No onnxruntime provider specified, using CPUExecutionProvider""" )
__lowerCamelCase = """CPUExecutionProvider"""
return ort.InferenceSession(UpperCamelCase_ , providers=[provider] , sess_options=UpperCamelCase_ )
def lowerCAmelCase__ ( self: int , UpperCamelCase_: Union[str, Path] , UpperCamelCase_: Optional[str] = None , **UpperCamelCase_: Optional[int] ):
__lowerCamelCase = file_name if file_name is not None else ONNX_WEIGHTS_NAME
__lowerCamelCase = self.model_save_dir.joinpath(self.latest_model_name )
__lowerCamelCase = Path(UpperCamelCase_ ).joinpath(UpperCamelCase_ )
try:
shutil.copyfile(UpperCamelCase_ , UpperCamelCase_ )
except shutil.SameFileError:
pass
# copy external weights (for models >2GB)
__lowerCamelCase = self.model_save_dir.joinpath(UpperCamelCase_ )
if src_path.exists():
__lowerCamelCase = Path(UpperCamelCase_ ).joinpath(UpperCamelCase_ )
try:
shutil.copyfile(UpperCamelCase_ , UpperCamelCase_ )
except shutil.SameFileError:
pass
def lowerCAmelCase__ ( self: List[Any] , UpperCamelCase_: Union[str, os.PathLike] , **UpperCamelCase_: Optional[Any] , ):
if os.path.isfile(UpperCamelCase_ ):
logger.error(F'Provided path ({save_directory}) should be a directory, not a file' )
return
os.makedirs(UpperCamelCase_ , exist_ok=UpperCamelCase_ )
# saving model weights/files
self._save_pretrained(UpperCamelCase_ , **UpperCamelCase_ )
@classmethod
def lowerCAmelCase__ ( cls: str , UpperCamelCase_: Union[str, Path] , UpperCamelCase_: Optional[Union[bool, str, None]] = None , UpperCamelCase_: Optional[Union[str, None]] = None , UpperCamelCase_: bool = False , UpperCamelCase_: Optional[str] = None , UpperCamelCase_: Optional[str] = None , UpperCamelCase_: Optional[str] = None , UpperCamelCase_: Optional["ort.SessionOptions"] = None , **UpperCamelCase_: int , ):
__lowerCamelCase = file_name if file_name is not None else ONNX_WEIGHTS_NAME
# load model from local directory
if os.path.isdir(UpperCamelCase_ ):
__lowerCamelCase = OnnxRuntimeModel.load_model(
os.path.join(UpperCamelCase_ , UpperCamelCase_ ) , provider=UpperCamelCase_ , sess_options=UpperCamelCase_ )
__lowerCamelCase = Path(UpperCamelCase_ )
# load model from hub
else:
# download model
__lowerCamelCase = hf_hub_download(
repo_id=UpperCamelCase_ , filename=UpperCamelCase_ , use_auth_token=UpperCamelCase_ , revision=UpperCamelCase_ , cache_dir=UpperCamelCase_ , force_download=UpperCamelCase_ , )
__lowerCamelCase = Path(UpperCamelCase_ ).parent
__lowerCamelCase = Path(UpperCamelCase_ ).name
__lowerCamelCase = OnnxRuntimeModel.load_model(UpperCamelCase_ , provider=UpperCamelCase_ , sess_options=UpperCamelCase_ )
return cls(model=UpperCamelCase_ , **UpperCamelCase_ )
@classmethod
def lowerCAmelCase__ ( cls: Optional[int] , UpperCamelCase_: Union[str, Path] , UpperCamelCase_: bool = True , UpperCamelCase_: Optional[str] = None , UpperCamelCase_: Optional[str] = None , **UpperCamelCase_: int , ):
__lowerCamelCase = None
if len(str(UpperCamelCase_ ).split("""@""" ) ) == 2:
__lowerCamelCase, __lowerCamelCase = model_id.split("""@""" )
return cls._from_pretrained(
model_id=UpperCamelCase_ , revision=UpperCamelCase_ , cache_dir=UpperCamelCase_ , force_download=UpperCamelCase_ , use_auth_token=UpperCamelCase_ , **UpperCamelCase_ , )
| 12 | 1 |
"""simple docstring"""
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
SqueezeBertModel,
)
class snake_case ( SCREAMING_SNAKE_CASE_ ):
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=13 , __UpperCAmelCase=7 , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase=False , __UpperCAmelCase=True , __UpperCAmelCase=99 , __UpperCAmelCase=32 , __UpperCAmelCase=5 , __UpperCAmelCase=4 , __UpperCAmelCase=64 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=5_12 , __UpperCAmelCase=16 , __UpperCAmelCase=2 , __UpperCAmelCase=0.02 , __UpperCAmelCase=3 , __UpperCAmelCase=4 , __UpperCAmelCase=None , __UpperCAmelCase=2 , __UpperCAmelCase=2 , __UpperCAmelCase=2 , __UpperCAmelCase=2 , __UpperCAmelCase=4 , __UpperCAmelCase=1 , ) ->Any:
a_ = parent
a_ = batch_size
a_ = seq_length
a_ = is_training
a_ = use_input_mask
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_ = q_groups
a_ = k_groups
a_ = v_groups
a_ = post_attention_groups
a_ = intermediate_groups
a_ = output_groups
def UpperCAmelCase__ ( self) ->Union[str, Any]:
a_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
a_ = None
if self.use_input_mask:
a_ = random_attention_mask([self.batch_size, self.seq_length])
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_ = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCAmelCase__ ( self) ->int:
return SqueezeBertConfig(
embedding_size=self.hidden_size , vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , attention_probs_dropout_prob=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , q_groups=self.q_groups , k_groups=self.k_groups , v_groups=self.v_groups , post_attention_groups=self.post_attention_groups , intermediate_groups=self.intermediate_groups , output_groups=self.output_groups , )
def UpperCAmelCase__ ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase) ->Dict:
a_ = SqueezeBertModel(config=__UpperCAmelCase)
model.to(__UpperCAmelCase)
model.eval()
a_ = model(__UpperCAmelCase , __UpperCAmelCase)
a_ = model(__UpperCAmelCase)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
def UpperCAmelCase__ ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase) ->Any:
a_ = SqueezeBertForMaskedLM(config=__UpperCAmelCase)
model.to(__UpperCAmelCase)
model.eval()
a_ = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , labels=__UpperCAmelCase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size))
def UpperCAmelCase__ ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase) ->Union[str, Any]:
a_ = SqueezeBertForQuestionAnswering(config=__UpperCAmelCase)
model.to(__UpperCAmelCase)
model.eval()
a_ = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , start_positions=__UpperCAmelCase , end_positions=__UpperCAmelCase)
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length))
def UpperCAmelCase__ ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase) ->Tuple:
a_ = self.num_labels
a_ = SqueezeBertForSequenceClassification(__UpperCAmelCase)
model.to(__UpperCAmelCase)
model.eval()
a_ = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , labels=__UpperCAmelCase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def UpperCAmelCase__ ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase) ->Dict:
a_ = self.num_labels
a_ = SqueezeBertForTokenClassification(config=__UpperCAmelCase)
model.to(__UpperCAmelCase)
model.eval()
a_ = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , labels=__UpperCAmelCase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels))
def UpperCAmelCase__ ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase) ->List[str]:
a_ = self.num_choices
a_ = SqueezeBertForMultipleChoice(config=__UpperCAmelCase)
model.to(__UpperCAmelCase)
model.eval()
a_ = input_ids.unsqueeze(1).expand(-1 , self.num_choices , -1).contiguous()
a_ = input_mask.unsqueeze(1).expand(-1 , self.num_choices , -1).contiguous()
a_ = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , labels=__UpperCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices))
def UpperCAmelCase__ ( self) ->Any:
a_ = self.prepare_config_and_inputs()
((a_) , (a_) , (a_) , (a_) , (a_) , (a_)) = config_and_inputs
a_ = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class snake_case ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
a_ : Optional[Any] = (
(
SqueezeBertModel,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
)
if is_torch_available()
else None
)
a_ : Union[str, Any] = (
{
"""feature-extraction""": SqueezeBertModel,
"""fill-mask""": SqueezeBertForMaskedLM,
"""question-answering""": SqueezeBertForQuestionAnswering,
"""text-classification""": SqueezeBertForSequenceClassification,
"""token-classification""": SqueezeBertForTokenClassification,
"""zero-shot""": SqueezeBertForSequenceClassification,
}
if is_torch_available()
else {}
)
a_ : List[Any] = False
a_ : Tuple = True
a_ : Optional[int] = False
def UpperCAmelCase__ ( self) ->Optional[Any]:
a_ = SqueezeBertModelTester(self)
a_ = ConfigTester(self , config_class=__UpperCAmelCase , dim=37)
def UpperCAmelCase__ ( self) ->Tuple:
self.config_tester.run_common_tests()
def UpperCAmelCase__ ( self) ->Tuple:
a_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_model(*__UpperCAmelCase)
def UpperCAmelCase__ ( self) ->List[str]:
a_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_masked_lm(*__UpperCAmelCase)
def UpperCAmelCase__ ( self) ->List[Any]:
a_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_question_answering(*__UpperCAmelCase)
def UpperCAmelCase__ ( self) ->Any:
a_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_sequence_classification(*__UpperCAmelCase)
def UpperCAmelCase__ ( self) ->str:
a_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_token_classification(*__UpperCAmelCase)
def UpperCAmelCase__ ( self) ->Tuple:
a_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_multiple_choice(*__UpperCAmelCase)
@slow
def UpperCAmelCase__ ( self) ->int:
for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a_ = SqueezeBertModel.from_pretrained(__UpperCAmelCase)
self.assertIsNotNone(__UpperCAmelCase)
@require_sentencepiece
@require_tokenizers
@require_torch
class snake_case ( unittest.TestCase ):
@slow
def UpperCAmelCase__ ( self) ->Union[str, Any]:
a_ = SqueezeBertForSequenceClassification.from_pretrained("squeezebert/squeezebert-mnli")
a_ = torch.tensor([[1, 2_94_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 13, 15_88, 2]])
a_ = model(__UpperCAmelCase)[0]
a_ = torch.Size((1, 3))
self.assertEqual(output.shape , __UpperCAmelCase)
a_ = torch.tensor([[0.6_401, -0.0_349, -0.6_041]])
self.assertTrue(torch.allclose(__UpperCAmelCase , __UpperCAmelCase , atol=1E-4)) | 303 |
"""simple docstring"""
import os
import numpy
import onnx
def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase ) ->List[str]:
"""simple docstring"""
a_ = a.name
a_ = b.name
a_ = ""
a_ = ""
a_ = a == b
a_ = name_a
a_ = name_b
return res
def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) ->List[Any]:
"""simple docstring"""
for i, input_name in enumerate(node_proto.input ):
if input_name == name:
node_proto.input.insert(UpperCAmelCase , UpperCAmelCase )
node_proto.input.pop(i + 1 )
if node_proto.op_type == "If":
_graph_replace_input_with(node_proto.attribute[0].g , UpperCAmelCase , UpperCAmelCase )
_graph_replace_input_with(node_proto.attribute[1].g , UpperCAmelCase , UpperCAmelCase )
if node_proto.op_type == "Loop":
_graph_replace_input_with(node_proto.attribute[0].g , UpperCAmelCase , UpperCAmelCase )
def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) ->Dict:
"""simple docstring"""
for n in graph_proto.node:
_node_replace_input_with(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) ->int:
"""simple docstring"""
a_ = list(model.graph.initializer )
a_ = list(model_without_ext.graph.initializer )
for i, ref_i in ind_to_replace:
assert inits_with_data[i].name == inits[i].name
assert inits_with_data[ref_i].name == inits[ref_i].name
assert i > ref_i
a_ = inits[i].name
a_ = inits[ref_i].name
model_without_ext.graph.initializer.remove(inits[i] )
# for n in model.graph.node:
_graph_replace_input_with(model_without_ext.graph , UpperCAmelCase , UpperCAmelCase )
def UpperCamelCase ( UpperCAmelCase ) ->Union[str, Any]:
"""simple docstring"""
a_ = os.path.dirname(UpperCAmelCase )
a_ = os.path.basename(UpperCAmelCase )
a_ = onnx.load(os.path.join(UpperCAmelCase , UpperCAmelCase ) )
a_ = list(model.graph.initializer )
a_ = set()
a_ = {}
a_ = []
a_ = 0
for i in range(len(UpperCAmelCase ) ):
if i in dup_set:
continue
for j in range(i + 1 , len(UpperCAmelCase ) ):
if j in dup_set:
continue
if _is_equal_tensor_proto(inits[i] , inits[j] ):
dup_set.add(UpperCAmelCase )
dup_set.add(UpperCAmelCase )
a_ = inits[j].data_type
a_ = numpy.prod(inits[j].dims )
if dtype == 1:
mem_size *= 4
elif dtype == 6:
mem_size *= 4
elif dtype == 7 or dtype == 11:
mem_size *= 8
else:
print("unexpected data type: " , UpperCAmelCase )
total_reduced_size += mem_size
a_ = inits[i].name
a_ = inits[j].name
if name_i in dup_map:
dup_map[name_i].append(UpperCAmelCase )
else:
a_ = [name_j]
ind_to_replace.append((j, i) )
print("total reduced size: " , total_reduced_size / 1_024 / 1_024 / 1_024 , "GB" )
a_ = sorted(UpperCAmelCase )
_remove_dup_initializers_from_model(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
a_ = "optimized_" + model_file_name
a_ = os.path.join(UpperCAmelCase , UpperCAmelCase )
onnx.save(UpperCAmelCase , UpperCAmelCase )
return new_model | 303 | 1 |
'''simple docstring'''
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
lowerCamelCase : List[Any] = "\\n @inproceedings{kakwani2020indicnlpsuite,\n title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},\n author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},\n year={2020},\n booktitle={Findings of EMNLP},\n}\n"
lowerCamelCase : Optional[Any] = "\\n IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide\n variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te.\n"
lowerCamelCase : int = "\nCompute IndicGLUE evaluation metric associated to each IndicGLUE dataset.\nArgs:\n predictions: list of predictions to score (as int64),\n except for 'cvit-mkb-clsr' where each prediction is a vector (of float32).\n references: list of ground truth labels corresponding to the predictions (as int64),\n except for 'cvit-mkb-clsr' where each reference is a vector (of float32).\nReturns: depending on the IndicGLUE subset, one or several of:\n \"accuracy\": Accuracy\n \"f1\": F1 score\n \"precision\": Precision@10\nExamples:\n\n >>> indic_glue_metric = datasets.load_metric('indic_glue', 'wnli') # 'wnli' or any of [\"copa\", \"sna\", \"csqa\", \"wstp\", \"inltkh\", \"bbca\", \"iitp-mr\", \"iitp-pr\", \"actsa-sc\", \"md\"]\n >>> references = [0, 1]\n >>> predictions = [0, 1]\n >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'accuracy': 1.0}\n\n >>> indic_glue_metric = datasets.load_metric('indic_glue', 'wiki-ner')\n >>> references = [0, 1]\n >>> predictions = [0, 1]\n >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'accuracy': 1.0, 'f1': 1.0}\n\n >>> indic_glue_metric = datasets.load_metric('indic_glue', 'cvit-mkb-clsr')\n >>> references = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]\n >>> predictions = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]\n >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'precision@10': 1.0}\n\n"
def _lowerCAmelCase ( _UpperCamelCase : List[str] , _UpperCamelCase : Tuple ) -> List[Any]:
"""simple docstring"""
return float((preds == labels).mean() )
def _lowerCAmelCase ( _UpperCamelCase : Union[str, Any] , _UpperCamelCase : Any ) -> List[str]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =simple_accuracy(_UpperCamelCase , _UpperCamelCase )
_SCREAMING_SNAKE_CASE =float(fa_score(y_true=_UpperCamelCase , y_pred=_UpperCamelCase ) )
return {
"accuracy": acc,
"f1": fa,
}
def _lowerCAmelCase ( _UpperCamelCase : Any , _UpperCamelCase : int ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =np.array(_UpperCamelCase )
_SCREAMING_SNAKE_CASE =np.array(_UpperCamelCase )
_SCREAMING_SNAKE_CASE =en_sentvecs.shape[0]
# mean centering
_SCREAMING_SNAKE_CASE =en_sentvecs - np.mean(_UpperCamelCase , axis=0 )
_SCREAMING_SNAKE_CASE =in_sentvecs - np.mean(_UpperCamelCase , axis=0 )
_SCREAMING_SNAKE_CASE =cdist(_UpperCamelCase , _UpperCamelCase , 'cosine' )
_SCREAMING_SNAKE_CASE =np.array(range(_UpperCamelCase ) )
_SCREAMING_SNAKE_CASE =sim.argsort(axis=1 )[:, :10]
_SCREAMING_SNAKE_CASE =np.any(preds == actual[:, None] , axis=1 )
return float(matches.mean() )
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class A__ ( datasets.Metric ):
def A ( self : Any ) -> List[str]:
'''simple docstring'''
if self.config_name not in [
"wnli",
"copa",
"sna",
"csqa",
"wstp",
"inltkh",
"bbca",
"cvit-mkb-clsr",
"iitp-mr",
"iitp-pr",
"actsa-sc",
"md",
"wiki-ner",
]:
raise KeyError(
'You should supply a configuration name selected in '
'["wnli", "copa", "sna", "csqa", "wstp", "inltkh", "bbca", '
'"cvit-mkb-clsr", "iitp-mr", "iitp-pr", "actsa-sc", "md", '
'"wiki-ner"]' )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('int64' )
if self.config_name != 'cvit-mkb-clsr'
else datasets.Sequence(datasets.Value('float32' ) ),
'references': datasets.Value('int64' )
if self.config_name != 'cvit-mkb-clsr'
else datasets.Sequence(datasets.Value('float32' ) ),
} ) , codebase_urls=[] , reference_urls=[] , format='numpy' if self.config_name != 'cvit-mkb-clsr' else None , )
def A ( self : List[str] , _a : Tuple , _a : Optional[int] ) -> int:
'''simple docstring'''
if self.config_name == "cvit-mkb-clsr":
return {"precision@10": precision_at_aa(_a , _a )}
elif self.config_name in ["wiki-ner"]:
return acc_and_fa(_a , _a )
elif self.config_name in [
"wnli",
"copa",
"sna",
"csqa",
"wstp",
"inltkh",
"bbca",
"iitp-mr",
"iitp-pr",
"actsa-sc",
"md",
]:
return {"accuracy": simple_accuracy(_a , _a )}
else:
raise KeyError(
'You should supply a configuration name selected in '
'["wnli", "copa", "sna", "csqa", "wstp", "inltkh", "bbca", '
'"cvit-mkb-clsr", "iitp-mr", "iitp-pr", "actsa-sc", "md", '
'"wiki-ner"]' )
| 47 |
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAME,
cached_file,
get_file_from_repo,
has_file,
)
_SCREAMING_SNAKE_CASE : List[str] = "hf-internal-testing/tiny-random-bert"
_SCREAMING_SNAKE_CASE : Optional[Any] = os.path.join(TRANSFORMERS_CACHE, "models--hf-internal-testing--tiny-random-bert")
_SCREAMING_SNAKE_CASE : Optional[int] = "9b8c223d42b2188cb49d29af482996f9d0f3e5a6"
class A__ ( unittest.TestCase ):
"""simple docstring"""
def a_ ( self ):
snake_case = cached_file(__snake_case , __snake_case )
# Should have downloaded the file in here
self.assertTrue(os.path.isdir(__snake_case ) )
# Cache should contain at least those three subfolders:
for subfolder in ["blobs", "refs", "snapshots"]:
self.assertTrue(os.path.isdir(os.path.join(__snake_case , __snake_case ) ) )
with open(os.path.join(__snake_case , '''refs''' , '''main''' ) ) as f:
snake_case = f.read()
self.assertEqual(__snake_case , os.path.join(__snake_case , '''snapshots''' , __snake_case , __snake_case ) )
self.assertTrue(os.path.isfile(__snake_case ) )
# File is cached at the same place the second time.
snake_case = cached_file(__snake_case , __snake_case )
self.assertEqual(__snake_case , __snake_case )
# Using a specific revision to test the full commit hash.
snake_case = cached_file(__snake_case , __snake_case , revision='''9b8c223''' )
self.assertEqual(__snake_case , os.path.join(__snake_case , '''snapshots''' , __snake_case , __snake_case ) )
def a_ ( self ):
with self.assertRaisesRegex(__snake_case , '''is not a valid model identifier''' ):
snake_case = cached_file('''tiny-random-bert''' , __snake_case )
with self.assertRaisesRegex(__snake_case , '''is not a valid git identifier''' ):
snake_case = cached_file(__snake_case , __snake_case , revision='''aaaa''' )
with self.assertRaisesRegex(__snake_case , '''does not appear to have a file named''' ):
snake_case = cached_file(__snake_case , '''conf''' )
def a_ ( self ):
with self.assertRaisesRegex(__snake_case , '''does not appear to have a file named''' ):
snake_case = cached_file(__snake_case , '''conf''' )
with open(os.path.join(__snake_case , '''refs''' , '''main''' ) ) as f:
snake_case = f.read()
self.assertTrue(os.path.isfile(os.path.join(__snake_case , '''.no_exist''' , __snake_case , '''conf''' ) ) )
snake_case = cached_file(__snake_case , '''conf''' , _raise_exceptions_for_missing_entries=__snake_case )
self.assertIsNone(__snake_case )
snake_case = cached_file(__snake_case , '''conf''' , local_files_only=__snake_case , _raise_exceptions_for_missing_entries=__snake_case )
self.assertIsNone(__snake_case )
snake_case = mock.Mock()
snake_case = 5_0_0
snake_case = {}
snake_case = HTTPError
snake_case = {}
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch('''requests.Session.request''' , return_value=__snake_case ) as mock_head:
snake_case = cached_file(__snake_case , '''conf''' , _raise_exceptions_for_connection_errors=__snake_case )
self.assertIsNone(__snake_case )
# This check we did call the fake head request
mock_head.assert_called()
def a_ ( self ):
self.assertTrue(has_file('''hf-internal-testing/tiny-bert-pt-only''' , __snake_case ) )
self.assertFalse(has_file('''hf-internal-testing/tiny-bert-pt-only''' , __snake_case ) )
self.assertFalse(has_file('''hf-internal-testing/tiny-bert-pt-only''' , __snake_case ) )
def a_ ( self ):
# `get_file_from_repo` returns None if the file does not exist
self.assertIsNone(get_file_from_repo('''bert-base-cased''' , '''ahah.txt''' ) )
# The function raises if the repository does not exist.
with self.assertRaisesRegex(__snake_case , '''is not a valid model identifier''' ):
get_file_from_repo('''bert-base-case''' , __snake_case )
# The function raises if the revision does not exist.
with self.assertRaisesRegex(__snake_case , '''is not a valid git identifier''' ):
get_file_from_repo('''bert-base-cased''' , __snake_case , revision='''ahaha''' )
snake_case = get_file_from_repo('''bert-base-cased''' , __snake_case )
# The name is the cached name which is not very easy to test, so instead we load the content.
snake_case = json.loads(open(__snake_case , '''r''' ).read() )
self.assertEqual(config['''hidden_size'''] , 7_6_8 )
def a_ ( self ):
with tempfile.TemporaryDirectory() as tmp_dir:
snake_case = Path(__snake_case ) / '''a.txt'''
filename.touch()
self.assertEqual(get_file_from_repo(__snake_case , '''a.txt''' ) , str(__snake_case ) )
self.assertIsNone(get_file_from_repo(__snake_case , '''b.txt''' ) )
| 127 | 0 |
"""simple docstring"""
import math
def lowercase__ ( _UpperCAmelCase ) -> int:
'''simple docstring'''
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
lowercase : Optional[Any] = f'''Input value of [number={number}] must be an integer'''
raise TypeError(_UpperCAmelCase )
if number < 1:
lowercase : int = f'''Input value of [number={number}] must be > 0'''
raise ValueError(_UpperCAmelCase )
elif number == 1:
return 3
elif number == 2:
return 5
else:
lowercase : Optional[int] = int(math.log(number // 3 , 2 ) ) + 2
lowercase : str = [3, 5]
lowercase : str = 2
lowercase : Optional[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(1_1):
_UpperCamelCase: Union[str, Any] = 0
try:
_UpperCamelCase: int = proth(number)
except ValueError:
print(f'''ValueError: there is no {number}th Proth number''')
continue
print(f'''The {number}th Proth number: {value}''')
| 53 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionInstructPixaPixPipeline,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class a__ ( SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, unittest.TestCase ):
_lowerCamelCase = StableDiffusionInstructPixaPixPipeline
_lowerCamelCase = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
_lowerCamelCase = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
_lowerCamelCase = IMAGE_TO_IMAGE_IMAGE_PARAMS
_lowerCamelCase = IMAGE_TO_IMAGE_IMAGE_PARAMS
def lowercase ( self : str ) -> str:
torch.manual_seed(0 )
lowercase : int = UNetaDConditionModel(
block_out_channels=(32, 64), layers_per_block=2, sample_size=32, in_channels=8, out_channels=4, down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D'), up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D'), cross_attention_dim=32, )
lowercase : int = PNDMScheduler(skip_prk_steps=lowerCAmelCase )
torch.manual_seed(0 )
lowercase : Union[str, Any] = AutoencoderKL(
block_out_channels=[32, 64], in_channels=3, out_channels=3, down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'], up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'], latent_channels=4, )
torch.manual_seed(0 )
lowercase : Union[str, Any] = CLIPTextConfig(
bos_token_id=0, eos_token_id=2, hidden_size=32, intermediate_size=37, layer_norm_eps=1e-05, num_attention_heads=4, num_hidden_layers=5, pad_token_id=1, vocab_size=1000, )
lowercase : Optional[int] = CLIPTextModel(lowerCAmelCase )
lowercase : str = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
lowercase : Union[str, Any] = {
'unet': unet,
'scheduler': scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'safety_checker': None,
'feature_extractor': None,
}
return components
def lowercase ( self : int, lowerCAmelCase : str, lowerCAmelCase : Tuple=0 ) -> Tuple:
lowercase : Optional[int] = floats_tensor((1, 3, 32, 32), rng=random.Random(lowerCAmelCase ) ).to(lowerCAmelCase )
lowercase : Dict = image.cpu().permute(0, 2, 3, 1 )[0]
lowercase : Any = Image.fromarray(np.uinta(lowerCAmelCase ) ).convert('RGB' )
if str(lowerCAmelCase ).startswith('mps' ):
lowercase : str = torch.manual_seed(lowerCAmelCase )
else:
lowercase : Optional[Any] = torch.Generator(device=lowerCAmelCase ).manual_seed(lowerCAmelCase )
lowercase : Union[str, Any] = {
'prompt': 'A painting of a squirrel eating a burger',
'image': image,
'generator': generator,
'num_inference_steps': 2,
'guidance_scale': 6.0,
'image_guidance_scale': 1,
'output_type': 'numpy',
}
return inputs
def lowercase ( self : Tuple ) -> Optional[Any]:
lowercase : str = 'cpu' # ensure determinism for the device-dependent torch.Generator
lowercase : int = self.get_dummy_components()
lowercase : str = StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase )
lowercase : Union[str, Any] = sd_pipe.to(lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase )
lowercase : Union[str, Any] = self.get_dummy_inputs(lowerCAmelCase )
lowercase : Union[str, Any] = sd_pipe(**lowerCAmelCase ).images
lowercase : str = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
lowercase : str = np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def lowercase ( self : Optional[Any] ) -> int:
lowercase : Union[str, Any] = 'cpu' # ensure determinism for the device-dependent torch.Generator
lowercase : Union[str, Any] = self.get_dummy_components()
lowercase : Optional[int] = StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase )
lowercase : Union[str, Any] = sd_pipe.to(lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase )
lowercase : Optional[Any] = self.get_dummy_inputs(lowerCAmelCase )
lowercase : Any = 'french fries'
lowercase : Tuple = sd_pipe(**lowerCAmelCase, negative_prompt=lowerCAmelCase )
lowercase : Optional[Any] = output.images
lowercase : List[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
lowercase : Dict = np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def lowercase ( self : Optional[int] ) -> str:
lowercase : List[Any] = 'cpu' # ensure determinism for the device-dependent torch.Generator
lowercase : str = self.get_dummy_components()
lowercase : Optional[int] = StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase )
lowercase : str = sd_pipe.to(lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase )
lowercase : Any = self.get_dummy_inputs(lowerCAmelCase )
lowercase : int = [inputs['prompt']] * 2
lowercase : Dict = np.array(inputs['image'] ).astype(np.floataa ) / 255.0
lowercase : Optional[int] = torch.from_numpy(lowerCAmelCase ).unsqueeze(0 ).to(lowerCAmelCase )
lowercase : List[Any] = image / 2 + 0.5
lowercase : List[str] = image.permute(0, 3, 1, 2 )
lowercase : List[str] = image.repeat(2, 1, 1, 1 )
lowercase : Dict = sd_pipe(**lowerCAmelCase ).images
lowercase : Union[str, Any] = image[-1, -3:, -3:, -1]
assert image.shape == (2, 32, 32, 3)
lowercase : Optional[int] = np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def lowercase ( self : Union[str, Any] ) -> Any:
lowercase : str = 'cpu' # ensure determinism for the device-dependent torch.Generator
lowercase : Optional[int] = self.get_dummy_components()
lowercase : str = EulerAncestralDiscreteScheduler(
beta_start=0.0_0085, beta_end=0.012, beta_schedule='scaled_linear' )
lowercase : Tuple = StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase )
lowercase : str = sd_pipe.to(lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase )
lowercase : Dict = self.get_dummy_inputs(lowerCAmelCase )
lowercase : Dict = sd_pipe(**lowerCAmelCase ).images
lowercase : Tuple = image[0, -3:, -3:, -1]
lowercase : Optional[int] = [round(lowerCAmelCase, 4 ) for x in image_slice.flatten().tolist()]
print(','.join([str(lowerCAmelCase ) for x in slice] ) )
assert image.shape == (1, 32, 32, 3)
lowercase : List[str] = np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def lowercase ( self : List[str] ) -> Dict:
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
def lowercase ( self : int ) -> Optional[Any]:
lowercase : List[Any] = self.get_dummy_components()
lowercase : Optional[Any] = StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase )
lowercase : List[str] = VaeImageProcessor(do_resize=lowerCAmelCase, do_normalize=lowerCAmelCase )
lowercase : int = pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
lowercase : Dict = pipe(**self.get_dummy_inputs_by_type(lowerCAmelCase, input_image_type='pt' ) )[0]
lowercase : Optional[Any] = components['vae']
lowercase : str = self.get_dummy_inputs_by_type(lowerCAmelCase, input_image_type='pt' )
for image_param in self.image_latents_params:
if image_param in inputs.keys():
lowercase : Optional[Any] = vae.encode(inputs[image_param] ).latent_dist.mode()
lowercase : Optional[int] = pipe(**lowerCAmelCase )[0]
lowercase : int = np.abs(out - out_latents_inputs ).max()
self.assertLess(lowerCAmelCase, 1e-4, 'passing latents as image input generate different result from passing image' )
@slow
@require_torch_gpu
class a__ ( unittest.TestCase ):
def lowercase ( self : List[str] ) -> Dict:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowercase ( self : List[str], lowerCAmelCase : int=0 ) -> str:
lowercase : Dict = torch.manual_seed(lowerCAmelCase )
lowercase : Optional[Any] = load_image(
'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg' )
lowercase : int = {
'prompt': 'turn him into a cyborg',
'image': image,
'generator': generator,
'num_inference_steps': 3,
'guidance_scale': 7.5,
'image_guidance_scale': 1.0,
'output_type': 'numpy',
}
return inputs
def lowercase ( self : int ) -> str:
lowercase : str = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'timbrooks/instruct-pix2pix', safety_checker=lowerCAmelCase )
pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
pipe.enable_attention_slicing()
lowercase : Optional[Any] = self.get_inputs()
lowercase : List[Any] = pipe(**lowerCAmelCase ).images
lowercase : Tuple = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
lowercase : List[str] = np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def lowercase ( self : Optional[Any] ) -> Optional[Any]:
lowercase : Any = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'timbrooks/instruct-pix2pix', safety_checker=lowerCAmelCase )
lowercase : List[Any] = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
pipe.enable_attention_slicing()
lowercase : List[Any] = self.get_inputs()
lowercase : Tuple = pipe(**lowerCAmelCase ).images
lowercase : Dict = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
lowercase : str = np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def lowercase ( self : Optional[Any] ) -> List[Any]:
lowercase : int = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'timbrooks/instruct-pix2pix', safety_checker=lowerCAmelCase )
lowercase : List[Any] = DDIMScheduler.from_config(pipe.scheduler.config )
pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
pipe.enable_attention_slicing()
lowercase : Any = self.get_inputs()
lowercase : Union[str, Any] = pipe(**lowerCAmelCase ).images
lowercase : List[Any] = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
lowercase : int = np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def lowercase ( self : Tuple ) -> Dict:
lowercase : Dict = 0
def callback_fn(lowerCAmelCase : int, lowerCAmelCase : int, lowerCAmelCase : torch.FloatTensor ) -> None:
lowercase : Optional[Any] = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
lowercase : str = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
lowercase : Union[str, Any] = latents[0, -3:, -3:, -1]
lowercase : List[str] = np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
elif step == 2:
lowercase : Optional[Any] = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
lowercase : Union[str, Any] = latents[0, -3:, -3:, -1]
lowercase : str = np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
lowercase : Union[str, Any] = False
lowercase : List[str] = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'timbrooks/instruct-pix2pix', safety_checker=lowerCAmelCase, torch_dtype=torch.floataa )
lowercase : Optional[int] = pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
pipe.enable_attention_slicing()
lowercase : Union[str, Any] = self.get_inputs()
pipe(**lowerCAmelCase, callback=lowerCAmelCase, callback_steps=1 )
assert callback_fn.has_been_called
assert number_of_steps == 3
def lowercase ( self : Union[str, Any] ) -> Union[str, Any]:
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
lowercase : Optional[int] = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'timbrooks/instruct-pix2pix', safety_checker=lowerCAmelCase, torch_dtype=torch.floataa )
lowercase : Dict = pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
lowercase : Dict = self.get_inputs()
lowercase : List[Any] = pipe(**lowerCAmelCase )
lowercase : Any = torch.cuda.max_memory_allocated()
# make sure that less than 2.2 GB is allocated
assert mem_bytes < 2.2 * 10**9
def lowercase ( self : Union[str, Any] ) -> Tuple:
lowercase : int = self.get_inputs()
# resize to resolution that is divisible by 8 but not 16 or 32
lowercase : Optional[Any] = inputs['image'].resize((504, 504) )
lowercase : Union[str, Any] = 'timbrooks/instruct-pix2pix'
lowercase : Any = StableDiffusionInstructPixaPixPipeline.from_pretrained(
lowerCAmelCase, safety_checker=lowerCAmelCase, )
pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
pipe.enable_attention_slicing()
lowercase : str = pipe(**lowerCAmelCase )
lowercase : int = output.images[0]
lowercase : int = image[255:258, 383:386, -1]
assert image.shape == (504, 504, 3)
lowercase : Union[str, Any] = np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-3
| 53 | 1 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import numpy
import tensorflow as tf
from transformers import (
TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
BertConfig,
DPRConfig,
TFDPRContextEncoder,
TFDPRQuestionEncoder,
TFDPRReader,
)
class snake_case__ :
"""simple docstring"""
def __init__( self , __lowercase , __lowercase=1_3 , __lowercase=7 , __lowercase=True , __lowercase=True , __lowercase=True , __lowercase=True , __lowercase=9_9 , __lowercase=3_2 , __lowercase=2 , __lowercase=4 , __lowercase=3_7 , __lowercase="gelu" , __lowercase=0.1 , __lowercase=0.1 , __lowercase=5_1_2 , __lowercase=1_6 , __lowercase=2 , __lowercase=0.0_2 , __lowercase=3 , __lowercase=4 , __lowercase=None , __lowercase=0 , ) -> Optional[Any]:
"""simple docstring"""
a__ : Optional[int] = parent
a__ : int = batch_size
a__ : Dict = seq_length
a__ : Optional[Any] = is_training
a__ : Optional[Any] = use_input_mask
a__ : str = use_token_type_ids
a__ : List[Any] = use_labels
a__ : int = vocab_size
a__ : List[Any] = hidden_size
a__ : int = num_hidden_layers
a__ : Optional[Any] = num_attention_heads
a__ : Tuple = intermediate_size
a__ : Dict = hidden_act
a__ : Any = hidden_dropout_prob
a__ : List[str] = attention_probs_dropout_prob
a__ : Optional[Any] = max_position_embeddings
a__ : List[Any] = type_vocab_size
a__ : Dict = type_sequence_label_size
a__ : List[Any] = initializer_range
a__ : Dict = num_labels
a__ : int = num_choices
a__ : Union[str, Any] = scope
a__ : str = projection_dim
def SCREAMING_SNAKE_CASE__( self ) -> List[str]:
"""simple docstring"""
a__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a__ : Optional[int] = None
if self.use_input_mask:
# follow test_modeling_tf_ctrl.py
a__ : str = random_attention_mask([self.batch_size, self.seq_length] )
a__ : Tuple = None
if self.use_token_type_ids:
a__ : Dict = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
a__ : str = None
a__ : List[str] = None
a__ : int = None
if self.use_labels:
a__ : List[str] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
a__ : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
a__ : Union[str, Any] = ids_tensor([self.batch_size] , self.num_choices )
a__ : int = BertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__lowercase , initializer_range=self.initializer_range , )
a__ : str = DPRConfig(projection_dim=self.projection_dim , **config.to_dict() )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def SCREAMING_SNAKE_CASE__( self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase ) -> str:
"""simple docstring"""
a__ : Tuple = TFDPRContextEncoder(config=__lowercase )
a__ : Optional[int] = model(__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase )
a__ : Dict = model(__lowercase , token_type_ids=__lowercase )
a__ : List[str] = model(__lowercase )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size) )
def SCREAMING_SNAKE_CASE__( self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase ) -> Optional[Any]:
"""simple docstring"""
a__ : List[str] = TFDPRQuestionEncoder(config=__lowercase )
a__ : Optional[Any] = model(__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase )
a__ : str = model(__lowercase , token_type_ids=__lowercase )
a__ : Optional[int] = model(__lowercase )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size) )
def SCREAMING_SNAKE_CASE__( self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase ) -> Union[str, Any]:
"""simple docstring"""
a__ : int = TFDPRReader(config=__lowercase )
a__ : List[Any] = model(__lowercase , attention_mask=__lowercase )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.relevance_logits.shape , (self.batch_size,) )
def SCREAMING_SNAKE_CASE__( self ) -> str:
"""simple docstring"""
a__ : Union[str, Any] = self.prepare_config_and_inputs()
(
(
a__
) , (
a__
) , (
a__
) , (
a__
) , (
a__
) , (
a__
) , (
a__
) ,
) : Union[str, Any] = config_and_inputs
a__ : str = {"""input_ids""": input_ids}
return config, inputs_dict
@require_tf
class snake_case__ (A__ , A__ , unittest.TestCase ):
"""simple docstring"""
__lowerCAmelCase :Optional[int] = (
(
TFDPRContextEncoder,
TFDPRQuestionEncoder,
TFDPRReader,
)
if is_tf_available()
else ()
)
__lowerCAmelCase :Dict = {"feature-extraction": TFDPRQuestionEncoder} if is_tf_available() else {}
__lowerCAmelCase :List[Any] = False
__lowerCAmelCase :Optional[Any] = False
__lowerCAmelCase :Dict = False
__lowerCAmelCase :int = False
__lowerCAmelCase :Optional[Any] = False
def SCREAMING_SNAKE_CASE__( self ) -> List[str]:
"""simple docstring"""
a__ : Optional[Any] = TFDPRModelTester(self )
a__ : Dict = ConfigTester(self , config_class=__lowercase , hidden_size=3_7 )
def SCREAMING_SNAKE_CASE__( self ) -> Any:
"""simple docstring"""
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE__( self ) -> Dict:
"""simple docstring"""
a__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_context_encoder(*__lowercase )
def SCREAMING_SNAKE_CASE__( self ) -> Dict:
"""simple docstring"""
a__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_question_encoder(*__lowercase )
def SCREAMING_SNAKE_CASE__( self ) -> Any:
"""simple docstring"""
a__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_reader(*__lowercase )
@slow
def SCREAMING_SNAKE_CASE__( self ) -> int:
"""simple docstring"""
for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a__ : Optional[Any] = TFDPRContextEncoder.from_pretrained(__lowercase )
self.assertIsNotNone(__lowercase )
for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a__ : str = TFDPRContextEncoder.from_pretrained(__lowercase )
self.assertIsNotNone(__lowercase )
for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a__ : Optional[Any] = TFDPRQuestionEncoder.from_pretrained(__lowercase )
self.assertIsNotNone(__lowercase )
for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a__ : int = TFDPRReader.from_pretrained(__lowercase )
self.assertIsNotNone(__lowercase )
@require_tf
class snake_case__ (unittest.TestCase ):
"""simple docstring"""
@slow
def SCREAMING_SNAKE_CASE__( self ) -> Tuple:
"""simple docstring"""
a__ : Any = TFDPRQuestionEncoder.from_pretrained("""facebook/dpr-question_encoder-single-nq-base""" )
a__ : Union[str, Any] = tf.constant(
[[1_0_1, 7_5_9_2, 1_0_1_0, 2_0_0_3, 2_0_2_6, 3_8_9_9, 1_0_1_4_0, 1_0_2_9, 1_0_2]] ) # [CLS] hello, is my dog cute? [SEP]
a__ : Any = model(__lowercase )[0] # embedding shape = (1, 768)
# compare the actual values for a slice.
a__ : Optional[int] = tf.constant(
[
[
0.0_3_2_3_6_2_5_3,
0.1_2_7_5_3_3_3_5,
0.1_6_8_1_8_5_0_9,
0.0_0_2_7_9_7_8_6,
0.3_8_9_6_9_3_3,
0.2_4_2_6_4_9_4_5,
0.2_1_7_8_9_7_1,
-0.0_2_3_3_5_2_2_7,
-0.0_8_4_8_1_9_5_9,
-0.1_4_3_2_4_1_1_7,
]
] )
self.assertTrue(numpy.allclose(output[:, :1_0].numpy() , expected_slice.numpy() , atol=1E-4 ) )
| 170 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import numpy
import tensorflow as tf
from transformers import (
TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
BertConfig,
DPRConfig,
TFDPRContextEncoder,
TFDPRQuestionEncoder,
TFDPRReader,
)
class snake_case__ :
"""simple docstring"""
def __init__( self , __lowercase , __lowercase=1_3 , __lowercase=7 , __lowercase=True , __lowercase=True , __lowercase=True , __lowercase=True , __lowercase=9_9 , __lowercase=3_2 , __lowercase=2 , __lowercase=4 , __lowercase=3_7 , __lowercase="gelu" , __lowercase=0.1 , __lowercase=0.1 , __lowercase=5_1_2 , __lowercase=1_6 , __lowercase=2 , __lowercase=0.0_2 , __lowercase=3 , __lowercase=4 , __lowercase=None , __lowercase=0 , ) -> Optional[Any]:
"""simple docstring"""
a__ : Optional[int] = parent
a__ : int = batch_size
a__ : Dict = seq_length
a__ : Optional[Any] = is_training
a__ : Optional[Any] = use_input_mask
a__ : str = use_token_type_ids
a__ : List[Any] = use_labels
a__ : int = vocab_size
a__ : List[Any] = hidden_size
a__ : int = num_hidden_layers
a__ : Optional[Any] = num_attention_heads
a__ : Tuple = intermediate_size
a__ : Dict = hidden_act
a__ : Any = hidden_dropout_prob
a__ : List[str] = attention_probs_dropout_prob
a__ : Optional[Any] = max_position_embeddings
a__ : List[Any] = type_vocab_size
a__ : Dict = type_sequence_label_size
a__ : List[Any] = initializer_range
a__ : Dict = num_labels
a__ : int = num_choices
a__ : Union[str, Any] = scope
a__ : str = projection_dim
def SCREAMING_SNAKE_CASE__( self ) -> List[str]:
"""simple docstring"""
a__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a__ : Optional[int] = None
if self.use_input_mask:
# follow test_modeling_tf_ctrl.py
a__ : str = random_attention_mask([self.batch_size, self.seq_length] )
a__ : Tuple = None
if self.use_token_type_ids:
a__ : Dict = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
a__ : str = None
a__ : List[str] = None
a__ : int = None
if self.use_labels:
a__ : List[str] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
a__ : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
a__ : Union[str, Any] = ids_tensor([self.batch_size] , self.num_choices )
a__ : int = BertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__lowercase , initializer_range=self.initializer_range , )
a__ : str = DPRConfig(projection_dim=self.projection_dim , **config.to_dict() )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def SCREAMING_SNAKE_CASE__( self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase ) -> str:
"""simple docstring"""
a__ : Tuple = TFDPRContextEncoder(config=__lowercase )
a__ : Optional[int] = model(__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase )
a__ : Dict = model(__lowercase , token_type_ids=__lowercase )
a__ : List[str] = model(__lowercase )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size) )
def SCREAMING_SNAKE_CASE__( self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase ) -> Optional[Any]:
"""simple docstring"""
a__ : List[str] = TFDPRQuestionEncoder(config=__lowercase )
a__ : Optional[Any] = model(__lowercase , attention_mask=__lowercase , token_type_ids=__lowercase )
a__ : str = model(__lowercase , token_type_ids=__lowercase )
a__ : Optional[int] = model(__lowercase )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size) )
def SCREAMING_SNAKE_CASE__( self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase ) -> Union[str, Any]:
"""simple docstring"""
a__ : int = TFDPRReader(config=__lowercase )
a__ : List[Any] = model(__lowercase , attention_mask=__lowercase )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.relevance_logits.shape , (self.batch_size,) )
def SCREAMING_SNAKE_CASE__( self ) -> str:
"""simple docstring"""
a__ : Union[str, Any] = self.prepare_config_and_inputs()
(
(
a__
) , (
a__
) , (
a__
) , (
a__
) , (
a__
) , (
a__
) , (
a__
) ,
) : Union[str, Any] = config_and_inputs
a__ : str = {"""input_ids""": input_ids}
return config, inputs_dict
@require_tf
class snake_case__ (A__ , A__ , unittest.TestCase ):
"""simple docstring"""
__lowerCAmelCase :Optional[int] = (
(
TFDPRContextEncoder,
TFDPRQuestionEncoder,
TFDPRReader,
)
if is_tf_available()
else ()
)
__lowerCAmelCase :Dict = {"feature-extraction": TFDPRQuestionEncoder} if is_tf_available() else {}
__lowerCAmelCase :List[Any] = False
__lowerCAmelCase :Optional[Any] = False
__lowerCAmelCase :Dict = False
__lowerCAmelCase :int = False
__lowerCAmelCase :Optional[Any] = False
def SCREAMING_SNAKE_CASE__( self ) -> List[str]:
"""simple docstring"""
a__ : Optional[Any] = TFDPRModelTester(self )
a__ : Dict = ConfigTester(self , config_class=__lowercase , hidden_size=3_7 )
def SCREAMING_SNAKE_CASE__( self ) -> Any:
"""simple docstring"""
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE__( self ) -> Dict:
"""simple docstring"""
a__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_context_encoder(*__lowercase )
def SCREAMING_SNAKE_CASE__( self ) -> Dict:
"""simple docstring"""
a__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_question_encoder(*__lowercase )
def SCREAMING_SNAKE_CASE__( self ) -> Any:
"""simple docstring"""
a__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_reader(*__lowercase )
@slow
def SCREAMING_SNAKE_CASE__( self ) -> int:
"""simple docstring"""
for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a__ : Optional[Any] = TFDPRContextEncoder.from_pretrained(__lowercase )
self.assertIsNotNone(__lowercase )
for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a__ : str = TFDPRContextEncoder.from_pretrained(__lowercase )
self.assertIsNotNone(__lowercase )
for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a__ : Optional[Any] = TFDPRQuestionEncoder.from_pretrained(__lowercase )
self.assertIsNotNone(__lowercase )
for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a__ : int = TFDPRReader.from_pretrained(__lowercase )
self.assertIsNotNone(__lowercase )
@require_tf
class snake_case__ (unittest.TestCase ):
"""simple docstring"""
@slow
def SCREAMING_SNAKE_CASE__( self ) -> Tuple:
"""simple docstring"""
a__ : Any = TFDPRQuestionEncoder.from_pretrained("""facebook/dpr-question_encoder-single-nq-base""" )
a__ : Union[str, Any] = tf.constant(
[[1_0_1, 7_5_9_2, 1_0_1_0, 2_0_0_3, 2_0_2_6, 3_8_9_9, 1_0_1_4_0, 1_0_2_9, 1_0_2]] ) # [CLS] hello, is my dog cute? [SEP]
a__ : Any = model(__lowercase )[0] # embedding shape = (1, 768)
# compare the actual values for a slice.
a__ : Optional[int] = tf.constant(
[
[
0.0_3_2_3_6_2_5_3,
0.1_2_7_5_3_3_3_5,
0.1_6_8_1_8_5_0_9,
0.0_0_2_7_9_7_8_6,
0.3_8_9_6_9_3_3,
0.2_4_2_6_4_9_4_5,
0.2_1_7_8_9_7_1,
-0.0_2_3_3_5_2_2_7,
-0.0_8_4_8_1_9_5_9,
-0.1_4_3_2_4_1_1_7,
]
] )
self.assertTrue(numpy.allclose(output[:, :1_0].numpy() , expected_slice.numpy() , atol=1E-4 ) )
| 170 | 1 |
import os
def SCREAMING_SNAKE_CASE ( ) -> Dict:
lowerCamelCase__ : int = os.path.dirname(os.path.realpath(_UpperCAmelCase ) )
lowerCamelCase__ : Optional[int] = os.path.join(_UpperCAmelCase , 'triangle.txt' )
with open(_UpperCAmelCase ) as f:
lowerCamelCase__ : Optional[Any] = f.readlines()
lowerCamelCase__ : str = []
for line in triangle:
lowerCamelCase__ : Union[str, Any] = []
for number in line.strip().split(' ' ):
numbers_from_line.append(int(_UpperCAmelCase ) )
a.append(_UpperCAmelCase )
for i in range(1 , len(_UpperCAmelCase ) ):
for j in range(len(a[i] ) ):
lowerCamelCase__ : Any = a[i - 1][j] if j != len(a[i - 1] ) else 0
lowerCamelCase__ : Dict = a[i - 1][j - 1] if j > 0 else 0
a[i][j] += max(_UpperCAmelCase , _UpperCAmelCase )
return max(a[-1] )
if __name__ == "__main__":
print(solution())
| 350 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , ) -> None:
lowerCamelCase__ : Optional[Any] = len(_UpperCAmelCase )
# If row is equal to the size of the board it means there are a queen in each row in
# the current board (possible_board)
if row == n:
# We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
# this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
boards.append(['. ' * i + 'Q ' + '. ' * (n - 1 - i) for i in possible_board] )
return
# We iterate each column in the row to find all possible results in each row
for col in range(_UpperCAmelCase ):
# We apply that we learned previously. First we check that in the current board
# (possible_board) there are not other same value because if there is it means
# that there are a collision in vertical. Then we apply the two formulas we
# learned before:
#
# 45º: y - x = b or 45: row - col = b
# 135º: y + x = b or row + col = b.
#
# And we verify if the results of this two formulas not exist in their variables
# respectively. (diagonal_right_collisions, diagonal_left_collisions)
#
# If any or these are True it means there is a collision so we continue to the
# next value in the for loop.
if (
col in possible_board
or row - col in diagonal_right_collisions
or row + col in diagonal_left_collisions
):
continue
# If it is False we call dfs function again and we update the inputs
depth_first_search(
[*possible_board, col] , [*diagonal_right_collisions, row - col] , [*diagonal_left_collisions, row + col] , _UpperCAmelCase , _UpperCAmelCase , )
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> None:
lowerCamelCase__ : list[list[str]] = []
depth_first_search([] , [] , [] , _UpperCAmelCase , _UpperCAmelCase )
# Print all the boards
for board in boards:
for column in board:
print(_UpperCAmelCase )
print('' )
print(len(_UpperCAmelCase ) , 'solutions were found.' )
if __name__ == "__main__":
import doctest
doctest.testmod()
n_queens_solution(4)
| 45 | 0 |
"""simple docstring"""
from math import factorial
a_ = {str(d): factorial(d) for d in range(10)}
def __lowercase ( snake_case_ : Optional[Any] ) ->int:
'''simple docstring'''
return sum(DIGIT_FACTORIAL[d] for d in str(snake_case_ ) )
def __lowercase ( ) ->int:
'''simple docstring'''
__A : Optional[Any] = 7 * factorial(9 ) + 1
return sum(i for i in range(3 ,snake_case_ ) if sum_of_digit_factorial(snake_case_ ) == i )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 179 |
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel
class __a ( __UpperCamelCase ):
__lowercase : Any = ['vqvae']
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , ) -> Union[str, Any]:
'''simple docstring'''
super().__init__()
self.register_modules(unet=lowerCAmelCase__ , scheduler=lowerCAmelCase__ , mel=lowerCAmelCase__ , vqvae=lowerCAmelCase__ )
def SCREAMING_SNAKE_CASE__ ( self ) -> int:
'''simple docstring'''
return 50 if isinstance(self.scheduler , lowerCAmelCase__ ) else 1_000
@torch.no_grad()
def __call__( self , lowerCAmelCase__ = 1 , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = 0 , lowerCAmelCase__ = 0 , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = 0 , lowerCAmelCase__ = 0 , lowerCAmelCase__ = None , lowerCAmelCase__ = 0 , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__=True , ) -> Union[
Union[AudioPipelineOutput, ImagePipelineOutput],
Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
]:
'''simple docstring'''
lowercase__: Union[str, Any] = steps or self.get_default_steps()
self.scheduler.set_timesteps(lowerCAmelCase__ )
lowercase__: Optional[int] = step_generator or generator
# For backwards compatibility
if type(self.unet.config.sample_size ) == int:
lowercase__: Optional[int] = (self.unet.config.sample_size, self.unet.config.sample_size)
if noise is None:
lowercase__: List[str] = randn_tensor(
(
batch_size,
self.unet.config.in_channels,
self.unet.config.sample_size[0],
self.unet.config.sample_size[1],
) , generator=lowerCAmelCase__ , device=self.device , )
lowercase__: List[Any] = noise
lowercase__: int = None
if audio_file is not None or raw_audio is not None:
self.mel.load_audio(lowerCAmelCase__ , lowerCAmelCase__ )
lowercase__: int = self.mel.audio_slice_to_image(lowerCAmelCase__ )
lowercase__: int = np.frombuffer(input_image.tobytes() , dtype='uint8' ).reshape(
(input_image.height, input_image.width) )
lowercase__: str = (input_image / 255) * 2 - 1
lowercase__: Union[str, Any] = torch.tensor(input_image[np.newaxis, :, :] , dtype=torch.float ).to(self.device )
if self.vqvae is not None:
lowercase__: Optional[int] = self.vqvae.encode(torch.unsqueeze(lowerCAmelCase__ , 0 ) ).latent_dist.sample(
generator=lowerCAmelCase__ )[0]
lowercase__: Dict = self.vqvae.config.scaling_factor * input_images
if start_step > 0:
lowercase__: List[Any] = self.scheduler.add_noise(lowerCAmelCase__ , lowerCAmelCase__ , self.scheduler.timesteps[start_step - 1] )
lowercase__: str = (
self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
)
lowercase__: Dict = int(mask_start_secs * pixels_per_second )
lowercase__: Tuple = int(mask_end_secs * pixels_per_second )
lowercase__: List[Any] = self.scheduler.add_noise(lowerCAmelCase__ , lowerCAmelCase__ , torch.tensor(self.scheduler.timesteps[start_step:] ) )
for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:] ) ):
if isinstance(self.unet , lowerCAmelCase__ ):
lowercase__: Union[str, Any] = self.unet(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )['sample']
else:
lowercase__: Optional[Any] = self.unet(lowerCAmelCase__ , lowerCAmelCase__ )['sample']
if isinstance(self.scheduler , lowerCAmelCase__ ):
lowercase__: List[str] = self.scheduler.step(
model_output=lowerCAmelCase__ , timestep=lowerCAmelCase__ , sample=lowerCAmelCase__ , eta=lowerCAmelCase__ , generator=lowerCAmelCase__ , )['prev_sample']
else:
lowercase__: int = self.scheduler.step(
model_output=lowerCAmelCase__ , timestep=lowerCAmelCase__ , sample=lowerCAmelCase__ , generator=lowerCAmelCase__ , )['prev_sample']
if mask is not None:
if mask_start > 0:
lowercase__: List[Any] = mask[:, step, :, :mask_start]
if mask_end > 0:
lowercase__: Optional[int] = mask[:, step, :, -mask_end:]
if self.vqvae is not None:
# 0.18215 was scaling factor used in training to ensure unit variance
lowercase__: Union[str, Any] = 1 / self.vqvae.config.scaling_factor * images
lowercase__: Optional[Any] = self.vqvae.decode(lowerCAmelCase__ )['sample']
lowercase__: Dict = (images / 2 + 0.5).clamp(0 , 1 )
lowercase__: List[Any] = images.cpu().permute(0 , 2 , 3 , 1 ).numpy()
lowercase__: int = (images * 255).round().astype('uint8' )
lowercase__: Optional[Any] = list(
(Image.fromarray(_[:, :, 0] ) for _ in images)
if images.shape[3] == 1
else (Image.fromarray(lowerCAmelCase__ , mode='RGB' ).convert('L' ) for _ in images) )
lowercase__: Dict = [self.mel.image_to_audio(lowerCAmelCase__ ) for _ in images]
if not return_dict:
return images, (self.mel.get_sample_rate(), audios)
return BaseOutput(**AudioPipelineOutput(np.array(lowerCAmelCase__ )[:, np.newaxis, :] ) , **ImagePipelineOutput(lowerCAmelCase__ ) )
@torch.no_grad()
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ , lowerCAmelCase__ = 50 ) -> np.ndarray:
'''simple docstring'''
assert isinstance(self.scheduler , lowerCAmelCase__ )
self.scheduler.set_timesteps(lowerCAmelCase__ )
lowercase__: List[str] = np.array(
[np.frombuffer(image.tobytes() , dtype='uint8' ).reshape((1, image.height, image.width) ) for image in images] )
lowercase__: str = (sample / 255) * 2 - 1
lowercase__: str = torch.Tensor(lowerCAmelCase__ ).to(self.device )
for t in self.progress_bar(torch.flip(self.scheduler.timesteps , (0,) ) ):
lowercase__: Union[str, Any] = t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
lowercase__: Optional[Any] = self.scheduler.alphas_cumprod[t]
lowercase__: str = (
self.scheduler.alphas_cumprod[prev_timestep]
if prev_timestep >= 0
else self.scheduler.final_alpha_cumprod
)
lowercase__: str = 1 - alpha_prod_t
lowercase__: int = self.unet(lowerCAmelCase__ , lowerCAmelCase__ )['sample']
lowercase__: int = (1 - alpha_prod_t_prev) ** 0.5 * model_output
lowercase__: Optional[int] = (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
lowercase__: Any = sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output
return sample
@staticmethod
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> torch.Tensor:
'''simple docstring'''
lowercase__: Any = acos(torch.dot(torch.flatten(lowerCAmelCase__ ) , torch.flatten(lowerCAmelCase__ ) ) / torch.norm(lowerCAmelCase__ ) / torch.norm(lowerCAmelCase__ ) )
return sin((1 - alpha) * theta ) * xa / sin(lowerCAmelCase__ ) + sin(alpha * theta ) * xa / sin(lowerCAmelCase__ )
| 196 | 0 |
'''simple docstring'''
import random
def lowercase (_A ):
"""simple docstring"""
_lowerCAmelCase : Union[str, Any] = num - 1
_lowerCAmelCase : Optional[Any] = 0
while s % 2 == 0:
_lowerCAmelCase : Optional[int] = s // 2
t += 1
for _ in range(5 ):
_lowerCAmelCase : Dict = random.randrange(2 , num - 1 )
_lowerCAmelCase : Dict = pow(_A , _A , _A )
if v != 1:
_lowerCAmelCase : str = 0
while v != (num - 1):
if i == t - 1:
return False
else:
_lowerCAmelCase : Tuple = i + 1
_lowerCAmelCase : Optional[Any] = (v**2) % num
return True
def lowercase (_A ):
"""simple docstring"""
if num < 2:
return False
_lowerCAmelCase : List[Any] = [
2,
3,
5,
7,
1_1,
1_3,
1_7,
1_9,
2_3,
2_9,
3_1,
3_7,
4_1,
4_3,
4_7,
5_3,
5_9,
6_1,
6_7,
7_1,
7_3,
7_9,
8_3,
8_9,
9_7,
1_0_1,
1_0_3,
1_0_7,
1_0_9,
1_1_3,
1_2_7,
1_3_1,
1_3_7,
1_3_9,
1_4_9,
1_5_1,
1_5_7,
1_6_3,
1_6_7,
1_7_3,
1_7_9,
1_8_1,
1_9_1,
1_9_3,
1_9_7,
1_9_9,
2_1_1,
2_2_3,
2_2_7,
2_2_9,
2_3_3,
2_3_9,
2_4_1,
2_5_1,
2_5_7,
2_6_3,
2_6_9,
2_7_1,
2_7_7,
2_8_1,
2_8_3,
2_9_3,
3_0_7,
3_1_1,
3_1_3,
3_1_7,
3_3_1,
3_3_7,
3_4_7,
3_4_9,
3_5_3,
3_5_9,
3_6_7,
3_7_3,
3_7_9,
3_8_3,
3_8_9,
3_9_7,
4_0_1,
4_0_9,
4_1_9,
4_2_1,
4_3_1,
4_3_3,
4_3_9,
4_4_3,
4_4_9,
4_5_7,
4_6_1,
4_6_3,
4_6_7,
4_7_9,
4_8_7,
4_9_1,
4_9_9,
5_0_3,
5_0_9,
5_2_1,
5_2_3,
5_4_1,
5_4_7,
5_5_7,
5_6_3,
5_6_9,
5_7_1,
5_7_7,
5_8_7,
5_9_3,
5_9_9,
6_0_1,
6_0_7,
6_1_3,
6_1_7,
6_1_9,
6_3_1,
6_4_1,
6_4_3,
6_4_7,
6_5_3,
6_5_9,
6_6_1,
6_7_3,
6_7_7,
6_8_3,
6_9_1,
7_0_1,
7_0_9,
7_1_9,
7_2_7,
7_3_3,
7_3_9,
7_4_3,
7_5_1,
7_5_7,
7_6_1,
7_6_9,
7_7_3,
7_8_7,
7_9_7,
8_0_9,
8_1_1,
8_2_1,
8_2_3,
8_2_7,
8_2_9,
8_3_9,
8_5_3,
8_5_7,
8_5_9,
8_6_3,
8_7_7,
8_8_1,
8_8_3,
8_8_7,
9_0_7,
9_1_1,
9_1_9,
9_2_9,
9_3_7,
9_4_1,
9_4_7,
9_5_3,
9_6_7,
9_7_1,
9_7_7,
9_8_3,
9_9_1,
9_9_7,
]
if num in low_primes:
return True
for prime in low_primes:
if (num % prime) == 0:
return False
return rabin_miller(_A )
def lowercase (_A = 1_0_2_4 ):
"""simple docstring"""
while True:
_lowerCAmelCase : Any = random.randrange(2 ** (keysize - 1) , 2 ** (keysize) )
if is_prime_low_num(_A ):
return num
if __name__ == "__main__":
lowerCAmelCase : Tuple = generate_large_prime()
print(("""Prime number:""", num))
print(("""is_prime_low_num:""", is_prime_low_num(num)))
| 353 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
)
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmelCase : Tuple = get_tests_dir("""fixtures/test_sentencepiece.model""")
if is_torch_available():
from transformers.models.mbart.modeling_mbart import shift_tokens_right
lowerCAmelCase : Union[str, Any] = 25_00_04
lowerCAmelCase : int = 25_00_20
@require_sentencepiece
@require_tokenizers
class UpperCamelCase__ ( SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
"""simple docstring"""
__magic_name__ = MBartaaTokenizer
__magic_name__ = MBartaaTokenizerFast
__magic_name__ = True
__magic_name__ = True
def a ( self ):
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
_lowerCAmelCase : List[Any] = MBartaaTokenizer(snake_case__ , src_lang='en_XX' , tgt_lang='ro_RO' , keep_accents=snake_case__ )
tokenizer.save_pretrained(self.tmpdirname )
def a ( self ):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = '<s>'
_lowerCAmelCase : str = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case__ ) , snake_case__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case__ ) , snake_case__ )
def a ( self ):
'''simple docstring'''
_lowerCAmelCase : Dict = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '<s>' )
self.assertEqual(vocab_keys[1] , '<pad>' )
self.assertEqual(vocab_keys[-1] , '<mask>' )
self.assertEqual(len(snake_case__ ) , 1054 )
def a ( self ):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 1054 )
def a ( self ):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = MBartaaTokenizer(snake_case__ , src_lang='en_XX' , tgt_lang='ro_RO' , keep_accents=snake_case__ )
_lowerCAmelCase : Any = tokenizer.tokenize('This is a test' )
self.assertListEqual(snake_case__ , ['▁This', '▁is', '▁a', '▁t', 'est'] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(snake_case__ ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
_lowerCAmelCase : Tuple = tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
snake_case__ , [SPIECE_UNDERLINE + 'I', SPIECE_UNDERLINE + 'was', SPIECE_UNDERLINE + 'b', 'or', 'n', SPIECE_UNDERLINE + 'in', SPIECE_UNDERLINE + '', '9', '2', '0', '0', '0', ',', SPIECE_UNDERLINE + 'and', SPIECE_UNDERLINE + 'this', SPIECE_UNDERLINE + 'is', SPIECE_UNDERLINE + 'f', 'al', 's', 'é', '.'] , )
_lowerCAmelCase : Optional[int] = tokenizer.convert_tokens_to_ids(snake_case__ )
self.assertListEqual(
snake_case__ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] , )
_lowerCAmelCase : Optional[Any] = tokenizer.convert_ids_to_tokens(snake_case__ )
self.assertListEqual(
snake_case__ , [SPIECE_UNDERLINE + 'I', SPIECE_UNDERLINE + 'was', SPIECE_UNDERLINE + 'b', 'or', 'n', SPIECE_UNDERLINE + 'in', SPIECE_UNDERLINE + '', '<unk>', '2', '0', '0', '0', ',', SPIECE_UNDERLINE + 'and', SPIECE_UNDERLINE + 'this', SPIECE_UNDERLINE + 'is', SPIECE_UNDERLINE + 'f', 'al', 's', '<unk>', '.'] , )
@slow
def a ( self ):
'''simple docstring'''
_lowerCAmelCase : Dict = {'input_ids': [[25_0004, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 5428, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 1_2399, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 12_2009, 11_5774, 23, 805, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [25_0004, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [25_0004, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=snake_case__ , model_name='facebook/mbart-large-50' , revision='d3913889c59cd5c9e456b269c376325eabad57e2' , )
def a ( self ):
'''simple docstring'''
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
_lowerCAmelCase : Optional[int] = (self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-mbart50', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})' ):
_lowerCAmelCase : Union[str, Any] = self.rust_tokenizer_class.from_pretrained(snake_case__ , **snake_case__ )
_lowerCAmelCase : Tuple = self.tokenizer_class.from_pretrained(snake_case__ , **snake_case__ )
_lowerCAmelCase : Optional[Any] = tempfile.mkdtemp()
_lowerCAmelCase : Tuple = tokenizer_r.save_pretrained(snake_case__ )
_lowerCAmelCase : str = tokenizer_p.save_pretrained(snake_case__ )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )
_lowerCAmelCase : Any = tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f )
self.assertSequenceEqual(snake_case__ , snake_case__ )
# Checks everything loads correctly in the same way
_lowerCAmelCase : List[str] = tokenizer_r.from_pretrained(snake_case__ )
_lowerCAmelCase : Optional[int] = tokenizer_p.from_pretrained(snake_case__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(snake_case__ , snake_case__ ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(snake_case__ )
# Save tokenizer rust, legacy_format=True
_lowerCAmelCase : Union[str, Any] = tempfile.mkdtemp()
_lowerCAmelCase : Dict = tokenizer_r.save_pretrained(snake_case__ , legacy_format=snake_case__ )
_lowerCAmelCase : Any = tokenizer_p.save_pretrained(snake_case__ )
# Checks it save with the same files
self.assertSequenceEqual(snake_case__ , snake_case__ )
# Checks everything loads correctly in the same way
_lowerCAmelCase : Dict = tokenizer_r.from_pretrained(snake_case__ )
_lowerCAmelCase : List[str] = tokenizer_p.from_pretrained(snake_case__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(snake_case__ , snake_case__ ) )
shutil.rmtree(snake_case__ )
# Save tokenizer rust, legacy_format=False
_lowerCAmelCase : Optional[int] = tempfile.mkdtemp()
_lowerCAmelCase : int = tokenizer_r.save_pretrained(snake_case__ , legacy_format=snake_case__ )
_lowerCAmelCase : Tuple = tokenizer_p.save_pretrained(snake_case__ )
# Checks it saved the tokenizer.json file
self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
_lowerCAmelCase : int = tokenizer_r.from_pretrained(snake_case__ )
_lowerCAmelCase : str = tokenizer_p.from_pretrained(snake_case__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(snake_case__ , snake_case__ ) )
shutil.rmtree(snake_case__ )
@require_torch
@require_sentencepiece
@require_tokenizers
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
__magic_name__ = "facebook/mbart-large-50-one-to-many-mmt"
__magic_name__ = [
" 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.",
]
__magic_name__ = [
"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.",
]
__magic_name__ = [EN_CODE, 8_2_7_4, 1_2_7_8_7_3, 2_5_9_1_6, 7, 8_6_2_2, 2_0_7_1, 4_3_8, 6_7_4_8_5, 5_3, 1_8_7_8_9_5, 2_3, 5_1_7_1_2, 2]
@classmethod
def a ( cls ):
'''simple docstring'''
_lowerCAmelCase : MBartaaTokenizer = MBartaaTokenizer.from_pretrained(
cls.checkpoint_name , src_lang='en_XX' , tgt_lang='ro_RO' )
_lowerCAmelCase : Dict = 1
return cls
def a ( self ):
'''simple docstring'''
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 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['mr_IN'] , 25_0038 )
def a ( self ):
'''simple docstring'''
_lowerCAmelCase : int = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , snake_case__ )
def a ( self ):
'''simple docstring'''
self.assertIn(snake_case__ , self.tokenizer.all_special_ids )
_lowerCAmelCase : Union[str, Any] = [RO_CODE, 884, 9019, 96, 9, 916, 8_6792, 36, 1_8743, 1_5596, 5, 2]
_lowerCAmelCase : List[str] = self.tokenizer.decode(snake_case__ , skip_special_tokens=snake_case__ )
_lowerCAmelCase : str = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=snake_case__ )
self.assertEqual(snake_case__ , snake_case__ )
self.assertNotIn(self.tokenizer.eos_token , snake_case__ )
def a ( self ):
'''simple docstring'''
_lowerCAmelCase : str = ['this is gunna be a long sentence ' * 20]
assert isinstance(src_text[0] , snake_case__ )
_lowerCAmelCase : List[str] = 10
_lowerCAmelCase : Any = self.tokenizer(snake_case__ , max_length=snake_case__ , truncation=snake_case__ ).input_ids[0]
self.assertEqual(ids[0] , snake_case__ )
self.assertEqual(ids[-1] , 2 )
self.assertEqual(len(snake_case__ ) , snake_case__ )
def a ( self ):
'''simple docstring'''
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR'] ) , [25_0053, 25_0001] )
def a ( self ):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = tempfile.mkdtemp()
_lowerCAmelCase : Dict = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(snake_case__ )
_lowerCAmelCase : Tuple = MBartaaTokenizer.from_pretrained(snake_case__ )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , snake_case__ )
@require_torch
def a ( self ):
'''simple docstring'''
_lowerCAmelCase : str = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=snake_case__ , return_tensors='pt' )
_lowerCAmelCase : Optional[int] = shift_tokens_right(batch['labels'] , self.tokenizer.pad_token_id )
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
assert batch.input_ids[1][0] == EN_CODE
assert batch.input_ids[1][-1] == 2
assert batch.labels[1][0] == RO_CODE
assert batch.labels[1][-1] == 2
assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]
@require_torch
def a ( self ):
'''simple docstring'''
_lowerCAmelCase : str = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=snake_case__ , truncation=snake_case__ , max_length=len(self.expected_src_tokens ) , return_tensors='pt' , )
_lowerCAmelCase : int = shift_tokens_right(batch['labels'] , self.tokenizer.pad_token_id )
self.assertIsInstance(snake_case__ , snake_case__ )
self.assertEqual((2, 14) , batch.input_ids.shape )
self.assertEqual((2, 14) , batch.attention_mask.shape )
_lowerCAmelCase : Union[str, Any] = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , snake_case__ )
self.assertEqual(2 , batch.decoder_input_ids[0, 0] ) # decoder_start_token_id
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [EN_CODE] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
def a ( self ):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = self.tokenizer(self.src_text , padding=snake_case__ , truncation=snake_case__ , max_length=3 , return_tensors='pt' )
_lowerCAmelCase : str = self.tokenizer(
text_target=self.tgt_text , padding=snake_case__ , truncation=snake_case__ , max_length=10 , return_tensors='pt' )
_lowerCAmelCase : List[Any] = targets['input_ids']
_lowerCAmelCase : Any = shift_tokens_right(snake_case__ , 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 a ( self ):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = self.tokenizer._build_translation_inputs(
'A test' , return_tensors='pt' , src_lang='en_XX' , tgt_lang='ar_AR' )
self.assertEqual(
nested_simplify(snake_case__ ) , {
# en_XX, A, test, EOS
'input_ids': [[25_0004, 62, 3034, 2]],
'attention_mask': [[1, 1, 1, 1]],
# ar_AR
'forced_bos_token_id': 25_0001,
} , )
| 25 | 0 |
def _lowercase ( UpperCamelCase_ ) -> str:
'''simple docstring'''
return "".join(chr(ord(__lowerCamelCase ) - 32 ) if 'a' <= char <= 'z' else char for char in word )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 176 |
__UpperCamelCase : Optional[int] = {
"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",
}
| 228 | 0 |
from __future__ import annotations
__UpperCAmelCase = tuple[int, int, int]
__UpperCAmelCase = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
__UpperCAmelCase = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
# -------------------------- default selection --------------------------
# rotors --------------------------
__UpperCAmelCase = 'EGZWVONAHDCLFQMSIPJBYUKXTR'
__UpperCAmelCase = 'FOBHMDKEXQNRAULPGSJVTYICZW'
__UpperCAmelCase = 'ZJXESIUQLHAVRMDOYGTNFWPBKC'
# reflector --------------------------
__UpperCAmelCase = {
'A': 'N',
'N': 'A',
'B': 'O',
'O': 'B',
'C': 'P',
'P': 'C',
'D': 'Q',
'Q': 'D',
'E': 'R',
'R': 'E',
'F': 'S',
'S': 'F',
'G': 'T',
'T': 'G',
'H': 'U',
'U': 'H',
'I': 'V',
'V': 'I',
'J': 'W',
'W': 'J',
'K': 'X',
'X': 'K',
'L': 'Y',
'Y': 'L',
'M': 'Z',
'Z': 'M',
}
# -------------------------- extra rotors --------------------------
__UpperCAmelCase = 'RMDJXFUWGISLHVTCQNKYPBEZOA'
__UpperCAmelCase = 'SGLCPQWZHKXAREONTFBVIYJUDM'
__UpperCAmelCase = 'HVSICLTYKQUBXDWAJZOMFGPREN'
__UpperCAmelCase = 'RZWQHFMVDBKICJLNTUXAGYPSOE'
__UpperCAmelCase = 'LFKIJODBEGAMQPXVUHYSTCZRWN'
__UpperCAmelCase = 'KOAEGVDHXPQZMLFTYWJNBRCIUS'
def lowercase__ ( __snake_case : RotorPositionT , __snake_case : RotorSelectionT , __snake_case : str ):
'''simple docstring'''
if (unique_rotsel := len(set(snake_case_ ) )) < 3:
UpperCAmelCase_ : int = F"Please use 3 unique rotors (not {unique_rotsel})"
raise Exception(snake_case_ )
# Checks if rotor positions are valid
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : List[str] = rotpos
if not 0 < rotorposa <= len(snake_case_ ):
UpperCAmelCase_ : Any = F"First rotor position is not within range of 1..26 ({rotorposa}"
raise ValueError(snake_case_ )
if not 0 < rotorposa <= len(snake_case_ ):
UpperCAmelCase_ : Any = F"Second rotor position is not within range of 1..26 ({rotorposa})"
raise ValueError(snake_case_ )
if not 0 < rotorposa <= len(snake_case_ ):
UpperCAmelCase_ : Dict = F"Third rotor position is not within range of 1..26 ({rotorposa})"
raise ValueError(snake_case_ )
# Validates string and returns dict
UpperCAmelCase_ : Tuple = _plugboard(snake_case_ )
return rotpos, rotsel, pbdict
def lowercase__ ( __snake_case : str ):
'''simple docstring'''
if not isinstance(snake_case_ , snake_case_ ):
UpperCAmelCase_ : List[str] = F"Plugboard setting isn't type string ({type(snake_case_ )})"
raise TypeError(snake_case_ )
elif len(snake_case_ ) % 2 != 0:
UpperCAmelCase_ : Tuple = F"Odd number of symbols ({len(snake_case_ )})"
raise Exception(snake_case_ )
elif pbstring == "":
return {}
pbstring.replace(' ' , '' )
# Checks if all characters are unique
UpperCAmelCase_ : List[str] = set()
for i in pbstring:
if i not in abc:
UpperCAmelCase_ : Tuple = F"'{i}' not in list of symbols"
raise Exception(snake_case_ )
elif i in tmppbl:
UpperCAmelCase_ : Dict = F"Duplicate symbol ({i})"
raise Exception(snake_case_ )
else:
tmppbl.add(snake_case_ )
del tmppbl
# Created the dictionary
UpperCAmelCase_ : Optional[Any] = {}
for j in range(0 , len(snake_case_ ) - 1 , 2 ):
UpperCAmelCase_ : List[Any] = pbstring[j + 1]
UpperCAmelCase_ : List[str] = pbstring[j]
return pb
def lowercase__ ( __snake_case : str , __snake_case : RotorPositionT , __snake_case : RotorSelectionT = (rotora, rotora, rotora) , __snake_case : str = "" , ):
'''simple docstring'''
UpperCAmelCase_ : Optional[int] = text.upper()
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : Dict = _validator(
snake_case_ , snake_case_ , plugb.upper() )
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : Optional[Any] = rotor_position
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : str = rotor_selection
rotorposa -= 1
rotorposa -= 1
rotorposa -= 1
UpperCAmelCase_ : Dict = []
# encryption/decryption process --------------------------
for symbol in text:
if symbol in abc:
# 1st plugboard --------------------------
if symbol in plugboard:
UpperCAmelCase_ : List[Any] = plugboard[symbol]
# rotor ra --------------------------
UpperCAmelCase_ : int = abc.index(snake_case_ ) + rotorposa
UpperCAmelCase_ : List[str] = rotora[index % len(snake_case_ )]
# rotor rb --------------------------
UpperCAmelCase_ : List[str] = abc.index(snake_case_ ) + rotorposa
UpperCAmelCase_ : Optional[int] = rotora[index % len(snake_case_ )]
# rotor rc --------------------------
UpperCAmelCase_ : Dict = abc.index(snake_case_ ) + rotorposa
UpperCAmelCase_ : Optional[int] = rotora[index % len(snake_case_ )]
# reflector --------------------------
# this is the reason you don't need another machine to decipher
UpperCAmelCase_ : Tuple = reflector[symbol]
# 2nd rotors
UpperCAmelCase_ : Tuple = abc[rotora.index(snake_case_ ) - rotorposa]
UpperCAmelCase_ : Dict = abc[rotora.index(snake_case_ ) - rotorposa]
UpperCAmelCase_ : Union[str, Any] = abc[rotora.index(snake_case_ ) - rotorposa]
# 2nd plugboard
if symbol in plugboard:
UpperCAmelCase_ : Optional[Any] = plugboard[symbol]
# moves/resets rotor positions
rotorposa += 1
if rotorposa >= len(snake_case_ ):
UpperCAmelCase_ : Optional[Any] = 0
rotorposa += 1
if rotorposa >= len(snake_case_ ):
UpperCAmelCase_ : int = 0
rotorposa += 1
if rotorposa >= len(snake_case_ ):
UpperCAmelCase_ : int = 0
# else:
# pass
# Error could be also raised
# raise ValueError(
# 'Invalid symbol('+repr(symbol)+')')
result.append(snake_case_ )
return "".join(snake_case_ )
if __name__ == "__main__":
__UpperCAmelCase = 'This is my Python script that emulates the Enigma machine from WWII.'
__UpperCAmelCase = (1, 1, 1)
__UpperCAmelCase = 'pictures'
__UpperCAmelCase = (rotora, rotora, rotora)
__UpperCAmelCase = enigma(message, rotor_pos, rotor_sel, pb)
print('Encrypted message:', en)
print('Decrypted message:', enigma(en, rotor_pos, rotor_sel, pb))
| 368 |
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase (_snake_case ):
'''simple docstring'''
_snake_case : Any = (DDPMParallelScheduler,)
def __UpperCAmelCase ( self , **_UpperCamelCase ) -> Any:
UpperCAmelCase_ : List[str] = {
'num_train_timesteps': 1_0_0_0,
'beta_start': 0.00_01,
'beta_end': 0.02,
'beta_schedule': 'linear',
'variance_type': 'fixed_small',
'clip_sample': True,
}
config.update(**_UpperCamelCase )
return config
def __UpperCAmelCase ( self ) -> Any:
for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=_UpperCamelCase )
def __UpperCAmelCase ( self ) -> int:
for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1] , [0.0_02, 0.02, 0.2, 2] ):
self.check_over_configs(beta_start=_UpperCamelCase , beta_end=_UpperCamelCase )
def __UpperCAmelCase ( self ) -> Any:
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=_UpperCamelCase )
def __UpperCAmelCase ( self ) -> Optional[Any]:
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=_UpperCamelCase )
def __UpperCAmelCase ( self ) -> Optional[int]:
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=_UpperCamelCase )
def __UpperCAmelCase ( self ) -> Union[str, Any]:
self.check_over_configs(thresholding=_UpperCamelCase )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=_UpperCamelCase , prediction_type=_UpperCamelCase , sample_max_value=_UpperCamelCase , )
def __UpperCAmelCase ( self ) -> Any:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=_UpperCamelCase )
def __UpperCAmelCase ( self ) -> Union[str, Any]:
for t in [0, 5_0_0, 9_9_9]:
self.check_over_forward(time_step=_UpperCamelCase )
def __UpperCAmelCase ( self ) -> Dict:
UpperCAmelCase_ : Dict = self.scheduler_classes[0]
UpperCAmelCase_ : int = self.get_scheduler_config()
UpperCAmelCase_ : Union[str, Any] = scheduler_class(**_UpperCamelCase )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(4_8_7 ) - 0.0_09_79 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(9_9_9 ) - 0.02 ) ) < 1E-5
def __UpperCAmelCase ( self ) -> Dict:
UpperCAmelCase_ : List[Any] = self.scheduler_classes[0]
UpperCAmelCase_ : Any = self.get_scheduler_config()
UpperCAmelCase_ : Union[str, Any] = scheduler_class(**_UpperCamelCase )
UpperCAmelCase_ : Optional[Any] = len(_UpperCamelCase )
UpperCAmelCase_ : Any = self.dummy_model()
UpperCAmelCase_ : List[Any] = self.dummy_sample_deter
UpperCAmelCase_ : Union[str, Any] = self.dummy_sample_deter + 0.1
UpperCAmelCase_ : str = self.dummy_sample_deter - 0.1
UpperCAmelCase_ : Tuple = samplea.shape[0]
UpperCAmelCase_ : Tuple = torch.stack([samplea, samplea, samplea] , dim=0 )
UpperCAmelCase_ : str = torch.arange(_UpperCamelCase )[0:3, None].repeat(1 , _UpperCamelCase )
UpperCAmelCase_ : Any = model(samples.flatten(0 , 1 ) , timesteps.flatten(0 , 1 ) )
UpperCAmelCase_ : Dict = scheduler.batch_step_no_noise(_UpperCamelCase , timesteps.flatten(0 , 1 ) , samples.flatten(0 , 1 ) )
UpperCAmelCase_ : List[Any] = torch.sum(torch.abs(_UpperCamelCase ) )
UpperCAmelCase_ : Any = torch.mean(torch.abs(_UpperCamelCase ) )
assert abs(result_sum.item() - 11_53.18_33 ) < 1E-2
assert abs(result_mean.item() - 0.50_05 ) < 1E-3
def __UpperCAmelCase ( self ) -> Union[str, Any]:
UpperCAmelCase_ : List[str] = self.scheduler_classes[0]
UpperCAmelCase_ : List[Any] = self.get_scheduler_config()
UpperCAmelCase_ : Any = scheduler_class(**_UpperCamelCase )
UpperCAmelCase_ : Optional[int] = len(_UpperCamelCase )
UpperCAmelCase_ : Optional[int] = self.dummy_model()
UpperCAmelCase_ : Optional[int] = self.dummy_sample_deter
UpperCAmelCase_ : Union[str, Any] = torch.manual_seed(0 )
for t in reversed(range(_UpperCamelCase ) ):
# 1. predict noise residual
UpperCAmelCase_ : Union[str, Any] = model(_UpperCamelCase , _UpperCamelCase )
# 2. predict previous mean of sample x_t-1
UpperCAmelCase_ : str = scheduler.step(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , generator=_UpperCamelCase ).prev_sample
UpperCAmelCase_ : str = pred_prev_sample
UpperCAmelCase_ : Optional[int] = torch.sum(torch.abs(_UpperCamelCase ) )
UpperCAmelCase_ : Optional[int] = torch.mean(torch.abs(_UpperCamelCase ) )
assert abs(result_sum.item() - 2_58.96_06 ) < 1E-2
assert abs(result_mean.item() - 0.33_72 ) < 1E-3
def __UpperCAmelCase ( self ) -> int:
UpperCAmelCase_ : List[Any] = self.scheduler_classes[0]
UpperCAmelCase_ : Optional[int] = self.get_scheduler_config(prediction_type='v_prediction' )
UpperCAmelCase_ : Tuple = scheduler_class(**_UpperCamelCase )
UpperCAmelCase_ : Tuple = len(_UpperCamelCase )
UpperCAmelCase_ : Tuple = self.dummy_model()
UpperCAmelCase_ : List[str] = self.dummy_sample_deter
UpperCAmelCase_ : Any = torch.manual_seed(0 )
for t in reversed(range(_UpperCamelCase ) ):
# 1. predict noise residual
UpperCAmelCase_ : Optional[Any] = model(_UpperCamelCase , _UpperCamelCase )
# 2. predict previous mean of sample x_t-1
UpperCAmelCase_ : Any = scheduler.step(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , generator=_UpperCamelCase ).prev_sample
UpperCAmelCase_ : List[str] = pred_prev_sample
UpperCAmelCase_ : Optional[int] = torch.sum(torch.abs(_UpperCamelCase ) )
UpperCAmelCase_ : Any = torch.mean(torch.abs(_UpperCamelCase ) )
assert abs(result_sum.item() - 2_02.02_96 ) < 1E-2
assert abs(result_mean.item() - 0.26_31 ) < 1E-3
def __UpperCAmelCase ( self ) -> List[Any]:
UpperCAmelCase_ : Any = self.scheduler_classes[0]
UpperCAmelCase_ : Optional[Any] = self.get_scheduler_config()
UpperCAmelCase_ : str = scheduler_class(**_UpperCamelCase )
UpperCAmelCase_ : Optional[int] = [1_0_0, 8_7, 5_0, 1, 0]
scheduler.set_timesteps(timesteps=_UpperCamelCase )
UpperCAmelCase_ : Tuple = scheduler.timesteps
for i, timestep in enumerate(_UpperCamelCase ):
if i == len(_UpperCamelCase ) - 1:
UpperCAmelCase_ : List[str] = -1
else:
UpperCAmelCase_ : int = timesteps[i + 1]
UpperCAmelCase_ : int = scheduler.previous_timestep(_UpperCamelCase )
UpperCAmelCase_ : Optional[Any] = prev_t.item()
self.assertEqual(_UpperCamelCase , _UpperCamelCase )
def __UpperCAmelCase ( self ) -> List[Any]:
UpperCAmelCase_ : str = self.scheduler_classes[0]
UpperCAmelCase_ : Tuple = self.get_scheduler_config()
UpperCAmelCase_ : Union[str, Any] = scheduler_class(**_UpperCamelCase )
UpperCAmelCase_ : Optional[int] = [1_0_0, 8_7, 5_0, 5_1, 0]
with self.assertRaises(_UpperCamelCase , msg='`custom_timesteps` must be in descending order.' ):
scheduler.set_timesteps(timesteps=_UpperCamelCase )
def __UpperCAmelCase ( self ) -> Tuple:
UpperCAmelCase_ : Any = self.scheduler_classes[0]
UpperCAmelCase_ : List[str] = self.get_scheduler_config()
UpperCAmelCase_ : List[str] = scheduler_class(**_UpperCamelCase )
UpperCAmelCase_ : Tuple = [1_0_0, 8_7, 5_0, 1, 0]
UpperCAmelCase_ : Optional[Any] = len(_UpperCamelCase )
with self.assertRaises(_UpperCamelCase , msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.' ):
scheduler.set_timesteps(num_inference_steps=_UpperCamelCase , timesteps=_UpperCamelCase )
def __UpperCAmelCase ( self ) -> Dict:
UpperCAmelCase_ : List[str] = self.scheduler_classes[0]
UpperCAmelCase_ : Any = self.get_scheduler_config()
UpperCAmelCase_ : Optional[Any] = scheduler_class(**_UpperCamelCase )
UpperCAmelCase_ : Dict = [scheduler.config.num_train_timesteps]
with self.assertRaises(
_UpperCamelCase , msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}' , ):
scheduler.set_timesteps(timesteps=_UpperCamelCase )
| 145 | 0 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class _UpperCamelCase ( __A ):
'''simple docstring'''
@slow
@require_torch
def __UpperCamelCase ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = EncoderDecoderModel.from_encoder_decoder_pretrained("prajjwal1/bert-tiny" , "prajjwal1/bert-tiny" )
SCREAMING_SNAKE_CASE : Any = BertTokenizer.from_pretrained("bert-base-uncased" )
SCREAMING_SNAKE_CASE : int = bertabert.config.encoder.vocab_size
SCREAMING_SNAKE_CASE : Optional[int] = tokenizer.sep_token_id
SCREAMING_SNAKE_CASE : Tuple = tokenizer.cls_token_id
SCREAMING_SNAKE_CASE : Optional[int] = 128
SCREAMING_SNAKE_CASE : str = datasets.load_dataset("cnn_dailymail" , "3.0.0" , split="train[:1%]" )
SCREAMING_SNAKE_CASE : Optional[int] = datasets.load_dataset("cnn_dailymail" , "3.0.0" , split="validation[:1%]" )
SCREAMING_SNAKE_CASE : List[str] = train_dataset.select(range(32 ) )
SCREAMING_SNAKE_CASE : Dict = val_dataset.select(range(16 ) )
SCREAMING_SNAKE_CASE : str = 4
def _map_to_encoder_decoder_inputs(a : List[str] ):
# Tokenizer will automatically set [BOS] <text> [EOS]
SCREAMING_SNAKE_CASE : Any = tokenizer(batch["article"] , padding="max_length" , truncation=a , max_length=512 )
SCREAMING_SNAKE_CASE : Union[str, Any] = tokenizer(batch["highlights"] , padding="max_length" , truncation=a , max_length=128 )
SCREAMING_SNAKE_CASE : Optional[Any] = inputs.input_ids
SCREAMING_SNAKE_CASE : List[str] = inputs.attention_mask
SCREAMING_SNAKE_CASE : str = outputs.input_ids
SCREAMING_SNAKE_CASE : List[str] = outputs.input_ids.copy()
SCREAMING_SNAKE_CASE : List[str] = [
[-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
]
SCREAMING_SNAKE_CASE : Any = outputs.attention_mask
assert all(len(a ) == 512 for x in inputs.input_ids )
assert all(len(a ) == 128 for x in outputs.input_ids )
return batch
def _compute_metrics(a : int ):
SCREAMING_SNAKE_CASE : Dict = pred.label_ids
SCREAMING_SNAKE_CASE : Optional[Any] = pred.predictions
# all unnecessary tokens are removed
SCREAMING_SNAKE_CASE : Optional[int] = tokenizer.batch_decode(a , skip_special_tokens=a )
SCREAMING_SNAKE_CASE : int = tokenizer.batch_decode(a , skip_special_tokens=a )
SCREAMING_SNAKE_CASE : int = sum([int(pred_str[i] == label_str[i] ) for i in range(len(a ) )] ) / len(a )
return {"accuracy": accuracy}
# map train dataset
SCREAMING_SNAKE_CASE : Optional[Any] = train_dataset.map(
_map_to_encoder_decoder_inputs , batched=a , batch_size=a , remove_columns=["article", "highlights"] , )
train_dataset.set_format(
type="torch" , columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"] , )
# same for validation dataset
SCREAMING_SNAKE_CASE : Union[str, Any] = val_dataset.map(
_map_to_encoder_decoder_inputs , batched=a , batch_size=a , remove_columns=["article", "highlights"] , )
val_dataset.set_format(
type="torch" , columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"] , )
SCREAMING_SNAKE_CASE : int = self.get_auto_remove_tmp_dir()
SCREAMING_SNAKE_CASE : Any = SeqaSeqTrainingArguments(
output_dir=a , per_device_train_batch_size=a , per_device_eval_batch_size=a , predict_with_generate=a , evaluation_strategy="steps" , do_train=a , do_eval=a , warmup_steps=0 , eval_steps=2 , logging_steps=2 , )
# instantiate trainer
SCREAMING_SNAKE_CASE : Optional[Any] = SeqaSeqTrainer(
model=a , args=a , compute_metrics=_compute_metrics , train_dataset=a , eval_dataset=a , tokenizer=a , )
# start training
trainer.train() | 76 |
def lowerCamelCase__ ( _a , _a):
_validate_point(_a)
_validate_point(_a)
if len(_a) != len(_a):
raise ValueError("Both points must be in the same n-dimensional space")
return float(sum(abs(a - b) for a, b in zip(_a , _a)))
def lowerCamelCase__ ( _a):
if point:
if isinstance(_a , _a):
for item in point:
if not isinstance(_a , (int, float)):
SCREAMING_SNAKE_CASE : List[Any] = (
"Expected a list of numbers as input, found "
f"{type(_a).__name__}"
)
raise TypeError(_a)
else:
SCREAMING_SNAKE_CASE : List[Any] = f"Expected a list of numbers as input, found {type(_a).__name__}"
raise TypeError(_a)
else:
raise ValueError("Missing an input")
def lowerCamelCase__ ( _a , _a):
_validate_point(_a)
_validate_point(_a)
if len(_a) != len(_a):
raise ValueError("Both points must be in the same n-dimensional space")
return float(sum(abs(x - y) for x, y in zip(_a , _a)))
if __name__ == "__main__":
import doctest
doctest.testmod() | 76 | 1 |
'''simple docstring'''
from __future__ import annotations
def a ( lowerCamelCase__ , lowerCamelCase__ = None ):
'''simple docstring'''
A_ : int = word_bank or []
# create a table
A_ : int = len(lowerCamelCase__ ) + 1
A_ : list[list[list[str]]] = []
for _ in range(lowerCamelCase__ ):
table.append([] )
# seed value
A_ : Dict = [[]] # because empty string has empty combination
# iterate through the indices
for i in range(lowerCamelCase__ ):
# condition
if table[i] != []:
for word in word_bank:
# slice condition
if target[i : i + len(lowerCamelCase__ )] == word:
A_ : list[list[str]] = [
[word, *way] for way in table[i]
]
# adds the word to every combination the current position holds
# now,push that combination to the table[i+len(word)]
table[i + len(lowerCamelCase__ )] += new_combinations
# combinations are in reverse order so reverse for better output
for combination in table[len(lowerCamelCase__ )]:
combination.reverse()
return table[len(lowerCamelCase__ )]
if __name__ == "__main__":
print(all_construct('''jwajalapa''', ['''jwa''', '''j''', '''w''', '''a''', '''la''', '''lapa''']))
print(all_construct('''rajamati''', ['''s''', '''raj''', '''amat''', '''raja''', '''ma''', '''i''', '''t''']))
print(
all_construct(
'''hexagonosaurus''',
['''h''', '''ex''', '''hex''', '''ag''', '''ago''', '''ru''', '''auru''', '''rus''', '''go''', '''no''', '''o''', '''s'''],
)
) | 361 |
'''simple docstring'''
def a ( lowerCamelCase__ ):
'''simple docstring'''
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod() | 135 | 0 |
'''simple docstring'''
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Union[str, Any]:
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
__lowerCamelCase = mf_knapsack(i - 1 , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
else:
__lowerCamelCase = max(
mf_knapsack(i - 1 , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) , mf_knapsack(i - 1 , UpperCamelCase__ , UpperCamelCase__ , j - wt[i - 1] ) + val[i - 1] , )
__lowerCamelCase = val
return f[i][j]
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> List[Any]:
__lowerCamelCase = [[0] * (w + 1) for _ in range(n + 1 )]
for i in range(1 , n + 1 ):
for w_ in range(1 , w + 1 ):
if wt[i - 1] <= w_:
__lowerCamelCase = max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]] , dp[i - 1][w_] )
else:
__lowerCamelCase = dp[i - 1][w_]
return dp[n][w_], dp
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> int:
if not (isinstance(UpperCamelCase__ , (list, tuple) ) and isinstance(UpperCamelCase__ , (list, tuple) )):
raise ValueError(
'''Both the weights and values vectors must be either lists or tuples''' )
__lowerCamelCase = len(UpperCamelCase__ )
if num_items != len(UpperCamelCase__ ):
__lowerCamelCase = (
'''The number of weights must be the same as the number of values.\n'''
f"""But got {num_items} weights and {len(UpperCamelCase__ )} values"""
)
raise ValueError(UpperCamelCase__ )
for i in range(UpperCamelCase__ ):
if not isinstance(wt[i] , UpperCamelCase__ ):
__lowerCamelCase = (
'''All weights must be integers but got weight of '''
f"""type {type(wt[i] )} at index {i}"""
)
raise TypeError(UpperCamelCase__ )
__lowerCamelCase , __lowerCamelCase = knapsack(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
__lowerCamelCase = set()
_construct_solution(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
return optimal_val, example_optional_set
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Union[str, Any]:
# for the current item i at a maximum weight j to be part of an optimal subset,
# the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
# where i - 1 means considering only the previous items at the given maximum weight
if i > 0 and j > 0:
if dp[i - 1][j] == dp[i][j]:
_construct_solution(UpperCamelCase__ , UpperCamelCase__ , i - 1 , UpperCamelCase__ , UpperCamelCase__ )
else:
optimal_set.add(UpperCamelCase__ )
_construct_solution(UpperCamelCase__ , UpperCamelCase__ , i - 1 , j - wt[i - 1] , UpperCamelCase__ )
if __name__ == "__main__":
__UpperCAmelCase =[3, 2, 4, 4]
__UpperCAmelCase =[4, 3, 2, 3]
__UpperCAmelCase =4
__UpperCAmelCase =6
__UpperCAmelCase =[[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
__UpperCAmelCase , __UpperCAmelCase =knapsack(w, wt, val, n)
print(optimal_solution)
print(mf_knapsack(n, wt, val, w)) # switched the n and w
# testing the dynamic programming problem with example
# the optimal subset for the above example are items 3 and 4
__UpperCAmelCase , __UpperCAmelCase =knapsack_with_example_solution(w, wt, val)
assert optimal_solution == 8
assert optimal_subset == {3, 4}
print("optimal_value = ", optimal_solution)
print("An optimal subset corresponding to the optimal value", optimal_subset)
| 67 | '''simple docstring'''
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ ) -> Optional[int]:
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(UpperCamelCase__ , int(b / 2 ) ) * actual_power(UpperCamelCase__ , int(b / 2 ) )
else:
return a * actual_power(UpperCamelCase__ , int(b / 2 ) ) * actual_power(UpperCamelCase__ , int(b / 2 ) )
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ ) -> float:
if b < 0:
return 1 / actual_power(UpperCamelCase__ , UpperCamelCase__ )
return actual_power(UpperCamelCase__ , UpperCamelCase__ )
if __name__ == "__main__":
print(power(-2, -3))
| 67 | 1 |
"""simple docstring"""
class __lowerCamelCase :
"""simple docstring"""
def __init__( self , UpperCAmelCase ) -> None:
'''simple docstring'''
lowercase_ = len(UpperCAmelCase )
lowercase_ = [0] * len_array
if len_array > 0:
lowercase_ = array[0]
for i in range(1 , UpperCAmelCase ):
lowercase_ = self.prefix_sum[i - 1] + array[i]
def A__ ( self , UpperCAmelCase , UpperCAmelCase ) -> int:
'''simple docstring'''
if start == 0:
return self.prefix_sum[end]
return self.prefix_sum[end] - self.prefix_sum[start - 1]
def A__ ( self , UpperCAmelCase ) -> bool:
'''simple docstring'''
lowercase_ = {0}
for sum_item in self.prefix_sum:
if sum_item - target_sum in sums:
return True
sums.add(UpperCAmelCase )
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 350 |
def SCREAMING_SNAKE_CASE_ ( __lowerCamelCase: int ):
'''simple docstring'''
if a < 0:
raise ValueError("Input value must be a positive integer" )
elif isinstance(__lowerCamelCase , __lowerCamelCase ):
raise TypeError("Input value must be a 'int' type" )
return bin(__lowerCamelCase ).count("1" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 297 | 0 |
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_torch_multi_gpu,
)
logging.basicConfig(level=logging.DEBUG)
UpperCamelCase__ = logging.getLogger()
UpperCamelCase__ = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class a__ ( snake_case__ ):
def __SCREAMING_SNAKE_CASE( self , _A ):
"""simple docstring"""
os.makedirs(_A , exist_ok=_A )
__lowerCAmelCase = {"source": "What is love ?", "target": "life"}
__lowerCAmelCase = {"train": 1_2, "val": 2, "test": 2}
for split in ["train", "test", "val"]:
for field in ["source", "target"]:
__lowerCAmelCase = "\n".join([contents[field]] * n_lines[split] )
with open(os.path.join(_A , f"""{split}.{field}""" ) , "w" ) as f:
f.write(_A )
def __SCREAMING_SNAKE_CASE( self , _A , _A = "pytorch" ):
"""simple docstring"""
__lowerCAmelCase = self.get_auto_remove_tmp_dir()
__lowerCAmelCase = os.path.join(_A , "output" )
__lowerCAmelCase = os.path.join(_A , "data" )
self._create_dummy_data(data_dir=_A )
__lowerCAmelCase = f"""
--data_dir {data_dir} \
--output_dir {output_dir} \
--model_name_or_path facebook/rag-sequence-base \
--model_type rag_sequence \
--do_train \
--do_predict \
--n_val -1 \
--val_check_interval 1.0 \
--train_batch_size 2 \
--eval_batch_size 1 \
--max_source_length 25 \
--max_target_length 25 \
--val_max_target_length 25 \
--test_max_target_length 25 \
--label_smoothing 0.1 \
--dropout 0.1 \
--attention_dropout 0.1 \
--weight_decay 0.001 \
--adam_epsilon 1e-08 \
--max_grad_norm 0.1 \
--lr_scheduler polynomial \
--learning_rate 3e-04 \
--num_train_epochs 1 \
--warmup_steps 4 \
--gradient_accumulation_steps 1 \
--distributed-port 8787 \
--use_dummy_dataset 1 \
--distributed_retriever {distributed_retriever} \
""".split()
if gpus > 0:
testargs.append(f"""--gpus={gpus}""" )
if is_apex_available():
testargs.append("--fp16" )
else:
testargs.append("--gpus=0" )
testargs.append("--distributed_backend=ddp_cpu" )
testargs.append("--num_processes=2" )
__lowerCAmelCase = [sys.executable, str(Path(finetune_rag.__file__ ).resolve() )] + testargs
execute_subprocess_async(_A , env=self.get_env() )
__lowerCAmelCase = os.path.join(_A , "metrics.json" )
with open(_A ) as f:
__lowerCAmelCase = json.load(_A )
return result
@require_torch_gpu
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self._run_finetune(gpus=1 )
self.assertGreaterEqual(result["test"][0]["test_avg_em"] , 0.2 )
@require_torch_multi_gpu
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self._run_finetune(gpus=2 )
self.assertGreaterEqual(result["test"][0]["test_avg_em"] , 0.2 )
@require_torch_gpu
@require_ray
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self._run_finetune(gpus=1 , distributed_retriever="ray" )
self.assertGreaterEqual(result["test"][0]["test_avg_em"] , 0.2 )
@require_torch_multi_gpu
@require_ray
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self._run_finetune(gpus=1 , distributed_retriever="ray" )
self.assertGreaterEqual(result["test"][0]["test_avg_em"] , 0.2 )
| 92 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM
@require_tf
@require_sentencepiece
@require_tokenizers
class a__ ( unittest.TestCase ):
@slow
def lowercase ( self : List[Any] ) -> List[Any]:
lowercase : Tuple = TFAutoModelForSeqaSeqLM.from_pretrained('google/mt5-small' )
lowercase : Dict = AutoTokenizer.from_pretrained('google/mt5-small' )
lowercase : List[Any] = tokenizer('Hello there', return_tensors='tf' ).input_ids
lowercase : Any = tokenizer('Hi I am', return_tensors='tf' ).input_ids
lowercase : Dict = model(lowerCAmelCase, labels=lowerCAmelCase ).loss
lowercase : Optional[int] = -tf.math.reduce_mean(lowerCAmelCase ).numpy()
lowercase : Tuple = -21.22_8168
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 2e-4 )
| 255 | 0 |
'''simple docstring'''
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_poolformer import PoolFormerConfig
UpperCamelCase_ = logging.get_logger(__name__)
# General docstring
UpperCamelCase_ = "PoolFormerConfig"
# Base docstring
UpperCamelCase_ = "sail/poolformer_s12"
UpperCamelCase_ = [1, 5_1_2, 7, 7]
# Image classification docstring
UpperCamelCase_ = "sail/poolformer_s12"
UpperCamelCase_ = "tabby, tabby cat"
UpperCamelCase_ = [
"sail/poolformer_s12",
# See all PoolFormer models at https://huggingface.co/models?filter=poolformer
]
def lowercase__( __UpperCamelCase: List[str] ,__UpperCamelCase: float = 0.0 ,__UpperCamelCase: bool = False ):
"""simple docstring"""
if drop_prob == 0.0 or not training:
return input
SCREAMING_SNAKE_CASE : List[str] = 1 - drop_prob
SCREAMING_SNAKE_CASE : Tuple = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
SCREAMING_SNAKE_CASE : Tuple = keep_prob + torch.rand(__UpperCamelCase ,dtype=input.dtype ,device=input.device )
random_tensor.floor_() # binarize
SCREAMING_SNAKE_CASE : Dict = input.div(__UpperCamelCase ) * random_tensor
return output
class _a ( nn.Module ):
'''simple docstring'''
def __init__( self, A = None ):
'''simple docstring'''
super().__init__()
SCREAMING_SNAKE_CASE : List[Any] = drop_prob
def UpperCamelCase_ ( self, A ):
'''simple docstring'''
return drop_path(A, self.drop_prob, self.training )
def UpperCamelCase_ ( self ):
'''simple docstring'''
return "p={}".format(self.drop_prob )
class _a ( nn.Module ):
'''simple docstring'''
def __init__( self, A, A, A, A, A, A=None ):
'''simple docstring'''
super().__init__()
SCREAMING_SNAKE_CASE : Optional[int] = patch_size if isinstance(A, collections.abc.Iterable ) else (patch_size, patch_size)
SCREAMING_SNAKE_CASE : Union[str, Any] = stride if isinstance(A, collections.abc.Iterable ) else (stride, stride)
SCREAMING_SNAKE_CASE : List[str] = padding if isinstance(A, collections.abc.Iterable ) else (padding, padding)
SCREAMING_SNAKE_CASE : Optional[int] = nn.Convad(A, A, kernel_size=A, stride=A, padding=A )
SCREAMING_SNAKE_CASE : List[str] = norm_layer(A ) if norm_layer else nn.Identity()
def UpperCamelCase_ ( self, A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = self.projection(A )
SCREAMING_SNAKE_CASE : List[str] = self.norm(A )
return embeddings
class _a ( nn.GroupNorm ):
'''simple docstring'''
def __init__( self, A, **A ):
'''simple docstring'''
super().__init__(1, A, **A )
class _a ( nn.Module ):
'''simple docstring'''
def __init__( self, A ):
'''simple docstring'''
super().__init__()
SCREAMING_SNAKE_CASE : Union[str, Any] = nn.AvgPoolad(A, stride=1, padding=pool_size // 2, count_include_pad=A )
def UpperCamelCase_ ( self, A ):
'''simple docstring'''
return self.pool(A ) - hidden_states
class _a ( nn.Module ):
'''simple docstring'''
def __init__( self, A, A, A, A ):
'''simple docstring'''
super().__init__()
SCREAMING_SNAKE_CASE : List[Any] = nn.Convad(A, A, 1 )
SCREAMING_SNAKE_CASE : Optional[Any] = nn.Convad(A, A, 1 )
SCREAMING_SNAKE_CASE : Union[str, Any] = PoolFormerDropPath(A )
if isinstance(config.hidden_act, A ):
SCREAMING_SNAKE_CASE : Any = ACTaFN[config.hidden_act]
else:
SCREAMING_SNAKE_CASE : List[str] = config.hidden_act
def UpperCamelCase_ ( self, A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = self.conva(A )
SCREAMING_SNAKE_CASE : Union[str, Any] = self.act_fn(A )
SCREAMING_SNAKE_CASE : Union[str, Any] = self.drop(A )
SCREAMING_SNAKE_CASE : Union[str, Any] = self.conva(A )
SCREAMING_SNAKE_CASE : List[str] = self.drop(A )
return hidden_states
class _a ( nn.Module ):
'''simple docstring'''
def __init__( self, A, A, A, A, A, A ):
'''simple docstring'''
super().__init__()
SCREAMING_SNAKE_CASE : Union[str, Any] = PoolFormerPooling(A )
SCREAMING_SNAKE_CASE : Any = PoolFormerOutput(A, A, A, A )
SCREAMING_SNAKE_CASE : Union[str, Any] = PoolFormerGroupNorm(A )
SCREAMING_SNAKE_CASE : Any = PoolFormerGroupNorm(A )
# Useful for training neural nets
SCREAMING_SNAKE_CASE : int = PoolFormerDropPath(A ) if drop_path > 0.0 else nn.Identity()
SCREAMING_SNAKE_CASE : List[Any] = config.use_layer_scale
if config.use_layer_scale:
SCREAMING_SNAKE_CASE : Union[str, Any] = nn.Parameter(
config.layer_scale_init_value * torch.ones((A) ), requires_grad=A )
SCREAMING_SNAKE_CASE : Optional[int] = nn.Parameter(
config.layer_scale_init_value * torch.ones((A) ), requires_grad=A )
def UpperCamelCase_ ( self, A ):
'''simple docstring'''
if self.use_layer_scale:
SCREAMING_SNAKE_CASE : str = self.pooling(self.before_norm(A ) )
SCREAMING_SNAKE_CASE : List[str] = self.layer_scale_a.unsqueeze(-1 ).unsqueeze(-1 ) * pooling_output
# First residual connection
SCREAMING_SNAKE_CASE : List[Any] = hidden_states + self.drop_path(A )
SCREAMING_SNAKE_CASE : Any = ()
SCREAMING_SNAKE_CASE : List[str] = self.output(self.after_norm(A ) )
SCREAMING_SNAKE_CASE : Tuple = self.layer_scale_a.unsqueeze(-1 ).unsqueeze(-1 ) * layer_output
# Second residual connection
SCREAMING_SNAKE_CASE : Optional[Any] = hidden_states + self.drop_path(A )
SCREAMING_SNAKE_CASE : Dict = (output,) + outputs
return outputs
else:
SCREAMING_SNAKE_CASE : Any = self.drop_path(self.pooling(self.before_norm(A ) ) )
# First residual connection
SCREAMING_SNAKE_CASE : List[str] = pooling_output + hidden_states
SCREAMING_SNAKE_CASE : List[str] = ()
# Second residual connection inside the PoolFormerOutput block
SCREAMING_SNAKE_CASE : Any = self.drop_path(self.output(self.after_norm(A ) ) )
SCREAMING_SNAKE_CASE : Tuple = hidden_states + layer_output
SCREAMING_SNAKE_CASE : Dict = (output,) + outputs
return outputs
class _a ( nn.Module ):
'''simple docstring'''
def __init__( self, A ):
'''simple docstring'''
super().__init__()
SCREAMING_SNAKE_CASE : Union[str, Any] = config
# stochastic depth decay rule
SCREAMING_SNAKE_CASE : Union[str, Any] = [x.item() for x in torch.linspace(0, config.drop_path_rate, sum(config.depths ) )]
# patch embeddings
SCREAMING_SNAKE_CASE : Any = []
for i in range(config.num_encoder_blocks ):
embeddings.append(
PoolFormerEmbeddings(
patch_size=config.patch_sizes[i], stride=config.strides[i], padding=config.padding[i], num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1], hidden_size=config.hidden_sizes[i], ) )
SCREAMING_SNAKE_CASE : int = nn.ModuleList(A )
# Transformer blocks
SCREAMING_SNAKE_CASE : Dict = []
SCREAMING_SNAKE_CASE : str = 0
for i in range(config.num_encoder_blocks ):
# each block consists of layers
SCREAMING_SNAKE_CASE : int = []
if i != 0:
cur += config.depths[i - 1]
for j in range(config.depths[i] ):
layers.append(
PoolFormerLayer(
A, num_channels=config.hidden_sizes[i], pool_size=config.pool_size, hidden_size=config.hidden_sizes[i], intermediate_size=int(config.hidden_sizes[i] * config.mlp_ratio ), drop_path=dpr[cur + j], ) )
blocks.append(nn.ModuleList(A ) )
SCREAMING_SNAKE_CASE : str = nn.ModuleList(A )
def UpperCamelCase_ ( self, A, A=False, A=True ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = () if output_hidden_states else None
SCREAMING_SNAKE_CASE : Tuple = pixel_values
for idx, layers in enumerate(zip(self.patch_embeddings, self.block ) ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Tuple = layers
# Get patch embeddings from hidden_states
SCREAMING_SNAKE_CASE : int = embedding_layer(A )
# Send the embeddings through the blocks
for _, blk in enumerate(A ):
SCREAMING_SNAKE_CASE : Any = blk(A )
SCREAMING_SNAKE_CASE : Any = layer_outputs[0]
if output_hidden_states:
SCREAMING_SNAKE_CASE : Union[str, Any] = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states] if v is not None )
return BaseModelOutputWithNoAttention(last_hidden_state=A, hidden_states=A )
class _a ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
A : int = PoolFormerConfig
A : Tuple = '''poolformer'''
A : int = '''pixel_values'''
A : Optional[Any] = True
def UpperCamelCase_ ( self, A ):
'''simple docstring'''
if isinstance(A, (nn.Linear, nn.Convad) ):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(A, nn.LayerNorm ):
module.bias.data.zero_()
module.weight.data.fill_(1.0 )
def UpperCamelCase_ ( self, A, A=False ):
'''simple docstring'''
if isinstance(A, A ):
SCREAMING_SNAKE_CASE : Union[str, Any] = value
UpperCamelCase_ = R"\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use\n it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n\n Parameters:\n config ([`PoolFormerConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"
UpperCamelCase_ = R"\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`PoolFormerImageProcessor.__call__`] for details.\n"
@add_start_docstrings(
'''The bare PoolFormer Model transformer outputting raw hidden-states without any specific head on top.''' , SCREAMING_SNAKE_CASE , )
class _a ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self, A ):
'''simple docstring'''
super().__init__(A )
SCREAMING_SNAKE_CASE : int = config
SCREAMING_SNAKE_CASE : Any = PoolFormerEncoder(A )
# Initialize weights and apply final processing
self.post_init()
def UpperCamelCase_ ( self ):
'''simple docstring'''
return self.embeddings.patch_embeddings
@add_start_docstrings_to_model_forward(A )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC, output_type=A, config_class=_CONFIG_FOR_DOC, modality='vision', expected_output=_EXPECTED_OUTPUT_SHAPE, )
def UpperCamelCase_ ( self, A = None, A = None, A = None, ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
SCREAMING_SNAKE_CASE : List[str] = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError('You have to specify pixel_values' )
SCREAMING_SNAKE_CASE : str = self.encoder(
A, output_hidden_states=A, return_dict=A, )
SCREAMING_SNAKE_CASE : List[Any] = encoder_outputs[0]
if not return_dict:
return (sequence_output, None) + encoder_outputs[1:]
return BaseModelOutputWithNoAttention(
last_hidden_state=A, hidden_states=encoder_outputs.hidden_states, )
class _a ( nn.Module ):
'''simple docstring'''
def __init__( self, A ):
'''simple docstring'''
super().__init__()
SCREAMING_SNAKE_CASE : Dict = nn.Linear(config.hidden_size, config.hidden_size )
def UpperCamelCase_ ( self, A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = self.dense(A )
return output
@add_start_docstrings(
'''
PoolFormer Model transformer with an image classification head on top
''' , SCREAMING_SNAKE_CASE , )
class _a ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self, A ):
'''simple docstring'''
super().__init__(A )
SCREAMING_SNAKE_CASE : Any = config.num_labels
SCREAMING_SNAKE_CASE : str = PoolFormerModel(A )
# Final norm
SCREAMING_SNAKE_CASE : Any = PoolFormerGroupNorm(config.hidden_sizes[-1] )
# Classifier head
SCREAMING_SNAKE_CASE : str = (
nn.Linear(config.hidden_sizes[-1], config.num_labels ) if config.num_labels > 0 else nn.Identity()
)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(A )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT, output_type=A, config_class=_CONFIG_FOR_DOC, expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT, )
def UpperCamelCase_ ( self, A = None, A = None, A = None, A = None, ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = return_dict if return_dict is not None else self.config.use_return_dict
SCREAMING_SNAKE_CASE : Any = self.poolformer(
A, output_hidden_states=A, return_dict=A, )
SCREAMING_SNAKE_CASE : Tuple = outputs[0]
SCREAMING_SNAKE_CASE : List[str] = self.classifier(self.norm(A ).mean([-2, -1] ) )
SCREAMING_SNAKE_CASE : Dict = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
SCREAMING_SNAKE_CASE : str = 'regression'
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
SCREAMING_SNAKE_CASE : int = 'single_label_classification'
else:
SCREAMING_SNAKE_CASE : List[str] = 'multi_label_classification'
if self.config.problem_type == "regression":
SCREAMING_SNAKE_CASE : Union[str, Any] = MSELoss()
if self.num_labels == 1:
SCREAMING_SNAKE_CASE : Union[str, Any] = loss_fct(logits.squeeze(), labels.squeeze() )
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = loss_fct(A, A )
elif self.config.problem_type == "single_label_classification":
SCREAMING_SNAKE_CASE : Dict = CrossEntropyLoss()
SCREAMING_SNAKE_CASE : int = loss_fct(logits.view(-1, self.num_labels ), labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
SCREAMING_SNAKE_CASE : Tuple = BCEWithLogitsLoss()
SCREAMING_SNAKE_CASE : List[str] = loss_fct(A, A )
if not return_dict:
SCREAMING_SNAKE_CASE : int = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=A, logits=A, hidden_states=outputs.hidden_states )
| 246 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/config.json"}
class _a ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
A : int = '''openai-gpt'''
A : Dict = {
'''max_position_embeddings''': '''n_positions''',
'''hidden_size''': '''n_embd''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self, A=40_478, A=512, A=768, A=12, A=12, A="gelu", A=0.1, A=0.1, A=0.1, A=1E-5, A=0.02, A="cls_index", A=True, A=None, A=True, A=0.1, **A, ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = vocab_size
SCREAMING_SNAKE_CASE : Union[str, Any] = n_positions
SCREAMING_SNAKE_CASE : Tuple = n_embd
SCREAMING_SNAKE_CASE : Any = n_layer
SCREAMING_SNAKE_CASE : Tuple = n_head
SCREAMING_SNAKE_CASE : Union[str, Any] = afn
SCREAMING_SNAKE_CASE : List[str] = resid_pdrop
SCREAMING_SNAKE_CASE : List[str] = embd_pdrop
SCREAMING_SNAKE_CASE : Tuple = attn_pdrop
SCREAMING_SNAKE_CASE : List[str] = layer_norm_epsilon
SCREAMING_SNAKE_CASE : Any = initializer_range
SCREAMING_SNAKE_CASE : List[Any] = summary_type
SCREAMING_SNAKE_CASE : int = summary_use_proj
SCREAMING_SNAKE_CASE : Union[str, Any] = summary_activation
SCREAMING_SNAKE_CASE : List[str] = summary_first_dropout
SCREAMING_SNAKE_CASE : Any = summary_proj_to_labels
super().__init__(**A )
| 246 | 1 |
"""simple docstring"""
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@maybe_allow_in_graph
class lowerCAmelCase_ (nn.Module ):
"""simple docstring"""
def __init__(self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = "geglu" , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = False , SCREAMING_SNAKE_CASE__ = False , SCREAMING_SNAKE_CASE__ = False , SCREAMING_SNAKE_CASE__ = False , SCREAMING_SNAKE_CASE__ = True , SCREAMING_SNAKE_CASE__ = "layer_norm" , SCREAMING_SNAKE_CASE__ = False , ) -> Optional[Any]:
"""simple docstring"""
super().__init__()
SCREAMING_SNAKE_CASE__ : List[Any] = only_cross_attention
SCREAMING_SNAKE_CASE__ : List[Any] = (num_embeds_ada_norm is not None) and norm_type == """ada_norm_zero"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = (num_embeds_ada_norm is not None) and norm_type == """ada_norm"""
if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
raise ValueError(
F'''`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to'''
F''' define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}.''' )
# Define 3 blocks. Each block has its own normalization layer.
# 1. Self-Attn
if self.use_ada_layer_norm:
SCREAMING_SNAKE_CASE__ : int = AdaLayerNorm(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
elif self.use_ada_layer_norm_zero:
SCREAMING_SNAKE_CASE__ : Tuple = AdaLayerNormZero(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = nn.LayerNorm(SCREAMING_SNAKE_CASE__ , elementwise_affine=SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Dict = Attention(
query_dim=SCREAMING_SNAKE_CASE__ , heads=SCREAMING_SNAKE_CASE__ , dim_head=SCREAMING_SNAKE_CASE__ , dropout=SCREAMING_SNAKE_CASE__ , bias=SCREAMING_SNAKE_CASE__ , cross_attention_dim=cross_attention_dim if only_cross_attention else None , upcast_attention=SCREAMING_SNAKE_CASE__ , )
# 2. Cross-Attn
if cross_attention_dim is not None or double_self_attention:
# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
# the second cross attention block.
SCREAMING_SNAKE_CASE__ : Any = (
AdaLayerNorm(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if self.use_ada_layer_norm
else nn.LayerNorm(SCREAMING_SNAKE_CASE__ , elementwise_affine=SCREAMING_SNAKE_CASE__ )
)
SCREAMING_SNAKE_CASE__ : Tuple = Attention(
query_dim=SCREAMING_SNAKE_CASE__ , cross_attention_dim=cross_attention_dim if not double_self_attention else None , heads=SCREAMING_SNAKE_CASE__ , dim_head=SCREAMING_SNAKE_CASE__ , dropout=SCREAMING_SNAKE_CASE__ , bias=SCREAMING_SNAKE_CASE__ , upcast_attention=SCREAMING_SNAKE_CASE__ , ) # is self-attn if encoder_hidden_states is none
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
SCREAMING_SNAKE_CASE__ : str = None
# 3. Feed-forward
SCREAMING_SNAKE_CASE__ : Optional[Any] = nn.LayerNorm(SCREAMING_SNAKE_CASE__ , elementwise_affine=SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Optional[int] = FeedForward(SCREAMING_SNAKE_CASE__ , dropout=SCREAMING_SNAKE_CASE__ , activation_fn=SCREAMING_SNAKE_CASE__ , final_dropout=SCREAMING_SNAKE_CASE__ )
# let chunk size default to None
SCREAMING_SNAKE_CASE__ : Tuple = None
SCREAMING_SNAKE_CASE__ : Dict = 0
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = chunk_size
SCREAMING_SNAKE_CASE__ : str = dim
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , ) -> Any:
"""simple docstring"""
if self.use_ada_layer_norm:
SCREAMING_SNAKE_CASE__ : Any = self.norma(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
elif self.use_ada_layer_norm_zero:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[Any] = self.norma(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , hidden_dtype=hidden_states.dtype )
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.norma(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : int = cross_attention_kwargs if cross_attention_kwargs is not None else {}
SCREAMING_SNAKE_CASE__ : List[Any] = self.attna(
SCREAMING_SNAKE_CASE__ , encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None , attention_mask=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ , )
if self.use_ada_layer_norm_zero:
SCREAMING_SNAKE_CASE__ : List[str] = gate_msa.unsqueeze(1 ) * attn_output
SCREAMING_SNAKE_CASE__ : Dict = attn_output + hidden_states
# 2. Cross-Attention
if self.attna is not None:
SCREAMING_SNAKE_CASE__ : str = (
self.norma(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) if self.use_ada_layer_norm else self.norma(SCREAMING_SNAKE_CASE__ )
)
SCREAMING_SNAKE_CASE__ : List[str] = self.attna(
SCREAMING_SNAKE_CASE__ , encoder_hidden_states=SCREAMING_SNAKE_CASE__ , attention_mask=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = attn_output + hidden_states
# 3. Feed-forward
SCREAMING_SNAKE_CASE__ : Any = self.norma(SCREAMING_SNAKE_CASE__ )
if self.use_ada_layer_norm_zero:
SCREAMING_SNAKE_CASE__ : Optional[int] = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
if self._chunk_size is not None:
# "feed_forward_chunk_size" can be used to save memory
if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
raise ValueError(
F'''`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`.''' )
SCREAMING_SNAKE_CASE__ : List[str] = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
SCREAMING_SNAKE_CASE__ : int = torch.cat(
[self.ff(SCREAMING_SNAKE_CASE__ ) for hid_slice in norm_hidden_states.chunk(SCREAMING_SNAKE_CASE__ , dim=self._chunk_dim )] , dim=self._chunk_dim , )
else:
SCREAMING_SNAKE_CASE__ : int = self.ff(SCREAMING_SNAKE_CASE__ )
if self.use_ada_layer_norm_zero:
SCREAMING_SNAKE_CASE__ : Tuple = gate_mlp.unsqueeze(1 ) * ff_output
SCREAMING_SNAKE_CASE__ : Optional[Any] = ff_output + hidden_states
return hidden_states
class lowerCAmelCase_ (nn.Module ):
"""simple docstring"""
def __init__(self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = 4 , SCREAMING_SNAKE_CASE__ = 0.0 , SCREAMING_SNAKE_CASE__ = "geglu" , SCREAMING_SNAKE_CASE__ = False , ) -> Tuple:
"""simple docstring"""
super().__init__()
SCREAMING_SNAKE_CASE__ : Optional[int] = int(dim * mult )
SCREAMING_SNAKE_CASE__ : Tuple = dim_out if dim_out is not None else dim
if activation_fn == "gelu":
SCREAMING_SNAKE_CASE__ : str = GELU(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if activation_fn == "gelu-approximate":
SCREAMING_SNAKE_CASE__ : List[str] = GELU(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , approximate="""tanh""" )
elif activation_fn == "geglu":
SCREAMING_SNAKE_CASE__ : Optional[Any] = GEGLU(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
elif activation_fn == "geglu-approximate":
SCREAMING_SNAKE_CASE__ : int = ApproximateGELU(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Any = nn.ModuleList([] )
# project in
self.net.append(SCREAMING_SNAKE_CASE__ )
# project dropout
self.net.append(nn.Dropout(SCREAMING_SNAKE_CASE__ ) )
# project out
self.net.append(nn.Linear(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) )
# FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
if final_dropout:
self.net.append(nn.Dropout(SCREAMING_SNAKE_CASE__ ) )
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ ) -> Union[str, Any]:
"""simple docstring"""
for module in self.net:
SCREAMING_SNAKE_CASE__ : Dict = module(SCREAMING_SNAKE_CASE__ )
return hidden_states
class lowerCAmelCase_ (nn.Module ):
"""simple docstring"""
def __init__(self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = "none" ) -> List[Any]:
"""simple docstring"""
super().__init__()
SCREAMING_SNAKE_CASE__ : Optional[int] = nn.Linear(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Any = approximate
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ ) -> Optional[int]:
"""simple docstring"""
if gate.device.type != "mps":
return F.gelu(SCREAMING_SNAKE_CASE__ , approximate=self.approximate )
# mps: gelu is not implemented for float16
return F.gelu(gate.to(dtype=torch.floataa ) , approximate=self.approximate ).to(dtype=gate.dtype )
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.proj(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : List[Any] = self.gelu(SCREAMING_SNAKE_CASE__ )
return hidden_states
class lowerCAmelCase_ (nn.Module ):
"""simple docstring"""
def __init__(self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Dict:
"""simple docstring"""
super().__init__()
SCREAMING_SNAKE_CASE__ : Dict = nn.Linear(SCREAMING_SNAKE_CASE__ , dim_out * 2 )
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ ) -> int:
"""simple docstring"""
if gate.device.type != "mps":
return F.gelu(SCREAMING_SNAKE_CASE__ )
# mps: gelu is not implemented for float16
return F.gelu(gate.to(dtype=torch.floataa ) ).to(dtype=gate.dtype )
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.proj(SCREAMING_SNAKE_CASE__ ).chunk(2 , dim=-1 )
return hidden_states * self.gelu(SCREAMING_SNAKE_CASE__ )
class lowerCAmelCase_ (nn.Module ):
"""simple docstring"""
def __init__(self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> List[Any]:
"""simple docstring"""
super().__init__()
SCREAMING_SNAKE_CASE__ : str = nn.Linear(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.proj(SCREAMING_SNAKE_CASE__ )
return x * torch.sigmoid(1.702 * x )
class lowerCAmelCase_ (nn.Module ):
"""simple docstring"""
def __init__(self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Optional[Any]:
"""simple docstring"""
super().__init__()
SCREAMING_SNAKE_CASE__ : List[Any] = nn.Embedding(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Dict = nn.SiLU()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = nn.Linear(SCREAMING_SNAKE_CASE__ , embedding_dim * 2 )
SCREAMING_SNAKE_CASE__ : List[str] = nn.LayerNorm(SCREAMING_SNAKE_CASE__ , elementwise_affine=SCREAMING_SNAKE_CASE__ )
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.linear(self.silu(self.emb(SCREAMING_SNAKE_CASE__ ) ) )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = torch.chunk(SCREAMING_SNAKE_CASE__ , 2 )
SCREAMING_SNAKE_CASE__ : Dict = self.norm(SCREAMING_SNAKE_CASE__ ) * (1 + scale) + shift
return x
class lowerCAmelCase_ (nn.Module ):
"""simple docstring"""
def __init__(self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> int:
"""simple docstring"""
super().__init__()
SCREAMING_SNAKE_CASE__ : Any = CombinedTimestepLabelEmbeddings(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = nn.SiLU()
SCREAMING_SNAKE_CASE__ : List[Any] = nn.Linear(SCREAMING_SNAKE_CASE__ , 6 * embedding_dim , bias=SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : List[Any] = nn.LayerNorm(SCREAMING_SNAKE_CASE__ , elementwise_affine=SCREAMING_SNAKE_CASE__ , eps=1E-6 )
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=None ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.linear(self.silu(self.emb(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , hidden_dtype=SCREAMING_SNAKE_CASE__ ) ) )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = emb.chunk(6 , dim=1 )
SCREAMING_SNAKE_CASE__ : Tuple = self.norm(SCREAMING_SNAKE_CASE__ ) * (1 + scale_msa[:, None]) + shift_msa[:, None]
return x, gate_msa, shift_mlp, scale_mlp, gate_mlp
class lowerCAmelCase_ (nn.Module ):
"""simple docstring"""
def __init__(self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = 1E-5 ) -> int:
"""simple docstring"""
super().__init__()
SCREAMING_SNAKE_CASE__ : List[Any] = num_groups
SCREAMING_SNAKE_CASE__ : Any = eps
if act_fn is None:
SCREAMING_SNAKE_CASE__ : Any = None
else:
SCREAMING_SNAKE_CASE__ : Dict = get_activation(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : List[Any] = nn.Linear(SCREAMING_SNAKE_CASE__ , out_dim * 2 )
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Any:
"""simple docstring"""
if self.act:
SCREAMING_SNAKE_CASE__ : Tuple = self.act(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Dict = self.linear(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Dict = emb[:, :, None, None]
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] = emb.chunk(2 , dim=1 )
SCREAMING_SNAKE_CASE__ : Dict = F.group_norm(SCREAMING_SNAKE_CASE__ , self.num_groups , eps=self.eps )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = x * (1 + scale) + shift
return x
| 25 |
"""simple docstring"""
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
@parameterized.expand([(None,), ('''foo.json''',)])
def _SCREAMING_SNAKE_CASE ( self : Dict , lowercase_ : Union[str, Any]):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : List[str] = GenerationConfig(
do_sample=lowercase_ , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowercase_ , config_name=lowercase_)
SCREAMING_SNAKE_CASE_ : Tuple = GenerationConfig.from_pretrained(lowercase_ , config_name=lowercase_)
# Checks parameters that were specified
self.assertEqual(loaded_config.do_sample , lowercase_)
self.assertEqual(loaded_config.temperature , 0.7)
self.assertEqual(loaded_config.length_penalty , 1.0)
self.assertEqual(loaded_config.bad_words_ids , [[1, 2, 3], [4, 5]])
# Checks parameters that were not specified (defaults)
self.assertEqual(loaded_config.top_k , 50)
self.assertEqual(loaded_config.max_length , 20)
self.assertEqual(loaded_config.max_time , lowercase_)
def _SCREAMING_SNAKE_CASE ( self : Optional[Any]):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Optional[Any] = AutoConfig.from_pretrained('''gpt2''')
SCREAMING_SNAKE_CASE_ : int = GenerationConfig.from_model_config(lowercase_)
SCREAMING_SNAKE_CASE_ : int = GenerationConfig()
# The generation config has loaded a few non-default parameters from the model config
self.assertNotEqual(lowercase_ , lowercase_)
# One of those parameters is eos_token_id -- check if it matches
self.assertNotEqual(generation_config_from_model.eos_token_id , default_generation_config.eos_token_id)
self.assertEqual(generation_config_from_model.eos_token_id , model_config.eos_token_id)
def _SCREAMING_SNAKE_CASE ( self : Dict):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : str = GenerationConfig()
SCREAMING_SNAKE_CASE_ : Any = {
'''max_new_tokens''': 1024,
'''foo''': '''bar''',
}
SCREAMING_SNAKE_CASE_ : str = copy.deepcopy(lowercase_)
SCREAMING_SNAKE_CASE_ : Tuple = generation_config.update(**lowercase_)
# update_kwargs was not modified (no side effects)
self.assertEqual(lowercase_ , lowercase_)
# update_kwargs was used to update the config on valid attributes
self.assertEqual(generation_config.max_new_tokens , 1024)
# `.update()` returns a dictionary of unused kwargs
self.assertEqual(lowercase_ , {'''foo''': '''bar'''})
def _SCREAMING_SNAKE_CASE ( self : Any):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Optional[Any] = GenerationConfig()
SCREAMING_SNAKE_CASE_ : List[str] = '''bar'''
with tempfile.TemporaryDirectory('''test-generation-config''') as tmp_dir:
generation_config.save_pretrained(lowercase_)
SCREAMING_SNAKE_CASE_ : List[Any] = GenerationConfig.from_pretrained(lowercase_)
# update_kwargs was used to update the config on valid attributes
self.assertEqual(new_config.foo , '''bar''')
SCREAMING_SNAKE_CASE_ : Optional[Any] = GenerationConfig.from_model_config(lowercase_)
assert not hasattr(lowercase_ , '''foo''') # no new kwargs should be initialized if from config
def _SCREAMING_SNAKE_CASE ( self : Tuple):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Tuple = GenerationConfig()
self.assertEqual(default_config.temperature , 1.0)
self.assertEqual(default_config.do_sample , lowercase_)
self.assertEqual(default_config.num_beams , 1)
SCREAMING_SNAKE_CASE_ : Dict = GenerationConfig(
do_sample=lowercase_ , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
self.assertEqual(config.temperature , 0.7)
self.assertEqual(config.do_sample , lowercase_)
self.assertEqual(config.num_beams , 1)
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowercase_)
SCREAMING_SNAKE_CASE_ : Tuple = GenerationConfig.from_pretrained(lowercase_ , temperature=1.0)
self.assertEqual(loaded_config.temperature , 1.0)
self.assertEqual(loaded_config.do_sample , lowercase_)
self.assertEqual(loaded_config.num_beams , 1) # default value
@is_staging_test
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
@classmethod
def _SCREAMING_SNAKE_CASE ( cls : Any):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : str = TOKEN
HfFolder.save_token(lowercase_)
@classmethod
def _SCREAMING_SNAKE_CASE ( cls : List[str]):
'''simple docstring'''
try:
delete_repo(token=cls._token , repo_id='''test-generation-config''')
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='''valid_org/test-generation-config-org''')
except HTTPError:
pass
def _SCREAMING_SNAKE_CASE ( self : Dict):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Tuple = GenerationConfig(
do_sample=lowercase_ , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub('''test-generation-config''' , use_auth_token=self._token)
SCREAMING_SNAKE_CASE_ : int = GenerationConfig.from_pretrained(F'{USER}/test-generation-config')
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_))
# Reset repo
delete_repo(token=self._token , repo_id='''test-generation-config''')
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
lowercase_ , repo_id='''test-generation-config''' , push_to_hub=lowercase_ , use_auth_token=self._token)
SCREAMING_SNAKE_CASE_ : Optional[int] = GenerationConfig.from_pretrained(F'{USER}/test-generation-config')
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_))
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any]):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Dict = GenerationConfig(
do_sample=lowercase_ , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub('''valid_org/test-generation-config-org''' , use_auth_token=self._token)
SCREAMING_SNAKE_CASE_ : Any = GenerationConfig.from_pretrained('''valid_org/test-generation-config-org''')
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_))
# Reset repo
delete_repo(token=self._token , repo_id='''valid_org/test-generation-config-org''')
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
lowercase_ , repo_id='''valid_org/test-generation-config-org''' , push_to_hub=lowercase_ , use_auth_token=self._token)
SCREAMING_SNAKE_CASE_ : Any = GenerationConfig.from_pretrained('''valid_org/test-generation-config-org''')
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase_ , getattr(lowercase_ , lowercase_))
| 91 | 0 |
def UpperCamelCase ( __magic_name__ : list ) -> list:
"""simple docstring"""
if len(__magic_name__ ) <= 1:
return lst
lowercase__ = 1
while i < len(__magic_name__ ):
if lst[i - 1] <= lst[i]:
i += 1
else:
lowercase__ , lowercase__ = lst[i], lst[i - 1]
i -= 1
if i == 0:
lowercase__ = 1
return lst
if __name__ == "__main__":
A : Union[str, Any] = input('Enter numbers separated by a comma:\n').strip()
A : Optional[int] = [int(item) for item in user_input.split(',')]
print(gnome_sort(unsorted))
| 146 |
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
@slow
@require_flax
class A ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase__ (self : Any , _UpperCAmelCase : List[str] , _UpperCAmelCase : Union[str, Any] ) -> int:
"""simple docstring"""
return f'''gaussian_noise_s={seed}_shape={"_".join([str(_UpperCAmelCase ) for s in shape] )}.npy'''
def lowerCamelCase__ (self : Any ) -> int:
"""simple docstring"""
super().tearDown()
gc.collect()
def lowerCamelCase__ (self : List[Any] , _UpperCAmelCase : Any=0 , _UpperCAmelCase : Optional[Any]=(4, 4, 64, 64) , _UpperCAmelCase : Any=False ) -> List[Any]:
"""simple docstring"""
lowercase__ = jnp.bfloataa if fpaa else jnp.floataa
lowercase__ = jnp.array(load_hf_numpy(self.get_file_format(_UpperCAmelCase , _UpperCAmelCase ) ) , dtype=_UpperCAmelCase )
return image
def lowerCamelCase__ (self : Optional[Any] , _UpperCAmelCase : str=False , _UpperCAmelCase : str="CompVis/stable-diffusion-v1-4" ) -> Dict:
"""simple docstring"""
lowercase__ = jnp.bfloataa if fpaa else jnp.floataa
lowercase__ = """bf16""" if fpaa else None
lowercase__ , lowercase__ = FlaxUNetaDConditionModel.from_pretrained(
_UpperCAmelCase , subfolder="""unet""" , dtype=_UpperCAmelCase , revision=_UpperCAmelCase )
return model, params
def lowerCamelCase__ (self : List[Any] , _UpperCAmelCase : int=0 , _UpperCAmelCase : Optional[int]=(4, 77, 768) , _UpperCAmelCase : Any=False ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = jnp.bfloataa if fpaa else jnp.floataa
lowercase__ = jnp.array(load_hf_numpy(self.get_file_format(_UpperCAmelCase , _UpperCAmelCase ) ) , dtype=_UpperCAmelCase )
return hidden_states
@parameterized.expand(
[
# fmt: off
[83, 4, [-0.2_323, -0.1_304, 0.0_813, -0.3_093, -0.0_919, -0.1_571, -0.1_125, -0.5_806]],
[17, 0.55, [-0.0_831, -0.2_443, 0.0_901, -0.0_919, 0.3_396, 0.0_103, -0.3_743, 0.0_701]],
[8, 0.89, [-0.4_863, 0.0_859, 0.0_875, -0.1_658, 0.9_199, -0.0_114, 0.4_839, 0.4_639]],
[3, 1000, [-0.5_649, 0.2_402, -0.5_518, 0.1_248, 1.1_328, -0.2_443, -0.0_325, -1.0_078]],
# fmt: on
] )
def lowerCamelCase__ (self : int , _UpperCAmelCase : str , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : int ) -> str:
"""simple docstring"""
lowercase__ , lowercase__ = self.get_unet_model(model_id="""CompVis/stable-diffusion-v1-4""" , fpaa=_UpperCAmelCase )
lowercase__ = self.get_latents(_UpperCAmelCase , fpaa=_UpperCAmelCase )
lowercase__ = self.get_encoder_hidden_states(_UpperCAmelCase , fpaa=_UpperCAmelCase )
lowercase__ = model.apply(
{"""params""": params} , _UpperCAmelCase , jnp.array(_UpperCAmelCase , dtype=jnp.intaa ) , encoder_hidden_states=_UpperCAmelCase , ).sample
assert sample.shape == latents.shape
lowercase__ = jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten()) ) , dtype=jnp.floataa )
lowercase__ = jnp.array(_UpperCAmelCase , dtype=jnp.floataa )
# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
assert jnp.allclose(_UpperCAmelCase , _UpperCAmelCase , atol=1E-2 )
@parameterized.expand(
[
# fmt: off
[83, 4, [0.1_514, 0.0_807, 0.1_624, 0.1_016, -0.1_896, 0.0_263, 0.0_677, 0.2_310]],
[17, 0.55, [0.1_164, -0.0_216, 0.0_170, 0.1_589, -0.3_120, 0.1_005, -0.0_581, -0.1_458]],
[8, 0.89, [-0.1_758, -0.0_169, 0.1_004, -0.1_411, 0.1_312, 0.1_103, -0.1_996, 0.2_139]],
[3, 1000, [0.1_214, 0.0_352, -0.0_731, -0.1_562, -0.0_994, -0.0_906, -0.2_340, -0.0_539]],
# fmt: on
] )
def lowerCamelCase__ (self : Optional[Any] , _UpperCAmelCase : Tuple , _UpperCAmelCase : int , _UpperCAmelCase : List[str] ) -> str:
"""simple docstring"""
lowercase__ , lowercase__ = self.get_unet_model(model_id="""stabilityai/stable-diffusion-2""" , fpaa=_UpperCAmelCase )
lowercase__ = self.get_latents(_UpperCAmelCase , shape=(4, 4, 96, 96) , fpaa=_UpperCAmelCase )
lowercase__ = self.get_encoder_hidden_states(_UpperCAmelCase , shape=(4, 77, 1024) , fpaa=_UpperCAmelCase )
lowercase__ = model.apply(
{"""params""": params} , _UpperCAmelCase , jnp.array(_UpperCAmelCase , dtype=jnp.intaa ) , encoder_hidden_states=_UpperCAmelCase , ).sample
assert sample.shape == latents.shape
lowercase__ = jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten()) ) , dtype=jnp.floataa )
lowercase__ = jnp.array(_UpperCAmelCase , dtype=jnp.floataa )
# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
assert jnp.allclose(_UpperCAmelCase , _UpperCAmelCase , atol=1E-2 )
| 146 | 1 |
'''simple docstring'''
# Lint as: python3
import itertools
import os
import re
lowerCamelCase : Any = re.compile(R'([A-Z]+)([A-Z][a-z])')
lowerCamelCase : str = re.compile(R'([a-z\d])([A-Z])')
lowerCamelCase : Optional[int] = re.compile(R'(?<!_)_(?!_)')
lowerCamelCase : List[Any] = re.compile(R'(_{2,})')
lowerCamelCase : str = R'^\w+(\.\w+)*$'
lowerCamelCase : Dict = R'<>:/\|?*'
def _SCREAMING_SNAKE_CASE (A ) -> Any:
"""simple docstring"""
lowercase__ = _uppercase_uppercase_re.sub(R'''\1_\2''' , A )
lowercase__ = _lowercase_uppercase_re.sub(R'''\1_\2''' , A )
return name.lower()
def _SCREAMING_SNAKE_CASE (A ) -> Tuple:
"""simple docstring"""
lowercase__ = _single_underscore_re.split(A )
lowercase__ = [_multiple_underscores_re.split(A ) for n in name]
return "".join(n.capitalize() for n in itertools.chain.from_iterable(A ) if n != '''''' )
def _SCREAMING_SNAKE_CASE (A ) -> Tuple:
"""simple docstring"""
if os.path.basename(A ) != name:
raise ValueError(f"Should be a dataset name, not a path: {name}" )
return camelcase_to_snakecase(A )
def _SCREAMING_SNAKE_CASE (A , A ) -> Optional[Any]:
"""simple docstring"""
if os.path.basename(A ) != name:
raise ValueError(f"Should be a dataset name, not a path: {name}" )
if not re.match(_split_re , A ):
raise ValueError(f"Split name should match '{_split_re}'' but got '{split}'." )
return f"{filename_prefix_for_name(A )}-{split}"
def _SCREAMING_SNAKE_CASE (A , A , A , A=None ) -> List[str]:
"""simple docstring"""
lowercase__ = filename_prefix_for_split(A , A )
if filetype_suffix:
prefix += f".{filetype_suffix}"
lowercase__ = os.path.join(A , A )
return f"{filepath}*"
def _SCREAMING_SNAKE_CASE (A , A , A , A=None , A=None ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = filename_prefix_for_split(A , A )
lowercase__ = os.path.join(A , A )
if shard_lengths:
lowercase__ = len(A )
lowercase__ = [f"{prefix}-{shard_id:05d}-of-{num_shards:05d}" for shard_id in range(A )]
if filetype_suffix:
lowercase__ = [filename + f".{filetype_suffix}" for filename in filenames]
return filenames
else:
lowercase__ = prefix
if filetype_suffix:
filename += f".{filetype_suffix}"
return [filename]
| 2 |
'''simple docstring'''
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
lowerCamelCase : List[str] = get_tests_dir('fixtures/spiece.model')
@require_sentencepiece
@require_tokenizers
class __lowerCAmelCase (lowercase_ , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase__ : int = DebertaVaTokenizer
lowerCAmelCase__ : List[Any] = DebertaVaTokenizerFast
lowerCAmelCase__ : str = True
lowerCAmelCase__ : Tuple = True
def UpperCamelCase__ (self : Tuple ):
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
lowercase__ = DebertaVaTokenizer(UpperCamelCase , unk_token='''<unk>''' )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCamelCase__ (self : Union[str, Any] , UpperCamelCase : str ):
'''simple docstring'''
lowercase__ = '''this is a test'''
lowercase__ = '''this is a test'''
return input_text, output_text
def UpperCamelCase__ (self : Optional[int] ):
'''simple docstring'''
lowercase__ = '''<pad>'''
lowercase__ = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase ) , UpperCamelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase ) , UpperCamelCase )
def UpperCamelCase__ (self : Dict ):
'''simple docstring'''
lowercase__ = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<pad>''' )
self.assertEqual(vocab_keys[1] , '''<unk>''' )
self.assertEqual(vocab_keys[-1] , '''[PAD]''' )
self.assertEqual(len(UpperCamelCase ) , 30001 )
def UpperCamelCase__ (self : int ):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 30000 )
def UpperCamelCase__ (self : Optional[Any] ):
'''simple docstring'''
lowercase__ = ''' \tHeLLo!how \n Are yoU? '''
lowercase__ = ['''▁hello''', '''!''', '''how''', '''▁are''', '''▁you''', '''?''']
# fmt: on
lowercase__ = DebertaVaTokenizer(UpperCamelCase , do_lower_case=UpperCamelCase )
lowercase__ = tokenizer.convert_ids_to_tokens(tokenizer.encode(UpperCamelCase , add_special_tokens=UpperCamelCase ) )
self.assertListEqual(UpperCamelCase , UpperCamelCase )
lowercase__ = DebertaVaTokenizerFast(UpperCamelCase , do_lower_case=UpperCamelCase )
lowercase__ = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(UpperCamelCase , add_special_tokens=UpperCamelCase ) )
self.assertListEqual(UpperCamelCase , UpperCamelCase )
@unittest.skip('''There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.''' )
def UpperCamelCase__ (self : List[Any] ):
'''simple docstring'''
pass
@unittest.skip('''There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.''' )
def UpperCamelCase__ (self : List[str] ):
'''simple docstring'''
pass
def UpperCamelCase__ (self : Optional[int] ):
'''simple docstring'''
lowercase__ = '''I was born in 92000, and this is falsé.'''
lowercase__ = ['''▁''', '''<unk>''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', '''▁''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''▁''', '''.''', ]
# fmt: on
lowercase__ = DebertaVaTokenizer(UpperCamelCase , split_by_punct=UpperCamelCase )
lowercase__ = tokenizer.convert_ids_to_tokens(tokenizer.encode(UpperCamelCase , add_special_tokens=UpperCamelCase ) )
self.assertListEqual(UpperCamelCase , UpperCamelCase )
lowercase__ = DebertaVaTokenizerFast(UpperCamelCase , split_by_punct=UpperCamelCase )
lowercase__ = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(UpperCamelCase , add_special_tokens=UpperCamelCase ) )
self.assertListEqual(UpperCamelCase , UpperCamelCase )
def UpperCamelCase__ (self : int ):
'''simple docstring'''
lowercase__ = '''I was born in 92000, and this is falsé.'''
lowercase__ = ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', '''▁''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''▁''', '''.''', ]
# fmt: on
lowercase__ = DebertaVaTokenizer(UpperCamelCase , do_lower_case=UpperCamelCase , split_by_punct=UpperCamelCase )
lowercase__ = tokenizer.convert_ids_to_tokens(tokenizer.encode(UpperCamelCase , add_special_tokens=UpperCamelCase ) )
self.assertListEqual(UpperCamelCase , UpperCamelCase )
lowercase__ = DebertaVaTokenizerFast(UpperCamelCase , do_lower_case=UpperCamelCase , split_by_punct=UpperCamelCase )
lowercase__ = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(UpperCamelCase , add_special_tokens=UpperCamelCase ) )
self.assertListEqual(UpperCamelCase , UpperCamelCase )
def UpperCamelCase__ (self : Optional[Any] ):
'''simple docstring'''
lowercase__ = '''I was born in 92000, and this is falsé.'''
lowercase__ = ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''', ]
# fmt: on
lowercase__ = DebertaVaTokenizer(UpperCamelCase , do_lower_case=UpperCamelCase , split_by_punct=UpperCamelCase )
lowercase__ = tokenizer.convert_ids_to_tokens(tokenizer.encode(UpperCamelCase , add_special_tokens=UpperCamelCase ) )
self.assertListEqual(UpperCamelCase , UpperCamelCase )
lowercase__ = DebertaVaTokenizerFast(UpperCamelCase , do_lower_case=UpperCamelCase , split_by_punct=UpperCamelCase )
lowercase__ = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(UpperCamelCase , add_special_tokens=UpperCamelCase ) )
self.assertListEqual(UpperCamelCase , UpperCamelCase )
def UpperCamelCase__ (self : Union[str, Any] ):
'''simple docstring'''
lowercase__ = '''I was born in 92000, and this is falsé.'''
lowercase__ = ['''▁''', '''<unk>''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', '''▁''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''▁''', '''.''', ]
# fmt: on
lowercase__ = DebertaVaTokenizer(UpperCamelCase , do_lower_case=UpperCamelCase , split_by_punct=UpperCamelCase )
lowercase__ = tokenizer.convert_ids_to_tokens(tokenizer.encode(UpperCamelCase , add_special_tokens=UpperCamelCase ) )
self.assertListEqual(UpperCamelCase , UpperCamelCase )
lowercase__ = DebertaVaTokenizerFast(UpperCamelCase , do_lower_case=UpperCamelCase , split_by_punct=UpperCamelCase )
lowercase__ = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(UpperCamelCase , add_special_tokens=UpperCamelCase ) )
self.assertListEqual(UpperCamelCase , UpperCamelCase )
def UpperCamelCase__ (self : Union[str, Any] ):
'''simple docstring'''
lowercase__ = ''' \tHeLLo!how \n Are yoU? '''
lowercase__ = ['''▁''', '''<unk>''', '''e''', '''<unk>''', '''o''', '''!''', '''how''', '''▁''', '''<unk>''', '''re''', '''▁yo''', '''<unk>''', '''?''']
# fmt: on
lowercase__ = DebertaVaTokenizer(UpperCamelCase , do_lower_case=UpperCamelCase , split_by_punct=UpperCamelCase )
lowercase__ = tokenizer.convert_ids_to_tokens(tokenizer.encode(UpperCamelCase , add_special_tokens=UpperCamelCase ) )
self.assertListEqual(UpperCamelCase , UpperCamelCase )
lowercase__ = DebertaVaTokenizerFast(UpperCamelCase , do_lower_case=UpperCamelCase , split_by_punct=UpperCamelCase )
lowercase__ = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(UpperCamelCase , add_special_tokens=UpperCamelCase ) )
self.assertListEqual(UpperCamelCase , UpperCamelCase )
def UpperCamelCase__ (self : Union[str, Any] ):
'''simple docstring'''
lowercase__ = self.get_tokenizer()
lowercase__ = self.get_rust_tokenizer()
lowercase__ = '''I was born in 92000, and this is falsé.'''
lowercase__ = tokenizer.convert_ids_to_tokens(tokenizer.encode(UpperCamelCase , add_special_tokens=UpperCamelCase ) )
lowercase__ = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(UpperCamelCase , add_special_tokens=UpperCamelCase ) )
self.assertListEqual(UpperCamelCase , UpperCamelCase )
lowercase__ = tokenizer.encode(UpperCamelCase , add_special_tokens=UpperCamelCase )
lowercase__ = rust_tokenizer.encode(UpperCamelCase , add_special_tokens=UpperCamelCase )
self.assertListEqual(UpperCamelCase , UpperCamelCase )
lowercase__ = self.get_rust_tokenizer()
lowercase__ = tokenizer.encode(UpperCamelCase )
lowercase__ = rust_tokenizer.encode(UpperCamelCase )
self.assertListEqual(UpperCamelCase , UpperCamelCase )
def UpperCamelCase__ (self : int ):
'''simple docstring'''
lowercase__ = '''This is a test'''
lowercase__ = [13, 1, 4398, 25, 21, 1289]
lowercase__ = ['''▁''', '''T''', '''his''', '''▁is''', '''▁a''', '''▁test''']
lowercase__ = ['''▁''', '''<unk>''', '''his''', '''▁is''', '''▁a''', '''▁test''']
lowercase__ = DebertaVaTokenizer(UpperCamelCase , keep_accents=UpperCamelCase )
lowercase__ = DebertaVaTokenizerFast(UpperCamelCase , keep_accents=UpperCamelCase )
lowercase__ = tokenizer.encode(UpperCamelCase , add_special_tokens=UpperCamelCase )
self.assertListEqual(UpperCamelCase , UpperCamelCase )
lowercase__ = tokenizer.tokenize(UpperCamelCase )
self.assertListEqual(UpperCamelCase , UpperCamelCase )
lowercase__ = tokenizer.convert_ids_to_tokens(UpperCamelCase )
self.assertListEqual(UpperCamelCase , UpperCamelCase )
lowercase__ = rust_tokenizer.encode(UpperCamelCase , add_special_tokens=UpperCamelCase )
self.assertListEqual(UpperCamelCase , UpperCamelCase )
lowercase__ = rust_tokenizer.tokenize(UpperCamelCase )
self.assertListEqual(UpperCamelCase , UpperCamelCase )
lowercase__ = rust_tokenizer.convert_ids_to_tokens(UpperCamelCase )
self.assertListEqual(UpperCamelCase , UpperCamelCase )
# fmt: off
lowercase__ = '''I was born in 92000, and this is falsé.'''
lowercase__ = [13, 1, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]
lowercase__ = ['''▁''', '''I''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''é''', '''.''', ]
lowercase__ = ['''▁''', '''<unk>''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''', ]
# fmt: on
lowercase__ = tokenizer.encode(UpperCamelCase , add_special_tokens=UpperCamelCase )
self.assertListEqual(UpperCamelCase , UpperCamelCase )
lowercase__ = tokenizer.tokenize(UpperCamelCase )
self.assertListEqual(UpperCamelCase , UpperCamelCase )
lowercase__ = tokenizer.convert_ids_to_tokens(UpperCamelCase )
self.assertListEqual(UpperCamelCase , UpperCamelCase )
lowercase__ = rust_tokenizer.encode(UpperCamelCase , add_special_tokens=UpperCamelCase )
self.assertListEqual(UpperCamelCase , UpperCamelCase )
lowercase__ = rust_tokenizer.tokenize(UpperCamelCase )
self.assertListEqual(UpperCamelCase , UpperCamelCase )
lowercase__ = rust_tokenizer.convert_ids_to_tokens(UpperCamelCase )
self.assertListEqual(UpperCamelCase , UpperCamelCase )
def UpperCamelCase__ (self : int ):
'''simple docstring'''
lowercase__ = DebertaVaTokenizer(UpperCamelCase )
lowercase__ = tokenizer.encode('''sequence builders''' )
lowercase__ = tokenizer.encode('''multi-sequence build''' )
lowercase__ = tokenizer.build_inputs_with_special_tokens(UpperCamelCase )
lowercase__ = tokenizer.build_inputs_with_special_tokens(UpperCamelCase , UpperCamelCase )
self.assertEqual([tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] , UpperCamelCase )
self.assertEqual(
[tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [tokenizer.sep_token_id] , UpperCamelCase , )
@slow
def UpperCamelCase__ (self : int ):
'''simple docstring'''
lowercase__ = {'''input_ids''': [[1, 39867, 36, 19390, 486, 27, 35052, 81436, 18, 60685, 1225, 7, 35052, 81436, 18, 9367, 16899, 18, 15937, 53, 594, 773, 18, 16287, 30465, 36, 15937, 6, 41139, 38, 36979, 60763, 191, 6, 34132, 99, 6, 50538, 390, 43230, 6, 34132, 2779, 20850, 14, 699, 1072, 1194, 36, 382, 10901, 53, 7, 699, 1072, 2084, 36, 20422, 630, 53, 19, 105, 3049, 1896, 1053, 16899, 1506, 11, 37978, 4243, 7, 1237, 31869, 200, 16566, 654, 6, 35052, 81436, 7, 55630, 13593, 4, 2], [1, 26, 15011, 13, 667, 8, 1053, 18, 23611, 1237, 72356, 12820, 34, 104134, 1209, 35, 13313, 6627, 21, 202, 347, 7, 164, 2399, 11, 46, 4485, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 5, 1232, 2864, 15785, 14951, 105, 5, 8581, 1250, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''token_type_ids''': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=UpperCamelCase , model_name='''microsoft/deberta-v2-xlarge''' , revision='''ad6e42c1532ddf3a15c39246b63f5559d558b670''' , )
| 2 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''sail/poolformer_s12''': '''https://huggingface.co/sail/poolformer_s12/resolve/main/config.json''',
# See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}
class lowercase ( A__ ):
"""simple docstring"""
_a = 'poolformer'
def __init__( self , UpperCamelCase_=3 , UpperCamelCase_=16 , UpperCamelCase_=16 , UpperCamelCase_=3 , UpperCamelCase_=4.0 , UpperCamelCase_=[2, 2, 6, 2] , UpperCamelCase_=[64, 128, 320, 512] , UpperCamelCase_=[7, 3, 3, 3] , UpperCamelCase_=[4, 2, 2, 2] , UpperCamelCase_=[2, 1, 1, 1] , UpperCamelCase_=4 , UpperCamelCase_=0.0 , UpperCamelCase_="gelu" , UpperCamelCase_=True , UpperCamelCase_=1e-5 , UpperCamelCase_=0.02 , **UpperCamelCase_ , ):
'''simple docstring'''
UpperCamelCase__ :str = num_channels
UpperCamelCase__ :Any = patch_size
UpperCamelCase__ :Optional[Any] = stride
UpperCamelCase__ :Optional[int] = padding
UpperCamelCase__ :List[Any] = pool_size
UpperCamelCase__ :str = hidden_sizes
UpperCamelCase__ :int = mlp_ratio
UpperCamelCase__ :List[Any] = depths
UpperCamelCase__ :Any = patch_sizes
UpperCamelCase__ :Optional[Any] = strides
UpperCamelCase__ :str = num_encoder_blocks
UpperCamelCase__ :List[str] = drop_path_rate
UpperCamelCase__ :Optional[int] = hidden_act
UpperCamelCase__ :List[str] = use_layer_scale
UpperCamelCase__ :Optional[int] = layer_scale_init_value
UpperCamelCase__ :Union[str, Any] = initializer_range
super().__init__(**UpperCamelCase_ )
class lowercase ( A__ ):
"""simple docstring"""
_a = version.parse('1.11' )
@property
def lowerCAmelCase__ ( self ):
'''simple docstring'''
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def lowerCAmelCase__ ( self ):
'''simple docstring'''
return 2e-3 | 219 |
'''simple docstring'''
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
__snake_case = 16
__snake_case = 32
def a ( __a , __a = 16 , __a = "bert-base-cased" ) -> Any:
'''simple docstring'''
UpperCamelCase__ :List[str] = AutoTokenizer.from_pretrained(__a )
UpperCamelCase__ :List[Any] = load_dataset('''glue''' , '''mrpc''' )
def tokenize_function(__a ):
# max_length=None => use the model max length (it's actually the default)
UpperCamelCase__ :Tuple = tokenizer(examples['''sentence1'''] , examples['''sentence2'''] , truncation=__a , max_length=__a )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
UpperCamelCase__ :Optional[int] = datasets.map(
__a , batched=__a , remove_columns=['''idx''', '''sentence1''', '''sentence2'''] , load_from_cache_file=__a )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
UpperCamelCase__ :Optional[int] = tokenized_datasets.rename_column('''label''' , '''labels''' )
def collate_fn(__a ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(__a , padding='''max_length''' , max_length=128 , return_tensors='''pt''' )
return tokenizer.pad(__a , padding='''longest''' , return_tensors='''pt''' )
# Instantiate dataloaders.
UpperCamelCase__ :Dict = DataLoader(
tokenized_datasets['''train'''] , shuffle=__a , collate_fn=__a , batch_size=__a )
UpperCamelCase__ :str = DataLoader(
tokenized_datasets['''validation'''] , shuffle=__a , collate_fn=__a , batch_size=__a )
return train_dataloader, eval_dataloader
def a ( __a , __a , __a , __a ) -> str:
'''simple docstring'''
model.eval()
UpperCamelCase__ :List[str] = 0
for step, batch in enumerate(__a ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
UpperCamelCase__ :int = model(**__a )
UpperCamelCase__ :Tuple = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
UpperCamelCase__ , UpperCamelCase__ :int = accelerator.gather(
(predictions, batch['''labels''']) ) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(__a ) - 1:
UpperCamelCase__ :Union[str, Any] = predictions[: len(eval_dataloader.dataset ) - samples_seen]
UpperCamelCase__ :List[str] = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=__a , references=__a , )
UpperCamelCase__ :Union[str, Any] = metric.compute()
return eval_metric["accuracy"]
def a ( __a , __a ) -> List[Any]:
'''simple docstring'''
UpperCamelCase__ :Any = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
UpperCamelCase__ :Any = config['''lr''']
UpperCamelCase__ :Optional[int] = int(config['''num_epochs'''] )
UpperCamelCase__ :List[Any] = int(config['''seed'''] )
UpperCamelCase__ :List[Any] = int(config['''batch_size'''] )
UpperCamelCase__ :List[Any] = args.model_name_or_path
set_seed(__a )
UpperCamelCase__ , UpperCamelCase__ :Any = get_dataloaders(__a , __a , __a )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
UpperCamelCase__ :Union[str, Any] = AutoModelForSequenceClassification.from_pretrained(__a , return_dict=__a )
# Instantiate optimizer
UpperCamelCase__ :Any = (
AdamW
if accelerator.state.deepspeed_plugin is None
or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
UpperCamelCase__ :Optional[Any] = optimizer_cls(params=model.parameters() , lr=__a )
if accelerator.state.deepspeed_plugin is not None:
UpperCamelCase__ :Tuple = accelerator.state.deepspeed_plugin.deepspeed_config[
'''gradient_accumulation_steps'''
]
else:
UpperCamelCase__ :Dict = 1
UpperCamelCase__ :Tuple = (len(__a ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
UpperCamelCase__ :Any = get_linear_schedule_with_warmup(
optimizer=__a , num_warmup_steps=0 , num_training_steps=__a , )
else:
UpperCamelCase__ :Any = DummyScheduler(__a , total_num_steps=__a , warmup_num_steps=0 )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ :Dict = accelerator.prepare(
__a , __a , __a , __a , __a )
# We need to keep track of how many total steps we have iterated over
UpperCamelCase__ :Tuple = 0
# We also need to keep track of the stating epoch so files are named properly
UpperCamelCase__ :Optional[Any] = 0
UpperCamelCase__ :Optional[int] = evaluate.load('''glue''' , '''mrpc''' )
UpperCamelCase__ :List[Any] = num_epochs
if args.partial_train_epoch is not None:
UpperCamelCase__ :Optional[Any] = args.partial_train_epoch
if args.resume_from_checkpoint:
accelerator.load_state(args.resume_from_checkpoint )
UpperCamelCase__ :Dict = args.resume_from_checkpoint.split('''epoch_''' )[1]
UpperCamelCase__ :Tuple = ''''''
for char in epoch_string:
if char.isdigit():
state_epoch_num += char
else:
break
UpperCamelCase__ :Any = int(__a ) + 1
UpperCamelCase__ :Dict = evaluation_loop(__a , __a , __a , __a )
accelerator.print('''resumed checkpoint performance:''' , __a )
accelerator.print('''resumed checkpoint\'s scheduler\'s lr:''' , lr_scheduler.get_lr()[0] )
accelerator.print('''resumed optimizers\'s lr:''' , optimizer.param_groups[0]['''lr'''] )
with open(os.path.join(args.output_dir , f'''state_{starting_epoch-1}.json''' ) , '''r''' ) as f:
UpperCamelCase__ :Optional[int] = json.load(__a )
assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
assert (
resumed_state["lr"] == lr_scheduler.get_lr()[0]
), "Scheduler learning rate mismatch, loading from checkpoint failed"
assert (
resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
), "Optimizer learning rate mismatch, loading from checkpoint failed"
assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
return
# Now we train the model
UpperCamelCase__ :Optional[Any] = {}
for epoch in range(__a , __a ):
model.train()
for step, batch in enumerate(__a ):
UpperCamelCase__ :Optional[int] = model(**__a )
UpperCamelCase__ :Optional[int] = outputs.loss
UpperCamelCase__ :str = loss / gradient_accumulation_steps
accelerator.backward(__a )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
UpperCamelCase__ :Union[str, Any] = f'''epoch_{epoch}'''
UpperCamelCase__ :List[Any] = os.path.join(args.output_dir , __a )
accelerator.save_state(__a )
UpperCamelCase__ :List[Any] = evaluation_loop(__a , __a , __a , __a )
UpperCamelCase__ :int = accuracy
UpperCamelCase__ :List[Any] = lr_scheduler.get_lr()[0]
UpperCamelCase__ :Any = optimizer.param_groups[0]['''lr''']
UpperCamelCase__ :int = epoch
UpperCamelCase__ :Tuple = overall_step
accelerator.print(f'''epoch {epoch}:''' , __a )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir , f'''state_{epoch}.json''' ) , '''w''' ) as f:
json.dump(__a , __a )
def a ( ) -> Tuple:
'''simple docstring'''
UpperCamelCase__ :List[Any] = argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.''' )
parser.add_argument(
'''--model_name_or_path''' , type=__a , default='''bert-base-cased''' , help='''Path to pretrained model or model identifier from huggingface.co/models.''' , required=__a , )
parser.add_argument(
'''--output_dir''' , type=__a , default='''.''' , help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.''' , )
parser.add_argument(
'''--resume_from_checkpoint''' , type=__a , default=__a , help='''If the training should continue from a checkpoint folder.''' , )
parser.add_argument(
'''--partial_train_epoch''' , type=__a , default=__a , help='''If passed, the training will stop after this number of epochs.''' , )
parser.add_argument(
'''--num_epochs''' , type=__a , default=2 , help='''Number of train epochs.''' , )
UpperCamelCase__ :Optional[int] = parser.parse_args()
UpperCamelCase__ :List[str] = {'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
training_function(__a , __a )
if __name__ == "__main__":
main() | 219 | 1 |
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase ( _lowerCAmelCase : str , _lowerCAmelCase : List[str] , _lowerCAmelCase : Any ):
"""simple docstring"""
return params[F'''{prefix}/{prefix}/relpos_bias/rel_embedding'''][:, i, :]
def lowerCAmelCase ( _lowerCAmelCase : int , _lowerCAmelCase : Dict , _lowerCAmelCase : Any , _lowerCAmelCase : List[str]="attention" ):
"""simple docstring"""
UpperCAmelCase__ = UpperCAmelCase__ = np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/key/kernel'''][:, i, :, :] )
UpperCAmelCase__ = k_tmp.reshape(k_tmp.shape[0] , k_tmp.shape[1] * k_tmp.shape[2] )
UpperCAmelCase__ = np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/out/kernel'''][:, i, :, :] )
UpperCAmelCase__ = o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1] , o_tmp.shape[2] )
UpperCAmelCase__ = np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/query/kernel'''][:, i, :, :] )
UpperCAmelCase__ = q_tmp.reshape(q_tmp.shape[0] , q_tmp.shape[1] * q_tmp.shape[2] )
UpperCAmelCase__ = np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/value/kernel'''][:, i, :, :] )
UpperCAmelCase__ = v_tmp.reshape(v_tmp.shape[0] , v_tmp.shape[1] * v_tmp.shape[2] )
return k, o, q, v
def lowerCAmelCase ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : List[str] , _lowerCAmelCase : str=False ):
"""simple docstring"""
if split_mlp_wi:
UpperCAmelCase__ = params[F'''{prefix}/{prefix}/mlp/wi_0/kernel'''][:, i, :]
UpperCAmelCase__ = params[F'''{prefix}/{prefix}/mlp/wi_1/kernel'''][:, i, :]
UpperCAmelCase__ = (wi_a, wi_a)
else:
UpperCAmelCase__ = params[F'''{prefix}/{prefix}/mlp/wi/kernel'''][:, i, :]
UpperCAmelCase__ = params[F'''{prefix}/{prefix}/mlp/wo/kernel'''][:, i, :]
return wi, wo
def lowerCAmelCase ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : str , _lowerCAmelCase : List[Any] ):
"""simple docstring"""
return params[F'''{prefix}/{prefix}/{layer_name}/scale'''][:, i]
def lowerCAmelCase ( _lowerCAmelCase : dict , *, _lowerCAmelCase : int , _lowerCAmelCase : bool , _lowerCAmelCase : bool = False ):
"""simple docstring"""
UpperCAmelCase__ = traverse_util.flatten_dict(variables["target"] )
UpperCAmelCase__ = {"/".join(_lowerCAmelCase ): v for k, v in old.items()}
# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
UpperCAmelCase__ = "encoder/encoder/mlp/wi_0/kernel" in old
print("Split MLP:" , _lowerCAmelCase )
UpperCAmelCase__ = collections.OrderedDict()
# Shared embeddings.
UpperCAmelCase__ = old["token_embedder/embedding"]
# Encoder.
for i in range(_lowerCAmelCase ):
# Block i, layer 0 (Self Attention).
UpperCAmelCase__ = tax_layer_norm_lookup(_lowerCAmelCase , _lowerCAmelCase , "encoder" , "pre_attention_layer_norm" )
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = tax_attention_lookup(_lowerCAmelCase , _lowerCAmelCase , "encoder" , "attention" )
UpperCAmelCase__ = layer_norm
UpperCAmelCase__ = k.T
UpperCAmelCase__ = o.T
UpperCAmelCase__ = q.T
UpperCAmelCase__ = v.T
# Block i, layer 1 (MLP).
UpperCAmelCase__ = tax_layer_norm_lookup(_lowerCAmelCase , _lowerCAmelCase , "encoder" , "pre_mlp_layer_norm" )
UpperCAmelCase__ , UpperCAmelCase__ = tax_mlp_lookup(_lowerCAmelCase , _lowerCAmelCase , "encoder" , _lowerCAmelCase )
UpperCAmelCase__ = layer_norm
if split_mlp_wi:
UpperCAmelCase__ = wi[0].T
UpperCAmelCase__ = wi[1].T
else:
UpperCAmelCase__ = wi.T
UpperCAmelCase__ = wo.T
if scalable_attention:
# convert the rel_embedding of each layer
UpperCAmelCase__ = tax_relpos_bias_lookup(
_lowerCAmelCase , _lowerCAmelCase , "encoder" ).T
UpperCAmelCase__ = old["encoder/encoder_norm/scale"]
if not scalable_attention:
UpperCAmelCase__ = tax_relpos_bias_lookup(
_lowerCAmelCase , 0 , "encoder" ).T
UpperCAmelCase__ = tax_relpos_bias_lookup(
_lowerCAmelCase , 0 , "decoder" ).T
if not is_encoder_only:
# Decoder.
for i in range(_lowerCAmelCase ):
# Block i, layer 0 (Self Attention).
UpperCAmelCase__ = tax_layer_norm_lookup(_lowerCAmelCase , _lowerCAmelCase , "decoder" , "pre_self_attention_layer_norm" )
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = tax_attention_lookup(_lowerCAmelCase , _lowerCAmelCase , "decoder" , "self_attention" )
UpperCAmelCase__ = layer_norm
UpperCAmelCase__ = k.T
UpperCAmelCase__ = o.T
UpperCAmelCase__ = q.T
UpperCAmelCase__ = v.T
# Block i, layer 1 (Cross Attention).
UpperCAmelCase__ = tax_layer_norm_lookup(_lowerCAmelCase , _lowerCAmelCase , "decoder" , "pre_cross_attention_layer_norm" )
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = tax_attention_lookup(_lowerCAmelCase , _lowerCAmelCase , "decoder" , "encoder_decoder_attention" )
UpperCAmelCase__ = layer_norm
UpperCAmelCase__ = k.T
UpperCAmelCase__ = o.T
UpperCAmelCase__ = q.T
UpperCAmelCase__ = v.T
# Block i, layer 2 (MLP).
UpperCAmelCase__ = tax_layer_norm_lookup(_lowerCAmelCase , _lowerCAmelCase , "decoder" , "pre_mlp_layer_norm" )
UpperCAmelCase__ , UpperCAmelCase__ = tax_mlp_lookup(_lowerCAmelCase , _lowerCAmelCase , "decoder" , _lowerCAmelCase )
UpperCAmelCase__ = layer_norm
if split_mlp_wi:
UpperCAmelCase__ = wi[0].T
UpperCAmelCase__ = wi[1].T
else:
UpperCAmelCase__ = wi.T
UpperCAmelCase__ = wo.T
if scalable_attention:
# convert the rel_embedding of each layer
UpperCAmelCase__ = tax_relpos_bias_lookup(_lowerCAmelCase , _lowerCAmelCase , "decoder" ).T
UpperCAmelCase__ = old["decoder/decoder_norm/scale"]
# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
if "decoder/logits_dense/kernel" in old:
UpperCAmelCase__ = old["decoder/logits_dense/kernel"].T
return new
def lowerCAmelCase ( _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : bool ):
"""simple docstring"""
UpperCAmelCase__ = collections.OrderedDict([(k, torch.from_numpy(v.copy() )) for (k, v) in converted_params.items()] )
# Add what is missing.
if "encoder.embed_tokens.weight" not in state_dict:
UpperCAmelCase__ = state_dict["shared.weight"]
if not is_encoder_only:
if "decoder.embed_tokens.weight" not in state_dict:
UpperCAmelCase__ = state_dict["shared.weight"]
if "lm_head.weight" not in state_dict: # For old 1.0 models.
print("Using shared word embeddings as lm_head." )
UpperCAmelCase__ = state_dict["shared.weight"]
return state_dict
def lowerCAmelCase ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : str , _lowerCAmelCase : List[str] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[int] ):
"""simple docstring"""
UpperCAmelCase__ = checkpoints.load_tax_checkpoint(_lowerCAmelCase )
UpperCAmelCase__ = convert_tax_to_pytorch(
_lowerCAmelCase , num_layers=config.num_layers , is_encoder_only=_lowerCAmelCase , scalable_attention=_lowerCAmelCase )
UpperCAmelCase__ = make_state_dict(_lowerCAmelCase , _lowerCAmelCase )
model.load_state_dict(_lowerCAmelCase , strict=_lowerCAmelCase )
def lowerCAmelCase ( _lowerCAmelCase : str , _lowerCAmelCase : List[str] , _lowerCAmelCase : Dict , _lowerCAmelCase : bool = False , _lowerCAmelCase : bool = False , ):
"""simple docstring"""
UpperCAmelCase__ = MTaConfig.from_json_file(_lowerCAmelCase )
print(F'''Building PyTorch model from configuration: {config}''' )
# Non-v1.1 checkpoints could also use T5Model, but this works for all.
# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
if is_encoder_only:
UpperCAmelCase__ = UMTaEncoderModel(_lowerCAmelCase )
else:
UpperCAmelCase__ = UMTaForConditionalGeneration(_lowerCAmelCase )
# Load weights from tf checkpoint
load_tax_weights_in_ta(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
model.save_pretrained(_lowerCAmelCase )
# Verify that we can load the checkpoint.
model.from_pretrained(_lowerCAmelCase )
print("Done" )
if __name__ == "__main__":
_lowerCAmelCase : str = argparse.ArgumentParser(description="Converts a native T5X checkpoint into a PyTorch checkpoint.")
# Required parameters
parser.add_argument(
"--t5x_checkpoint_path", default=None, type=str, required=True, help="Path to the T5X checkpoint."
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help="The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.",
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
parser.add_argument(
"--is_encoder_only", action="store_true", help="Check if the model is encoder-decoder model", default=False
)
parser.add_argument(
"--scalable_attention",
action="store_true",
help="Whether the model uses scaled attention (umt5 model)",
default=False,
)
_lowerCAmelCase : Optional[int] = parser.parse_args()
convert_tax_checkpoint_to_pytorch(
args.tax_checkpoint_path,
args.config_file,
args.pytorch_dump_path,
args.is_encoder_only,
args.scalable_attention,
)
| 169 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : List[str] = logging.get_logger(__name__)
_lowerCAmelCase : Optional[Any] = {
"microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json",
"microsoft/markuplm-large": "https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json",
}
class _UpperCamelCase ( lowerCAmelCase ):
UpperCAmelCase_ = """markuplm"""
def __init__( self :Dict , lowerCamelCase :List[Any]=3_0522 , lowerCamelCase :List[Any]=768 , lowerCamelCase :Union[str, Any]=12 , lowerCamelCase :Optional[int]=12 , lowerCamelCase :List[str]=3072 , lowerCamelCase :Dict="gelu" , lowerCamelCase :List[str]=0.1 , lowerCamelCase :Union[str, Any]=0.1 , lowerCamelCase :int=512 , lowerCamelCase :Union[str, Any]=2 , lowerCamelCase :int=0.02 , lowerCamelCase :int=1e-12 , lowerCamelCase :Tuple=0 , lowerCamelCase :List[str]=0 , lowerCamelCase :int=2 , lowerCamelCase :Optional[int]=256 , lowerCamelCase :List[str]=1024 , lowerCamelCase :Optional[Any]=216 , lowerCamelCase :str=1001 , lowerCamelCase :List[str]=32 , lowerCamelCase :Dict=50 , lowerCamelCase :int="absolute" , lowerCamelCase :Union[str, Any]=True , lowerCamelCase :Dict=None , **lowerCamelCase :List[Any] , ) -> int:
super().__init__(
pad_token_id=lowerCamelCase , bos_token_id=lowerCamelCase , eos_token_id=lowerCamelCase , **lowerCamelCase , )
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
# additional properties
UpperCAmelCase__ = max_depth
UpperCAmelCase__ = max_xpath_tag_unit_embeddings
UpperCAmelCase__ = max_xpath_subs_unit_embeddings
UpperCAmelCase__ = tag_pad_id
UpperCAmelCase__ = subs_pad_id
UpperCAmelCase__ = xpath_unit_hidden_size
| 169 | 1 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, require_faiss
a__ = pytest.mark.integration
@require_faiss
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
def __lowercase ( self ) -> List[Any]:
_a : Tuple = Dataset.from_dict({'''filename''': ['''my_name-train''' + '''_''' + str(_a ) for x in np.arange(3_0 ).tolist()]} )
return dset
def __lowercase ( self ) -> List[Any]:
import faiss
_a : Dataset = self._create_dummy_dataset()
_a : Union[str, Any] = dset.map(
lambda _a , _a : {"vecs": i * np.ones(5 , dtype=np.floataa )} , with_indices=_a , keep_in_memory=_a )
_a : List[str] = dset.add_faiss_index('''vecs''' , batch_size=1_0_0 , metric_type=faiss.METRIC_INNER_PRODUCT )
_a : Optional[Any] = dset.get_nearest_examples('''vecs''' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['''filename'''][0] , '''my_name-train_29''' )
dset.drop_index('''vecs''' )
def __lowercase ( self ) -> Optional[int]:
import faiss
_a : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((3_0, 5) ) * np.arange(3_0 ).reshape(-1 , 1 ) , index_name='''vecs''' , batch_size=1_0_0 , metric_type=faiss.METRIC_INNER_PRODUCT , )
_a : Union[str, Any] = dset.get_nearest_examples('''vecs''' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['''filename'''][0] , '''my_name-train_29''' )
def __lowercase ( self ) -> str:
import faiss
_a : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((3_0, 5) ) * np.arange(3_0 ).reshape(-1 , 1 ) , index_name='''vecs''' , metric_type=faiss.METRIC_INNER_PRODUCT , )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=_a ) as tmp_file:
dset.save_faiss_index('''vecs''' , tmp_file.name )
dset.load_faiss_index('''vecs2''' , tmp_file.name )
os.unlink(tmp_file.name )
_a : str = dset.get_nearest_examples('''vecs2''' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['''filename'''][0] , '''my_name-train_29''' )
def __lowercase ( self ) -> Optional[int]:
_a : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((3_0, 5) ) * np.arange(3_0 ).reshape(-1 , 1 ) , index_name='''vecs''' )
dset.drop_index('''vecs''' )
self.assertRaises(_a , partial(dset.get_nearest_examples , '''vecs2''' , np.ones(5 , dtype=np.floataa ) ) )
def __lowercase ( self ) -> Any:
from elasticsearch import Elasticsearch
_a : Dataset = self._create_dummy_dataset()
with patch('''elasticsearch.Elasticsearch.search''' ) as mocked_search, patch(
'''elasticsearch.client.IndicesClient.create''' ) as mocked_index_create, patch('''elasticsearch.helpers.streaming_bulk''' ) as mocked_bulk:
_a : Dict = {'''acknowledged''': True}
mocked_bulk.return_value([(True, None)] * 3_0 )
_a : Tuple = {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 2_9}]}}
_a : Optional[int] = Elasticsearch()
dset.add_elasticsearch_index('''filename''' , es_client=_a )
_a : Optional[Any] = dset.get_nearest_examples('''filename''' , '''my_name-train_29''' )
self.assertEqual(examples['''filename'''][0] , '''my_name-train_29''' )
@require_faiss
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
def __lowercase ( self ) -> Tuple:
import faiss
_a : Tuple = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
# add vectors
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsNotNone(index.faiss_index )
self.assertEqual(index.faiss_index.ntotal , 5 )
index.add_vectors(np.zeros((5, 5) , dtype=np.floataa ) )
self.assertEqual(index.faiss_index.ntotal , 1_0 )
# single query
_a : Optional[int] = np.zeros(5 , dtype=np.floataa )
_a : Optional[int] = 1
_a : int = index.search(_a )
self.assertRaises(_a , index.search , query.reshape(-1 , 1 ) )
self.assertGreater(scores[0] , 0 )
self.assertEqual(indices[0] , 1 )
# batched queries
_a : Dict = np.eye(5 , dtype=np.floataa )[::-1]
_a : Any = index.search_batch(_a )
self.assertRaises(_a , index.search_batch , queries[0] )
_a : Dict = [scores[0] for scores in total_scores]
_a : Any = [indices[0] for indices in total_indices]
self.assertGreater(np.min(_a ) , 0 )
self.assertListEqual([4, 3, 2, 1, 0] , _a )
def __lowercase ( self ) -> str:
import faiss
_a : int = FaissIndex(string_factory='''Flat''' )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexFlat )
_a : Optional[Any] = FaissIndex(string_factory='''LSH''' )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexLSH )
with self.assertRaises(_a ):
_a : List[str] = FaissIndex(string_factory='''Flat''' , custom_index=faiss.IndexFlat(5 ) )
def __lowercase ( self ) -> str:
import faiss
_a : int = faiss.IndexFlat(5 )
_a : str = FaissIndex(custom_index=_a )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexFlat )
def __lowercase ( self ) -> str:
import faiss
_a : Tuple = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=_a ) as tmp_file:
index.save(tmp_file.name )
_a : Union[str, Any] = FaissIndex.load(tmp_file.name )
os.unlink(tmp_file.name )
_a : List[Any] = np.zeros(5 , dtype=np.floataa )
_a : int = 1
_a : Union[str, Any] = index.search(_a )
self.assertGreater(scores[0] , 0 )
self.assertEqual(indices[0] , 1 )
@require_faiss
def __UpperCAmelCase ( __a : Optional[int] ) -> List[Any]:
"""simple docstring"""
import faiss
_a : Optional[Any] = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 ,dtype=np.floataa ) )
_a : str = '''index.faiss'''
_a : int = F"""mock://{index_name}"""
index.save(__a ,storage_options=mockfs.storage_options )
_a : int = FaissIndex.load(__a ,storage_options=mockfs.storage_options )
_a : Tuple = np.zeros(5 ,dtype=np.floataa )
_a : str = 1
_a : Union[str, Any] = index.search(__a )
assert scores[0] > 0
assert indices[0] == 1
@require_elasticsearch
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
def __lowercase ( self ) -> List[str]:
from elasticsearch import Elasticsearch
with patch('''elasticsearch.Elasticsearch.search''' ) as mocked_search, patch(
'''elasticsearch.client.IndicesClient.create''' ) as mocked_index_create, patch('''elasticsearch.helpers.streaming_bulk''' ) as mocked_bulk:
_a : int = Elasticsearch()
_a : List[Any] = {'''acknowledged''': True}
_a : List[str] = ElasticSearchIndex(es_client=_a )
mocked_bulk.return_value([(True, None)] * 3 )
index.add_documents(['''foo''', '''bar''', '''foobar'''] )
# single query
_a : str = '''foo'''
_a : List[str] = {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 0}]}}
_a : Optional[Any] = index.search(_a )
self.assertEqual(scores[0] , 1 )
self.assertEqual(indices[0] , 0 )
# single query with timeout
_a : Any = '''foo'''
_a : List[str] = {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 0}]}}
_a : Optional[int] = index.search(_a , request_timeout=3_0 )
self.assertEqual(scores[0] , 1 )
self.assertEqual(indices[0] , 0 )
# batched queries
_a : str = ['''foo''', '''bar''', '''foobar''']
_a : Optional[int] = {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 1}]}}
_a : Union[str, Any] = index.search_batch(_a )
_a : Union[str, Any] = [scores[0] for scores in total_scores]
_a : Optional[int] = [indices[0] for indices in total_indices]
self.assertGreater(np.min(_a ) , 0 )
self.assertListEqual([1, 1, 1] , _a )
# batched queries with timeout
_a : Tuple = ['''foo''', '''bar''', '''foobar''']
_a : str = {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 1}]}}
_a : int = index.search_batch(_a , request_timeout=3_0 )
_a : Tuple = [scores[0] for scores in total_scores]
_a : Optional[int] = [indices[0] for indices in total_indices]
self.assertGreater(np.min(_a ) , 0 )
self.assertListEqual([1, 1, 1] , _a )
| 351 |
from math import ceil
def __UpperCAmelCase ( __a : int = 1_001 ) -> int:
"""simple docstring"""
_a : Dict = 1
for i in range(1 ,int(ceil(n / 2.0 ) ) ):
_a : int = 2 * i + 1
_a : str = 2 * i
_a : Any = total + 4 * odd**2 - 6 * even
return total
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
print(solution())
else:
try:
a__ = int(sys.argv[1])
print(solution(n))
except ValueError:
print('''Invalid entry - please enter a number''')
| 15 | 0 |
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
ROBERTA_INPUTS_DOCSTRING,
ROBERTA_START_DOCSTRING,
RobertaEmbeddings,
)
from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy
@add_start_docstrings(
'''The RoBERTa Model transformer with early exiting (DeeRoBERTa). ''' , lowerCamelCase__ , )
class __lowerCAmelCase ( lowerCamelCase__ ):
__lowerCamelCase = RobertaConfig
__lowerCamelCase = '''roberta'''
def __init__( self , _snake_case ):
"""simple docstring"""
super().__init__(_snake_case )
_lowerCAmelCase = RobertaEmbeddings(_snake_case )
self.init_weights()
@add_start_docstrings(
'''RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,
also takes care of multi-layer training. ''' , lowerCamelCase__ , )
class __lowerCAmelCase ( lowerCamelCase__ ):
__lowerCamelCase = RobertaConfig
__lowerCamelCase = '''roberta'''
def __init__( self , _snake_case ):
"""simple docstring"""
super().__init__(_snake_case )
_lowerCAmelCase = config.num_labels
_lowerCAmelCase = config.num_hidden_layers
_lowerCAmelCase = DeeRobertaModel(_snake_case )
_lowerCAmelCase = nn.Dropout(config.hidden_dropout_prob )
_lowerCAmelCase = nn.Linear(config.hidden_size , self.config.num_labels )
@add_start_docstrings_to_model_forward(_snake_case )
def snake_case ( self , _snake_case=None , _snake_case=None , _snake_case=None , _snake_case=None , _snake_case=None , _snake_case=None , _snake_case=None , _snake_case=-1 , _snake_case=False , ):
"""simple docstring"""
_lowerCAmelCase = self.num_layers
try:
_lowerCAmelCase = self.roberta(
_snake_case , attention_mask=_snake_case , token_type_ids=_snake_case , position_ids=_snake_case , head_mask=_snake_case , inputs_embeds=_snake_case , )
_lowerCAmelCase = outputs[1]
_lowerCAmelCase = self.dropout(_snake_case )
_lowerCAmelCase = self.classifier(_snake_case )
_lowerCAmelCase = (logits,) + outputs[2:] # add hidden states and attention if they are here
except HighwayException as e:
_lowerCAmelCase = e.message
_lowerCAmelCase = e.exit_layer
_lowerCAmelCase = outputs[0]
if not self.training:
_lowerCAmelCase = entropy(_snake_case )
_lowerCAmelCase = []
_lowerCAmelCase = []
if labels is not None:
if self.num_labels == 1:
# We are doing regression
_lowerCAmelCase = MSELoss()
_lowerCAmelCase = loss_fct(logits.view(-1 ) , labels.view(-1 ) )
else:
_lowerCAmelCase = CrossEntropyLoss()
_lowerCAmelCase = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
# work with highway exits
_lowerCAmelCase = []
for highway_exit in outputs[-1]:
_lowerCAmelCase = highway_exit[0]
if not self.training:
highway_logits_all.append(_snake_case )
highway_entropy.append(highway_exit[2] )
if self.num_labels == 1:
# We are doing regression
_lowerCAmelCase = MSELoss()
_lowerCAmelCase = loss_fct(highway_logits.view(-1 ) , labels.view(-1 ) )
else:
_lowerCAmelCase = CrossEntropyLoss()
_lowerCAmelCase = loss_fct(highway_logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
highway_losses.append(_snake_case )
if train_highway:
_lowerCAmelCase = (sum(highway_losses[:-1] ),) + outputs
# exclude the final highway, of course
else:
_lowerCAmelCase = (loss,) + outputs
if not self.training:
_lowerCAmelCase = outputs + ((original_entropy, highway_entropy), exit_layer)
if output_layer >= 0:
_lowerCAmelCase = (
(outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
) # use the highway of the last layer
return outputs # (loss), logits, (hidden_states), (attentions), entropy
| 82 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
class snake_case__ ( lowerCAmelCase_ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = """philschmid/bart-large-cnn-samsum"""
_SCREAMING_SNAKE_CASE = (
"""This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, """
"""and returns a summary of the text."""
)
_SCREAMING_SNAKE_CASE = """summarizer"""
_SCREAMING_SNAKE_CASE = AutoTokenizer
_SCREAMING_SNAKE_CASE = AutoModelForSeqaSeqLM
_SCREAMING_SNAKE_CASE = ["""text"""]
_SCREAMING_SNAKE_CASE = ["""text"""]
def lowercase_ ( self : Optional[Any], _snake_case : str ) ->Any:
return self.pre_processor(_snake_case, return_tensors='pt', truncation=_snake_case )
def lowercase_ ( self : int, _snake_case : List[Any] ) ->Any:
return self.model.generate(**_snake_case )[0]
def lowercase_ ( self : int, _snake_case : int ) ->str:
return self.pre_processor.decode(_snake_case, skip_special_tokens=_snake_case, clean_up_tokenization_spaces=_snake_case )
| 277 | 0 |
'''simple docstring'''
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures
# Importing the dataset
a__ : Tuple = pd.read_csv(
'https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'
'position_salaries.csv'
)
a__ : Any = dataset.iloc[:, 1:2].values
a__ : int = dataset.iloc[:, 2].values
a__ , a__ , a__ , a__ : List[Any] = train_test_split(X, y, test_size=0.2, random_state=0)
a__ : Any = PolynomialFeatures(degree=4)
a__ : Optional[Any] = poly_reg.fit_transform(X)
a__ : Optional[int] = LinearRegression()
pol_reg.fit(X_poly, y)
def _lowercase ( ):
'''simple docstring'''
plt.scatter(__A ,__A ,color="""red""" )
plt.plot(__A ,pol_reg.predict(poly_reg.fit_transform(__A ) ) ,color="""blue""" )
plt.title("""Truth or Bluff (Linear Regression)""" )
plt.xlabel("""Position level""" )
plt.ylabel("""Salary""" )
plt.show()
if __name__ == "__main__":
viz_polymonial()
# Predicting a new result with Polymonial Regression
pol_reg.predict(poly_reg.fit_transform([[5.5]]))
# output should be 132148.43750003
| 243 |
'''simple docstring'''
def _lowercase ( __A = 10 ,__A = 22 ):
'''simple docstring'''
__UpperCamelCase = range(1 ,__A )
__UpperCamelCase = range(1 ,__A )
return sum(
1 for power in powers for base in bases if len(str(base**power ) ) == power )
if __name__ == "__main__":
print(f'''{solution(1_0, 2_2) = }''')
| 243 | 1 |
def UpperCamelCase__ ( A__ , A__ ) -> Optional[int]:
snake_case__ : Union[str, Any] = len(_UpperCAmelCase )
print('The following activities are selected:' )
# The first activity is always selected
snake_case__ : Any = 0
print(_UpperCAmelCase , end=',' )
# Consider rest of the activities
for j in range(_UpperCAmelCase ):
# If this activity has start time greater than
# or equal to the finish time of previously
# selected activity, then select it
if start[j] >= finish[i]:
print(_UpperCAmelCase , end=',' )
snake_case__ : int = j
if __name__ == "__main__":
import doctest
doctest.testmod()
lowerCAmelCase__ : Union[str, Any] = [1, 3, 0, 5, 8, 5]
lowerCAmelCase__ : List[str] = [2, 4, 6, 7, 9, 9]
print_max_activities(start, finish)
| 143 |
"""simple docstring"""
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class lowercase ( __UpperCAmelCase):
__lowerCAmelCase : str = ["""image_processor""", """tokenizer"""]
__lowerCAmelCase : Optional[Any] = """OwlViTImageProcessor"""
__lowerCAmelCase : Any = ("""CLIPTokenizer""", """CLIPTokenizerFast""")
def __init__( self : Union[str, Any] , _lowerCamelCase : str=None , _lowerCamelCase : Tuple=None , **_lowerCamelCase : List[Any] ):
"""simple docstring"""
A_ : 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.''' , _lowerCamelCase , )
A_ : List[Any] = kwargs.pop('''feature_extractor''' )
A_ : 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__(_lowerCamelCase , _lowerCamelCase )
def __call__( self : Optional[int] , _lowerCamelCase : List[Any]=None , _lowerCamelCase : Dict=None , _lowerCamelCase : int=None , _lowerCamelCase : str="max_length" , _lowerCamelCase : List[Any]="np" , **_lowerCamelCase : Optional[int] ):
"""simple docstring"""
if text is None and query_images is None and images is None:
raise ValueError(
'''You have to specify at least one text or query image or image. All three cannot be none.''' )
if text is not None:
if isinstance(_lowerCamelCase , _lowerCamelCase ) or (isinstance(_lowerCamelCase , _lowerCamelCase ) and not isinstance(text[0] , _lowerCamelCase )):
A_ : List[str] = [self.tokenizer(_lowerCamelCase , padding=_lowerCamelCase , return_tensors=_lowerCamelCase , **_lowerCamelCase )]
elif isinstance(_lowerCamelCase , _lowerCamelCase ) and isinstance(text[0] , _lowerCamelCase ):
A_ : Optional[int] = []
# Maximum number of queries across batch
A_ : Any = max([len(_lowerCamelCase ) for t in text] )
# Pad all batch samples to max number of text queries
for t in text:
if len(_lowerCamelCase ) != max_num_queries:
A_ : Optional[int] = t + [''' '''] * (max_num_queries - len(_lowerCamelCase ))
A_ : Tuple = self.tokenizer(_lowerCamelCase , padding=_lowerCamelCase , return_tensors=_lowerCamelCase , **_lowerCamelCase )
encodings.append(_lowerCamelCase )
else:
raise TypeError('''Input text should be a string, a list of strings or a nested list of strings''' )
if return_tensors == "np":
A_ : Union[str, Any] = np.concatenate([encoding['''input_ids'''] for encoding in encodings] , axis=0 )
A_ : Dict = np.concatenate([encoding['''attention_mask'''] for encoding in encodings] , axis=0 )
elif return_tensors == "jax" and is_flax_available():
import jax.numpy as jnp
A_ : List[Any] = jnp.concatenate([encoding['''input_ids'''] for encoding in encodings] , axis=0 )
A_ : Any = jnp.concatenate([encoding['''attention_mask'''] for encoding in encodings] , axis=0 )
elif return_tensors == "pt" and is_torch_available():
import torch
A_ : Any = torch.cat([encoding['''input_ids'''] for encoding in encodings] , dim=0 )
A_ : Union[str, Any] = torch.cat([encoding['''attention_mask'''] for encoding in encodings] , dim=0 )
elif return_tensors == "tf" and is_tf_available():
import tensorflow as tf
A_ : Tuple = tf.stack([encoding['''input_ids'''] for encoding in encodings] , axis=0 )
A_ : str = tf.stack([encoding['''attention_mask'''] for encoding in encodings] , axis=0 )
else:
raise ValueError('''Target return tensor type could not be returned''' )
A_ : Any = BatchEncoding()
A_ : Optional[Any] = input_ids
A_ : str = attention_mask
if query_images is not None:
A_ : Union[str, Any] = BatchEncoding()
A_ : Optional[Any] = self.image_processor(
_lowerCamelCase , return_tensors=_lowerCamelCase , **_lowerCamelCase ).pixel_values
A_ : Dict = query_pixel_values
if images is not None:
A_ : int = self.image_processor(_lowerCamelCase , return_tensors=_lowerCamelCase , **_lowerCamelCase )
if text is not None and images is not None:
A_ : str = image_features.pixel_values
return encoding
elif query_images is not None and images is not None:
A_ : Union[str, Any] = image_features.pixel_values
return encoding
elif text is not None or query_images is not None:
return encoding
else:
return BatchEncoding(data=dict(**_lowerCamelCase ) , tensor_type=_lowerCamelCase )
def a_ ( self : Optional[Any] , *_lowerCamelCase : int , **_lowerCamelCase : Dict ):
"""simple docstring"""
return self.image_processor.post_process(*_lowerCamelCase , **_lowerCamelCase )
def a_ ( self : Optional[Any] , *_lowerCamelCase : Optional[Any] , **_lowerCamelCase : Dict ):
"""simple docstring"""
return self.image_processor.post_process_object_detection(*_lowerCamelCase , **_lowerCamelCase )
def a_ ( self : List[Any] , *_lowerCamelCase : List[str] , **_lowerCamelCase : Optional[int] ):
"""simple docstring"""
return self.image_processor.post_process_image_guided_detection(*_lowerCamelCase , **_lowerCamelCase )
def a_ ( self : str , *_lowerCamelCase : Tuple , **_lowerCamelCase : List[Any] ):
"""simple docstring"""
return self.tokenizer.batch_decode(*_lowerCamelCase , **_lowerCamelCase )
def a_ ( self : Dict , *_lowerCamelCase : Any , **_lowerCamelCase : Union[str, Any] ):
"""simple docstring"""
return self.tokenizer.decode(*_lowerCamelCase , **_lowerCamelCase )
@property
def a_ ( self : List[str] ):
"""simple docstring"""
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 a_ ( self : Any ):
"""simple docstring"""
warnings.warn(
'''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' , _lowerCamelCase , )
return self.image_processor
| 167 | 0 |
"""simple docstring"""
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
CONFIG_MAPPING_NAMES,
AutoConfig,
model_type_to_module_name,
replace_list_option_in_docstrings,
)
__A = logging.get_logger(__name__)
__A = OrderedDict(
[
("align", "EfficientNetImageProcessor"),
("beit", "BeitImageProcessor"),
("bit", "BitImageProcessor"),
("blip", "BlipImageProcessor"),
("blip-2", "BlipImageProcessor"),
("bridgetower", "BridgeTowerImageProcessor"),
("chinese_clip", "ChineseCLIPImageProcessor"),
("clip", "CLIPImageProcessor"),
("clipseg", "ViTImageProcessor"),
("conditional_detr", "ConditionalDetrImageProcessor"),
("convnext", "ConvNextImageProcessor"),
("convnextv2", "ConvNextImageProcessor"),
("cvt", "ConvNextImageProcessor"),
("data2vec-vision", "BeitImageProcessor"),
("deformable_detr", "DeformableDetrImageProcessor"),
("deit", "DeiTImageProcessor"),
("deta", "DetaImageProcessor"),
("detr", "DetrImageProcessor"),
("dinat", "ViTImageProcessor"),
("donut-swin", "DonutImageProcessor"),
("dpt", "DPTImageProcessor"),
("efficientformer", "EfficientFormerImageProcessor"),
("efficientnet", "EfficientNetImageProcessor"),
("flava", "FlavaImageProcessor"),
("focalnet", "BitImageProcessor"),
("git", "CLIPImageProcessor"),
("glpn", "GLPNImageProcessor"),
("groupvit", "CLIPImageProcessor"),
("imagegpt", "ImageGPTImageProcessor"),
("instructblip", "BlipImageProcessor"),
("layoutlmv2", "LayoutLMv2ImageProcessor"),
("layoutlmv3", "LayoutLMv3ImageProcessor"),
("levit", "LevitImageProcessor"),
("mask2former", "Mask2FormerImageProcessor"),
("maskformer", "MaskFormerImageProcessor"),
("mgp-str", "ViTImageProcessor"),
("mobilenet_v1", "MobileNetV1ImageProcessor"),
("mobilenet_v2", "MobileNetV2ImageProcessor"),
("mobilevit", "MobileViTImageProcessor"),
("mobilevit", "MobileViTImageProcessor"),
("mobilevitv2", "MobileViTImageProcessor"),
("nat", "ViTImageProcessor"),
("oneformer", "OneFormerImageProcessor"),
("owlvit", "OwlViTImageProcessor"),
("perceiver", "PerceiverImageProcessor"),
("pix2struct", "Pix2StructImageProcessor"),
("poolformer", "PoolFormerImageProcessor"),
("regnet", "ConvNextImageProcessor"),
("resnet", "ConvNextImageProcessor"),
("sam", "SamImageProcessor"),
("segformer", "SegformerImageProcessor"),
("swiftformer", "ViTImageProcessor"),
("swin", "ViTImageProcessor"),
("swin2sr", "Swin2SRImageProcessor"),
("swinv2", "ViTImageProcessor"),
("table-transformer", "DetrImageProcessor"),
("timesformer", "VideoMAEImageProcessor"),
("tvlt", "TvltImageProcessor"),
("upernet", "SegformerImageProcessor"),
("van", "ConvNextImageProcessor"),
("videomae", "VideoMAEImageProcessor"),
("vilt", "ViltImageProcessor"),
("vit", "ViTImageProcessor"),
("vit_hybrid", "ViTHybridImageProcessor"),
("vit_mae", "ViTImageProcessor"),
("vit_msn", "ViTImageProcessor"),
("xclip", "CLIPImageProcessor"),
("yolos", "YolosImageProcessor"),
]
)
__A = _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)
def a__ ( __SCREAMING_SNAKE_CASE ) -> Tuple:
for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
if class_name in extractors:
__lowerCAmelCase: Any = model_type_to_module_name(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Dict = importlib.import_module(F".{module_name}" , "transformers.models" )
try:
return getattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
except AttributeError:
continue
for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
if getattr(__SCREAMING_SNAKE_CASE , "__name__" , __SCREAMING_SNAKE_CASE ) == class_name:
return extractor
# We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
# init and we return the proper dummy to get an appropriate error message.
__lowerCAmelCase: str = importlib.import_module("transformers" )
if hasattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
return getattr(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
return None
def a__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = None , __SCREAMING_SNAKE_CASE = False , __SCREAMING_SNAKE_CASE = False , __SCREAMING_SNAKE_CASE = None , __SCREAMING_SNAKE_CASE = None , __SCREAMING_SNAKE_CASE = None , __SCREAMING_SNAKE_CASE = False , **__SCREAMING_SNAKE_CASE , ) -> Any:
__lowerCAmelCase: List[Any] = get_file_from_repo(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , cache_dir=__SCREAMING_SNAKE_CASE , force_download=__SCREAMING_SNAKE_CASE , resume_download=__SCREAMING_SNAKE_CASE , proxies=__SCREAMING_SNAKE_CASE , use_auth_token=__SCREAMING_SNAKE_CASE , revision=__SCREAMING_SNAKE_CASE , local_files_only=__SCREAMING_SNAKE_CASE , )
if resolved_config_file is None:
logger.info(
"Could not locate the image processor configuration file, will try to use the model config instead." )
return {}
with open(__SCREAMING_SNAKE_CASE , encoding="utf-8" ) as reader:
return json.load(__SCREAMING_SNAKE_CASE )
class snake_case :
def __init__( self : List[str])-> str:
'''simple docstring'''
raise EnvironmentError(
"AutoImageProcessor is designed to be instantiated "
"using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method.")
@classmethod
@replace_list_option_in_docstrings(UpperCamelCase__)
def lowercase_ ( cls : Tuple , UpperCamelCase__ : str , **UpperCamelCase__ : Optional[int])-> List[Any]:
'''simple docstring'''
__lowerCAmelCase: Any = kwargs.pop("config" , UpperCamelCase__)
__lowerCAmelCase: Tuple = kwargs.pop("trust_remote_code" , UpperCamelCase__)
__lowerCAmelCase: List[str] = True
__lowerCAmelCase: List[str] = ImageProcessingMixin.get_image_processor_dict(UpperCamelCase__ , **UpperCamelCase__)
__lowerCAmelCase: Optional[int] = config_dict.get("image_processor_type" , UpperCamelCase__)
__lowerCAmelCase: int = None
if "AutoImageProcessor" in config_dict.get("auto_map" , {}):
__lowerCAmelCase: str = config_dict["auto_map"]["AutoImageProcessor"]
# If we still don't have the image processor class, check if we're loading from a previous feature extractor config
# and if so, infer the image processor class from there.
if image_processor_class is None and image_processor_auto_map is None:
__lowerCAmelCase: Dict = config_dict.pop("feature_extractor_type" , UpperCamelCase__)
if feature_extractor_class is not None:
logger.warning(
"Could not find image processor class in the image processor config or the model config. Loading"
" based on pattern matching with the model's feature extractor configuration.")
__lowerCAmelCase: Tuple = feature_extractor_class.replace("FeatureExtractor" , "ImageProcessor")
if "AutoFeatureExtractor" in config_dict.get("auto_map" , {}):
__lowerCAmelCase: List[str] = config_dict["auto_map"]["AutoFeatureExtractor"]
__lowerCAmelCase: Dict = feature_extractor_auto_map.replace("FeatureExtractor" , "ImageProcessor")
logger.warning(
"Could not find image processor auto map in the image processor config or the model config."
" Loading based on pattern matching with the model's feature extractor configuration.")
# If we don't find the image processor class in the image processor config, let's try the model config.
if image_processor_class is None and image_processor_auto_map is None:
if not isinstance(UpperCamelCase__ , UpperCamelCase__):
__lowerCAmelCase: Any = AutoConfig.from_pretrained(UpperCamelCase__ , **UpperCamelCase__)
# It could be in `config.image_processor_type``
__lowerCAmelCase: str = getattr(UpperCamelCase__ , "image_processor_type" , UpperCamelCase__)
if hasattr(UpperCamelCase__ , "auto_map") and "AutoImageProcessor" in config.auto_map:
__lowerCAmelCase: List[str] = config.auto_map["AutoImageProcessor"]
if image_processor_class is not None:
__lowerCAmelCase: Tuple = image_processor_class_from_name(UpperCamelCase__)
__lowerCAmelCase: int = image_processor_auto_map is not None
__lowerCAmelCase: str = image_processor_class is not None or type(UpperCamelCase__) in IMAGE_PROCESSOR_MAPPING
__lowerCAmelCase: List[str] = resolve_trust_remote_code(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__)
if has_remote_code and trust_remote_code:
__lowerCAmelCase: List[Any] = get_class_from_dynamic_module(
UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__)
__lowerCAmelCase: Optional[int] = kwargs.pop("code_revision" , UpperCamelCase__)
if os.path.isdir(UpperCamelCase__):
image_processor_class.register_for_auto_class()
return image_processor_class.from_dict(UpperCamelCase__ , **UpperCamelCase__)
elif image_processor_class is not None:
return image_processor_class.from_dict(UpperCamelCase__ , **UpperCamelCase__)
# Last try: we use the IMAGE_PROCESSOR_MAPPING.
elif type(UpperCamelCase__) in IMAGE_PROCESSOR_MAPPING:
__lowerCAmelCase: Dict = IMAGE_PROCESSOR_MAPPING[type(UpperCamelCase__)]
return image_processor_class.from_dict(UpperCamelCase__ , **UpperCamelCase__)
raise ValueError(
f"Unrecognized image processor in {pretrained_model_name_or_path}. Should have a "
f"`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following "
f"`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys())}")
@staticmethod
def lowercase_ ( UpperCamelCase__ : str , UpperCamelCase__ : Optional[Any])-> Any:
'''simple docstring'''
IMAGE_PROCESSOR_MAPPING.register(UpperCamelCase__ , UpperCamelCase__)
| 365 |
"""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
__A = logging.get_logger(__name__)
__A = {
"facebook/data2vec-vision-base-ft": (
"https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"
),
}
class snake_case ( __snake_case ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = """data2vec-vision"""
def __init__( self : Optional[int] , UpperCamelCase__ : str=7_6_8 , UpperCamelCase__ : Any=1_2 , UpperCamelCase__ : str=1_2 , UpperCamelCase__ : Optional[Any]=3_0_7_2 , UpperCamelCase__ : Union[str, Any]="gelu" , UpperCamelCase__ : List[Any]=0.0 , UpperCamelCase__ : Dict=0.0 , UpperCamelCase__ : Union[str, Any]=0.02 , UpperCamelCase__ : str=1e-12 , UpperCamelCase__ : Dict=2_2_4 , UpperCamelCase__ : List[Any]=1_6 , UpperCamelCase__ : Optional[Any]=3 , UpperCamelCase__ : List[str]=False , UpperCamelCase__ : List[Any]=False , UpperCamelCase__ : str=False , UpperCamelCase__ : List[str]=False , UpperCamelCase__ : int=0.1 , UpperCamelCase__ : List[Any]=0.1 , UpperCamelCase__ : Optional[Any]=True , UpperCamelCase__ : List[Any]=[3, 5, 7, 1_1] , UpperCamelCase__ : List[str]=[1, 2, 3, 6] , UpperCamelCase__ : Union[str, Any]=True , UpperCamelCase__ : Any=0.4 , UpperCamelCase__ : Union[str, Any]=2_5_6 , UpperCamelCase__ : List[Any]=1 , UpperCamelCase__ : str=False , UpperCamelCase__ : Optional[int]=2_5_5 , **UpperCamelCase__ : Dict , )-> List[str]:
'''simple docstring'''
super().__init__(**UpperCamelCase__)
__lowerCAmelCase: List[str] = hidden_size
__lowerCAmelCase: Union[str, Any] = num_hidden_layers
__lowerCAmelCase: Dict = num_attention_heads
__lowerCAmelCase: Optional[int] = intermediate_size
__lowerCAmelCase: int = hidden_act
__lowerCAmelCase: Union[str, Any] = hidden_dropout_prob
__lowerCAmelCase: Any = attention_probs_dropout_prob
__lowerCAmelCase: Dict = initializer_range
__lowerCAmelCase: Any = layer_norm_eps
__lowerCAmelCase: Union[str, Any] = image_size
__lowerCAmelCase: Tuple = patch_size
__lowerCAmelCase: List[str] = num_channels
__lowerCAmelCase: Optional[Any] = use_mask_token
__lowerCAmelCase: str = use_absolute_position_embeddings
__lowerCAmelCase: Optional[int] = use_relative_position_bias
__lowerCAmelCase: str = use_shared_relative_position_bias
__lowerCAmelCase: Union[str, Any] = layer_scale_init_value
__lowerCAmelCase: Any = drop_path_rate
__lowerCAmelCase: Dict = use_mean_pooling
# decode head attributes (semantic segmentation)
__lowerCAmelCase: int = out_indices
__lowerCAmelCase: Any = pool_scales
# auxiliary head attributes (semantic segmentation)
__lowerCAmelCase: List[Any] = use_auxiliary_head
__lowerCAmelCase: int = auxiliary_loss_weight
__lowerCAmelCase: Dict = auxiliary_channels
__lowerCAmelCase: Any = auxiliary_num_convs
__lowerCAmelCase: Any = auxiliary_concat_input
__lowerCAmelCase: Any = semantic_loss_ignore_index
class snake_case ( __snake_case ):
SCREAMING_SNAKE_CASE_ : Dict = version.parse("""1.11""" )
@property
def lowercase_ ( self : Optional[Any])-> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
])
@property
def lowercase_ ( self : Any)-> float:
'''simple docstring'''
return 1e-4
| 108 | 0 |
'''simple docstring'''
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class UpperCAmelCase_ ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
lowerCamelCase : List[Any] = [('''size''', ctypes.c_int), ('''visible''', ctypes.c_byte)]
def a_ ( ):
if os.name == "nt":
lowerCAmelCase = CursorInfo()
lowerCAmelCase = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(lowerCamelCase , ctypes.byref(lowerCamelCase ) )
lowerCAmelCase = False
ctypes.windll.kernelaa.SetConsoleCursorInfo(lowerCamelCase , ctypes.byref(lowerCamelCase ) )
elif os.name == "posix":
sys.stdout.write('\033[?25l' )
sys.stdout.flush()
def a_ ( ):
if os.name == "nt":
lowerCAmelCase = CursorInfo()
lowerCAmelCase = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(lowerCamelCase , ctypes.byref(lowerCamelCase ) )
lowerCAmelCase = True
ctypes.windll.kernelaa.SetConsoleCursorInfo(lowerCamelCase , ctypes.byref(lowerCamelCase ) )
elif os.name == "posix":
sys.stdout.write('\033[?25h' )
sys.stdout.flush()
@contextmanager
def a_ ( ):
try:
hide_cursor()
yield
finally:
show_cursor()
| 4 |
import datasets
from .evaluate import evaluate
_A : Optional[int] = '\\n@inproceedings{Rajpurkar2016SQuAD10,\n title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n booktitle={EMNLP},\n year={2016}\n}\n'
_A : int = '\nThis metric wrap the official scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by\ncrowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,\nfrom the corresponding reading passage, or the question might be unanswerable.\n'
_A : int = '\nComputes SQuAD scores (F1 and EM).\nArgs:\n predictions: List of question-answers dictionaries with the following key-values:\n - \'id\': id of the question-answer pair as given in the references (see below)\n - \'prediction_text\': the text of the answer\n references: List of question-answers dictionaries with the following key-values:\n - \'id\': id of the question-answer pair (see above),\n - \'answers\': a Dict in the SQuAD dataset format\n {\n \'text\': list of possible texts for the answer, as a list of strings\n \'answer_start\': list of start positions for the answer, as a list of ints\n }\n Note that answer_start values are not taken into account to compute the metric.\nReturns:\n \'exact_match\': Exact match (the normalized answer exactly match the gold answer)\n \'f1\': The F-score of predicted tokens versus the gold answer\nExamples:\n\n >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\'}]\n >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]\n >>> squad_metric = datasets.load_metric("squad")\n >>> results = squad_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'exact_match\': 100.0, \'f1\': 100.0}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,_KWARGS_DESCRIPTION )
class __SCREAMING_SNAKE_CASE ( datasets.Metric ):
def __lowerCamelCase ( self : Optional[Any] ) ->List[Any]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': {'''id''': datasets.Value('''string''' ), '''prediction_text''': datasets.Value('''string''' )},
'''references''': {
'''id''': datasets.Value('''string''' ),
'''answers''': datasets.features.Sequence(
{
'''text''': datasets.Value('''string''' ),
'''answer_start''': datasets.Value('''int32''' ),
} ),
},
} ) , codebase_urls=['''https://rajpurkar.github.io/SQuAD-explorer/'''] , reference_urls=['''https://rajpurkar.github.io/SQuAD-explorer/'''] , )
def __lowerCamelCase ( self : Dict , A : Tuple , A : Tuple ) ->int:
lowerCamelCase__ : Optional[Any] = {prediction['''id''']: prediction['''prediction_text'''] for prediction in predictions}
lowerCamelCase__ : int = [
{
'''paragraphs''': [
{
'''qas''': [
{
'''answers''': [{'''text''': answer_text} for answer_text in ref['''answers''']['''text''']],
'''id''': ref['''id'''],
}
for ref in references
]
}
]
}
]
lowerCamelCase__ : Tuple = evaluate(dataset=A , predictions=A )
return score
| 142 | 0 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def snake_case_ ( snake_case = 3 ) -> qiskit.result.counts.Counts:
if isinstance(snake_case , snake_case ):
raise TypeError('number of qubits must be a integer.' )
if number_of_qubits <= 0:
raise ValueError('number of qubits must be > 0.' )
if math.floor(snake_case ) != number_of_qubits:
raise ValueError('number of qubits must be exact integer.' )
if number_of_qubits > 10:
raise ValueError('number of qubits too large to simulate(>10).' )
lowercase__: str = QuantumRegister(snake_case , 'qr' )
lowercase__: str = ClassicalRegister(snake_case , 'cr' )
lowercase__: List[Any] = QuantumCircuit(snake_case , snake_case )
lowercase__: int = number_of_qubits
for i in range(snake_case ):
quantum_circuit.h(number_of_qubits - i - 1 )
counter -= 1
for j in range(snake_case ):
quantum_circuit.cp(np.pi / 2 ** (counter - j) , snake_case , snake_case )
for k in range(number_of_qubits // 2 ):
quantum_circuit.swap(snake_case , number_of_qubits - k - 1 )
# measure all the qubits
quantum_circuit.measure(snake_case , snake_case )
# simulate with 10000 shots
lowercase__: str = Aer.get_backend('qasm_simulator' )
lowercase__: Union[str, Any] = execute(snake_case , snake_case , shots=1_00_00 )
return job.result().get_counts(snake_case )
if __name__ == "__main__":
print(
F'''Total count for quantum fourier transform state is: \
{quantum_fourier_transform(3)}'''
)
| 366 |
from collections import deque
from math import floor
from random import random
from time import time
class __a :
def __init__( self ) -> Dict:
'''simple docstring'''
lowercase__: Dict = {}
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__=1 ) -> Optional[int]:
'''simple docstring'''
if self.graph.get(lowerCAmelCase__ ):
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
lowercase__: int = [[w, v]]
if not self.graph.get(lowerCAmelCase__ ):
lowercase__: Union[str, Any] = []
def SCREAMING_SNAKE_CASE__ ( self ) -> Any:
'''simple docstring'''
return list(self.graph )
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ , lowerCAmelCase__ ) -> Optional[int]:
'''simple docstring'''
if self.graph.get(lowerCAmelCase__ ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(lowerCAmelCase__ )
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__=-2 , lowerCAmelCase__=-1 ) -> Union[str, Any]:
'''simple docstring'''
if s == d:
return []
lowercase__: Tuple = []
lowercase__: Tuple = []
if s == -2:
lowercase__: Any = list(self.graph )[0]
stack.append(lowerCAmelCase__ )
visited.append(lowerCAmelCase__ )
lowercase__: Dict = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
lowercase__: Dict = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(lowerCAmelCase__ )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
lowercase__: Union[str, Any] = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(lowerCAmelCase__ ) != 0:
lowercase__: Optional[int] = stack[len(lowerCAmelCase__ ) - 1]
else:
lowercase__: Dict = ss
# check if se have reached the starting point
if len(lowerCAmelCase__ ) == 0:
return visited
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__=-1 ) -> List[str]:
'''simple docstring'''
if c == -1:
lowercase__: int = floor(random() * 10_000 ) + 10
for i in range(lowerCAmelCase__ ):
# every vertex has max 100 edges
for _ in range(floor(random() * 102 ) + 1 ):
lowercase__: str = floor(random() * c ) + 1
if n != i:
self.add_pair(lowerCAmelCase__ , lowerCAmelCase__ , 1 )
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__=-2 ) -> Dict:
'''simple docstring'''
lowercase__: int = deque()
lowercase__: Dict = []
if s == -2:
lowercase__: Optional[int] = list(self.graph )[0]
d.append(lowerCAmelCase__ )
visited.append(lowerCAmelCase__ )
while d:
lowercase__: str = d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ ) -> Dict:
'''simple docstring'''
lowercase__: Tuple = 0
for x in self.graph:
for y in self.graph[x]:
if y[1] == u:
count += 1
return count
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ ) -> Union[str, Any]:
'''simple docstring'''
return len(self.graph[u] )
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__=-2 ) -> Optional[Any]:
'''simple docstring'''
lowercase__: Tuple = []
lowercase__: str = []
if s == -2:
lowercase__: Dict = list(self.graph )[0]
stack.append(lowerCAmelCase__ )
visited.append(lowerCAmelCase__ )
lowercase__: List[Any] = s
lowercase__: Any = []
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
lowercase__: List[str] = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
lowercase__: Dict = node[1]
break
# check if all the children are visited
if s == ss:
sorted_nodes.append(stack.pop() )
if len(lowerCAmelCase__ ) != 0:
lowercase__: int = stack[len(lowerCAmelCase__ ) - 1]
else:
lowercase__: Optional[int] = ss
# check if se have reached the starting point
if len(lowerCAmelCase__ ) == 0:
return sorted_nodes
def SCREAMING_SNAKE_CASE__ ( self ) -> Any:
'''simple docstring'''
lowercase__: List[Any] = []
lowercase__: int = []
lowercase__: List[str] = list(self.graph )[0]
stack.append(lowerCAmelCase__ )
visited.append(lowerCAmelCase__ )
lowercase__: Dict = -2
lowercase__: Union[str, Any] = []
lowercase__: List[str] = s
lowercase__: Dict = False
lowercase__: Union[str, Any] = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
lowercase__: Any = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
lowercase__: List[Any] = len(lowerCAmelCase__ ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
lowercase__: Union[str, Any] = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
lowercase__: Any = True
if len(lowerCAmelCase__ ) != 0:
lowercase__: Optional[Any] = stack[len(lowerCAmelCase__ ) - 1]
else:
lowercase__: Union[str, Any] = False
indirect_parents.append(lowerCAmelCase__ )
lowercase__: int = s
lowercase__: str = ss
# check if se have reached the starting point
if len(lowerCAmelCase__ ) == 0:
return list(lowerCAmelCase__ )
def SCREAMING_SNAKE_CASE__ ( self ) -> Optional[Any]:
'''simple docstring'''
lowercase__: Any = []
lowercase__: int = []
lowercase__: Dict = list(self.graph )[0]
stack.append(lowerCAmelCase__ )
visited.append(lowerCAmelCase__ )
lowercase__: Optional[int] = -2
lowercase__: List[Any] = []
lowercase__: List[str] = s
lowercase__: List[Any] = False
lowercase__: str = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
lowercase__: Dict = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
lowercase__: Any = len(lowerCAmelCase__ ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
lowercase__: List[Any] = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
lowercase__: Optional[Any] = True
if len(lowerCAmelCase__ ) != 0:
lowercase__: Any = stack[len(lowerCAmelCase__ ) - 1]
else:
lowercase__: Optional[Any] = False
indirect_parents.append(lowerCAmelCase__ )
lowercase__: Dict = s
lowercase__: Dict = ss
# check if se have reached the starting point
if len(lowerCAmelCase__ ) == 0:
return False
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__=-2 , lowerCAmelCase__=-1 ) -> Dict:
'''simple docstring'''
lowercase__: Union[str, Any] = time()
self.dfs(lowerCAmelCase__ , lowerCAmelCase__ )
lowercase__: Optional[Any] = time()
return end - begin
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__=-2 ) -> List[str]:
'''simple docstring'''
lowercase__: str = time()
self.bfs(lowerCAmelCase__ )
lowercase__: List[str] = time()
return end - begin
class __a :
def __init__( self ) -> Tuple:
'''simple docstring'''
lowercase__: Dict = {}
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__=1 ) -> List[Any]:
'''simple docstring'''
# check if the u exists
if self.graph.get(lowerCAmelCase__ ):
# if there already is a edge
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
# if u does not exist
lowercase__: str = [[w, v]]
# add the other way
if self.graph.get(lowerCAmelCase__ ):
# if there already is a edge
if self.graph[v].count([w, u] ) == 0:
self.graph[v].append([w, u] )
else:
# if u does not exist
lowercase__: Union[str, Any] = [[w, u]]
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ , lowerCAmelCase__ ) -> str:
'''simple docstring'''
if self.graph.get(lowerCAmelCase__ ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(lowerCAmelCase__ )
# the other way round
if self.graph.get(lowerCAmelCase__ ):
for _ in self.graph[v]:
if _[1] == u:
self.graph[v].remove(lowerCAmelCase__ )
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__=-2 , lowerCAmelCase__=-1 ) -> List[str]:
'''simple docstring'''
if s == d:
return []
lowercase__: str = []
lowercase__: int = []
if s == -2:
lowercase__: Tuple = list(self.graph )[0]
stack.append(lowerCAmelCase__ )
visited.append(lowerCAmelCase__ )
lowercase__: int = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
lowercase__: int = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(lowerCAmelCase__ )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
lowercase__: List[Any] = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(lowerCAmelCase__ ) != 0:
lowercase__: Union[str, Any] = stack[len(lowerCAmelCase__ ) - 1]
else:
lowercase__: Optional[Any] = ss
# check if se have reached the starting point
if len(lowerCAmelCase__ ) == 0:
return visited
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__=-1 ) -> Optional[Any]:
'''simple docstring'''
if c == -1:
lowercase__: Any = floor(random() * 10_000 ) + 10
for i in range(lowerCAmelCase__ ):
# every vertex has max 100 edges
for _ in range(floor(random() * 102 ) + 1 ):
lowercase__: Optional[Any] = floor(random() * c ) + 1
if n != i:
self.add_pair(lowerCAmelCase__ , lowerCAmelCase__ , 1 )
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__=-2 ) -> List[Any]:
'''simple docstring'''
lowercase__: str = deque()
lowercase__: List[Any] = []
if s == -2:
lowercase__: str = list(self.graph )[0]
d.append(lowerCAmelCase__ )
visited.append(lowerCAmelCase__ )
while d:
lowercase__: Union[str, Any] = d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ ) -> List[Any]:
'''simple docstring'''
return len(self.graph[u] )
def SCREAMING_SNAKE_CASE__ ( self ) -> str:
'''simple docstring'''
lowercase__: str = []
lowercase__: Dict = []
lowercase__: Optional[int] = list(self.graph )[0]
stack.append(lowerCAmelCase__ )
visited.append(lowerCAmelCase__ )
lowercase__: Dict = -2
lowercase__: Dict = []
lowercase__: List[Any] = s
lowercase__: Union[str, Any] = False
lowercase__: List[str] = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
lowercase__: Optional[int] = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
lowercase__: Any = len(lowerCAmelCase__ ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
lowercase__: List[str] = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
lowercase__: str = True
if len(lowerCAmelCase__ ) != 0:
lowercase__: Dict = stack[len(lowerCAmelCase__ ) - 1]
else:
lowercase__: int = False
indirect_parents.append(lowerCAmelCase__ )
lowercase__: Tuple = s
lowercase__: List[Any] = ss
# check if se have reached the starting point
if len(lowerCAmelCase__ ) == 0:
return list(lowerCAmelCase__ )
def SCREAMING_SNAKE_CASE__ ( self ) -> Any:
'''simple docstring'''
lowercase__: Tuple = []
lowercase__: Optional[int] = []
lowercase__: Optional[Any] = list(self.graph )[0]
stack.append(lowerCAmelCase__ )
visited.append(lowerCAmelCase__ )
lowercase__: Tuple = -2
lowercase__: Any = []
lowercase__: int = s
lowercase__: Optional[int] = False
lowercase__: List[Any] = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
lowercase__: Optional[int] = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
lowercase__: Union[str, Any] = len(lowerCAmelCase__ ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
lowercase__: Any = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
lowercase__: List[str] = True
if len(lowerCAmelCase__ ) != 0:
lowercase__: List[str] = stack[len(lowerCAmelCase__ ) - 1]
else:
lowercase__: Dict = False
indirect_parents.append(lowerCAmelCase__ )
lowercase__: Optional[Any] = s
lowercase__: Optional[int] = ss
# check if se have reached the starting point
if len(lowerCAmelCase__ ) == 0:
return False
def SCREAMING_SNAKE_CASE__ ( self ) -> List[Any]:
'''simple docstring'''
return list(self.graph )
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__=-2 , lowerCAmelCase__=-1 ) -> Union[str, Any]:
'''simple docstring'''
lowercase__: Dict = time()
self.dfs(lowerCAmelCase__ , lowerCAmelCase__ )
lowercase__: List[Any] = time()
return end - begin
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__=-2 ) -> List[Any]:
'''simple docstring'''
lowercase__: str = time()
self.bfs(lowerCAmelCase__ )
lowercase__: List[str] = time()
return end - begin
| 288 | 0 |
'''simple docstring'''
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''facebook/detr-resnet-50''': '''https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json''',
# See all DETR models at https://huggingface.co/models?filter=detr
}
class lowercase_ (lowerCamelCase__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = 'detr'
SCREAMING_SNAKE_CASE : List[Any] = ['past_key_values']
SCREAMING_SNAKE_CASE : Optional[int] = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self : Tuple ,lowercase__ : int=True ,lowercase__ : Union[str, Any]=None ,lowercase__ : Optional[Any]=3 ,lowercase__ : Optional[Any]=1_0_0 ,lowercase__ : Optional[Any]=6 ,lowercase__ : Optional[int]=2_0_4_8 ,lowercase__ : List[str]=8 ,lowercase__ : Optional[int]=6 ,lowercase__ : Dict=2_0_4_8 ,lowercase__ : List[Any]=8 ,lowercase__ : Optional[int]=0.0 ,lowercase__ : str=0.0 ,lowercase__ : List[str]=True ,lowercase__ : Union[str, Any]="relu" ,lowercase__ : List[str]=2_5_6 ,lowercase__ : Optional[int]=0.1 ,lowercase__ : Tuple=0.0 ,lowercase__ : str=0.0 ,lowercase__ : Dict=0.0_2 ,lowercase__ : Tuple=1.0 ,lowercase__ : List[str]=False ,lowercase__ : Optional[int]="sine" ,lowercase__ : Any="resnet50" ,lowercase__ : str=True ,lowercase__ : Any=False ,lowercase__ : Tuple=1 ,lowercase__ : Optional[int]=5 ,lowercase__ : Any=2 ,lowercase__ : Union[str, Any]=1 ,lowercase__ : Dict=1 ,lowercase__ : List[str]=5 ,lowercase__ : int=2 ,lowercase__ : Union[str, Any]=0.1 ,**lowercase__ : Tuple ,):
if backbone_config is not None and use_timm_backbone:
raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.''' )
if not use_timm_backbone:
if backbone_config is None:
logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''' )
__lowercase = CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''] )
elif isinstance(lowercase__ ,lowercase__ ):
__lowercase = backbone_config.get('''model_type''' )
__lowercase = CONFIG_MAPPING[backbone_model_type]
__lowercase = config_class.from_dict(lowercase__ )
# set timm attributes to None
__lowercase , __lowercase , __lowercase = None, None, None
__lowercase = use_timm_backbone
__lowercase = backbone_config
__lowercase = num_channels
__lowercase = num_queries
__lowercase = d_model
__lowercase = encoder_ffn_dim
__lowercase = encoder_layers
__lowercase = encoder_attention_heads
__lowercase = decoder_ffn_dim
__lowercase = decoder_layers
__lowercase = decoder_attention_heads
__lowercase = dropout
__lowercase = attention_dropout
__lowercase = activation_dropout
__lowercase = activation_function
__lowercase = init_std
__lowercase = init_xavier_std
__lowercase = encoder_layerdrop
__lowercase = decoder_layerdrop
__lowercase = encoder_layers
__lowercase = auxiliary_loss
__lowercase = position_embedding_type
__lowercase = backbone
__lowercase = use_pretrained_backbone
__lowercase = dilation
# Hungarian matcher
__lowercase = class_cost
__lowercase = bbox_cost
__lowercase = giou_cost
# Loss coefficients
__lowercase = mask_loss_coefficient
__lowercase = dice_loss_coefficient
__lowercase = bbox_loss_coefficient
__lowercase = giou_loss_coefficient
__lowercase = eos_coefficient
super().__init__(is_encoder_decoder=lowercase__ ,**lowercase__ )
@property
def SCREAMING_SNAKE_CASE ( self : Tuple ):
return self.encoder_attention_heads
@property
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
return self.d_model
@classmethod
def SCREAMING_SNAKE_CASE ( cls : List[str] ,lowercase__ : PretrainedConfig ,**lowercase__ : int ):
return cls(backbone_config=lowercase__ ,**lowercase__ )
def SCREAMING_SNAKE_CASE ( self : Any ):
__lowercase = copy.deepcopy(self.__dict__ )
if output["backbone_config"] is not None:
__lowercase = self.backbone_config.to_dict()
__lowercase = self.__class__.model_type
return output
class lowercase_ (lowerCamelCase__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = version.parse('1.11' )
@property
def SCREAMING_SNAKE_CASE ( self : List[str] ):
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
('''pixel_mask''', {0: '''batch'''}),
] )
@property
def SCREAMING_SNAKE_CASE ( self : Optional[int] ):
return 1e-5
@property
def SCREAMING_SNAKE_CASE ( self : Any ):
return 1_2
| 104 |
'''simple docstring'''
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, 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 ViTMAEForPreTraining, ViTMAEModel
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 lowercase_ :
"""simple docstring"""
def __init__( self : str ,lowercase__ : Tuple ,lowercase__ : Dict=1_3 ,lowercase__ : List[str]=3_0 ,lowercase__ : Tuple=2 ,lowercase__ : Optional[int]=3 ,lowercase__ : List[str]=True ,lowercase__ : Tuple=True ,lowercase__ : int=3_2 ,lowercase__ : List[str]=5 ,lowercase__ : Tuple=4 ,lowercase__ : Any=3_7 ,lowercase__ : Any="gelu" ,lowercase__ : Union[str, Any]=0.1 ,lowercase__ : Optional[int]=0.1 ,lowercase__ : str=1_0 ,lowercase__ : Optional[int]=0.0_2 ,lowercase__ : Union[str, Any]=3 ,lowercase__ : Optional[int]=0.6 ,lowercase__ : List[Any]=None ,):
__lowercase = parent
__lowercase = batch_size
__lowercase = image_size
__lowercase = patch_size
__lowercase = num_channels
__lowercase = is_training
__lowercase = use_labels
__lowercase = hidden_size
__lowercase = num_hidden_layers
__lowercase = num_attention_heads
__lowercase = intermediate_size
__lowercase = hidden_act
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = type_sequence_label_size
__lowercase = initializer_range
__lowercase = mask_ratio
__lowercase = scope
# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
# (we add 1 for the [CLS] token)
__lowercase = (image_size // patch_size) ** 2
__lowercase = int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )
def SCREAMING_SNAKE_CASE ( self : Optional[int] ):
__lowercase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__lowercase = None
if self.use_labels:
__lowercase = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
__lowercase = self.get_config()
return config, pixel_values, labels
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
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 ,hidden_act=self.hidden_act ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,is_decoder=lowercase__ ,initializer_range=self.initializer_range ,mask_ratio=self.mask_ratio ,)
def SCREAMING_SNAKE_CASE ( self : List[Any] ,lowercase__ : str ,lowercase__ : Optional[int] ,lowercase__ : List[str] ):
__lowercase = ViTMAEModel(config=lowercase__ )
model.to(lowercase__ )
model.eval()
__lowercase = model(lowercase__ )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE ( self : Any ,lowercase__ : List[Any] ,lowercase__ : int ,lowercase__ : Optional[Any] ):
__lowercase = ViTMAEForPreTraining(lowercase__ )
model.to(lowercase__ )
model.eval()
__lowercase = model(lowercase__ )
__lowercase = (self.image_size // self.patch_size) ** 2
__lowercase = self.patch_size**2 * self.num_channels
self.parent.assertEqual(result.logits.shape ,(self.batch_size, num_patches, expected_num_channels) )
# test greyscale images
__lowercase = 1
__lowercase = ViTMAEForPreTraining(lowercase__ )
model.to(lowercase__ )
model.eval()
__lowercase = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
__lowercase = model(lowercase__ )
__lowercase = self.patch_size**2
self.parent.assertEqual(result.logits.shape ,(self.batch_size, num_patches, expected_num_channels) )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
__lowercase = self.prepare_config_and_inputs()
__lowercase , __lowercase , __lowercase = config_and_inputs
__lowercase = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class lowercase_ (lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
SCREAMING_SNAKE_CASE : Dict = {'feature-extraction': ViTMAEModel} if is_torch_available() else {}
SCREAMING_SNAKE_CASE : Any = False
SCREAMING_SNAKE_CASE : Optional[int] = False
SCREAMING_SNAKE_CASE : List[str] = False
SCREAMING_SNAKE_CASE : Union[str, Any] = False
def SCREAMING_SNAKE_CASE ( self : int ):
__lowercase = ViTMAEModelTester(self )
__lowercase = ConfigTester(self ,config_class=lowercase__ ,has_text_modality=lowercase__ ,hidden_size=3_7 )
def SCREAMING_SNAKE_CASE ( self : Optional[int] ):
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViTMAE does not use inputs_embeds''' )
def SCREAMING_SNAKE_CASE ( self : Any ):
pass
def SCREAMING_SNAKE_CASE ( self : Tuple ):
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = model_class(lowercase__ )
self.assertIsInstance(model.get_input_embeddings() ,(nn.Module) )
__lowercase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowercase__ ,nn.Linear ) )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = model_class(lowercase__ )
__lowercase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowercase = [*signature.parameters.keys()]
__lowercase = ['''pixel_values''']
self.assertListEqual(arg_names[:1] ,lowercase__ )
def SCREAMING_SNAKE_CASE ( self : List[Any] ):
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase__ )
def SCREAMING_SNAKE_CASE ( self : Any ):
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*lowercase__ )
def SCREAMING_SNAKE_CASE ( self : Any ,lowercase__ : Optional[int] ,lowercase__ : List[str] ,lowercase__ : Optional[Any] ):
# make masks reproducible
np.random.seed(2 )
__lowercase = int((pt_model.config.image_size // pt_model.config.patch_size) ** 2 )
__lowercase = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
__lowercase = torch.from_numpy(lowercase__ )
# Add `noise` argument.
# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
__lowercase = pt_noise
super().check_pt_tf_models(lowercase__ ,lowercase__ ,lowercase__ )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = model_class(lowercase__ )
model.to(lowercase__ )
model.eval()
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
__lowercase = model(**self._prepare_for_class(lowercase__ ,lowercase__ ) )
__lowercase = outputs[0].cpu().numpy()
__lowercase = 0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(lowercase__ )
__lowercase = model_class.from_pretrained(lowercase__ )
model.to(lowercase__ )
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
__lowercase = model(**self._prepare_for_class(lowercase__ ,lowercase__ ) )
# Make sure we don't have nans
__lowercase = after_outputs[0].cpu().numpy()
__lowercase = 0
__lowercase = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(lowercase__ ,1e-5 )
@unittest.skip(
reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
to get deterministic results.''' )
def SCREAMING_SNAKE_CASE ( self : Tuple ):
pass
@unittest.skip(
reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
to get deterministic results.''' )
def SCREAMING_SNAKE_CASE ( self : int ):
pass
@unittest.skip(
reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
to get deterministic results.''' )
def SCREAMING_SNAKE_CASE ( self : Any ):
pass
@unittest.skip(reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load''' )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def SCREAMING_SNAKE_CASE ( self : Any ):
pass
@slow
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowercase = ViTMAEModel.from_pretrained(lowercase__ )
self.assertIsNotNone(lowercase__ )
def _A ( ):
"""simple docstring"""
__lowercase = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class lowercase_ (unittest.TestCase ):
"""simple docstring"""
@cached_property
def SCREAMING_SNAKE_CASE ( self : str ):
return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base''' ) if is_vision_available() else None
@slow
def SCREAMING_SNAKE_CASE ( self : str ):
# make random mask reproducible across the PT and TF model
np.random.seed(2 )
__lowercase = ViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base''' ).to(lowercase__ )
__lowercase = self.default_image_processor
__lowercase = prepare_img()
__lowercase = image_processor(images=lowercase__ ,return_tensors='''pt''' ).to(lowercase__ )
# 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)
__lowercase = ViTMAEConfig()
__lowercase = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
__lowercase = np.random.uniform(size=(1, num_patches) )
# forward pass
with torch.no_grad():
__lowercase = model(**lowercase__ ,noise=torch.from_numpy(lowercase__ ).to(device=lowercase__ ) )
# verify the logits
__lowercase = torch.Size((1, 1_9_6, 7_6_8) )
self.assertEqual(outputs.logits.shape ,lowercase__ )
__lowercase = torch.tensor(
[[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]] )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] ,expected_slice.to(lowercase__ ) ,atol=1e-4 ) )
| 104 | 1 |
import os
import tempfile
import unittest
import uuid
from pathlib import Path
from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available
if is_torch_available():
import torch
if is_soundfile_availble():
import soundfile as sf
if is_vision_available():
from PIL import Image
def SCREAMING_SNAKE_CASE ( __UpperCamelCase="") -> str:
a = tempfile.mkdtemp()
return os.path.join(__UpperCamelCase , str(uuid.uuida()) + suffix)
@require_soundfile
@require_torch
class a__ ( unittest.TestCase ):
def lowerCAmelCase_ ( self ) -> Any:
'''simple docstring'''
a = torch.rand(12 , dtype=torch.floataa ) - 0.5
a = AgentAudio(A )
a = str(agent_type.to_string() )
# Ensure that the tensor and the agent_type's tensor are the same
self.assertTrue(torch.allclose(A , agent_type.to_raw() , atol=1e-4 ) )
del agent_type
# Ensure the path remains even after the object deletion
self.assertTrue(os.path.exists(A ) )
# Ensure that the file contains the same value as the original tensor
a , a = sf.read(A )
self.assertTrue(torch.allclose(A , torch.tensor(A ) , atol=1e-4 ) )
def lowerCAmelCase_ ( self ) -> str:
'''simple docstring'''
a = torch.rand(12 , dtype=torch.floataa ) - 0.5
a = get_new_path(suffix=".wav" )
sf.write(A , A , 16000 )
a = AgentAudio(A )
self.assertTrue(torch.allclose(A , agent_type.to_raw() , atol=1e-4 ) )
self.assertEqual(agent_type.to_string() , A )
@require_vision
@require_torch
class a__ ( unittest.TestCase ):
def lowerCAmelCase_ ( self ) -> Tuple:
'''simple docstring'''
a = torch.randint(0 , 256 , (64, 64, 3) )
a = AgentImage(A )
a = str(agent_type.to_string() )
# Ensure that the tensor and the agent_type's tensor are the same
self.assertTrue(torch.allclose(A , agent_type._tensor , atol=1e-4 ) )
self.assertIsInstance(agent_type.to_raw() , Image.Image )
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(A ) )
def lowerCAmelCase_ ( self ) -> Union[str, Any]:
'''simple docstring'''
a = Path(get_tests_dir("fixtures/tests_samples/COCO" ) ) / "000000039769.png"
a = Image.open(A )
a = AgentImage(A )
self.assertTrue(path.samefile(agent_type.to_string() ) )
self.assertTrue(image == agent_type.to_raw() )
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(A ) )
def lowerCAmelCase_ ( self ) -> Dict:
'''simple docstring'''
a = Path(get_tests_dir("fixtures/tests_samples/COCO" ) ) / "000000039769.png"
a = Image.open(A )
a = AgentImage(A )
self.assertFalse(path.samefile(agent_type.to_string() ) )
self.assertTrue(image == agent_type.to_raw() )
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(A ) )
class a__ ( unittest.TestCase ):
def lowerCAmelCase_ ( self ) -> Tuple:
'''simple docstring'''
a = "Hey!"
a = AgentText(A )
self.assertEqual(A , agent_type.to_string() )
self.assertEqual(A , agent_type.to_raw() )
self.assertEqual(A , A )
| 180 |
lowercase__ : List[Any] = {
"a": "AAAAA",
"b": "AAAAB",
"c": "AAABA",
"d": "AAABB",
"e": "AABAA",
"f": "AABAB",
"g": "AABBA",
"h": "AABBB",
"i": "ABAAA",
"j": "BBBAA",
"k": "ABAAB",
"l": "ABABA",
"m": "ABABB",
"n": "ABBAA",
"o": "ABBAB",
"p": "ABBBA",
"q": "ABBBB",
"r": "BAAAA",
"s": "BAAAB",
"t": "BAABA",
"u": "BAABB",
"v": "BBBAB",
"w": "BABAA",
"x": "BABAB",
"y": "BABBA",
"z": "BABBB",
" ": " ",
}
lowercase__ : str = {value: key for key, value in encode_dict.items()}
def SCREAMING_SNAKE_CASE ( __UpperCamelCase) -> str:
a = ""
for letter in word.lower():
if letter.isalpha() or letter == " ":
encoded += encode_dict[letter]
else:
raise Exception("encode() accepts only letters of the alphabet and spaces")
return encoded
def SCREAMING_SNAKE_CASE ( __UpperCamelCase) -> str:
if set(__UpperCamelCase) - {"A", "B", " "} != set():
raise Exception("decode() accepts only 'A', 'B' and spaces")
a = ""
for word in coded.split():
while len(__UpperCamelCase) != 0:
decoded += decode_dict[word[:5]]
a = word[5:]
decoded += " "
return decoded.strip()
if __name__ == "__main__":
from doctest import testmod
testmod()
| 180 | 1 |
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
a_ = logging.get_logger(__name__)
class _lowercase ( snake_case_ ):
def __init__( self : Union[str, Any] , *snake_case : Tuple , **snake_case : List[str] ) -> None:
"""simple docstring"""
warnings.warn(
'The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
' use DeiTImageProcessor instead.' , snake_case , )
super().__init__(*snake_case , **snake_case )
| 175 | def __lowercase ( lowerCamelCase : str , lowerCamelCase : str ):
def get_matched_characters(lowerCamelCase : str , lowerCamelCase : str ) -> str:
UpperCamelCase_ : Tuple = []
UpperCamelCase_ : List[Any] = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumerate(_stra ):
UpperCamelCase_ : int = int(max(0 , i - limit ) )
UpperCamelCase_ : Dict = int(min(i + limit + 1 , len(_stra ) ) )
if l in _stra[left:right]:
matched.append(lowerCamelCase )
UpperCamelCase_ : Dict = F"{_stra[0:_stra.index(lowerCamelCase )]} {_stra[_stra.index(lowerCamelCase ) + 1:]}"
return "".join(lowerCamelCase )
# matching characters
UpperCamelCase_ : str = get_matched_characters(lowerCamelCase , lowerCamelCase )
UpperCamelCase_ : str = get_matched_characters(lowerCamelCase , lowerCamelCase )
UpperCamelCase_ : Union[str, Any] = len(lowerCamelCase )
# transposition
UpperCamelCase_ : int = (
len([(ca, ca) for ca, ca in zip(lowerCamelCase , lowerCamelCase ) if ca != ca] ) // 2
)
if not match_count:
UpperCamelCase_ : Union[str, Any] = 0.0
else:
UpperCamelCase_ : str = (
1
/ 3
* (
match_count / len(lowerCamelCase )
+ match_count / len(lowerCamelCase )
+ (match_count - transpositions) / match_count
)
)
# common prefix up to 4 characters
UpperCamelCase_ : Dict = 0
for ca, ca in zip(stra[:4] , stra[:4] ):
if ca == ca:
prefix_len += 1
else:
break
return jaro + 0.1 * prefix_len * (1 - jaro)
if __name__ == "__main__":
import doctest
doctest.testmod()
print(jaro_winkler('hello', 'world'))
| 175 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFDebertaVaForMaskedLM,
TFDebertaVaForQuestionAnswering,
TFDebertaVaForSequenceClassification,
TFDebertaVaForTokenClassification,
TFDebertaVaModel,
)
class A_ :
def __init__( self: Dict ,__lowerCAmelCase: Optional[Any] ,__lowerCAmelCase: Tuple=13 ,__lowerCAmelCase: List[Any]=7 ,__lowerCAmelCase: Optional[Any]=True ,__lowerCAmelCase: int=True ,__lowerCAmelCase: Union[str, Any]=True ,__lowerCAmelCase: Dict=True ,__lowerCAmelCase: Optional[int]=99 ,__lowerCAmelCase: Union[str, Any]=32 ,__lowerCAmelCase: Tuple=2 ,__lowerCAmelCase: Any=4 ,__lowerCAmelCase: List[Any]=37 ,__lowerCAmelCase: Optional[Any]="gelu" ,__lowerCAmelCase: Union[str, Any]=0.1 ,__lowerCAmelCase: str=0.1 ,__lowerCAmelCase: Dict=512 ,__lowerCAmelCase: List[Any]=16 ,__lowerCAmelCase: Union[str, Any]=2 ,__lowerCAmelCase: int=0.02 ,__lowerCAmelCase: Optional[int]=False ,__lowerCAmelCase: Any=True ,__lowerCAmelCase: List[str]="None" ,__lowerCAmelCase: Optional[int]=3 ,__lowerCAmelCase: Optional[int]=4 ,__lowerCAmelCase: Dict=None ,):
'''simple docstring'''
_lowerCamelCase : Dict = parent
_lowerCamelCase : int = batch_size
_lowerCamelCase : Union[str, Any] = seq_length
_lowerCamelCase : List[str] = is_training
_lowerCamelCase : List[str] = use_input_mask
_lowerCamelCase : Dict = use_token_type_ids
_lowerCamelCase : Optional[int] = use_labels
_lowerCamelCase : List[Any] = vocab_size
_lowerCamelCase : List[str] = hidden_size
_lowerCamelCase : Any = num_hidden_layers
_lowerCamelCase : Optional[Any] = num_attention_heads
_lowerCamelCase : str = intermediate_size
_lowerCamelCase : int = hidden_act
_lowerCamelCase : Any = hidden_dropout_prob
_lowerCamelCase : Any = attention_probs_dropout_prob
_lowerCamelCase : Union[str, Any] = max_position_embeddings
_lowerCamelCase : List[Any] = type_vocab_size
_lowerCamelCase : List[str] = type_sequence_label_size
_lowerCamelCase : Optional[Any] = initializer_range
_lowerCamelCase : List[Any] = num_labels
_lowerCamelCase : Any = num_choices
_lowerCamelCase : Optional[int] = relative_attention
_lowerCamelCase : Any = position_biased_input
_lowerCamelCase : List[Any] = pos_att_type
_lowerCamelCase : int = scope
def _lowercase ( self: Tuple ):
'''simple docstring'''
_lowerCamelCase : str = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
_lowerCamelCase : List[Any] = None
if self.use_input_mask:
_lowerCamelCase : Dict = random_attention_mask([self.batch_size, self.seq_length] )
_lowerCamelCase : Tuple = None
if self.use_token_type_ids:
_lowerCamelCase : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] ,self.type_vocab_size )
_lowerCamelCase : List[Any] = None
_lowerCamelCase : Dict = None
_lowerCamelCase : str = None
if self.use_labels:
_lowerCamelCase : Union[str, Any] = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
_lowerCamelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] ,self.num_labels )
_lowerCamelCase : Dict = DebertaVaConfig(
vocab_size=self.vocab_size ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,hidden_act=self.hidden_act ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,type_vocab_size=self.type_vocab_size ,relative_attention=self.relative_attention ,position_biased_input=self.position_biased_input ,initializer_range=self.initializer_range ,return_dict=__lowerCAmelCase ,)
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _lowercase ( self: List[str] ,__lowerCAmelCase: str ,__lowerCAmelCase: Optional[Any] ,__lowerCAmelCase: List[str] ,__lowerCAmelCase: Optional[Any] ,__lowerCAmelCase: str ,__lowerCAmelCase: Dict ,__lowerCAmelCase: Optional[int] ):
'''simple docstring'''
_lowerCamelCase : str = TFDebertaVaModel(config=__lowerCAmelCase )
_lowerCamelCase : List[Any] = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_lowerCamelCase : List[str] = [input_ids, input_mask]
_lowerCamelCase : int = model(__lowerCAmelCase )
_lowerCamelCase : str = model(__lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def _lowercase ( self: Tuple ,__lowerCAmelCase: Tuple ,__lowerCAmelCase: Dict ,__lowerCAmelCase: List[str] ,__lowerCAmelCase: int ,__lowerCAmelCase: List[str] ,__lowerCAmelCase: Tuple ,__lowerCAmelCase: Any ):
'''simple docstring'''
_lowerCamelCase : Union[str, Any] = TFDebertaVaForMaskedLM(config=__lowerCAmelCase )
_lowerCamelCase : List[Any] = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
_lowerCamelCase : List[Any] = model(__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def _lowercase ( self: List[str] ,__lowerCAmelCase: Any ,__lowerCAmelCase: Optional[Any] ,__lowerCAmelCase: List[Any] ,__lowerCAmelCase: int ,__lowerCAmelCase: Optional[int] ,__lowerCAmelCase: List[str] ,__lowerCAmelCase: Any ):
'''simple docstring'''
_lowerCamelCase : Dict = self.num_labels
_lowerCamelCase : str = TFDebertaVaForSequenceClassification(config=__lowerCAmelCase )
_lowerCamelCase : List[str] = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
_lowerCamelCase : Any = model(__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def _lowercase ( self: Dict ,__lowerCAmelCase: Optional[int] ,__lowerCAmelCase: str ,__lowerCAmelCase: Optional[Any] ,__lowerCAmelCase: str ,__lowerCAmelCase: Optional[Any] ,__lowerCAmelCase: List[Any] ,__lowerCAmelCase: int ):
'''simple docstring'''
_lowerCamelCase : Any = self.num_labels
_lowerCamelCase : List[str] = TFDebertaVaForTokenClassification(config=__lowerCAmelCase )
_lowerCamelCase : Tuple = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
_lowerCamelCase : Tuple = model(__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.num_labels) )
def _lowercase ( self: int ,__lowerCAmelCase: Dict ,__lowerCAmelCase: int ,__lowerCAmelCase: Optional[int] ,__lowerCAmelCase: List[Any] ,__lowerCAmelCase: Dict ,__lowerCAmelCase: str ,__lowerCAmelCase: Dict ):
'''simple docstring'''
_lowerCamelCase : str = TFDebertaVaForQuestionAnswering(config=__lowerCAmelCase )
_lowerCamelCase : Dict = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
_lowerCamelCase : Dict = model(__lowerCAmelCase )
self.parent.assertEqual(result.start_logits.shape ,(self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape ,(self.batch_size, self.seq_length) )
def _lowercase ( self: str ):
'''simple docstring'''
_lowerCamelCase : List[Any] = self.prepare_config_and_inputs()
(
(
_lowerCamelCase
), (
_lowerCamelCase
), (
_lowerCamelCase
), (
_lowerCamelCase
), (
_lowerCamelCase
), (
_lowerCamelCase
), (
_lowerCamelCase
),
) : List[str] = config_and_inputs
_lowerCamelCase : Union[str, Any] = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class A_ ( a_ , a_ , unittest.TestCase ):
lowerCAmelCase__ = (
(
TFDebertaVaModel,
TFDebertaVaForMaskedLM,
TFDebertaVaForQuestionAnswering,
TFDebertaVaForSequenceClassification,
TFDebertaVaForTokenClassification,
)
if is_tf_available()
else ()
)
lowerCAmelCase__ = (
{
"feature-extraction": TFDebertaVaModel,
"fill-mask": TFDebertaVaForMaskedLM,
"question-answering": TFDebertaVaForQuestionAnswering,
"text-classification": TFDebertaVaForSequenceClassification,
"token-classification": TFDebertaVaForTokenClassification,
"zero-shot": TFDebertaVaForSequenceClassification,
}
if is_tf_available()
else {}
)
lowerCAmelCase__ = False
lowerCAmelCase__ = False
def _lowercase ( self: Tuple ):
'''simple docstring'''
_lowerCamelCase : Tuple = TFDebertaVaModelTester(self )
_lowerCamelCase : Tuple = ConfigTester(self ,config_class=__lowerCAmelCase ,hidden_size=37 )
def _lowercase ( self: Tuple ):
'''simple docstring'''
self.config_tester.run_common_tests()
def _lowercase ( self: str ):
'''simple docstring'''
_lowerCamelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowerCAmelCase )
def _lowercase ( self: List[str] ):
'''simple docstring'''
_lowerCamelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__lowerCAmelCase )
def _lowercase ( self: Union[str, Any] ):
'''simple docstring'''
_lowerCamelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__lowerCAmelCase )
def _lowercase ( self: Any ):
'''simple docstring'''
_lowerCamelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__lowerCAmelCase )
def _lowercase ( self: Tuple ):
'''simple docstring'''
_lowerCamelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase )
@slow
def _lowercase ( self: Optional[Any] ):
'''simple docstring'''
_lowerCamelCase : str = TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge" )
self.assertIsNotNone(__lowerCAmelCase )
@require_tf
class A_ ( unittest.TestCase ):
@unittest.skip(reason="Model not available yet" )
def _lowercase ( self: Tuple ):
'''simple docstring'''
pass
@slow
def _lowercase ( self: List[Any] ):
'''simple docstring'''
_lowerCamelCase : Any = TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge" )
_lowerCamelCase : Optional[Any] = tf.constant([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] )
_lowerCamelCase : str = tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
_lowerCamelCase : int = model(__lowerCAmelCase ,attention_mask=__lowerCAmelCase )[0]
_lowerCamelCase : Union[str, Any] = tf.constant(
[[[0.23_56, 0.19_48, 0.03_69], [-0.10_63, 0.35_86, -0.51_52], [-0.63_99, -0.02_59, -0.25_25]]] )
tf.debugging.assert_near(output[:, 1:4, 1:4] ,__lowerCAmelCase ,atol=1e-4 ) | 350 |
"""simple docstring"""
from collections import defaultdict
def lowerCamelCase_( _lowerCamelCase ) -> int:
'''simple docstring'''
_lowerCamelCase : Optional[int] = 1
_lowerCamelCase : str = True
for v in tree[start]:
if v not in visited:
ret += dfs(_lowerCamelCase )
if ret % 2 == 0:
cuts.append(_lowerCamelCase )
return ret
def lowerCamelCase_( ) -> int:
'''simple docstring'''
dfs(1 )
if __name__ == "__main__":
_lowerCAmelCase , _lowerCAmelCase : Optional[Any] = 10, 9
_lowerCAmelCase : str = defaultdict(list)
_lowerCAmelCase : dict[int, bool] = {}
_lowerCAmelCase : list[int] = []
_lowerCAmelCase : Any = 0
_lowerCAmelCase : Any = [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
for u, v in edges:
tree[u].append(v)
tree[v].append(u)
even_tree()
print(len(cuts) - 1) | 340 | 0 |
"""simple docstring"""
def lowercase ( _snake_case : Optional[Any] ) ->List[str]:
"""simple docstring"""
__snake_case , __snake_case : List[str] = [], []
while len(_snake_case ) > 1:
__snake_case , __snake_case : Optional[Any] = min(_snake_case ), max(_snake_case )
start.append(_snake_case )
end.append(_snake_case )
collection.remove(_snake_case )
collection.remove(_snake_case )
end.reverse()
return start + collection + end
if __name__ == "__main__":
SCREAMING_SNAKE_CASE : Any = input("""Enter numbers separated by a comma:\n""").strip()
SCREAMING_SNAKE_CASE : int = [int(item) for item in user_input.split(""",""")]
print(*merge_sort(unsorted), sep=""",""")
| 102 |
"""simple docstring"""
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_SNAKE_CASE : str = """▁"""
SCREAMING_SNAKE_CASE : List[str] = get_tests_dir("""fixtures/test_sentencepiece.model""")
@require_sentencepiece
@require_tokenizers
class _UpperCAmelCase ( __snake_case, unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ =BigBirdTokenizer
lowerCamelCase__ =BigBirdTokenizerFast
lowerCamelCase__ =True
lowerCamelCase__ =True
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
super().setUp()
__snake_case : List[Any] = self.tokenizer_class(a_ , keep_accents=a_ )
tokenizer.save_pretrained(self.tmpdirname )
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : Optional[Any] = '''<s>'''
__snake_case : Optional[Any] = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(a_ ) , a_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(a_ ) , a_ )
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : Dict = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<unk>''' )
self.assertEqual(vocab_keys[1] , '''<s>''' )
self.assertEqual(vocab_keys[-1] , '''[MASK]''' )
self.assertEqual(len(a_ ) , 10_04 )
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 10_00 )
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
if not self.test_rust_tokenizer:
return
__snake_case : str = self.get_tokenizer()
__snake_case : Dict = self.get_rust_tokenizer()
__snake_case : Dict = '''I was born in 92000, and this is falsé.'''
__snake_case : int = tokenizer.tokenize(a_ )
__snake_case : str = rust_tokenizer.tokenize(a_ )
self.assertListEqual(a_ , a_ )
__snake_case : Tuple = tokenizer.encode(a_ , add_special_tokens=a_ )
__snake_case : Tuple = rust_tokenizer.encode(a_ , add_special_tokens=a_ )
self.assertListEqual(a_ , a_ )
__snake_case : Optional[Any] = self.get_rust_tokenizer()
__snake_case : Optional[int] = tokenizer.encode(a_ )
__snake_case : Dict = rust_tokenizer.encode(a_ )
self.assertListEqual(a_ , a_ )
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : List[Any] = BigBirdTokenizer(a_ , keep_accents=a_ )
__snake_case : Optional[int] = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(a_ , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(a_ ) , [2_85, 46, 10, 1_70, 3_82] , )
__snake_case : Union[str, Any] = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
a_ , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
__snake_case : Tuple = tokenizer.convert_tokens_to_ids(a_ )
self.assertListEqual(
a_ , [8, 21, 84, 55, 24, 19, 7, 0, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 0, 4] , )
__snake_case : Optional[Any] = tokenizer.convert_ids_to_tokens(a_ )
self.assertListEqual(
a_ , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
] , )
@cached_property
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
return BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base''' )
@slow
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : List[str] = '''Hello World!'''
__snake_case : List[Any] = [65, 1_85_36, 22_60, 1_01, 66]
self.assertListEqual(a_ , self.big_tokenizer.encode(a_ ) )
@slow
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : Optional[Any] = (
'''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
)
# fmt: off
__snake_case : Optional[int] = [65, 8_71, 4_19, 3_58, 9_46, 9_91, 25_21, 4_52, 3_58, 13_57, 3_87, 77_51, 35_36, 1_12, 9_85, 4_56, 1_26, 8_65, 9_38, 54_00, 57_34, 4_58, 13_68, 4_67, 7_86, 24_62, 52_46, 11_59, 6_33, 8_65, 45_19, 4_57, 5_82, 8_52, 25_57, 4_27, 9_16, 5_08, 4_05, 3_43_24, 4_97, 3_91, 4_08, 1_13_42, 12_44, 3_85, 1_00, 9_38, 9_85, 4_56, 5_74, 3_62, 1_25_97, 32_00, 31_29, 11_72, 66] # noqa: E231
# fmt: on
self.assertListEqual(a_ , self.big_tokenizer.encode(a_ ) )
@require_torch
@slow
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
import torch
from transformers import BigBirdConfig, BigBirdModel
# Build sequence
__snake_case : str = list(self.big_tokenizer.get_vocab().keys() )[:10]
__snake_case : Tuple = ''' '''.join(a_ )
__snake_case : Tuple = self.big_tokenizer.encode_plus(a_ , return_tensors='''pt''' , return_token_type_ids=a_ )
__snake_case : List[Any] = self.big_tokenizer.batch_encode_plus(
[sequence + ''' ''' + sequence] , return_tensors='''pt''' , return_token_type_ids=a_ )
__snake_case : Optional[int] = BigBirdConfig(attention_type='''original_full''' )
__snake_case : str = BigBirdModel(a_ )
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**a_ )
model(**a_ )
@slow
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : List[Any] = BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base''' )
__snake_case : Any = tokenizer.decode(tokenizer('''Paris is the [MASK].''' ).input_ids )
self.assertTrue(decoded_text == '''[CLS] Paris is the[MASK].[SEP]''' )
@slow
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : Tuple = {'''input_ids''': [[65, 3_92_86, 4_58, 3_63_35, 20_01, 4_56, 1_30_73, 1_32_66, 4_55, 1_13, 77_46, 17_41, 1_11_57, 3_91, 1_30_73, 1_32_66, 4_55, 1_13, 39_67, 3_54_12, 1_13, 49_36, 1_09, 38_70, 23_77, 1_13, 3_00_84, 4_57_20, 4_58, 1_34, 1_74_96, 1_12, 5_03, 1_16_72, 1_13, 1_18, 1_12, 56_65, 1_33_47, 3_86_87, 1_12, 14_96, 3_13_89, 1_12, 32_68, 4_72_64, 1_34, 9_62, 1_12, 1_63_77, 80_35, 2_31_30, 4_30, 1_21_69, 1_55_18, 2_85_92, 4_58, 1_46, 4_16_97, 1_09, 3_91, 1_21_69, 1_55_18, 1_66_89, 4_58, 1_46, 4_13_58, 1_09, 4_52, 7_26, 40_34, 1_11, 7_63, 3_54_12, 50_82, 3_88, 19_03, 1_11, 90_51, 3_91, 28_70, 4_89_18, 19_00, 11_23, 5_50, 9_98, 1_12, 95_86, 1_59_85, 4_55, 3_91, 4_10, 2_29_55, 3_76_36, 1_14, 66], [65, 4_48, 1_74_96, 4_19, 36_63, 3_85, 7_63, 1_13, 2_75_33, 28_70, 32_83, 1_30_43, 16_39, 2_47_13, 5_23, 6_56, 2_40_13, 1_85_50, 25_21, 5_17, 2_70_14, 2_12_44, 4_20, 12_12, 14_65, 3_91, 9_27, 48_33, 3_88, 5_78, 1_17_86, 1_14, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 4_84, 21_69, 76_87, 2_19_32, 1_81_46, 7_26, 3_63, 1_70_32, 33_91, 1_14, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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], [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]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=a_ , model_name='''google/bigbird-roberta-base''' , revision='''215c99f1600e06f83acce68422f2035b2b5c3510''' , )
| 102 | 1 |
"""simple docstring"""
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import stable_softmax
if is_torch_available():
import torch
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
@add_end_docstrings(
lowercase__ , r'''
top_k (`int`, defaults to 5):
The number of predictions to return.
targets (`str` or `List[str]`, *optional*):
When passed, the model will limit the scores to the passed targets instead of looking up in the whole
vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting
token will be used (with a warning, and that might be slower).
''' , )
class SCREAMING_SNAKE_CASE__ ( lowercase__ ):
def SCREAMING_SNAKE_CASE ( self : Optional[int] , SCREAMING_SNAKE_CASE__ : GenericTensor ) -> np.ndarray:
if self.framework == "tf":
a_ : Optional[Any] = tf.where(input_ids == self.tokenizer.mask_token_id ).numpy()
elif self.framework == "pt":
a_ : Dict = torch.nonzero(input_ids == self.tokenizer.mask_token_id , as_tuple=SCREAMING_SNAKE_CASE__ )
else:
raise ValueError('Unsupported framework' )
return masked_index
def SCREAMING_SNAKE_CASE ( self : str , SCREAMING_SNAKE_CASE__ : GenericTensor ) -> np.ndarray:
a_ : List[str] = self.get_masked_index(SCREAMING_SNAKE_CASE__ )
a_ : Optional[Any] = np.prod(masked_index.shape )
if numel < 1:
raise PipelineException(
'fill-mask' , self.model.base_model_prefix , F"""No mask_token ({self.tokenizer.mask_token}) found on the input""" , )
def SCREAMING_SNAKE_CASE ( self : Tuple , SCREAMING_SNAKE_CASE__ : GenericTensor ) -> Tuple:
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
for model_input in model_inputs:
self._ensure_exactly_one_mask_token(model_input['input_ids'][0] )
else:
for input_ids in model_inputs["input_ids"]:
self._ensure_exactly_one_mask_token(SCREAMING_SNAKE_CASE__ )
def SCREAMING_SNAKE_CASE ( self : str , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : int=None , **SCREAMING_SNAKE_CASE__ : Tuple ) -> Dict[str, GenericTensor]:
if return_tensors is None:
a_ : str = self.framework
a_ : Tuple = self.tokenizer(SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ )
self.ensure_exactly_one_mask_token(SCREAMING_SNAKE_CASE__ )
return model_inputs
def SCREAMING_SNAKE_CASE ( self : Any , SCREAMING_SNAKE_CASE__ : Optional[int] ) -> str:
a_ : Dict = self.model(**SCREAMING_SNAKE_CASE__ )
a_ : List[Any] = model_inputs['input_ids']
return model_outputs
def SCREAMING_SNAKE_CASE ( self : int , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : str=5 , SCREAMING_SNAKE_CASE__ : Optional[int]=None ) -> Optional[Any]:
# Cap top_k if there are targets
if target_ids is not None and target_ids.shape[0] < top_k:
a_ : str = target_ids.shape[0]
a_ : Dict = model_outputs['input_ids'][0]
a_ : Tuple = model_outputs['logits']
if self.framework == "tf":
a_ : Tuple = tf.where(input_ids == self.tokenizer.mask_token_id ).numpy()[:, 0]
a_ : Any = outputs.numpy()
a_ : Tuple = outputs[0, masked_index, :]
a_ : List[Any] = stable_softmax(SCREAMING_SNAKE_CASE__ , axis=-1 )
if target_ids is not None:
a_ : List[Any] = tf.gather_nd(tf.squeeze(SCREAMING_SNAKE_CASE__ , 0 ) , target_ids.reshape(-1 , 1 ) )
a_ : Tuple = tf.expand_dims(SCREAMING_SNAKE_CASE__ , 0 )
a_ : Dict = tf.math.top_k(SCREAMING_SNAKE_CASE__ , k=SCREAMING_SNAKE_CASE__ )
a_ : Union[str, Any] = topk.values.numpy(), topk.indices.numpy()
else:
a_ : Dict = torch.nonzero(input_ids == self.tokenizer.mask_token_id , as_tuple=SCREAMING_SNAKE_CASE__ ).squeeze(-1 )
# Fill mask pipeline supports only one ${mask_token} per sample
a_ : List[str] = outputs[0, masked_index, :]
a_ : Dict = logits.softmax(dim=-1 )
if target_ids is not None:
a_ : Optional[int] = probs[..., target_ids]
a_ : str = probs.topk(SCREAMING_SNAKE_CASE__ )
a_ : Tuple = []
a_ : Tuple = values.shape[0] == 1
for i, (_values, _predictions) in enumerate(zip(values.tolist() , predictions.tolist() ) ):
a_ : Optional[Any] = []
for v, p in zip(_values , _predictions ):
# Copy is important since we're going to modify this array in place
a_ : Dict = input_ids.numpy().copy()
if target_ids is not None:
a_ : Dict = target_ids[p].tolist()
a_ : Optional[int] = p
# Filter padding out:
a_ : Optional[int] = tokens[np.where(tokens != self.tokenizer.pad_token_id )]
# Originally we skip special tokens to give readable output.
# For multi masks though, the other [MASK] would be removed otherwise
# making the output look odd, so we add them back
a_ : Union[str, Any] = self.tokenizer.decode(SCREAMING_SNAKE_CASE__ , skip_special_tokens=SCREAMING_SNAKE_CASE__ )
a_ : List[str] = {'score': v, 'token': p, 'token_str': self.tokenizer.decode([p] ), 'sequence': sequence}
row.append(SCREAMING_SNAKE_CASE__ )
result.append(SCREAMING_SNAKE_CASE__ )
if single_mask:
return result[0]
return result
def SCREAMING_SNAKE_CASE ( self : int , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : int=None ) -> str:
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
a_ : Union[str, Any] = [targets]
try:
a_ : Union[str, Any] = self.tokenizer.get_vocab()
except Exception:
a_ : Optional[int] = {}
a_ : Any = []
for target in targets:
a_ : Optional[int] = vocab.get(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if id_ is None:
a_ : str = self.tokenizer(
SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ , return_attention_mask=SCREAMING_SNAKE_CASE__ , return_token_type_ids=SCREAMING_SNAKE_CASE__ , max_length=1 , truncation=SCREAMING_SNAKE_CASE__ , )['input_ids']
if len(SCREAMING_SNAKE_CASE__ ) == 0:
logger.warning(
F"""The specified target token `{target}` does not exist in the model vocabulary. """
'We cannot replace it with anything meaningful, ignoring it' )
continue
a_ : Optional[Any] = input_ids[0]
# XXX: If users encounter this pass
# it becomes pretty slow, so let's make sure
# The warning enables them to fix the input to
# get faster performance.
logger.warning(
F"""The specified target token `{target}` does not exist in the model vocabulary. """
F"""Replacing with `{self.tokenizer.convert_ids_to_tokens(id_ )}`.""" )
target_ids.append(id_ )
a_ : Optional[int] = list(set(SCREAMING_SNAKE_CASE__ ) )
if len(SCREAMING_SNAKE_CASE__ ) == 0:
raise ValueError('At least one target must be provided when passed.' )
a_ : List[str] = np.array(SCREAMING_SNAKE_CASE__ )
return target_ids
def SCREAMING_SNAKE_CASE ( self : Optional[int] , SCREAMING_SNAKE_CASE__ : Any=None , SCREAMING_SNAKE_CASE__ : Optional[int]=None ) -> List[str]:
a_ : Any = {}
if targets is not None:
a_ : List[str] = self.get_target_ids(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
a_ : Any = target_ids
if top_k is not None:
a_ : Tuple = top_k
if self.tokenizer.mask_token_id is None:
raise PipelineException(
'fill-mask' , self.model.base_model_prefix , 'The tokenizer does not define a `mask_token`.' )
return {}, {}, postprocess_params
def __call__( self : int , SCREAMING_SNAKE_CASE__ : List[str] , *SCREAMING_SNAKE_CASE__ : List[Any] , **SCREAMING_SNAKE_CASE__ : Dict ) -> Dict:
a_ : Optional[Any] = super().__call__(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) and len(SCREAMING_SNAKE_CASE__ ) == 1:
return outputs[0]
return outputs
| 366 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase_ : str = logging.get_logger(__name__)
UpperCAmelCase_ : str = {
'Salesforce/instruct-blip-flan-t5': 'https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json',
}
class SCREAMING_SNAKE_CASE__ ( lowercase__ ):
snake_case__ : int = '''instructblip_vision_model'''
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE__ : List[Any]=1_4_0_8 , SCREAMING_SNAKE_CASE__ : int=6_1_4_4 , SCREAMING_SNAKE_CASE__ : Dict=3_9 , SCREAMING_SNAKE_CASE__ : Union[str, Any]=1_6 , SCREAMING_SNAKE_CASE__ : Union[str, Any]=2_2_4 , SCREAMING_SNAKE_CASE__ : Optional[int]=1_4 , SCREAMING_SNAKE_CASE__ : List[Any]="gelu" , SCREAMING_SNAKE_CASE__ : List[str]=1E-6 , SCREAMING_SNAKE_CASE__ : Tuple=0.0 , SCREAMING_SNAKE_CASE__ : List[str]=1E-10 , SCREAMING_SNAKE_CASE__ : str=True , **SCREAMING_SNAKE_CASE__ : Dict , ) -> int:
super().__init__(**SCREAMING_SNAKE_CASE__ )
a_ : List[str] = hidden_size
a_ : Any = intermediate_size
a_ : str = num_hidden_layers
a_ : Dict = num_attention_heads
a_ : str = patch_size
a_ : Any = image_size
a_ : Dict = initializer_range
a_ : List[Any] = attention_dropout
a_ : Union[str, Any] = layer_norm_eps
a_ : Optional[Any] = hidden_act
a_ : Optional[Any] = qkv_bias
@classmethod
def SCREAMING_SNAKE_CASE ( cls : Any , SCREAMING_SNAKE_CASE__ : Union[str, os.PathLike] , **SCREAMING_SNAKE_CASE__ : List[str] ) -> "PretrainedConfig":
cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE__ )
a_ , a_ : Dict = cls.get_config_dict(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
# get the vision config dict if we are loading from InstructBlipConfig
if config_dict.get('model_type' ) == "instructblip":
a_ : List[Any] = config_dict['vision_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
class SCREAMING_SNAKE_CASE__ ( lowercase__ ):
snake_case__ : List[Any] = '''instructblip_qformer'''
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE__ : Union[str, Any]=3_0_5_2_2 , SCREAMING_SNAKE_CASE__ : str=7_6_8 , SCREAMING_SNAKE_CASE__ : List[str]=1_2 , SCREAMING_SNAKE_CASE__ : Any=1_2 , SCREAMING_SNAKE_CASE__ : Optional[int]=3_0_7_2 , SCREAMING_SNAKE_CASE__ : List[str]="gelu" , SCREAMING_SNAKE_CASE__ : Dict=0.1 , SCREAMING_SNAKE_CASE__ : int=0.1 , SCREAMING_SNAKE_CASE__ : Optional[int]=5_1_2 , SCREAMING_SNAKE_CASE__ : Optional[Any]=0.02 , SCREAMING_SNAKE_CASE__ : int=1E-12 , SCREAMING_SNAKE_CASE__ : Dict=0 , SCREAMING_SNAKE_CASE__ : Optional[Any]="absolute" , SCREAMING_SNAKE_CASE__ : Union[str, Any]=2 , SCREAMING_SNAKE_CASE__ : str=1_4_0_8 , **SCREAMING_SNAKE_CASE__ : Dict , ) -> Optional[Any]:
super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
a_ : Union[str, Any] = vocab_size
a_ : List[str] = hidden_size
a_ : Any = num_hidden_layers
a_ : Dict = num_attention_heads
a_ : Optional[int] = hidden_act
a_ : List[str] = intermediate_size
a_ : Tuple = hidden_dropout_prob
a_ : Union[str, Any] = attention_probs_dropout_prob
a_ : List[str] = max_position_embeddings
a_ : List[str] = initializer_range
a_ : Any = layer_norm_eps
a_ : Tuple = position_embedding_type
a_ : List[str] = cross_attention_frequency
a_ : Union[str, Any] = encoder_hidden_size
@classmethod
def SCREAMING_SNAKE_CASE ( cls : str , SCREAMING_SNAKE_CASE__ : Union[str, os.PathLike] , **SCREAMING_SNAKE_CASE__ : Union[str, Any] ) -> "PretrainedConfig":
cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE__ )
a_ , a_ : Union[str, Any] = cls.get_config_dict(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
# get the qformer config dict if we are loading from InstructBlipConfig
if config_dict.get('model_type' ) == "instructblip":
a_ : Dict = config_dict['qformer_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
class SCREAMING_SNAKE_CASE__ ( lowercase__ ):
snake_case__ : Union[str, Any] = '''instructblip'''
snake_case__ : List[str] = True
def __init__( self : Tuple , SCREAMING_SNAKE_CASE__ : Optional[Any]=None , SCREAMING_SNAKE_CASE__ : Dict=None , SCREAMING_SNAKE_CASE__ : List[Any]=None , SCREAMING_SNAKE_CASE__ : int=3_2 , **SCREAMING_SNAKE_CASE__ : Union[str, Any] ) -> Tuple:
super().__init__(**SCREAMING_SNAKE_CASE__ )
if vision_config is None:
a_ : Dict = {}
logger.info('vision_config is None. initializing the InstructBlipVisionConfig with default values.' )
if qformer_config is None:
a_ : List[str] = {}
logger.info('qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.' )
if text_config is None:
a_ : Any = {}
logger.info('text_config is None. Initializing the text config with default values (`OPTConfig`).' )
a_ : int = InstructBlipVisionConfig(**SCREAMING_SNAKE_CASE__ )
a_ : int = InstructBlipQFormerConfig(**SCREAMING_SNAKE_CASE__ )
a_ : Optional[Any] = text_config['model_type'] if 'model_type' in text_config else 'opt'
a_ : List[Any] = CONFIG_MAPPING[text_model_type](**SCREAMING_SNAKE_CASE__ )
a_ : Tuple = self.text_config.tie_word_embeddings
a_ : List[Any] = self.text_config.is_encoder_decoder
a_ : Optional[Any] = num_query_tokens
a_ : int = self.vision_config.hidden_size
a_ : List[str] = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
a_ : Optional[int] = 1.0
a_ : Any = 0.02
@classmethod
def SCREAMING_SNAKE_CASE ( cls : List[Any] , SCREAMING_SNAKE_CASE__ : InstructBlipVisionConfig , SCREAMING_SNAKE_CASE__ : InstructBlipQFormerConfig , SCREAMING_SNAKE_CASE__ : PretrainedConfig , **SCREAMING_SNAKE_CASE__ : List[str] , ) -> Any:
return cls(
vision_config=vision_config.to_dict() , qformer_config=qformer_config.to_dict() , text_config=text_config.to_dict() , **SCREAMING_SNAKE_CASE__ , )
def SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> Union[str, Any]:
a_ : List[Any] = copy.deepcopy(self.__dict__ )
a_ : Optional[int] = self.vision_config.to_dict()
a_ : Optional[Any] = self.qformer_config.to_dict()
a_ : Union[str, Any] = self.text_config.to_dict()
a_ : Optional[Any] = self.__class__.model_type
return output
| 120 | 0 |
def lowercase_ (A : int ):
snake_case__ : str = generate_pascal_triangle(A )
for row_idx in range(A ):
# Print left spaces
for _ in range(num_rows - row_idx - 1 ):
print(end=' ' )
# Print row values
for col_idx in range(row_idx + 1 ):
if col_idx != row_idx:
print(triangle[row_idx][col_idx] , end=' ' )
else:
print(triangle[row_idx][col_idx] , end='' )
print()
def lowercase_ (A : int ):
if not isinstance(A , A ):
raise TypeError('The input value of \'num_rows\' should be \'int\'' )
if num_rows == 0:
return []
elif num_rows < 0:
raise ValueError(
'The input value of \'num_rows\' should be greater than or equal to 0' )
snake_case__ : list[list[int]] = []
for current_row_idx in range(A ):
snake_case__ : Optional[int] = populate_current_row(A , A )
triangle.append(A )
return triangle
def lowercase_ (A : list[list[int]] , A : int ):
snake_case__ : str = [-1] * (current_row_idx + 1)
# first and last elements of current row are equal to 1
snake_case__ , snake_case__ : Optional[Any] = 1, 1
for current_col_idx in range(1 , A ):
calculate_current_element(
A , A , A , A )
return current_row
def lowercase_ (A : list[list[int]] , A : list[int] , A : int , A : int , ):
snake_case__ : Optional[int] = triangle[current_row_idx - 1][current_col_idx - 1]
snake_case__ : Any = triangle[current_row_idx - 1][current_col_idx]
snake_case__ : List[Any] = above_to_left_elt + above_to_right_elt
def lowercase_ (A : int ):
if not isinstance(A , A ):
raise TypeError('The input value of \'num_rows\' should be \'int\'' )
if num_rows == 0:
return []
elif num_rows < 0:
raise ValueError(
'The input value of \'num_rows\' should be greater than or equal to 0' )
snake_case__ : list[list[int]] = [[1]]
for row_index in range(1 , A ):
snake_case__ : int = [0] + result[-1] + [0]
snake_case__ : int = row_index + 1
# Calculate the number of distinct elements in a row
snake_case__ : int = sum(divmod(A , 2 ) )
snake_case__ : Optional[Any] = [
temp_row[i - 1] + temp_row[i] for i in range(1 , distinct_elements + 1 )
]
snake_case__ : str = row_first_half[: (row_index + 1) // 2]
row_second_half.reverse()
snake_case__ : Optional[int] = row_first_half + row_second_half
result.append(A )
return result
def lowercase_ ():
from collections.abc import Callable
from timeit import timeit
def benchmark_a_function(A : Callable , A : int ) -> None:
snake_case__ : Optional[int] = F'''{func.__name__}({value})'''
snake_case__ : Optional[Any] = timeit(F'''__main__.{call}''' , setup='import __main__' )
# print(f"{call:38} = {func(value)} -- {timing:.4f} seconds")
print(F'''{call:38} -- {timing:.4f} seconds''' )
for value in range(1_5 ): # (1, 7, 14):
for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
benchmark_a_function(A , A )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 277 |
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class snake_case__ :
"""simple docstring"""
def __init__( self : Tuple, _snake_case : Any, _snake_case : int=1_3, _snake_case : Optional[int]=3_2, _snake_case : Tuple=2, _snake_case : Any=3, _snake_case : Tuple=1_6, _snake_case : Tuple=[1, 2, 1], _snake_case : Dict=[2, 2, 4], _snake_case : str=2, _snake_case : Union[str, Any]=2.0, _snake_case : Dict=True, _snake_case : Dict=0.0, _snake_case : str=0.0, _snake_case : str=0.1, _snake_case : List[str]="gelu", _snake_case : int=False, _snake_case : Optional[Any]=True, _snake_case : List[Any]=0.0_2, _snake_case : Union[str, Any]=1e-5, _snake_case : Union[str, Any]=True, _snake_case : List[Any]=None, _snake_case : Any=True, _snake_case : List[Any]=1_0, _snake_case : str=8, ) ->Union[str, Any]:
snake_case__ : Any = parent
snake_case__ : Tuple = batch_size
snake_case__ : Tuple = image_size
snake_case__ : Any = patch_size
snake_case__ : Optional[int] = num_channels
snake_case__ : Tuple = embed_dim
snake_case__ : Any = depths
snake_case__ : Any = num_heads
snake_case__ : List[str] = window_size
snake_case__ : Dict = mlp_ratio
snake_case__ : Optional[int] = qkv_bias
snake_case__ : Optional[Any] = hidden_dropout_prob
snake_case__ : List[str] = attention_probs_dropout_prob
snake_case__ : Union[str, Any] = drop_path_rate
snake_case__ : str = hidden_act
snake_case__ : Union[str, Any] = use_absolute_embeddings
snake_case__ : Union[str, Any] = patch_norm
snake_case__ : Any = layer_norm_eps
snake_case__ : Tuple = initializer_range
snake_case__ : Dict = is_training
snake_case__ : Any = scope
snake_case__ : Optional[Any] = use_labels
snake_case__ : str = type_sequence_label_size
snake_case__ : List[Any] = encoder_stride
def lowercase_ ( self : Tuple ) ->str:
snake_case__ : Tuple = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case__ : List[Any] = None
if self.use_labels:
snake_case__ : Optional[Any] = ids_tensor([self.batch_size], self.type_sequence_label_size )
snake_case__ : Any = self.get_config()
return config, pixel_values, labels
def lowercase_ ( self : Optional[int] ) ->Optional[int]:
return SwinvaConfig(
image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, embed_dim=self.embed_dim, depths=self.depths, num_heads=self.num_heads, window_size=self.window_size, mlp_ratio=self.mlp_ratio, qkv_bias=self.qkv_bias, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, drop_path_rate=self.drop_path_rate, hidden_act=self.hidden_act, use_absolute_embeddings=self.use_absolute_embeddings, path_norm=self.patch_norm, layer_norm_eps=self.layer_norm_eps, initializer_range=self.initializer_range, encoder_stride=self.encoder_stride, )
def lowercase_ ( self : Optional[int], _snake_case : str, _snake_case : List[str], _snake_case : int ) ->Dict:
snake_case__ : List[Any] = SwinvaModel(config=_snake_case )
model.to(_snake_case )
model.eval()
snake_case__ : Optional[int] = model(_snake_case )
snake_case__ : List[Any] = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
snake_case__ : List[Any] = int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, expected_seq_len, expected_dim) )
def lowercase_ ( self : Optional[Any], _snake_case : Any, _snake_case : List[str], _snake_case : Dict ) ->List[Any]:
snake_case__ : List[str] = SwinvaForMaskedImageModeling(config=_snake_case )
model.to(_snake_case )
model.eval()
snake_case__ : Union[str, Any] = model(_snake_case )
self.parent.assertEqual(
result.logits.shape, (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
snake_case__ : Optional[Any] = 1
snake_case__ : Optional[int] = SwinvaForMaskedImageModeling(_snake_case )
model.to(_snake_case )
model.eval()
snake_case__ : Tuple = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
snake_case__ : Any = model(_snake_case )
self.parent.assertEqual(result.logits.shape, (self.batch_size, 1, self.image_size, self.image_size) )
def lowercase_ ( self : List[str], _snake_case : int, _snake_case : List[Any], _snake_case : Optional[int] ) ->Any:
snake_case__ : Tuple = self.type_sequence_label_size
snake_case__ : int = SwinvaForImageClassification(_snake_case )
model.to(_snake_case )
model.eval()
snake_case__ : Tuple = model(_snake_case, labels=_snake_case )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size) )
def lowercase_ ( self : Any ) ->Dict:
snake_case__ : str = self.prepare_config_and_inputs()
snake_case__ , snake_case__ , snake_case__ : List[str] = config_and_inputs
snake_case__ : Union[str, Any] = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class snake_case__ ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = (
(SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
)
_SCREAMING_SNAKE_CASE = (
{"""feature-extraction""": SwinvaModel, """image-classification""": SwinvaForImageClassification}
if is_torch_available()
else {}
)
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = False
def lowercase_ ( self : Union[str, Any] ) ->Dict:
snake_case__ : Optional[int] = SwinvaModelTester(self )
snake_case__ : int = ConfigTester(self, config_class=_snake_case, embed_dim=3_7 )
def lowercase_ ( self : Tuple ) ->int:
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 : Any ) ->str:
snake_case__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_snake_case )
@unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.' )
def lowercase_ ( self : Any ) ->Union[str, Any]:
pass
@unittest.skip(reason='Swinv2 does not use inputs_embeds' )
def lowercase_ ( self : str ) ->Union[str, Any]:
pass
def lowercase_ ( self : Optional[Any] ) ->Union[str, Any]:
snake_case__ , snake_case__ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case__ : Union[str, Any] = model_class(_snake_case )
self.assertIsInstance(model.get_input_embeddings(), (nn.Module) )
snake_case__ : Union[str, Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_snake_case, nn.Linear ) )
def lowercase_ ( self : List[str] ) ->Optional[int]:
snake_case__ , snake_case__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case__ : Any = model_class(_snake_case )
snake_case__ : Dict = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case__ : Optional[Any] = [*signature.parameters.keys()]
snake_case__ : List[Any] = ['pixel_values']
self.assertListEqual(arg_names[:1], _snake_case )
def lowercase_ ( self : str ) ->Union[str, Any]:
snake_case__ , snake_case__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
snake_case__ : int = True
for model_class in self.all_model_classes:
snake_case__ : str = True
snake_case__ : Union[str, Any] = False
snake_case__ : Tuple = True
snake_case__ : int = model_class(_snake_case )
model.to(_snake_case )
model.eval()
with torch.no_grad():
snake_case__ : Optional[int] = model(**self._prepare_for_class(_snake_case, _snake_case ) )
snake_case__ : List[str] = outputs.attentions
snake_case__ : List[Any] = len(self.model_tester.depths )
self.assertEqual(len(_snake_case ), _snake_case )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
snake_case__ : str = True
snake_case__ : Tuple = config.window_size**2
snake_case__ : Optional[int] = model_class(_snake_case )
model.to(_snake_case )
model.eval()
with torch.no_grad():
snake_case__ : str = model(**self._prepare_for_class(_snake_case, _snake_case ) )
snake_case__ : Tuple = outputs.attentions
self.assertEqual(len(_snake_case ), _snake_case )
self.assertListEqual(
list(attentions[0].shape[-3:] ), [self.model_tester.num_heads[0], window_size_squared, window_size_squared], )
snake_case__ : Optional[Any] = len(_snake_case )
# Check attention is always last and order is fine
snake_case__ : Optional[int] = True
snake_case__ : Dict = True
snake_case__ : List[Any] = model_class(_snake_case )
model.to(_snake_case )
model.eval()
with torch.no_grad():
snake_case__ : Optional[int] = model(**self._prepare_for_class(_snake_case, _snake_case ) )
if hasattr(self.model_tester, 'num_hidden_states_types' ):
snake_case__ : str = self.model_tester.num_hidden_states_types
else:
# also another +1 for reshaped_hidden_states
snake_case__ : Dict = 2
self.assertEqual(out_len + added_hidden_states, len(_snake_case ) )
snake_case__ : Any = outputs.attentions
self.assertEqual(len(_snake_case ), _snake_case )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ), [self.model_tester.num_heads[0], window_size_squared, window_size_squared], )
def lowercase_ ( self : Dict, _snake_case : Tuple, _snake_case : Any, _snake_case : int, _snake_case : Optional[int] ) ->str:
snake_case__ : Dict = model_class(_snake_case )
model.to(_snake_case )
model.eval()
with torch.no_grad():
snake_case__ : List[Any] = model(**self._prepare_for_class(_snake_case, _snake_case ) )
snake_case__ : Dict = outputs.hidden_states
snake_case__ : int = getattr(
self.model_tester, 'expected_num_hidden_layers', len(self.model_tester.depths ) + 1 )
self.assertEqual(len(_snake_case ), _snake_case )
# Swinv2 has a different seq_length
snake_case__ : int = (
config.patch_size
if isinstance(config.patch_size, collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
snake_case__ : Optional[Any] = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ), [num_patches, self.model_tester.embed_dim], )
snake_case__ : Union[str, Any] = outputs.reshaped_hidden_states
self.assertEqual(len(_snake_case ), _snake_case )
snake_case__ , snake_case__ , snake_case__ , snake_case__ : str = reshaped_hidden_states[0].shape
snake_case__ : Any = (
reshaped_hidden_states[0].view(_snake_case, _snake_case, height * width ).permute(0, 2, 1 )
)
self.assertListEqual(
list(reshaped_hidden_states.shape[-2:] ), [num_patches, self.model_tester.embed_dim], )
def lowercase_ ( self : str ) ->List[Any]:
snake_case__ , snake_case__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
snake_case__ : List[Any] = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size, collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes:
snake_case__ : Optional[int] = True
self.check_hidden_states_output(_snake_case, _snake_case, _snake_case, _snake_case )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case__ : Dict = True
self.check_hidden_states_output(_snake_case, _snake_case, _snake_case, _snake_case )
def lowercase_ ( self : List[str] ) ->str:
snake_case__ , snake_case__ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
snake_case__ : List[str] = 3
snake_case__ : Union[str, Any] = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size, collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
snake_case__ : str = (
config.patch_size
if isinstance(config.patch_size, collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
snake_case__ : Tuple = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
snake_case__ : Optional[Any] = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
snake_case__ : int = True
self.check_hidden_states_output(_snake_case, _snake_case, _snake_case, (padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case__ : List[str] = True
self.check_hidden_states_output(_snake_case, _snake_case, _snake_case, (padded_height, padded_width) )
def lowercase_ ( self : List[str] ) ->Optional[int]:
snake_case__ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*_snake_case )
def lowercase_ ( self : List[Any] ) ->str:
snake_case__ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_snake_case )
@slow
def lowercase_ ( self : str ) ->Union[str, Any]:
for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case__ : Dict = SwinvaModel.from_pretrained(_snake_case )
self.assertIsNotNone(_snake_case )
def lowercase_ ( self : Optional[int] ) ->List[str]:
snake_case__ , snake_case__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
snake_case__ : List[Any] = _config_zero_init(_snake_case )
for model_class in self.all_model_classes:
snake_case__ : List[str] = model_class(config=_snake_case )
for name, param in model.named_parameters():
if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(), [0.0, 1.0], msg=F'''Parameter {name} of model {model_class} seems not properly initialized''', )
@require_vision
@require_torch
class snake_case__ ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def lowercase_ ( self : Union[str, Any] ) ->List[str]:
return (
AutoImageProcessor.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256' )
if is_vision_available()
else None
)
@slow
def lowercase_ ( self : int ) ->List[Any]:
snake_case__ : Any = SwinvaForImageClassification.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256' ).to(
_snake_case )
snake_case__ : int = self.default_image_processor
snake_case__ : Union[str, Any] = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
snake_case__ : Optional[Any] = image_processor(images=_snake_case, return_tensors='pt' ).to(_snake_case )
# forward pass
with torch.no_grad():
snake_case__ : List[str] = model(**_snake_case )
# verify the logits
snake_case__ : int = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape, _snake_case )
snake_case__ : Optional[int] = torch.tensor([-0.3_9_4_7, -0.4_3_0_6, 0.0_0_2_6] ).to(_snake_case )
self.assertTrue(torch.allclose(outputs.logits[0, :3], _snake_case, atol=1e-4 ) )
| 277 | 1 |
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
UpperCAmelCase__ : str =logging.get_logger(__name__)
UpperCAmelCase__ : int ={'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
UpperCAmelCase__ : List[Any] ={
'''vocab_file''': {
'''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
},
'''merges_file''': {
'''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
},
}
UpperCAmelCase__ : List[Any] ={'''allegro/herbert-base-cased''': 5_14}
UpperCAmelCase__ : int ={}
class __A ( a ):
__A = VOCAB_FILES_NAMES
__A = PRETRAINED_VOCAB_FILES_MAP
__A = PRETRAINED_INIT_CONFIGURATION
__A = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__A = HerbertTokenizer
def __init__( self , UpperCAmelCase_=None , UpperCAmelCase_=None , UpperCAmelCase_=None , UpperCAmelCase_="<s>" , UpperCAmelCase_="<unk>" , UpperCAmelCase_="<pad>" , UpperCAmelCase_="<mask>" , UpperCAmelCase_="</s>" , **UpperCAmelCase_ , ):
super().__init__(
UpperCAmelCase_ , UpperCAmelCase_ , tokenizer_file=UpperCAmelCase_ , cls_token=UpperCAmelCase_ , unk_token=UpperCAmelCase_ , pad_token=UpperCAmelCase_ , mask_token=UpperCAmelCase_ , sep_token=UpperCAmelCase_ , **UpperCAmelCase_ , )
def _snake_case ( self , UpperCAmelCase_ , UpperCAmelCase_ = None ):
lowerCamelCase =[self.cls_token_id]
lowerCamelCase =[self.sep_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def _snake_case ( self , UpperCAmelCase_ , UpperCAmelCase_ = None , UpperCAmelCase_ = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=UpperCAmelCase_ , token_ids_a=UpperCAmelCase_ , already_has_special_tokens=UpperCAmelCase_ )
if token_ids_a is None:
return [1] + ([0] * len(UpperCAmelCase_ )) + [1]
return [1] + ([0] * len(UpperCAmelCase_ )) + [1] + ([0] * len(UpperCAmelCase_ )) + [1]
def _snake_case ( self , UpperCAmelCase_ , UpperCAmelCase_ = None ):
lowerCamelCase =[self.sep_token_id]
lowerCamelCase =[self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def _snake_case ( self , UpperCAmelCase_ , UpperCAmelCase_ = None ):
lowerCamelCase =self._tokenizer.model.save(UpperCAmelCase_ , name=UpperCAmelCase_ )
return tuple(UpperCAmelCase_ )
| 262 |
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def _lowercase ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = 1 / sqrt(2 ) ) -> IIRFilter:
lowerCamelCase =tau * frequency / samplerate
lowerCamelCase =sin(_UpperCAmelCase )
lowerCamelCase =cos(_UpperCAmelCase )
lowerCamelCase =_sin / (2 * q_factor)
lowerCamelCase =(1 - _cos) / 2
lowerCamelCase =1 - _cos
lowerCamelCase =1 + alpha
lowerCamelCase =-2 * _cos
lowerCamelCase =1 - alpha
lowerCamelCase =IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def _lowercase ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = 1 / sqrt(2 ) ) -> IIRFilter:
lowerCamelCase =tau * frequency / samplerate
lowerCamelCase =sin(_UpperCAmelCase )
lowerCamelCase =cos(_UpperCAmelCase )
lowerCamelCase =_sin / (2 * q_factor)
lowerCamelCase =(1 + _cos) / 2
lowerCamelCase =-1 - _cos
lowerCamelCase =1 + alpha
lowerCamelCase =-2 * _cos
lowerCamelCase =1 - alpha
lowerCamelCase =IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def _lowercase ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = 1 / sqrt(2 ) ) -> IIRFilter:
lowerCamelCase =tau * frequency / samplerate
lowerCamelCase =sin(_UpperCAmelCase )
lowerCamelCase =cos(_UpperCAmelCase )
lowerCamelCase =_sin / (2 * q_factor)
lowerCamelCase =_sin / 2
lowerCamelCase =0
lowerCamelCase =-ba
lowerCamelCase =1 + alpha
lowerCamelCase =-2 * _cos
lowerCamelCase =1 - alpha
lowerCamelCase =IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def _lowercase ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = 1 / sqrt(2 ) ) -> IIRFilter:
lowerCamelCase =tau * frequency / samplerate
lowerCamelCase =sin(_UpperCAmelCase )
lowerCamelCase =cos(_UpperCAmelCase )
lowerCamelCase =_sin / (2 * q_factor)
lowerCamelCase =1 - alpha
lowerCamelCase =-2 * _cos
lowerCamelCase =1 + alpha
lowerCamelCase =IIRFilter(2 )
filt.set_coefficients([ba, ba, ba] , [ba, ba, ba] )
return filt
def _lowercase ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = 1 / sqrt(2 ) , ) -> IIRFilter:
lowerCamelCase =tau * frequency / samplerate
lowerCamelCase =sin(_UpperCAmelCase )
lowerCamelCase =cos(_UpperCAmelCase )
lowerCamelCase =_sin / (2 * q_factor)
lowerCamelCase =10 ** (gain_db / 40)
lowerCamelCase =1 + alpha * big_a
lowerCamelCase =-2 * _cos
lowerCamelCase =1 - alpha * big_a
lowerCamelCase =1 + alpha / big_a
lowerCamelCase =-2 * _cos
lowerCamelCase =1 - alpha / big_a
lowerCamelCase =IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def _lowercase ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = 1 / sqrt(2 ) , ) -> IIRFilter:
lowerCamelCase =tau * frequency / samplerate
lowerCamelCase =sin(_UpperCAmelCase )
lowerCamelCase =cos(_UpperCAmelCase )
lowerCamelCase =_sin / (2 * q_factor)
lowerCamelCase =10 ** (gain_db / 40)
lowerCamelCase =(big_a + 1) - (big_a - 1) * _cos
lowerCamelCase =(big_a + 1) + (big_a - 1) * _cos
lowerCamelCase =(big_a - 1) - (big_a + 1) * _cos
lowerCamelCase =(big_a - 1) + (big_a + 1) * _cos
lowerCamelCase =2 * sqrt(_UpperCAmelCase ) * alpha
lowerCamelCase =big_a * (pmc + aaa)
lowerCamelCase =2 * big_a * mpc
lowerCamelCase =big_a * (pmc - aaa)
lowerCamelCase =ppmc + aaa
lowerCamelCase =-2 * pmpc
lowerCamelCase =ppmc - aaa
lowerCamelCase =IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def _lowercase ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = 1 / sqrt(2 ) , ) -> IIRFilter:
lowerCamelCase =tau * frequency / samplerate
lowerCamelCase =sin(_UpperCAmelCase )
lowerCamelCase =cos(_UpperCAmelCase )
lowerCamelCase =_sin / (2 * q_factor)
lowerCamelCase =10 ** (gain_db / 40)
lowerCamelCase =(big_a + 1) - (big_a - 1) * _cos
lowerCamelCase =(big_a + 1) + (big_a - 1) * _cos
lowerCamelCase =(big_a - 1) - (big_a + 1) * _cos
lowerCamelCase =(big_a - 1) + (big_a + 1) * _cos
lowerCamelCase =2 * sqrt(_UpperCAmelCase ) * alpha
lowerCamelCase =big_a * (ppmc + aaa)
lowerCamelCase =-2 * big_a * pmpc
lowerCamelCase =big_a * (ppmc - aaa)
lowerCamelCase =pmc + aaa
lowerCamelCase =2 * mpc
lowerCamelCase =pmc - aaa
lowerCamelCase =IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
| 262 | 1 |
'''simple docstring'''
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
_lowerCAmelCase = TypeVar('''T''')
def _SCREAMING_SNAKE_CASE ( UpperCamelCase ):
"""simple docstring"""
return (position - 1) // 2
def _SCREAMING_SNAKE_CASE ( UpperCamelCase ):
"""simple docstring"""
return (2 * position) + 1
def _SCREAMING_SNAKE_CASE ( UpperCamelCase ):
"""simple docstring"""
return (2 * position) + 2
class lowerCAmelCase_( Generic[T] ):
'''simple docstring'''
def __init__( self ) -> None:
lowerCAmelCase__ : list[tuple[T, int]] = []
lowerCAmelCase__ : dict[T, int] = {}
lowerCAmelCase__ : int = 0
def __len__( self ) -> int:
return self.elements
def __repr__( self ) -> str:
return str(self.heap )
def UpperCAmelCase_ ( self ) -> bool:
# Check if the priority queue is empty
return self.elements == 0
def UpperCAmelCase_ ( self ,__UpperCAmelCase ,__UpperCAmelCase ) -> None:
# Add an element with given priority to the queue
self.heap.append((elem, weight) )
lowerCAmelCase__ : str = self.elements
self.elements += 1
self._bubble_up(__UpperCAmelCase )
def UpperCAmelCase_ ( self ) -> T:
# Remove and return the element with lowest weight (highest priority)
if self.elements > 1:
self._swap_nodes(0 ,self.elements - 1 )
lowerCAmelCase__ , lowerCAmelCase__ : Optional[int] = self.heap.pop()
del self.position_map[elem]
self.elements -= 1
if self.elements > 0:
lowerCAmelCase__ , lowerCAmelCase__ : str = self.heap[0]
self._bubble_down(__UpperCAmelCase )
return elem
def UpperCAmelCase_ ( self ,__UpperCAmelCase ,__UpperCAmelCase ) -> None:
# Update the weight of the given key
lowerCAmelCase__ : int = self.position_map[elem]
lowerCAmelCase__ : List[Any] = (elem, weight)
if position > 0:
lowerCAmelCase__ : Dict = get_parent_position(__UpperCAmelCase )
lowerCAmelCase__ , lowerCAmelCase__ : Optional[int] = self.heap[parent_position]
if parent_weight > weight:
self._bubble_up(__UpperCAmelCase )
else:
self._bubble_down(__UpperCAmelCase )
else:
self._bubble_down(__UpperCAmelCase )
def UpperCAmelCase_ ( self ,__UpperCAmelCase ) -> None:
# Place a node at the proper position (upward movement) [to be used internally
# only]
lowerCAmelCase__ : List[str] = self.position_map[elem]
if curr_pos == 0:
return None
lowerCAmelCase__ : int = get_parent_position(__UpperCAmelCase )
lowerCAmelCase__ , lowerCAmelCase__ : Any = self.heap[curr_pos]
lowerCAmelCase__ , lowerCAmelCase__ : List[str] = self.heap[parent_position]
if parent_weight > weight:
self._swap_nodes(__UpperCAmelCase ,__UpperCAmelCase )
return self._bubble_up(__UpperCAmelCase )
return None
def UpperCAmelCase_ ( self ,__UpperCAmelCase ) -> None:
# Place a node at the proper position (downward movement) [to be used
# internally only]
lowerCAmelCase__ : List[Any] = self.position_map[elem]
lowerCAmelCase__ , lowerCAmelCase__ : Dict = self.heap[curr_pos]
lowerCAmelCase__ : str = get_child_left_position(__UpperCAmelCase )
lowerCAmelCase__ : Union[str, Any] = get_child_right_position(__UpperCAmelCase )
if child_left_position < self.elements and child_right_position < self.elements:
lowerCAmelCase__ , lowerCAmelCase__ : Dict = self.heap[child_left_position]
lowerCAmelCase__ , lowerCAmelCase__ : Any = self.heap[child_right_position]
if child_right_weight < child_left_weight and child_right_weight < weight:
self._swap_nodes(__UpperCAmelCase ,__UpperCAmelCase )
return self._bubble_down(__UpperCAmelCase )
if child_left_position < self.elements:
lowerCAmelCase__ , lowerCAmelCase__ : List[str] = self.heap[child_left_position]
if child_left_weight < weight:
self._swap_nodes(__UpperCAmelCase ,__UpperCAmelCase )
return self._bubble_down(__UpperCAmelCase )
else:
return None
if child_right_position < self.elements:
lowerCAmelCase__ , lowerCAmelCase__ : Any = self.heap[child_right_position]
if child_right_weight < weight:
self._swap_nodes(__UpperCAmelCase ,__UpperCAmelCase )
return self._bubble_down(__UpperCAmelCase )
return None
def UpperCAmelCase_ ( self ,__UpperCAmelCase ,__UpperCAmelCase ) -> None:
# Swap the nodes at the given positions
lowerCAmelCase__ : str = self.heap[nodea_pos][0]
lowerCAmelCase__ : Dict = self.heap[nodea_pos][0]
lowerCAmelCase__ , lowerCAmelCase__ : Tuple = (
self.heap[nodea_pos],
self.heap[nodea_pos],
)
lowerCAmelCase__ : int = nodea_pos
lowerCAmelCase__ : int = nodea_pos
class lowerCAmelCase_( Generic[T] ):
'''simple docstring'''
def __init__( self ) -> None:
lowerCAmelCase__ : dict[T, dict[T, int]] = {}
lowerCAmelCase__ : int = 0
def __repr__( self ) -> str:
return str(self.connections )
def __len__( self ) -> int:
return self.nodes
def UpperCAmelCase_ ( self ,__UpperCAmelCase ) -> None:
# Add a node in the graph if it is not in the graph
if node not in self.connections:
lowerCAmelCase__ : Optional[int] = {}
self.nodes += 1
def UpperCAmelCase_ ( self ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ) -> None:
# Add an edge between 2 nodes in the graph
self.add_node(__UpperCAmelCase )
self.add_node(__UpperCAmelCase )
lowerCAmelCase__ : Any = weight
lowerCAmelCase__ : Tuple = weight
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , ):
"""simple docstring"""
lowerCAmelCase__ : dict[T, int] = {node: maxsize for node in graph.connections}
lowerCAmelCase__ : dict[T, T | None] = {node: None for node in graph.connections}
lowerCAmelCase__ : MinPriorityQueue[T] = MinPriorityQueue()
for node, weight in dist.items():
priority_queue.push(UpperCamelCase , UpperCamelCase )
if priority_queue.is_empty():
return dist, parent
# initialization
lowerCAmelCase__ : List[Any] = priority_queue.extract_min()
lowerCAmelCase__ : str = 0
for neighbour in graph.connections[node]:
if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
lowerCAmelCase__ : Any = dist[node] + graph.connections[node][neighbour]
priority_queue.update_key(UpperCamelCase , dist[neighbour] )
lowerCAmelCase__ : List[str] = node
# running prim's algorithm
while not priority_queue.is_empty():
lowerCAmelCase__ : Any = priority_queue.extract_min()
for neighbour in graph.connections[node]:
if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
lowerCAmelCase__ : Optional[int] = dist[node] + graph.connections[node][neighbour]
priority_queue.update_key(UpperCamelCase , dist[neighbour] )
lowerCAmelCase__ : Optional[int] = node
return dist, parent
| 37 |
'''simple docstring'''
def _A ( ):
lowercase__ = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
lowercase__ = 6
lowercase__ = 1
lowercase__ = 1901
lowercase__ = 0
while year < 2001:
day += 7
if (year % 4 == 0 and year % 100 != 0) or (year % 400 == 0):
if day > days_per_month[month - 1] and month != 2:
month += 1
lowercase__ = day - days_per_month[month - 2]
elif day > 29 and month == 2:
month += 1
lowercase__ = day - 29
else:
if day > days_per_month[month - 1]:
month += 1
lowercase__ = day - days_per_month[month - 2]
if month > 12:
year += 1
lowercase__ = 1
if year < 2001 and day == 1:
sundays += 1
return sundays
if __name__ == "__main__":
print(solution())
| 164 | 0 |
def lowerCamelCase__ ( _lowercase , _lowercase ):
'''simple docstring'''
return number | (1 << position)
def lowerCamelCase__ ( _lowercase , _lowercase ):
'''simple docstring'''
return number & ~(1 << position)
def lowerCamelCase__ ( _lowercase , _lowercase ):
'''simple docstring'''
return number ^ (1 << position)
def lowerCamelCase__ ( _lowercase , _lowercase ):
'''simple docstring'''
return ((number >> position) & 1) == 1
def lowerCamelCase__ ( _lowercase , _lowercase ):
'''simple docstring'''
return int((number & (1 << position)) != 0 )
if __name__ == "__main__":
import doctest
doctest.testmod() | 365 |
from __future__ import annotations
from fractions import Fraction
def lowerCamelCase__ ( _lowercase , _lowercase ):
'''simple docstring'''
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
UpperCAmelCase_ : Optional[int] = []
UpperCAmelCase_ : Tuple = 11
UpperCAmelCase_ : int = int('''1''' + '''0''' * digit_len )
for num in range(_lowercase , _lowercase ):
while den <= 99:
if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
if is_digit_cancelling(_lowercase , _lowercase ):
solutions.append(f'''{num}/{den}''' )
den += 1
num += 1
UpperCAmelCase_ : Any = 10
return solutions
def lowerCamelCase__ ( _lowercase = 2 ):
'''simple docstring'''
UpperCAmelCase_ : Tuple = 1.0
for fraction in fraction_list(_lowercase ):
UpperCAmelCase_ : Optional[Any] = Fraction(_lowercase )
result *= frac.denominator / frac.numerator
return int(_lowercase )
if __name__ == "__main__":
print(solution()) | 235 | 0 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
get_from_cache,
http_get,
http_head,
)
UpperCAmelCase_ = "\\n Text data.\n Second line of data."
UpperCAmelCase_ = "file"
@pytest.fixture(scope="""session""" )
def lowerCamelCase__ ( A__ : Dict ):
'''simple docstring'''
__lowerCamelCase = tmp_path_factory.mktemp("""data""" ) / (FILE_PATH + """.zstd""")
__lowerCamelCase = bytes(_UpperCamelCase , """utf-8""" )
with zstd.open(_UpperCamelCase , """wb""" ) as f:
f.write(_UpperCamelCase )
return path
@pytest.fixture
def lowerCamelCase__ ( A__ : List[str] ):
'''simple docstring'''
with open(os.path.join(tmpfs.local_root_dir , _UpperCamelCase ) , """w""" ) as f:
f.write(_UpperCamelCase )
return FILE_PATH
@pytest.mark.parametrize("""compression_format""" , ["""gzip""", """xz""", """zstd"""] )
def lowerCamelCase__ ( A__ : Optional[int] , A__ : Optional[Any] , A__ : Optional[Any] , A__ : Any , A__ : Union[str, Any] , A__ : str ):
'''simple docstring'''
__lowerCamelCase = {"""gzip""": gz_file, """xz""": xz_file, """zstd""": zstd_path}
__lowerCamelCase = input_paths[compression_format]
__lowerCamelCase = tmp_path / """cache"""
__lowerCamelCase = DownloadConfig(cache_dir=_UpperCamelCase , extract_compressed_file=_UpperCamelCase )
__lowerCamelCase = cached_path(_UpperCamelCase , download_config=_UpperCamelCase )
with open(_UpperCamelCase ) as f:
__lowerCamelCase = f.read()
with open(_UpperCamelCase ) as f:
__lowerCamelCase = f.read()
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize("""default_extracted""" , [True, False] )
@pytest.mark.parametrize("""default_cache_dir""" , [True, False] )
def lowerCamelCase__ ( A__ : List[str] , A__ : str , A__ : Tuple , A__ : List[str] , A__ : Optional[int] ):
'''simple docstring'''
__lowerCamelCase = """custom_cache"""
__lowerCamelCase = """custom_extracted_dir"""
__lowerCamelCase = tmp_path / """custom_extracted_path"""
if default_extracted:
__lowerCamelCase = ("""downloads""" if default_cache_dir else custom_cache_dir, """extracted""")
else:
monkeypatch.setattr("""datasets.config.EXTRACTED_DATASETS_DIR""" , _UpperCamelCase )
monkeypatch.setattr("""datasets.config.EXTRACTED_DATASETS_PATH""" , str(_UpperCamelCase ) )
__lowerCamelCase = custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)
__lowerCamelCase = xz_file
__lowerCamelCase = (
DownloadConfig(extract_compressed_file=_UpperCamelCase )
if default_cache_dir
else DownloadConfig(cache_dir=tmp_path / custom_cache_dir , extract_compressed_file=_UpperCamelCase )
)
__lowerCamelCase = cached_path(_UpperCamelCase , download_config=_UpperCamelCase )
assert Path(_UpperCamelCase ).parent.parts[-2:] == expected
def lowerCamelCase__ ( A__ : Optional[Any] ):
'''simple docstring'''
__lowerCamelCase = str(Path(_UpperCamelCase ).resolve() )
assert cached_path(_UpperCamelCase ) == text_file
# relative path
__lowerCamelCase = str(Path(_UpperCamelCase ).resolve().relative_to(Path(os.getcwd() ) ) )
assert cached_path(_UpperCamelCase ) == text_file
def lowerCamelCase__ ( A__ : List[str] ):
'''simple docstring'''
__lowerCamelCase = str(tmp_path.resolve() / """__missing_file__.txt""" )
with pytest.raises(_UpperCamelCase ):
cached_path(_UpperCamelCase )
# relative path
__lowerCamelCase = """./__missing_file__.txt"""
with pytest.raises(_UpperCamelCase ):
cached_path(_UpperCamelCase )
def lowerCamelCase__ ( A__ : Optional[Any] ):
'''simple docstring'''
__lowerCamelCase = get_from_cache(f'tmp://{tmpfs_file}' )
with open(_UpperCamelCase ) as f:
__lowerCamelCase = f.read()
assert output_file_content == FILE_CONTENT
@patch("""datasets.config.HF_DATASETS_OFFLINE""" , _UpperCamelCase )
def lowerCamelCase__ ( ):
'''simple docstring'''
with pytest.raises(_UpperCamelCase ):
cached_path("""https://huggingface.co""" )
@patch("""datasets.config.HF_DATASETS_OFFLINE""" , _UpperCamelCase )
def lowerCamelCase__ ( A__ : List[str] ):
'''simple docstring'''
__lowerCamelCase = tmp_path_factory.mktemp("""data""" ) / """file.html"""
with pytest.raises(_UpperCamelCase ):
http_get("""https://huggingface.co""" , temp_file=_UpperCamelCase )
with pytest.raises(_UpperCamelCase ):
http_head("""https://huggingface.co""" )
@patch("""datasets.config.HF_DATASETS_OFFLINE""" , _UpperCamelCase )
def lowerCamelCase__ ( A__ : List[Any] ):
'''simple docstring'''
__lowerCamelCase = tmp_path_factory.mktemp("""data""" ) / """file.html"""
with pytest.raises(_UpperCamelCase ):
ftp_get("""ftp://huggingface.co""" , temp_file=_UpperCamelCase )
with pytest.raises(_UpperCamelCase ):
ftp_head("""ftp://huggingface.co""" )
@patch("""datasets.config.HF_DATASETS_OFFLINE""" , _UpperCamelCase )
def lowerCamelCase__ ( A__ : Tuple ):
'''simple docstring'''
__lowerCamelCase = tmp_path_factory.mktemp("""data""" ) / """file.html"""
with pytest.raises(_UpperCamelCase ):
fsspec_get("""s3://huggingface.co""" , temp_file=_UpperCamelCase )
with pytest.raises(_UpperCamelCase ):
fsspec_head("""s3://huggingface.co""" )
| 12 |
'''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 json
import os
from ...utils.constants import SAGEMAKER_PARALLEL_EC2_INSTANCES, TORCH_DYNAMO_MODES
from ...utils.dataclasses import ComputeEnvironment, SageMakerDistributedType
from ...utils.imports import is_botoa_available
from .config_args import SageMakerConfig
from .config_utils import (
DYNAMO_BACKENDS,
_ask_field,
_ask_options,
_convert_dynamo_backend,
_convert_mixed_precision,
_convert_sagemaker_distributed_mode,
_convert_yes_no_to_bool,
)
if is_botoa_available():
import botoa # noqa: F401
def _lowerCAmelCase ( _UpperCamelCase : Optional[int] ) -> List[str]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =botoa.client('iam' )
_SCREAMING_SNAKE_CASE ={
'Version': '2012-10-17',
'Statement': [
{'Effect': 'Allow', 'Principal': {'Service': 'sagemaker.amazonaws.com'}, 'Action': 'sts:AssumeRole'}
],
}
try:
# create the role, associated with the chosen trust policy
iam_client.create_role(
RoleName=_UpperCamelCase , AssumeRolePolicyDocument=json.dumps(_UpperCamelCase , indent=2 ) )
_SCREAMING_SNAKE_CASE ={
'Version': '2012-10-17',
'Statement': [
{
'Effect': 'Allow',
'Action': [
'sagemaker:*',
'ecr:GetDownloadUrlForLayer',
'ecr:BatchGetImage',
'ecr:BatchCheckLayerAvailability',
'ecr:GetAuthorizationToken',
'cloudwatch:PutMetricData',
'cloudwatch:GetMetricData',
'cloudwatch:GetMetricStatistics',
'cloudwatch:ListMetrics',
'logs:CreateLogGroup',
'logs:CreateLogStream',
'logs:DescribeLogStreams',
'logs:PutLogEvents',
'logs:GetLogEvents',
's3:CreateBucket',
's3:ListBucket',
's3:GetBucketLocation',
's3:GetObject',
's3:PutObject',
],
'Resource': '*',
}
],
}
# attach policy to role
iam_client.put_role_policy(
RoleName=_UpperCamelCase , PolicyName=f"{role_name}_policy_permission" , PolicyDocument=json.dumps(_UpperCamelCase , indent=2 ) , )
except iam_client.exceptions.EntityAlreadyExistsException:
print(f"role {role_name} already exists. Using existing one" )
def _lowerCAmelCase ( _UpperCamelCase : List[str] ) -> Optional[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =botoa.client('iam' )
return iam_client.get_role(RoleName=_UpperCamelCase )["Role"]["Arn"]
def _lowerCAmelCase ( ) -> Optional[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =_ask_options(
'How do you want to authorize?' , ['AWS Profile', 'Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) '] , _UpperCamelCase , )
_SCREAMING_SNAKE_CASE =None
if credentials_configuration == 0:
_SCREAMING_SNAKE_CASE =_ask_field('Enter your AWS Profile name: [default] ' , default='default' )
_SCREAMING_SNAKE_CASE =aws_profile
else:
print(
'Note you will need to provide AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY when you launch you training script with,'
'`accelerate launch --aws_access_key_id XXX --aws_secret_access_key YYY`' )
_SCREAMING_SNAKE_CASE =_ask_field('AWS Access Key ID: ' )
_SCREAMING_SNAKE_CASE =aws_access_key_id
_SCREAMING_SNAKE_CASE =_ask_field('AWS Secret Access Key: ' )
_SCREAMING_SNAKE_CASE =aws_secret_access_key
_SCREAMING_SNAKE_CASE =_ask_field('Enter your AWS Region: [us-east-1]' , default='us-east-1' )
_SCREAMING_SNAKE_CASE =aws_region
_SCREAMING_SNAKE_CASE =_ask_options(
'Do you already have an IAM Role for executing Amazon SageMaker Training Jobs?' , ['Provide IAM Role name', 'Create new IAM role using credentials'] , _UpperCamelCase , )
if role_management == 0:
_SCREAMING_SNAKE_CASE =_ask_field('Enter your IAM role name: ' )
else:
_SCREAMING_SNAKE_CASE ='accelerate_sagemaker_execution_role'
print(f"Accelerate will create an iam role \"{iam_role_name}\" using the provided credentials" )
_create_iam_role_for_sagemaker(_UpperCamelCase )
_SCREAMING_SNAKE_CASE =_ask_field(
'Do you want to use custom Docker image? [yes/NO]: ' , _convert_yes_no_to_bool , default=_UpperCamelCase , error_message='Please enter yes or no.' , )
_SCREAMING_SNAKE_CASE =None
if is_custom_docker_image:
_SCREAMING_SNAKE_CASE =_ask_field('Enter your Docker image: ' , lambda _UpperCamelCase : str(_UpperCamelCase ).lower() )
_SCREAMING_SNAKE_CASE =_ask_field(
'Do you want to provide SageMaker input channels with data locations? [yes/NO]: ' , _convert_yes_no_to_bool , default=_UpperCamelCase , error_message='Please enter yes or no.' , )
_SCREAMING_SNAKE_CASE =None
if is_sagemaker_inputs_enabled:
_SCREAMING_SNAKE_CASE =_ask_field(
'Enter the path to the SageMaker inputs TSV file with columns (channel_name, data_location): ' , lambda _UpperCamelCase : str(_UpperCamelCase ).lower() , )
_SCREAMING_SNAKE_CASE =_ask_field(
'Do you want to enable SageMaker metrics? [yes/NO]: ' , _convert_yes_no_to_bool , default=_UpperCamelCase , error_message='Please enter yes or no.' , )
_SCREAMING_SNAKE_CASE =None
if is_sagemaker_metrics_enabled:
_SCREAMING_SNAKE_CASE =_ask_field(
'Enter the path to the SageMaker metrics TSV file with columns (metric_name, metric_regex): ' , lambda _UpperCamelCase : str(_UpperCamelCase ).lower() , )
_SCREAMING_SNAKE_CASE =_ask_options(
'What is the distributed mode?' , ['No distributed training', 'Data parallelism'] , _convert_sagemaker_distributed_mode , )
_SCREAMING_SNAKE_CASE ={}
_SCREAMING_SNAKE_CASE =_ask_field(
'Do you wish to optimize your script with torch dynamo?[yes/NO]:' , _convert_yes_no_to_bool , default=_UpperCamelCase , error_message='Please enter yes or no.' , )
if use_dynamo:
_SCREAMING_SNAKE_CASE ='dynamo_'
_SCREAMING_SNAKE_CASE =_ask_options(
'Which dynamo backend would you like to use?' , [x.lower() for x in DYNAMO_BACKENDS] , _convert_dynamo_backend , default=2 , )
_SCREAMING_SNAKE_CASE =_ask_field(
'Do you want to customize the defaults sent to torch.compile? [yes/NO]: ' , _convert_yes_no_to_bool , default=_UpperCamelCase , error_message='Please enter yes or no.' , )
if use_custom_options:
_SCREAMING_SNAKE_CASE =_ask_options(
'Which mode do you want to use?' , _UpperCamelCase , lambda _UpperCamelCase : TORCH_DYNAMO_MODES[int(_UpperCamelCase )] , default='default' , )
_SCREAMING_SNAKE_CASE =_ask_field(
'Do you want the fullgraph mode or it is ok to break model into several subgraphs? [yes/NO]: ' , _convert_yes_no_to_bool , default=_UpperCamelCase , error_message='Please enter yes or no.' , )
_SCREAMING_SNAKE_CASE =_ask_field(
'Do you want to enable dynamic shape tracing? [yes/NO]: ' , _convert_yes_no_to_bool , default=_UpperCamelCase , error_message='Please enter yes or no.' , )
_SCREAMING_SNAKE_CASE ='Which EC2 instance type you want to use for your training?'
if distributed_type != SageMakerDistributedType.NO:
_SCREAMING_SNAKE_CASE =_ask_options(
_UpperCamelCase , _UpperCamelCase , lambda _UpperCamelCase : SAGEMAKER_PARALLEL_EC2_INSTANCES[int(_UpperCamelCase )] )
else:
eca_instance_query += "? [ml.p3.2xlarge]:"
_SCREAMING_SNAKE_CASE =_ask_field(_UpperCamelCase , lambda _UpperCamelCase : str(_UpperCamelCase ).lower() , default='ml.p3.2xlarge' )
_SCREAMING_SNAKE_CASE =1
if distributed_type in (SageMakerDistributedType.DATA_PARALLEL, SageMakerDistributedType.MODEL_PARALLEL):
_SCREAMING_SNAKE_CASE =_ask_field(
'How many machines do you want use? [1]: ' , _UpperCamelCase , default=1 , )
_SCREAMING_SNAKE_CASE =_ask_options(
'Do you wish to use FP16 or BF16 (mixed precision)?' , ['no', 'fp16', 'bf16', 'fp8'] , _convert_mixed_precision , )
if use_dynamo and mixed_precision == "no":
print(
'Torch dynamo used without mixed precision requires TF32 to be efficient. Accelerate will enable it by default when launching your scripts.' )
return SageMakerConfig(
image_uri=_UpperCamelCase , compute_environment=ComputeEnvironment.AMAZON_SAGEMAKER , distributed_type=_UpperCamelCase , use_cpu=_UpperCamelCase , dynamo_config=_UpperCamelCase , eca_instance_type=_UpperCamelCase , profile=_UpperCamelCase , region=_UpperCamelCase , iam_role_name=_UpperCamelCase , mixed_precision=_UpperCamelCase , num_machines=_UpperCamelCase , sagemaker_inputs_file=_UpperCamelCase , sagemaker_metrics_file=_UpperCamelCase , )
| 47 | 0 |
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def UpperCamelCase__( UpperCamelCase__ : BertModel , UpperCamelCase__ : str , UpperCamelCase__ : str )->List[Any]:
A__ = ('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')
A__ = (
('''layer.''', '''layer_'''),
('''word_embeddings.weight''', '''word_embeddings'''),
('''position_embeddings.weight''', '''position_embeddings'''),
('''token_type_embeddings.weight''', '''token_type_embeddings'''),
('''.''', '''/'''),
('''LayerNorm/weight''', '''LayerNorm/gamma'''),
('''LayerNorm/bias''', '''LayerNorm/beta'''),
('''weight''', '''kernel'''),
)
if not os.path.isdir(UpperCamelCase__ ):
os.makedirs(UpperCamelCase__ )
A__ = model.state_dict()
def to_tf_var_name(UpperCamelCase__ : str ):
for patt, repl in iter(UpperCamelCase__ ):
A__ = name.replace(UpperCamelCase__ , UpperCamelCase__ )
return f"bert/{name}"
def create_tf_var(UpperCamelCase__ : np.ndarray , UpperCamelCase__ : str , UpperCamelCase__ : tf.Session ):
A__ = tf.dtypes.as_dtype(tensor.dtype )
A__ = tf.get_variable(dtype=UpperCamelCase__ , shape=tensor.shape , name=UpperCamelCase__ , initializer=tf.zeros_initializer() )
session.run(tf.variables_initializer([tf_var] ) )
session.run(UpperCamelCase__ )
return tf_var
tf.reset_default_graph()
with tf.Session() as session:
for var_name in state_dict:
A__ = to_tf_var_name(UpperCamelCase__ )
A__ = state_dict[var_name].numpy()
if any(x in var_name for x in tensors_to_transpose ):
A__ = torch_tensor.T
A__ = create_tf_var(tensor=UpperCamelCase__ , name=UpperCamelCase__ , session=UpperCamelCase__ )
tf.keras.backend.set_value(UpperCamelCase__ , UpperCamelCase__ )
A__ = session.run(UpperCamelCase__ )
print(f"Successfully created {tf_name}: {np.allclose(UpperCamelCase__ , UpperCamelCase__ )}" )
A__ = tf.train.Saver(tf.trainable_variables() )
saver.save(UpperCamelCase__ , os.path.join(UpperCamelCase__ , model_name.replace('''-''' , '''_''' ) + '''.ckpt''' ) )
def UpperCamelCase__( UpperCamelCase__ : int=None )->Tuple:
A__ = argparse.ArgumentParser()
parser.add_argument('''--model_name''' , type=UpperCamelCase__ , required=UpperCamelCase__ , help='''model name e.g. bert-base-uncased''' )
parser.add_argument(
'''--cache_dir''' , type=UpperCamelCase__ , default=UpperCamelCase__ , required=UpperCamelCase__ , help='''Directory containing pytorch model''' )
parser.add_argument('''--pytorch_model_path''' , type=UpperCamelCase__ , required=UpperCamelCase__ , help='''/path/to/<pytorch-model-name>.bin''' )
parser.add_argument('''--tf_cache_dir''' , type=UpperCamelCase__ , required=UpperCamelCase__ , help='''Directory in which to save tensorflow model''' )
A__ = parser.parse_args(UpperCamelCase__ )
A__ = BertModel.from_pretrained(
pretrained_model_name_or_path=args.model_name , state_dict=torch.load(args.pytorch_model_path ) , cache_dir=args.cache_dir , )
convert_pytorch_checkpoint_to_tf(model=UpperCamelCase__ , ckpt_dir=args.tf_cache_dir , model_name=args.model_name )
if __name__ == "__main__":
main()
| 39 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__: List[Any] = logging.get_logger(__name__)
a__: Optional[Any] = {
'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 SCREAMING_SNAKE_CASE__ ( UpperCamelCase__ ):
__SCREAMING_SNAKE_CASE = '''unispeech'''
def __init__( self,__lowerCamelCase=32,__lowerCamelCase=768,__lowerCamelCase=12,__lowerCamelCase=12,__lowerCamelCase=3072,__lowerCamelCase="gelu",__lowerCamelCase=0.1,__lowerCamelCase=0.1,__lowerCamelCase=0.1,__lowerCamelCase=0.0,__lowerCamelCase=0.0,__lowerCamelCase=0.1,__lowerCamelCase=0.1,__lowerCamelCase=0.02,__lowerCamelCase=1E-5,__lowerCamelCase="group",__lowerCamelCase="gelu",__lowerCamelCase=(512, 512, 512, 512, 512, 512, 512),__lowerCamelCase=(5, 2, 2, 2, 2, 2, 2),__lowerCamelCase=(10, 3, 3, 3, 3, 2, 2),__lowerCamelCase=False,__lowerCamelCase=128,__lowerCamelCase=16,__lowerCamelCase=False,__lowerCamelCase=True,__lowerCamelCase=0.05,__lowerCamelCase=10,__lowerCamelCase=2,__lowerCamelCase=0.0,__lowerCamelCase=10,__lowerCamelCase=0,__lowerCamelCase=320,__lowerCamelCase=2,__lowerCamelCase=0.1,__lowerCamelCase=100,__lowerCamelCase=256,__lowerCamelCase=256,__lowerCamelCase=0.1,__lowerCamelCase="mean",__lowerCamelCase=False,__lowerCamelCase=False,__lowerCamelCase=256,__lowerCamelCase=80,__lowerCamelCase=0,__lowerCamelCase=1,__lowerCamelCase=2,__lowerCamelCase=0.5,**__lowerCamelCase,):
super().__init__(**__lowerCamelCase,pad_token_id=__lowerCamelCase,bos_token_id=__lowerCamelCase,eos_token_id=__lowerCamelCase )
A__ = hidden_size
A__ = feat_extract_norm
A__ = feat_extract_activation
A__ = list(__lowerCamelCase )
A__ = list(__lowerCamelCase )
A__ = list(__lowerCamelCase )
A__ = conv_bias
A__ = num_conv_pos_embeddings
A__ = num_conv_pos_embedding_groups
A__ = len(self.conv_dim )
A__ = num_hidden_layers
A__ = intermediate_size
A__ = hidden_act
A__ = num_attention_heads
A__ = hidden_dropout
A__ = attention_dropout
A__ = activation_dropout
A__ = feat_proj_dropout
A__ = final_dropout
A__ = layerdrop
A__ = layer_norm_eps
A__ = initializer_range
A__ = num_ctc_classes
A__ = vocab_size
A__ = do_stable_layer_norm
A__ = use_weighted_layer_sum
A__ = 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
A__ = apply_spec_augment
A__ = mask_time_prob
A__ = mask_time_length
A__ = mask_time_min_masks
A__ = mask_feature_prob
A__ = mask_feature_length
A__ = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
A__ = num_codevectors_per_group
A__ = num_codevector_groups
A__ = contrastive_logits_temperature
A__ = feat_quantizer_dropout
A__ = num_negatives
A__ = codevector_dim
A__ = proj_codevector_dim
A__ = diversity_loss_weight
# ctc loss
A__ = ctc_loss_reduction
A__ = ctc_zero_infinity
# pretraining loss
A__ = replace_prob
@property
def UpperCamelCase ( self ):
return functools.reduce(operator.mul,self.conv_stride,1 )
| 39 | 1 |
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ImageGPTImageProcessor
class __A ( unittest.TestCase ):
'''simple docstring'''
def __init__( self , __lowerCAmelCase , __lowerCAmelCase=7 , __lowerCAmelCase=3 , __lowerCAmelCase=1_8 , __lowerCAmelCase=3_0 , __lowerCAmelCase=4_0_0 , __lowerCAmelCase=True , __lowerCAmelCase=None , __lowerCAmelCase=True , ):
'''simple docstring'''
lowerCamelCase__ = size if size is not None else {'height': 1_8, 'width': 1_8}
lowerCamelCase__ = parent
lowerCamelCase__ = batch_size
lowerCamelCase__ = num_channels
lowerCamelCase__ = image_size
lowerCamelCase__ = min_resolution
lowerCamelCase__ = max_resolution
lowerCamelCase__ = do_resize
lowerCamelCase__ = size
lowerCamelCase__ = do_normalize
def __lowerCamelCase ( self ):
'''simple docstring'''
return {
# here we create 2 clusters for the sake of simplicity
"clusters": np.asarray(
[
[0.8866_4436_3403_3203, 0.6618_8293_6954_4983, 0.3891_7464_0178_6804],
[-0.6042_5591_4688_1104, -0.0_2295_0088_6052_8469, 0.5423_7973_6900_3296],
] ),
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
}
@require_torch
@require_vision
class __A ( _a , unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase_ = ImageGPTImageProcessor if is_vision_available() else None
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ = ImageGPTImageProcessingTester(self )
@property
def __lowerCamelCase ( self ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__lowerCAmelCase , '''clusters''' ) )
self.assertTrue(hasattr(__lowerCAmelCase , '''do_resize''' ) )
self.assertTrue(hasattr(__lowerCAmelCase , '''size''' ) )
self.assertTrue(hasattr(__lowerCAmelCase , '''do_normalize''' ) )
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''height''': 1_8, '''width''': 1_8} )
lowerCamelCase__ = self.image_processing_class.from_dict(self.image_processor_dict , size=4_2 )
self.assertEqual(image_processor.size , {'''height''': 4_2, '''width''': 4_2} )
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ = self.image_processing_class(**self.image_processor_dict )
lowerCamelCase__ = json.loads(image_processor.to_json_string() )
for key, value in self.image_processor_dict.items():
if key == "clusters":
self.assertTrue(np.array_equal(__lowerCAmelCase , obj[key] ) )
else:
self.assertEqual(obj[key] , __lowerCAmelCase )
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ = self.image_processing_class(**self.image_processor_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCamelCase__ = os.path.join(__lowerCAmelCase , '''image_processor.json''' )
image_processor_first.to_json_file(__lowerCAmelCase )
lowerCamelCase__ = self.image_processing_class.from_json_file(__lowerCAmelCase ).to_dict()
lowerCamelCase__ = image_processor_first.to_dict()
for key, value in image_processor_first.items():
if key == "clusters":
self.assertTrue(np.array_equal(__lowerCAmelCase , image_processor_second[key] ) )
else:
self.assertEqual(image_processor_first[key] , __lowerCAmelCase )
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ = self.image_processing_class(**self.image_processor_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
image_processor_first.save_pretrained(__lowerCAmelCase )
lowerCamelCase__ = self.image_processing_class.from_pretrained(__lowerCAmelCase ).to_dict()
lowerCamelCase__ = image_processor_first.to_dict()
for key, value in image_processor_first.items():
if key == "clusters":
self.assertTrue(np.array_equal(__lowerCAmelCase , image_processor_second[key] ) )
else:
self.assertEqual(image_processor_first[key] , __lowerCAmelCase )
@unittest.skip('''ImageGPT requires clusters at initialization''' )
def __lowerCamelCase ( self ):
'''simple docstring'''
pass
def lowerCAmelCase__() -> Tuple:
'''simple docstring'''
lowerCamelCase__ = load_dataset('''hf-internal-testing/fixtures_image_utils''' ,split='''test''' )
lowerCamelCase__ = Image.open(dataset[4]['''file'''] )
lowerCamelCase__ = Image.open(dataset[5]['''file'''] )
lowerCamelCase__ = [imagea, imagea]
return images
@require_vision
@require_torch
class __A ( unittest.TestCase ):
'''simple docstring'''
@slow
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ = ImageGPTImageProcessor.from_pretrained('''openai/imagegpt-small''' )
lowerCamelCase__ = prepare_images()
# test non-batched
lowerCamelCase__ = image_processing(images[0] , return_tensors='''pt''' )
self.assertIsInstance(encoding.input_ids , torch.LongTensor )
self.assertEqual(encoding.input_ids.shape , (1, 1_0_2_4) )
lowerCamelCase__ = [3_0_6, 1_9_1, 1_9_1]
self.assertEqual(encoding.input_ids[0, :3].tolist() , __lowerCAmelCase )
# test batched
lowerCamelCase__ = image_processing(__lowerCAmelCase , return_tensors='''pt''' )
self.assertIsInstance(encoding.input_ids , torch.LongTensor )
self.assertEqual(encoding.input_ids.shape , (2, 1_0_2_4) )
lowerCamelCase__ = [3_0_3, 1_3, 1_3]
self.assertEqual(encoding.input_ids[1, -3:].tolist() , __lowerCAmelCase )
| 209 |
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
SqueezeBertModel,
)
class _lowerCamelCase( _a ):
def __init__( self, lowerCamelCase, lowerCamelCase=13, lowerCamelCase=7, lowerCamelCase=True, lowerCamelCase=True, lowerCamelCase=False, lowerCamelCase=True, lowerCamelCase=99, lowerCamelCase=32, lowerCamelCase=5, lowerCamelCase=4, lowerCamelCase=64, lowerCamelCase="gelu", lowerCamelCase=0.1, lowerCamelCase=0.1, lowerCamelCase=5_12, lowerCamelCase=16, lowerCamelCase=2, lowerCamelCase=0.0_2, lowerCamelCase=3, lowerCamelCase=4, lowerCamelCase=None, lowerCamelCase=2, lowerCamelCase=2, lowerCamelCase=2, lowerCamelCase=2, lowerCamelCase=4, lowerCamelCase=1, ) -> Union[str, Any]:
"""simple docstring"""
_lowercase : Dict = parent
_lowercase : Optional[Any] = batch_size
_lowercase : Any = seq_length
_lowercase : Optional[Any] = is_training
_lowercase : Optional[Any] = use_input_mask
_lowercase : List[Any] = use_token_type_ids
_lowercase : List[str] = use_labels
_lowercase : str = vocab_size
_lowercase : List[str] = hidden_size
_lowercase : Dict = num_hidden_layers
_lowercase : List[str] = num_attention_heads
_lowercase : int = intermediate_size
_lowercase : Union[str, Any] = hidden_act
_lowercase : int = hidden_dropout_prob
_lowercase : List[Any] = attention_probs_dropout_prob
_lowercase : Dict = max_position_embeddings
_lowercase : Union[str, Any] = type_vocab_size
_lowercase : List[Any] = type_sequence_label_size
_lowercase : Any = initializer_range
_lowercase : List[str] = num_labels
_lowercase : Any = num_choices
_lowercase : Tuple = scope
_lowercase : Optional[Any] = q_groups
_lowercase : List[str] = k_groups
_lowercase : Optional[int] = v_groups
_lowercase : List[str] = post_attention_groups
_lowercase : Union[str, Any] = intermediate_groups
_lowercase : int = output_groups
def UpperCamelCase ( self) -> Optional[int]:
"""simple docstring"""
_lowercase : int = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
_lowercase : Any = None
if self.use_input_mask:
_lowercase : Tuple = random_attention_mask([self.batch_size, self.seq_length])
_lowercase : Dict = None
_lowercase : int = None
_lowercase : List[Any] = None
if self.use_labels:
_lowercase : List[Any] = ids_tensor([self.batch_size], self.type_sequence_label_size)
_lowercase : int = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
_lowercase : Dict = ids_tensor([self.batch_size], self.num_choices)
_lowercase : Optional[Any] = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCamelCase ( self) -> Optional[int]:
"""simple docstring"""
return SqueezeBertConfig(
embedding_size=self.hidden_size, vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, attention_probs_dropout_prob=self.hidden_dropout_prob, attention_dropout=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, initializer_range=self.initializer_range, q_groups=self.q_groups, k_groups=self.k_groups, v_groups=self.v_groups, post_attention_groups=self.post_attention_groups, intermediate_groups=self.intermediate_groups, output_groups=self.output_groups, )
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase) -> Union[str, Any]:
"""simple docstring"""
_lowercase : List[str] = SqueezeBertModel(config=lowerCamelCase)
model.to(lowerCamelCase)
model.eval()
_lowercase : Dict = model(lowerCamelCase, lowerCamelCase)
_lowercase : Any = model(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, lowerCamelCase, lowerCamelCase) -> int:
"""simple docstring"""
_lowercase : Dict = SqueezeBertForMaskedLM(config=lowerCamelCase)
model.to(lowerCamelCase)
model.eval()
_lowercase : Optional[Any] = model(lowerCamelCase, attention_mask=lowerCamelCase, labels=lowerCamelCase)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase) -> Union[str, Any]:
"""simple docstring"""
_lowercase : Union[str, Any] = SqueezeBertForQuestionAnswering(config=lowerCamelCase)
model.to(lowerCamelCase)
model.eval()
_lowercase : List[Any] = model(
lowerCamelCase, attention_mask=lowerCamelCase, start_positions=lowerCamelCase, end_positions=lowerCamelCase)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase) -> str:
"""simple docstring"""
_lowercase : Optional[Any] = self.num_labels
_lowercase : int = SqueezeBertForSequenceClassification(lowerCamelCase)
model.to(lowerCamelCase)
model.eval()
_lowercase : Any = model(lowerCamelCase, attention_mask=lowerCamelCase, labels=lowerCamelCase)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase) -> List[Any]:
"""simple docstring"""
_lowercase : Union[str, Any] = self.num_labels
_lowercase : List[str] = SqueezeBertForTokenClassification(config=lowerCamelCase)
model.to(lowerCamelCase)
model.eval()
_lowercase : Union[str, Any] = model(lowerCamelCase, attention_mask=lowerCamelCase, labels=lowerCamelCase)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase) -> Dict:
"""simple docstring"""
_lowercase : str = self.num_choices
_lowercase : str = SqueezeBertForMultipleChoice(config=lowerCamelCase)
model.to(lowerCamelCase)
model.eval()
_lowercase : Dict = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
_lowercase : int = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
_lowercase : Optional[Any] = model(
lowerCamelCase, attention_mask=lowerCamelCase, labels=lowerCamelCase, )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def UpperCamelCase ( self) -> List[str]:
"""simple docstring"""
_lowercase : Optional[int] = self.prepare_config_and_inputs()
((_lowercase) , (_lowercase) , (_lowercase) , (_lowercase) , (_lowercase) , (_lowercase)) : Dict = config_and_inputs
_lowercase : Tuple = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class _lowerCamelCase( _a, _a, unittest.TestCase ):
lowercase_ : Union[str, Any] = (
(
SqueezeBertModel,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
)
if is_torch_available()
else None
)
lowercase_ : Optional[int] = (
{
"""feature-extraction""": SqueezeBertModel,
"""fill-mask""": SqueezeBertForMaskedLM,
"""question-answering""": SqueezeBertForQuestionAnswering,
"""text-classification""": SqueezeBertForSequenceClassification,
"""token-classification""": SqueezeBertForTokenClassification,
"""zero-shot""": SqueezeBertForSequenceClassification,
}
if is_torch_available()
else {}
)
lowercase_ : Tuple = False
lowercase_ : List[str] = True
lowercase_ : int = False
def UpperCamelCase ( self) -> int:
"""simple docstring"""
_lowercase : str = SqueezeBertModelTester(self)
_lowercase : Dict = ConfigTester(self, config_class=lowerCamelCase, dim=37)
def UpperCamelCase ( self) -> List[str]:
"""simple docstring"""
self.config_tester.run_common_tests()
def UpperCamelCase ( self) -> Tuple:
"""simple docstring"""
_lowercase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_model(*lowerCamelCase)
def UpperCamelCase ( self) -> List[str]:
"""simple docstring"""
_lowercase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_masked_lm(*lowerCamelCase)
def UpperCamelCase ( self) -> Any:
"""simple docstring"""
_lowercase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_question_answering(*lowerCamelCase)
def UpperCamelCase ( self) -> Union[str, Any]:
"""simple docstring"""
_lowercase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_sequence_classification(*lowerCamelCase)
def UpperCamelCase ( self) -> Any:
"""simple docstring"""
_lowercase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_token_classification(*lowerCamelCase)
def UpperCamelCase ( self) -> Optional[int]:
"""simple docstring"""
_lowercase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_multiple_choice(*lowerCamelCase)
@slow
def UpperCamelCase ( self) -> Dict:
"""simple docstring"""
for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowercase : List[Any] = SqueezeBertModel.from_pretrained(lowerCamelCase)
self.assertIsNotNone(lowerCamelCase)
@require_sentencepiece
@require_tokenizers
@require_torch
class _lowerCamelCase( unittest.TestCase ):
@slow
def UpperCamelCase ( self) -> Optional[Any]:
"""simple docstring"""
_lowercase : Union[str, Any] = SqueezeBertForSequenceClassification.from_pretrained('squeezebert/squeezebert-mnli')
_lowercase : Optional[int] = torch.tensor([[1, 2_94_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 13, 15_88, 2]])
_lowercase : List[str] = model(lowerCamelCase)[0]
_lowercase : Union[str, Any] = torch.Size((1, 3))
self.assertEqual(output.shape, lowerCamelCase)
_lowercase : Tuple = torch.tensor([[0.6_4_0_1, -0.0_3_4_9, -0.6_0_4_1]])
self.assertTrue(torch.allclose(lowerCamelCase, lowerCamelCase, atol=1E-4))
| 21 | 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, PreTrainedTokenizer
from ...utils import logging
a : Any = logging.get_logger(__name__)
a : List[Any] = {"""vocab_file""": """sentencepiece.bpe.model"""}
a : Optional[Any] = {
"""vocab_file""": {
"""moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
"""moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
"""moussaKam/barthez-orangesum-title""": (
"""https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
),
},
}
a : str = {
"""moussaKam/mbarthez""": 1024,
"""moussaKam/barthez""": 1024,
"""moussaKam/barthez-orangesum-title""": 1024,
}
a : Union[str, Any] = """▁"""
class a ( __A ):
snake_case_ = VOCAB_FILES_NAMES
snake_case_ = PRETRAINED_VOCAB_FILES_MAP
snake_case_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case_ = ['input_ids', 'attention_mask']
def __init__( self : int , lowercase_ : List[str] , lowercase_ : List[str]="<s>" , lowercase_ : Union[str, Any]="</s>" , lowercase_ : str="</s>" , lowercase_ : List[str]="<s>" , lowercase_ : Dict="<unk>" , lowercase_ : Optional[int]="<pad>" , lowercase_ : List[str]="<mask>" , lowercase_ : str = None , **lowercase_ : Any , ):
# Mask token behave like a normal word, i.e. include the space before it
snake_case_ = AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase ) if isinstance(__lowercase , __lowercase ) else mask_token
snake_case_ = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=__lowercase , eos_token=__lowercase , unk_token=__lowercase , sep_token=__lowercase , cls_token=__lowercase , pad_token=__lowercase , mask_token=__lowercase , sp_model_kwargs=self.sp_model_kwargs , **__lowercase , )
snake_case_ = vocab_file
snake_case_ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(__lowercase ) )
snake_case_ = {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}
snake_case_ = len(self.sp_model ) - 1
snake_case_ = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def A_ ( self : Dict , lowercase_ : Any , lowercase_ : int = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
snake_case_ = [self.cls_token_id]
snake_case_ = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def A_ ( self : Optional[Any] , lowercase_ : Union[str, Any] , lowercase_ : List[Any] = None , lowercase_ : Any = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__lowercase , token_ids_a=__lowercase , already_has_special_tokens=__lowercase )
if token_ids_a is None:
return [1] + ([0] * len(__lowercase )) + [1]
return [1] + ([0] * len(__lowercase )) + [1, 1] + ([0] * len(__lowercase )) + [1]
def A_ ( self : Any , lowercase_ : int , lowercase_ : Any = None ):
snake_case_ = [self.sep_token_id]
snake_case_ = [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 A_ ( self : Optional[Any] ):
return len(self.sp_model )
def A_ ( self : int ):
snake_case_ = {self.convert_ids_to_tokens(__lowercase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def A_ ( self : Optional[int] , lowercase_ : List[Any] ):
return self.sp_model.encode(__lowercase , out_type=__lowercase )
def A_ ( self : Optional[int] , lowercase_ : int ):
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
snake_case_ = self.sp_model.PieceToId(__lowercase )
return spm_id if spm_id else self.unk_token_id
def A_ ( self : Dict , lowercase_ : str ):
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(__lowercase )
def A_ ( self : Optional[int] , lowercase_ : Optional[Any] ):
snake_case_ = []
snake_case_ = ''''''
snake_case_ = 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(__lowercase ) + token
snake_case_ = True
snake_case_ = []
else:
current_sub_tokens.append(__lowercase )
snake_case_ = False
out_string += self.sp_model.decode(__lowercase )
return out_string.strip()
def __getstate__( self : int ):
snake_case_ = self.__dict__.copy()
snake_case_ = None
return state
def __setstate__( self : int , lowercase_ : Dict ):
snake_case_ = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
snake_case_ = {}
snake_case_ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def A_ ( self : List[str] , lowercase_ : Dict , lowercase_ : Optional[Any] = None ):
if not os.path.isdir(__lowercase ):
logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
return
snake_case_ = os.path.join(
__lowercase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__lowercase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , __lowercase )
elif not os.path.isfile(self.vocab_file ):
with open(__lowercase , '''wb''' ) as fi:
snake_case_ = self.sp_model.serialized_model_proto()
fi.write(__lowercase )
return (out_vocab_file,)
| 359 |
'''simple docstring'''
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, StoppingCriteria, StoppingCriteriaList
a : str = ['\nclass', '\ndef', '\n#', '\n@', '\nprint', '\nif']
class a ( _lowerCamelCase ):
def __init__( self : List[Any] , lowercase_ : Union[str, Any] , lowercase_ : Dict , lowercase_ : Optional[int]=None , lowercase_ : str=1 ):
snake_case_ = tokenizer
snake_case_ = dataset
snake_case_ = len(lowercase_ ) if n_tasks is None else n_tasks
snake_case_ = n_copies
def __iter__( self : Optional[Any] ):
snake_case_ = []
for task in range(self.n_tasks ):
# without strip, the model generate commented codes ...
prompts.append(self.tokenizer.eos_token + self.dataset[task]['''prompt'''].strip() )
snake_case_ = self.tokenizer(lowercase_ , padding=lowercase_ , return_tensors='''pt''' )
for task in range(self.n_tasks ):
for _ in range(self.n_copies ):
yield {
"ids": outputs.input_ids[task],
"task_id": task,
"input_len": outputs.attention_mask[task].sum(),
}
class a ( _lowerCamelCase ):
def __init__( self : str , lowercase_ : Tuple , lowercase_ : Optional[int] , lowercase_ : int ):
snake_case_ = start_length
snake_case_ = eof_strings
snake_case_ = tokenizer
def __call__( self : List[Any] , lowercase_ : str , lowercase_ : Optional[int] , **lowercase_ : List[str] ):
snake_case_ = self.tokenizer.batch_decode(input_ids[:, self.start_length :] )
snake_case_ = []
for decoded_generation in decoded_generations:
done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings ) )
return all(lowercase_ )
def __magic_name__ ( __UpperCAmelCase ) -> Any:
'''simple docstring'''
snake_case_ = re.split('''(%s)''' % '''|'''.join(__UpperCAmelCase ), __UpperCAmelCase )
# last string should be ""
return "".join(string_list[:-2] )
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase=20, **__UpperCAmelCase ) -> str:
'''simple docstring'''
snake_case_ = defaultdict(__UpperCAmelCase ) # dict of list of generated tokens
for step, batch in tqdm(enumerate(__UpperCAmelCase ) ):
with torch.no_grad():
snake_case_ = batch['''ids'''].shape[-1]
snake_case_ = accelerator.unwrap_model(__UpperCAmelCase ).generate(
input_ids=batch['''ids'''][:, : batch['''input_len''']], num_return_sequences=__UpperCAmelCase, **__UpperCAmelCase )
# each task is generated batch_size times
snake_case_ = batch['''task_id'''].repeat(__UpperCAmelCase )
snake_case_ = accelerator.pad_across_processes(
__UpperCAmelCase, dim=1, pad_index=tokenizer.pad_token_id )
snake_case_ ,snake_case_ = accelerator.gather((generated_tokens, generated_tasks) )
snake_case_ = generated_tokens.cpu().numpy()
snake_case_ = generated_tasks.cpu().numpy()
for task, generated_tokens in zip(__UpperCAmelCase, __UpperCAmelCase ):
gen_token_dict[task].append(__UpperCAmelCase )
snake_case_ = [[] for _ in range(__UpperCAmelCase )]
for task, generated_tokens in gen_token_dict.items():
for s in generated_tokens:
snake_case_ = tokenizer.decode(__UpperCAmelCase, skip_special_tokens=__UpperCAmelCase, clean_up_tokenization_spaces=__UpperCAmelCase )
code_gens[task].append(remove_last_block(__UpperCAmelCase ) )
return code_gens
def __magic_name__ ( ) -> Tuple:
'''simple docstring'''
snake_case_ = HfArgumentParser(__UpperCAmelCase )
snake_case_ = parser.parse_args()
transformers.logging.set_verbosity_error()
# enables code execution in code_eval metric
snake_case_ = args.HF_ALLOW_CODE_EVAL
# make sure tokenizer plays nice with multiprocessing
snake_case_ = '''false'''
if args.num_workers is None:
snake_case_ = multiprocessing.cpu_count()
# Use dataset load to feed to accelerate
snake_case_ = Accelerator()
set_seed(args.seed, device_specific=__UpperCAmelCase )
# Load model and tokenizer
snake_case_ = AutoTokenizer.from_pretrained(args.model_ckpt )
snake_case_ = tokenizer.eos_token
snake_case_ = AutoModelForCausalLM.from_pretrained(args.model_ckpt )
# Generation settings
snake_case_ = {
'''do_sample''': args.do_sample,
'''temperature''': args.temperature,
'''max_new_tokens''': args.max_new_tokens,
'''top_p''': args.top_p,
'''top_k''': args.top_k,
'''stopping_criteria''': StoppingCriteriaList([EndOfFunctionCriteria(0, __UpperCAmelCase, __UpperCAmelCase )] ),
}
# Load evaluation dataset and metric
snake_case_ = load_dataset('''openai_humaneval''' )
snake_case_ = load_metric('''code_eval''' )
snake_case_ = args.num_tasks if args.num_tasks is not None else len(human_eval['''test'''] )
snake_case_ = args.n_samples // args.batch_size
snake_case_ = TokenizedDataset(__UpperCAmelCase, human_eval['''test'''], n_copies=__UpperCAmelCase, n_tasks=__UpperCAmelCase )
# do not confuse args.batch_size, which is actually the num_return_sequences
snake_case_ = DataLoader(__UpperCAmelCase, batch_size=1 )
# Run a quick test to see if code evaluation is enabled
try:
snake_case_ = code_eval_metric.compute(references=[''''''], predictions=[['''''']] )
except ValueError as exception:
print(
'''Code evaluation not enabled. Read the warning below carefully and then use `--HF_ALLOW_CODE_EVAL="1"`'''
''' flag to enable code evaluation.''' )
raise exception
snake_case_ ,snake_case_ = accelerator.prepare(__UpperCAmelCase, __UpperCAmelCase )
snake_case_ = complete_code(
__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, n_tasks=__UpperCAmelCase, batch_size=args.batch_size, **__UpperCAmelCase, )
if accelerator.is_main_process:
snake_case_ = []
for task in tqdm(range(__UpperCAmelCase ) ):
snake_case_ = human_eval['''test'''][task]['''test''']
snake_case_ = F"check({human_eval['test'][task]['entry_point']})"
references.append('''\n''' + test_func + '''\n''' + entry_point )
# Evaluate completions with "code_eval" metric
snake_case_ ,snake_case_ = code_eval_metric.compute(
references=__UpperCAmelCase, predictions=__UpperCAmelCase, num_workers=args.num_workers )
print(F"Results: {pass_at_k}" )
# Save results to json file
with open(args.output_file, '''w''' ) as fp:
json.dump(__UpperCAmelCase, __UpperCAmelCase )
# For some reason the folliwng seems to be necessary sometimes for code_eval to work nice with multiprocessing
# https://stackoverflow.com/questions/60804599/python-multiprocessing-keeps-spawning-the-whole-script
if __name__ == "__main__":
main()
| 72 | 0 |
'''simple docstring'''
from __future__ import annotations
from PIL import Image
# Define glider example
a : Optional[int] = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[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],
]
# Define blinker example
a : List[Any] = [[0, 1, 0], [0, 1, 0], [0, 1, 0]]
def __magic_name__ ( __UpperCAmelCase ) -> list[list[int]]:
'''simple docstring'''
snake_case_ = []
for i in range(len(__UpperCAmelCase ) ):
snake_case_ = []
for j in range(len(cells[i] ) ):
# Get the number of live neighbours
snake_case_ = 0
if i > 0 and j > 0:
neighbour_count += cells[i - 1][j - 1]
if i > 0:
neighbour_count += cells[i - 1][j]
if i > 0 and j < len(cells[i] ) - 1:
neighbour_count += cells[i - 1][j + 1]
if j > 0:
neighbour_count += cells[i][j - 1]
if j < len(cells[i] ) - 1:
neighbour_count += cells[i][j + 1]
if i < len(__UpperCAmelCase ) - 1 and j > 0:
neighbour_count += cells[i + 1][j - 1]
if i < len(__UpperCAmelCase ) - 1:
neighbour_count += cells[i + 1][j]
if i < len(__UpperCAmelCase ) - 1 and j < len(cells[i] ) - 1:
neighbour_count += cells[i + 1][j + 1]
# Rules of the game of life (excerpt from Wikipedia):
# 1. Any live cell with two or three live neighbours survives.
# 2. Any dead cell with three live neighbours becomes a live cell.
# 3. All other live cells die in the next generation.
# Similarly, all other dead cells stay dead.
snake_case_ = cells[i][j] == 1
if (
(alive and 2 <= neighbour_count <= 3)
or not alive
and neighbour_count == 3
):
next_generation_row.append(1 )
else:
next_generation_row.append(0 )
next_generation.append(__UpperCAmelCase )
return next_generation
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> list[Image.Image]:
'''simple docstring'''
snake_case_ = []
for _ in range(__UpperCAmelCase ):
# Create output image
snake_case_ = Image.new('''RGB''', (len(cells[0] ), len(__UpperCAmelCase )) )
snake_case_ = img.load()
# Save cells to image
for x in range(len(__UpperCAmelCase ) ):
for y in range(len(cells[0] ) ):
snake_case_ = 255 - cells[y][x] * 255
snake_case_ = (colour, colour, colour)
# Save image
images.append(__UpperCAmelCase )
snake_case_ = new_generation(__UpperCAmelCase )
return images
if __name__ == "__main__":
a : Dict = generate_images(GLIDER, 16)
images[0].save('out.gif', save_all=True, append_images=images[1:])
| 56 | import requests
def UpperCAmelCase ( lowercase , lowercase ):
"""simple docstring"""
__lowercase = {'''Content-Type''': '''application/json'''}
__lowercase = requests.post(lowercase , json={'''text''': message_body} , headers=lowercase )
if response.status_code != 200:
__lowercase = (
'''Request to slack returned an error '''
F"{response.status_code}, the response is:\n{response.text}"
)
raise ValueError(lowercase )
if __name__ == "__main__":
# Set the slack url to the one provided by Slack when you create the webhook at
# https://my.slack.com/services/new/incoming-webhook/
send_slack_message("""<YOUR MESSAGE BODY>""", """<SLACK CHANNEL URL>""") | 210 | 0 |
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class _snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE__ ( self) -> List[Any]:
SCREAMING_SNAKE_CASE = inspect.getfile(accelerate.test_utils)
SCREAMING_SNAKE_CASE = os.path.sep.join(mod_file.split(os.path.sep)[:-1] + ['scripts', 'test_script.py'])
SCREAMING_SNAKE_CASE = os.path.sep.join(
mod_file.split(os.path.sep)[:-1] + ['scripts', 'test_distributed_data_loop.py'])
SCREAMING_SNAKE_CASE = os.path.sep.join(mod_file.split(os.path.sep)[:-1] + ['scripts', 'test_ops.py'])
@require_multi_gpu
def SCREAMING_SNAKE_CASE__ ( self) -> Union[str, Any]:
print(f'''Found {torch.cuda.device_count()} devices.''')
SCREAMING_SNAKE_CASE = ['torchrun', f'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
with patch_environment(omp_num_threads=1):
execute_subprocess_async(A__ , env=os.environ.copy())
@require_multi_gpu
def SCREAMING_SNAKE_CASE__ ( self) -> List[str]:
print(f'''Found {torch.cuda.device_count()} devices.''')
SCREAMING_SNAKE_CASE = ['torchrun', f'''--nproc_per_node={torch.cuda.device_count()}''', self.operation_file_path]
print(f'''Command: {cmd}''')
with patch_environment(omp_num_threads=1):
execute_subprocess_async(A__ , env=os.environ.copy())
@require_multi_gpu
def SCREAMING_SNAKE_CASE__ ( self) -> Optional[Any]:
SCREAMING_SNAKE_CASE = ['torchrun', f'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__)]
with patch_environment(omp_num_threads=1):
execute_subprocess_async(A__ , env=os.environ.copy())
@require_multi_gpu
def SCREAMING_SNAKE_CASE__ ( self) -> List[str]:
print(f'''Found {torch.cuda.device_count()} devices, using 2 devices only''')
SCREAMING_SNAKE_CASE = ['torchrun', f'''--nproc_per_node={torch.cuda.device_count()}''', self.data_loop_file_path]
with patch_environment(omp_num_threads=1 , cuda_visible_devices='0,1'):
execute_subprocess_async(A__ , env=os.environ.copy())
if __name__ == "__main__":
a_ : Union[str, Any] = Accelerator()
a_ : int = (accelerator.state.process_index + 2, 10)
a_ : Any = torch.randint(0, 10, shape).to(accelerator.device)
a_ : str = ""
a_ : List[str] = accelerator.pad_across_processes(tensor)
if tensora.shape[0] != accelerator.state.num_processes + 1:
error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
error_msg += "Tensors have different values."
if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
error_msg += "Padding was not done with the right value (0)."
a_ : List[Any] = accelerator.pad_across_processes(tensor, pad_first=True)
if tensora.shape[0] != accelerator.state.num_processes + 1:
error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
a_ : int = accelerator.state.num_processes - accelerator.state.process_index - 1
if not torch.equal(tensora[index:], tensor):
error_msg += "Tensors have different values."
if not torch.all(tensora[:index] == 0):
error_msg += "Padding was not done with the right value (0)."
# Raise error at the end to make sure we don't stop at the first failure.
if len(error_msg) > 0:
raise ValueError(error_msg)
| 359 |
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
a_ : Dict = logging.getLogger(__name__)
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class _snake_case ( A__ ):
def __init__( self , *a , a=None , a=None , a=None , **a) -> List[Any]:
super().__init__(*a , **a)
SCREAMING_SNAKE_CASE = eval_examples
SCREAMING_SNAKE_CASE = post_process_function
SCREAMING_SNAKE_CASE = quant_trainer_args
SCREAMING_SNAKE_CASE = 128 # default number of calibration samples
def SCREAMING_SNAKE_CASE__ ( self , a=None) -> Union[str, Any]:
if calib_dataset is None and self.calib_dataset is None:
raise ValueError('Trainer: calibration requires an calib_dataset.')
SCREAMING_SNAKE_CASE = calib_dataset if calib_dataset is not None else self.calib_dataset
SCREAMING_SNAKE_CASE = self._remove_unused_columns(a , description='Calibration')
return DataLoader(
a , batch_size=self.args.eval_batch_size , collate_fn=self.data_collator , drop_last=self.args.dataloader_drop_last , num_workers=self.args.dataloader_num_workers , pin_memory=self.args.dataloader_pin_memory , shuffle=a , )
def SCREAMING_SNAKE_CASE__ ( self , a=None) -> Optional[Any]:
SCREAMING_SNAKE_CASE = self.train_dataset if calib_dataset is None else calib_dataset
SCREAMING_SNAKE_CASE = self.get_calib_dataloader(a)
SCREAMING_SNAKE_CASE = self.model
quant_trainer.configure_model(a , self.quant_trainer_args , calib=a)
model.eval()
quant_trainer.enable_calibration(a)
logger.info('***** Running calibration *****')
logger.info(f''' Num examples = {self.calib_num}''')
logger.info(f''' Batch size = {calib_dataloader.batch_size}''')
for step, inputs in enumerate(a):
# Prediction step
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.prediction_step(a , a , prediction_loss_only=a)
if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
break
quant_trainer.finish_calibration(a , self.quant_trainer_args)
SCREAMING_SNAKE_CASE = model
def SCREAMING_SNAKE_CASE__ ( self , a=None , a=None , a=None , a = "eval") -> str:
SCREAMING_SNAKE_CASE = self.eval_dataset if eval_dataset is None else eval_dataset
SCREAMING_SNAKE_CASE = self.get_eval_dataloader(a)
SCREAMING_SNAKE_CASE = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
SCREAMING_SNAKE_CASE = self.compute_metrics
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
SCREAMING_SNAKE_CASE = eval_loop(
a , description='Evaluation' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=a , )
finally:
SCREAMING_SNAKE_CASE = compute_metrics
if self.post_process_function is not None and self.compute_metrics is not None:
SCREAMING_SNAKE_CASE = self.post_process_function(a , a , output.predictions)
SCREAMING_SNAKE_CASE = self.compute_metrics(a)
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys()):
if not key.startswith(f'''{metric_key_prefix}_'''):
SCREAMING_SNAKE_CASE = metrics.pop(a)
self.log(a)
else:
SCREAMING_SNAKE_CASE = {}
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())
SCREAMING_SNAKE_CASE = self.callback_handler.on_evaluate(self.args , self.state , self.control , a)
return metrics
def SCREAMING_SNAKE_CASE__ ( self , a , a , a=None , a = "test") -> Optional[Any]:
SCREAMING_SNAKE_CASE = self.get_test_dataloader(a)
# Temporarily disable metric computation, we will do it in the loop here.
SCREAMING_SNAKE_CASE = self.compute_metrics
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
SCREAMING_SNAKE_CASE = eval_loop(
a , description='Prediction' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=a , )
finally:
SCREAMING_SNAKE_CASE = compute_metrics
if self.post_process_function is None or self.compute_metrics is None:
return output
SCREAMING_SNAKE_CASE = self.post_process_function(a , a , output.predictions , 'predict')
SCREAMING_SNAKE_CASE = self.compute_metrics(a)
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys()):
if not key.startswith(f'''{metric_key_prefix}_'''):
SCREAMING_SNAKE_CASE = metrics.pop(a)
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=a)
def SCREAMING_SNAKE_CASE__ ( self , a="./") -> List[Any]:
SCREAMING_SNAKE_CASE = self.eval_dataset
SCREAMING_SNAKE_CASE = self.get_eval_dataloader(a)
SCREAMING_SNAKE_CASE = next(iter(a))
# saving device - to make it consistent
SCREAMING_SNAKE_CASE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# convert to tuple
SCREAMING_SNAKE_CASE = tuple(v.to(a) for k, v in batch.items())
logger.info('Converting model to be onnx compatible')
from pytorch_quantization.nn import TensorQuantizer
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = self.model.to(a)
model.eval()
model.float()
SCREAMING_SNAKE_CASE = model.module if hasattr(a , 'module') else model
quant_trainer.configure_model(a , self.quant_trainer_args)
SCREAMING_SNAKE_CASE = os.path.join(a , 'model.onnx')
logger.info(f'''exporting model to {output_model_file}''')
SCREAMING_SNAKE_CASE = {0: 'batch_size', 1: 'seq_len'}
torch.onnx.export(
a , a , a , export_params=a , opset_version=13 , do_constant_folding=a , input_names=['input_ids', 'attention_mask', 'token_type_ids'] , output_names=['output_start_logits', 'output_end_logits'] , dynamic_axes={
'input_ids': axes,
'attention_mask': axes,
'token_type_ids': axes,
'output_start_logits': axes,
'output_end_logits': axes,
} , verbose=a , )
logger.info('onnx export finished')
| 327 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tokenization_utils_base import PreTrainedTokenizerBase
lowerCAmelCase__ : Optional[Any] =logging.get_logger(__name__)
lowerCAmelCase__ : Tuple ={
'''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json''',
'''allenai/longformer-large-4096''': '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json''',
'''allenai/longformer-large-4096-finetuned-triviaqa''': (
'''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'''
),
'''allenai/longformer-base-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'''
),
'''allenai/longformer-large-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'''
),
}
class UpperCAmelCase_ ( UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ : Optional[int] = "longformer"
def __init__( self , _A = 512 , _A = 2 , _A = 1 , _A = 0 , _A = 2 , _A = 30_522 , _A = 768 , _A = 12 , _A = 12 , _A = 3_072 , _A = "gelu" , _A = 0.1 , _A = 0.1 , _A = 512 , _A = 2 , _A = 0.0_2 , _A = 1e-12 , _A = False , **_A , ):
'''simple docstring'''
super().__init__(pad_token_id=snake_case__ , **snake_case__ )
__SCREAMING_SNAKE_CASE = attention_window
__SCREAMING_SNAKE_CASE = sep_token_id
__SCREAMING_SNAKE_CASE = bos_token_id
__SCREAMING_SNAKE_CASE = eos_token_id
__SCREAMING_SNAKE_CASE = vocab_size
__SCREAMING_SNAKE_CASE = hidden_size
__SCREAMING_SNAKE_CASE = num_hidden_layers
__SCREAMING_SNAKE_CASE = num_attention_heads
__SCREAMING_SNAKE_CASE = hidden_act
__SCREAMING_SNAKE_CASE = intermediate_size
__SCREAMING_SNAKE_CASE = hidden_dropout_prob
__SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
__SCREAMING_SNAKE_CASE = max_position_embeddings
__SCREAMING_SNAKE_CASE = type_vocab_size
__SCREAMING_SNAKE_CASE = initializer_range
__SCREAMING_SNAKE_CASE = layer_norm_eps
__SCREAMING_SNAKE_CASE = onnx_export
class UpperCAmelCase_ ( UpperCamelCase_ ):
'''simple docstring'''
def __init__( self , _A , _A = "default" , _A = None ):
'''simple docstring'''
super().__init__(snake_case__ , snake_case__ , snake_case__ )
__SCREAMING_SNAKE_CASE = True
@property
def _A ( self ):
'''simple docstring'''
if self.task == "multiple-choice":
__SCREAMING_SNAKE_CASE = {0: "batch", 1: "choice", 2: "sequence"}
else:
__SCREAMING_SNAKE_CASE = {0: "batch", 1: "sequence"}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
('global_attention_mask', dynamic_axis),
] )
@property
def _A ( self ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = super().outputs
if self.task == "default":
__SCREAMING_SNAKE_CASE = {0: "batch"}
return outputs
@property
def _A ( self ):
'''simple docstring'''
return 1e-4
@property
def _A ( self ):
'''simple docstring'''
return max(super().default_onnx_opset , 14 )
def _A ( self , _A , _A = -1 , _A = -1 , _A = False , _A = None , ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = super().generate_dummy_inputs(
preprocessor=snake_case__ , batch_size=snake_case__ , seq_length=snake_case__ , is_pair=snake_case__ , framework=snake_case__ )
import torch
# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
# makes the export fail randomly
__SCREAMING_SNAKE_CASE = torch.zeros_like(inputs['input_ids'] )
# make every second token global
__SCREAMING_SNAKE_CASE = 1
return inputs
| 257 |
"""simple docstring"""
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
a : int
a : TreeNode | None =None
a : TreeNode | None =None
lowerCAmelCase__ = namedtuple('''CoinsDistribResult''', '''moves excess''')
def a__ ( SCREAMING_SNAKE_CASE : TreeNode | None ):
'''simple docstring'''
if root is None:
return 0
# Validation
def count_nodes(SCREAMING_SNAKE_CASE : TreeNode | None ) -> int:
if node is None:
return 0
return count_nodes(node.left ) + count_nodes(node.right ) + 1
def count_coins(SCREAMING_SNAKE_CASE : TreeNode | None ) -> int:
if node is None:
return 0
return count_coins(node.left ) + count_coins(node.right ) + node.data
if count_nodes(SCREAMING_SNAKE_CASE ) != count_coins(SCREAMING_SNAKE_CASE ):
raise ValueError("The nodes number should be same as the number of coins" )
# Main calculation
def get_distrib(SCREAMING_SNAKE_CASE : TreeNode | None ) -> CoinsDistribResult:
if node is None:
return CoinsDistribResult(0 , 1 )
lowerCAmelCase , lowerCAmelCase : List[str] = get_distrib(node.left )
lowerCAmelCase , lowerCAmelCase : List[str] = get_distrib(node.right )
lowerCAmelCase : Union[str, Any] = 1 - left_distrib_excess
lowerCAmelCase : List[Any] = 1 - right_distrib_excess
lowerCAmelCase : int = (
left_distrib_moves
+ right_distrib_moves
+ abs(SCREAMING_SNAKE_CASE )
+ abs(SCREAMING_SNAKE_CASE )
)
lowerCAmelCase : int = node.data - coins_to_left - coins_to_right
return CoinsDistribResult(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
return get_distrib(SCREAMING_SNAKE_CASE )[0]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 108 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import DonutImageProcessor
class _snake_case ( unittest.TestCase ):
def __init__( self , _a , _a=7 , _a=3 , _a=18 , _a=30 , _a=400 , _a=True , _a=None , _a=True , _a=False , _a=True , _a=True , _a=[0.5, 0.5, 0.5] , _a=[0.5, 0.5, 0.5] , ):
__magic_name__ : Optional[Any] = parent
__magic_name__ : Optional[Any] = batch_size
__magic_name__ : Any = num_channels
__magic_name__ : List[str] = image_size
__magic_name__ : List[str] = min_resolution
__magic_name__ : Optional[Any] = max_resolution
__magic_name__ : Optional[int] = do_resize
__magic_name__ : Union[str, Any] = size if size is not None else {"height": 18, "width": 20}
__magic_name__ : int = do_thumbnail
__magic_name__ : Dict = do_align_axis
__magic_name__ : Any = do_pad
__magic_name__ : Any = do_normalize
__magic_name__ : str = image_mean
__magic_name__ : Union[str, Any] = image_std
def SCREAMING_SNAKE_CASE ( self ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_thumbnail": self.do_thumbnail,
"do_align_long_axis": self.do_align_axis,
"do_pad": self.do_pad,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
}
@require_torch
@require_vision
class _snake_case ( snake_case , unittest.TestCase ):
UpperCamelCase__ = DonutImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : List[str] = DonutImageProcessingTester(self )
@property
def SCREAMING_SNAKE_CASE ( self ):
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_a , "do_resize" ) )
self.assertTrue(hasattr(_a , "size" ) )
self.assertTrue(hasattr(_a , "do_thumbnail" ) )
self.assertTrue(hasattr(_a , "do_align_long_axis" ) )
self.assertTrue(hasattr(_a , "do_pad" ) )
self.assertTrue(hasattr(_a , "do_normalize" ) )
self.assertTrue(hasattr(_a , "image_mean" ) )
self.assertTrue(hasattr(_a , "image_std" ) )
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : int = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"height": 18, "width": 20} )
__magic_name__ : Dict = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {"height": 42, "width": 42} )
# Previous config had dimensions in (width, height) order
__magic_name__ : List[str] = self.image_processing_class.from_dict(self.image_processor_dict , size=(42, 84) )
self.assertEqual(image_processor.size , {"height": 84, "width": 42} )
def SCREAMING_SNAKE_CASE ( self ):
pass
@is_flaky()
def SCREAMING_SNAKE_CASE ( self ):
# Initialize image_processing
__magic_name__ : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__magic_name__ : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_a )
for image in image_inputs:
self.assertIsInstance(_a , Image.Image )
# Test not batched input
__magic_name__ : str = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
) , )
# Test batched
__magic_name__ : List[Any] = image_processing(_a , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
) , )
@is_flaky()
def SCREAMING_SNAKE_CASE ( self ):
# Initialize image_processing
__magic_name__ : Dict = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__magic_name__ : Tuple = prepare_image_inputs(self.image_processor_tester , equal_resolution=_a , numpify=_a )
for image in image_inputs:
self.assertIsInstance(_a , np.ndarray )
# Test not batched input
__magic_name__ : Dict = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
) , )
# Test batched
__magic_name__ : int = image_processing(_a , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
) , )
@is_flaky()
def SCREAMING_SNAKE_CASE ( self ):
# Initialize image_processing
__magic_name__ : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__magic_name__ : Optional[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_a , torchify=_a )
for image in image_inputs:
self.assertIsInstance(_a , torch.Tensor )
# Test not batched input
__magic_name__ : List[Any] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
) , )
# Test batched
__magic_name__ : Dict = image_processing(_a , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
) , )
| 41 |
import numpy
class _snake_case :
def __init__( self , _a , _a ):
__magic_name__ : Optional[Any] = input_array
# Random initial weights are assigned where first argument is the
# number of nodes in previous layer and second argument is the
# number of nodes in the next layer.
# Random initial weights are assigned.
# self.input_array.shape[1] is used to represent number of nodes in input layer.
# First hidden layer consists of 4 nodes.
__magic_name__ : Any = numpy.random.rand(
self.input_array.shape[1] , 4 )
# Random initial values for the first hidden layer.
# First hidden layer has 4 nodes.
# Second hidden layer has 3 nodes.
__magic_name__ : List[str] = numpy.random.rand(
4 , 3 )
# Random initial values for the second hidden layer.
# Second hidden layer has 3 nodes.
# Output layer has 1 node.
__magic_name__ : Any = numpy.random.rand(3 , 1 )
# Real output values provided.
__magic_name__ : Dict = output_array
# Predicted output values by the neural network.
# Predicted_output array initially consists of zeroes.
__magic_name__ : Tuple = numpy.zeros(output_array.shape )
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : List[str] = sigmoid(
numpy.dot(self.input_array , self.input_layer_and_first_hidden_layer_weights ) )
# layer_between_first_hidden_layer_and_second_hidden_layer is the layer
# connecting the first hidden set of nodes with the second hidden set of nodes.
__magic_name__ : int = sigmoid(
numpy.dot(
self.layer_between_input_and_first_hidden_layer , self.first_hidden_layer_and_second_hidden_layer_weights , ) )
# layer_between_second_hidden_layer_and_output is the layer connecting
# second hidden layer with the output node.
__magic_name__ : Tuple = sigmoid(
numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer , self.second_hidden_layer_and_output_layer_weights , ) )
return self.layer_between_second_hidden_layer_and_output
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Optional[int] = numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer.T , 2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output ) , )
__magic_name__ : Optional[int] = numpy.dot(
self.layer_between_input_and_first_hidden_layer.T , numpy.dot(
2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output ) , self.second_hidden_layer_and_output_layer_weights.T , )
* sigmoid_derivative(
self.layer_between_first_hidden_layer_and_second_hidden_layer ) , )
__magic_name__ : int = numpy.dot(
self.input_array.T , numpy.dot(
numpy.dot(
2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output ) , self.second_hidden_layer_and_output_layer_weights.T , )
* sigmoid_derivative(
self.layer_between_first_hidden_layer_and_second_hidden_layer ) , self.first_hidden_layer_and_second_hidden_layer_weights.T , )
* sigmoid_derivative(self.layer_between_input_and_first_hidden_layer ) , )
self.input_layer_and_first_hidden_layer_weights += (
updated_input_layer_and_first_hidden_layer_weights
)
self.first_hidden_layer_and_second_hidden_layer_weights += (
updated_first_hidden_layer_and_second_hidden_layer_weights
)
self.second_hidden_layer_and_output_layer_weights += (
updated_second_hidden_layer_and_output_layer_weights
)
def SCREAMING_SNAKE_CASE ( self , _a , _a , _a ):
for iteration in range(1 , iterations + 1 ):
__magic_name__ : Any = self.feedforward()
self.back_propagation()
if give_loss:
__magic_name__ : int = numpy.mean(numpy.square(output - self.feedforward() ) )
print(f'''Iteration {iteration} Loss: {loss}''' )
def SCREAMING_SNAKE_CASE ( self , _a ):
__magic_name__ : str = input_arr
__magic_name__ : Optional[Any] = sigmoid(
numpy.dot(self.array , self.input_layer_and_first_hidden_layer_weights ) )
__magic_name__ : Union[str, Any] = sigmoid(
numpy.dot(
self.layer_between_input_and_first_hidden_layer , self.first_hidden_layer_and_second_hidden_layer_weights , ) )
__magic_name__ : Optional[Any] = sigmoid(
numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer , self.second_hidden_layer_and_output_layer_weights , ) )
return int(self.layer_between_second_hidden_layer_and_output > 0.6 )
def lowerCAmelCase_ ( _snake_case : numpy.ndarray ) -> numpy.ndarray:
'''simple docstring'''
return 1 / (1 + numpy.exp(-value ))
def lowerCAmelCase_ ( _snake_case : numpy.ndarray ) -> numpy.ndarray:
'''simple docstring'''
return (value) * (1 - (value))
def lowerCAmelCase_ ( ) -> int:
'''simple docstring'''
__magic_name__ : Tuple = numpy.array(
(
[0, 0, 0],
[0, 0, 1],
[0, 1, 0],
[0, 1, 1],
[1, 0, 0],
[1, 0, 1],
[1, 1, 0],
[1, 1, 1],
) , dtype=numpy.floataa , )
# True output values for the given input values.
__magic_name__ : List[str] = numpy.array(([0], [1], [1], [0], [1], [0], [0], [1]) , dtype=numpy.floataa )
# Calling neural network class.
__magic_name__ : List[Any] = TwoHiddenLayerNeuralNetwork(
input_array=_snake_case , output_array=_snake_case )
# Calling training function.
# Set give_loss to True if you want to see loss in every iteration.
neural_network.train(output=_snake_case , iterations=10 , give_loss=_snake_case )
return neural_network.predict(numpy.array(([1, 1, 1]) , dtype=numpy.floataa ) )
if __name__ == "__main__":
example()
| 41 | 1 |
def lowerCamelCase__ ( A__ : Any , A__ : Any ):
'''simple docstring'''
__lowerCamelCase = int(_lowerCAmelCase )
# Initialize Result
__lowerCamelCase = []
# Traverse through all denomination
for denomination in reversed(_lowerCAmelCase ):
# Find denominations
while int(_lowerCAmelCase ) >= int(_lowerCAmelCase ):
total_value -= int(_lowerCAmelCase )
answer.append(_lowerCAmelCase ) # Append the "answers" array
return answer
# Driver Code
if __name__ == "__main__":
UpperCAmelCase_ = []
UpperCAmelCase_ = '0'
if (
input('Do you want to enter your denominations ? (yY/n): ').strip().lower()
== "y"
):
UpperCAmelCase_ = int(input('Enter the number of denominations you want to add: ').strip())
for i in range(0, n):
denominations.append(int(input(f"""Denomination {i}: """).strip()))
UpperCAmelCase_ = input('Enter the change you want to make in Indian Currency: ').strip()
else:
# All denominations of Indian Currency if user does not enter
UpperCAmelCase_ = [1, 2, 5, 10, 20, 50, 100, 500, 2_000]
UpperCAmelCase_ = input('Enter the change you want to make: ').strip()
if int(value) == 0 or int(value) < 0:
print('The total value cannot be zero or negative.')
else:
print(f"""Following is minimal change for {value}: """)
UpperCAmelCase_ = find_minimum_change(denominations, value)
# Print result
for i in range(len(answer)):
print(answer[i], end=' ')
| 12 |
"""simple docstring"""
import os
from distutils.util import strtobool
def lowercase (_lowerCAmelCase , _lowerCAmelCase ):
for e in env_keys:
__lowerCAmelCase = int(os.environ.get(_lowerCAmelCase , -1 ) )
if val >= 0:
return val
return default
def lowercase (_lowerCAmelCase , _lowerCAmelCase=False ):
__lowerCAmelCase = os.environ.get(_lowerCAmelCase , str(_lowerCAmelCase ) )
return strtobool(_lowerCAmelCase ) == 1 # As its name indicates `strtobool` actually returns an int...
def lowercase (_lowerCAmelCase , _lowerCAmelCase="no" ):
__lowerCAmelCase = os.environ.get(_lowerCAmelCase , str(_lowerCAmelCase ) )
return value
| 301 | 0 |
"""simple docstring"""
from arguments import InitializationArguments
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
# Configuration
UpperCAmelCase : Tuple = HfArgumentParser(InitializationArguments)
UpperCAmelCase : str = parser.parse_args()
# Load codeparrot tokenizer trained for Python code tokenization
UpperCAmelCase : Optional[Any] = AutoTokenizer.from_pretrained(args.tokenizer_name)
# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
UpperCAmelCase : int = {
"vocab_size": len(tokenizer),
"scale_attn_by_inverse_layer_idx": True,
"reorder_and_upcast_attn": True,
}
# Load model config (GPT-2 large in this case)
UpperCAmelCase : Tuple = AutoConfig.from_pretrained(args.config_name, **config_kwargs)
# Initialize new model with config
UpperCAmelCase : List[str] = AutoModelForCausalLM.from_config(config)
# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)
| 365 |
"""simple docstring"""
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
EfficientNetConfig,
EfficientNetForImageClassification,
EfficientNetImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase : List[str] = logging.get_logger(__name__)
UpperCAmelCase : List[str] = {
'b0': efficientnet.EfficientNetBa,
'b1': efficientnet.EfficientNetBa,
'b2': efficientnet.EfficientNetBa,
'b3': efficientnet.EfficientNetBa,
'b4': efficientnet.EfficientNetBa,
'b5': efficientnet.EfficientNetBa,
'b6': efficientnet.EfficientNetBa,
'b7': efficientnet.EfficientNetBa,
}
UpperCAmelCase : List[str] = {
'b0': {
'hidden_dim': 1280,
'width_coef': 1.0,
'depth_coef': 1.0,
'image_size': 224,
'dropout_rate': 0.2,
'dw_padding': [],
},
'b1': {
'hidden_dim': 1280,
'width_coef': 1.0,
'depth_coef': 1.1,
'image_size': 240,
'dropout_rate': 0.2,
'dw_padding': [16],
},
'b2': {
'hidden_dim': 1408,
'width_coef': 1.1,
'depth_coef': 1.2,
'image_size': 260,
'dropout_rate': 0.3,
'dw_padding': [5, 8, 16],
},
'b3': {
'hidden_dim': 1536,
'width_coef': 1.2,
'depth_coef': 1.4,
'image_size': 300,
'dropout_rate': 0.3,
'dw_padding': [5, 18],
},
'b4': {
'hidden_dim': 1792,
'width_coef': 1.4,
'depth_coef': 1.8,
'image_size': 380,
'dropout_rate': 0.4,
'dw_padding': [6],
},
'b5': {
'hidden_dim': 2048,
'width_coef': 1.6,
'depth_coef': 2.2,
'image_size': 456,
'dropout_rate': 0.4,
'dw_padding': [13, 27],
},
'b6': {
'hidden_dim': 2304,
'width_coef': 1.8,
'depth_coef': 2.6,
'image_size': 528,
'dropout_rate': 0.5,
'dw_padding': [31],
},
'b7': {
'hidden_dim': 2560,
'width_coef': 2.0,
'depth_coef': 3.1,
'image_size': 600,
'dropout_rate': 0.5,
'dw_padding': [18],
},
}
def lowerCamelCase ( _UpperCamelCase : List[Any] ) -> Optional[int]:
'''simple docstring'''
__UpperCAmelCase : List[Any] = EfficientNetConfig()
__UpperCAmelCase : Dict = CONFIG_MAP[model_name]["""hidden_dim"""]
__UpperCAmelCase : Dict = CONFIG_MAP[model_name]["""width_coef"""]
__UpperCAmelCase : str = CONFIG_MAP[model_name]["""depth_coef"""]
__UpperCAmelCase : Dict = CONFIG_MAP[model_name]["""image_size"""]
__UpperCAmelCase : Dict = CONFIG_MAP[model_name]["""dropout_rate"""]
__UpperCAmelCase : Union[str, Any] = CONFIG_MAP[model_name]["""dw_padding"""]
__UpperCAmelCase : int = """huggingface/label-files"""
__UpperCAmelCase : Optional[int] = """imagenet-1k-id2label.json"""
__UpperCAmelCase : str = 1_0_0_0
__UpperCAmelCase : Dict = json.load(open(hf_hub_download(_UpperCamelCase , _UpperCamelCase , repo_type="""dataset""" ) , """r""" ) )
__UpperCAmelCase : int = {int(_UpperCamelCase ): v for k, v in idalabel.items()}
__UpperCAmelCase : Dict = idalabel
__UpperCAmelCase : Tuple = {v: k for k, v in idalabel.items()}
return config
def lowerCamelCase ( ) -> Union[str, Any]:
'''simple docstring'''
__UpperCAmelCase : Dict = """http://images.cocodataset.org/val2017/000000039769.jpg"""
__UpperCAmelCase : Optional[Any] = Image.open(requests.get(_UpperCamelCase , stream=_UpperCamelCase ).raw )
return im
def lowerCamelCase ( _UpperCamelCase : Any ) -> str:
'''simple docstring'''
__UpperCAmelCase : Tuple = CONFIG_MAP[model_name]["""image_size"""]
__UpperCAmelCase : List[str] = EfficientNetImageProcessor(
size={"""height""": size, """width""": size} , image_mean=[0.485, 0.456, 0.406] , image_std=[0.47_853_944, 0.4_732_864, 0.47_434_163] , do_center_crop=_UpperCamelCase , )
return preprocessor
def lowerCamelCase ( _UpperCamelCase : Dict ) -> Optional[int]:
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] = [v.split("""_""" )[0].split("""block""" )[1] for v in original_param_names if v.startswith("""block""" )]
__UpperCAmelCase : str = sorted(set(_UpperCamelCase ) )
__UpperCAmelCase : Optional[int] = len(_UpperCamelCase )
__UpperCAmelCase : Any = {b: str(_UpperCamelCase ) for b, i in zip(_UpperCamelCase , range(_UpperCamelCase ) )}
__UpperCAmelCase : Any = []
rename_keys.append(("""stem_conv/kernel:0""", """embeddings.convolution.weight""") )
rename_keys.append(("""stem_bn/gamma:0""", """embeddings.batchnorm.weight""") )
rename_keys.append(("""stem_bn/beta:0""", """embeddings.batchnorm.bias""") )
rename_keys.append(("""stem_bn/moving_mean:0""", """embeddings.batchnorm.running_mean""") )
rename_keys.append(("""stem_bn/moving_variance:0""", """embeddings.batchnorm.running_var""") )
for b in block_names:
__UpperCAmelCase : List[str] = block_name_mapping[b]
rename_keys.append((f'''block{b}_expand_conv/kernel:0''', f'''encoder.blocks.{hf_b}.expansion.expand_conv.weight''') )
rename_keys.append((f'''block{b}_expand_bn/gamma:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.weight''') )
rename_keys.append((f'''block{b}_expand_bn/beta:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.bias''') )
rename_keys.append(
(f'''block{b}_expand_bn/moving_mean:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.running_mean''') )
rename_keys.append(
(f'''block{b}_expand_bn/moving_variance:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.running_var''') )
rename_keys.append(
(f'''block{b}_dwconv/depthwise_kernel:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight''') )
rename_keys.append((f'''block{b}_bn/gamma:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight''') )
rename_keys.append((f'''block{b}_bn/beta:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias''') )
rename_keys.append(
(f'''block{b}_bn/moving_mean:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean''') )
rename_keys.append(
(f'''block{b}_bn/moving_variance:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var''') )
rename_keys.append((f'''block{b}_se_reduce/kernel:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.reduce.weight''') )
rename_keys.append((f'''block{b}_se_reduce/bias:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.reduce.bias''') )
rename_keys.append((f'''block{b}_se_expand/kernel:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.expand.weight''') )
rename_keys.append((f'''block{b}_se_expand/bias:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.expand.bias''') )
rename_keys.append(
(f'''block{b}_project_conv/kernel:0''', f'''encoder.blocks.{hf_b}.projection.project_conv.weight''') )
rename_keys.append((f'''block{b}_project_bn/gamma:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.weight''') )
rename_keys.append((f'''block{b}_project_bn/beta:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.bias''') )
rename_keys.append(
(f'''block{b}_project_bn/moving_mean:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.running_mean''') )
rename_keys.append(
(f'''block{b}_project_bn/moving_variance:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.running_var''') )
rename_keys.append(("""top_conv/kernel:0""", """encoder.top_conv.weight""") )
rename_keys.append(("""top_bn/gamma:0""", """encoder.top_bn.weight""") )
rename_keys.append(("""top_bn/beta:0""", """encoder.top_bn.bias""") )
rename_keys.append(("""top_bn/moving_mean:0""", """encoder.top_bn.running_mean""") )
rename_keys.append(("""top_bn/moving_variance:0""", """encoder.top_bn.running_var""") )
__UpperCAmelCase : Optional[int] = {}
for item in rename_keys:
if item[0] in original_param_names:
__UpperCAmelCase : Optional[Any] = """efficientnet.""" + item[1]
__UpperCAmelCase : Tuple = """classifier.weight"""
__UpperCAmelCase : Optional[int] = """classifier.bias"""
return key_mapping
def lowerCamelCase ( _UpperCamelCase : Any , _UpperCamelCase : Dict , _UpperCamelCase : int ) -> Tuple:
'''simple docstring'''
for key, value in tf_params.items():
if "normalization" in key:
continue
__UpperCAmelCase : List[Any] = key_mapping[key]
if "_conv" in key and "kernel" in key:
__UpperCAmelCase : int = torch.from_numpy(_UpperCamelCase ).permute(3 , 2 , 0 , 1 )
elif "depthwise_kernel" in key:
__UpperCAmelCase : Optional[Any] = torch.from_numpy(_UpperCamelCase ).permute(2 , 3 , 0 , 1 )
elif "kernel" in key:
__UpperCAmelCase : List[str] = torch.from_numpy(np.transpose(_UpperCamelCase ) )
else:
__UpperCAmelCase : Tuple = torch.from_numpy(_UpperCamelCase )
# Replace HF parameters with original TF model parameters
assert hf_params[hf_key].shape == new_hf_value.shape
hf_params[hf_key].copy_(_UpperCamelCase )
@torch.no_grad()
def lowerCamelCase ( _UpperCamelCase : Optional[int] , _UpperCamelCase : Union[str, Any] , _UpperCamelCase : Union[str, Any] , _UpperCamelCase : List[str] ) -> Tuple:
'''simple docstring'''
__UpperCAmelCase : int = model_classes[model_name](
include_top=_UpperCamelCase , weights="""imagenet""" , input_tensor=_UpperCamelCase , input_shape=_UpperCamelCase , pooling=_UpperCamelCase , classes=1_0_0_0 , classifier_activation="""softmax""" , )
__UpperCAmelCase : List[str] = original_model.trainable_variables
__UpperCAmelCase : List[Any] = original_model.non_trainable_variables
__UpperCAmelCase : Union[str, Any] = {param.name: param.numpy() for param in tf_params}
for param in tf_non_train_params:
__UpperCAmelCase : int = param.numpy()
__UpperCAmelCase : Dict = list(tf_params.keys() )
# Load HuggingFace model
__UpperCAmelCase : Optional[Any] = get_efficientnet_config(_UpperCamelCase )
__UpperCAmelCase : Optional[Any] = EfficientNetForImageClassification(_UpperCamelCase ).eval()
__UpperCAmelCase : Any = hf_model.state_dict()
# Create src-to-dst parameter name mapping dictionary
print("""Converting parameters...""" )
__UpperCAmelCase : Tuple = rename_keys(_UpperCamelCase )
replace_params(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
# Initialize preprocessor and preprocess input image
__UpperCAmelCase : List[Any] = convert_image_processor(_UpperCamelCase )
__UpperCAmelCase : Union[str, Any] = preprocessor(images=prepare_img() , return_tensors="""pt""" )
# HF model inference
hf_model.eval()
with torch.no_grad():
__UpperCAmelCase : Optional[int] = hf_model(**_UpperCamelCase )
__UpperCAmelCase : Any = outputs.logits.detach().numpy()
# Original model inference
__UpperCAmelCase : Union[str, Any] = False
__UpperCAmelCase : Dict = CONFIG_MAP[model_name]["""image_size"""]
__UpperCAmelCase : str = prepare_img().resize((image_size, image_size) , resample=PIL.Image.NEAREST )
__UpperCAmelCase : Optional[Any] = image.img_to_array(_UpperCamelCase )
__UpperCAmelCase : Tuple = np.expand_dims(_UpperCamelCase , axis=0 )
__UpperCAmelCase : str = original_model.predict(_UpperCamelCase )
# Check whether original and HF model outputs match -> np.allclose
assert np.allclose(_UpperCamelCase , _UpperCamelCase , atol=1E-3 ), "The predicted logits are not the same."
print("""Model outputs match!""" )
if save_model:
# Create folder to save model
if not os.path.isdir(_UpperCamelCase ):
os.mkdir(_UpperCamelCase )
# Save converted model and image processor
hf_model.save_pretrained(_UpperCamelCase )
preprocessor.save_pretrained(_UpperCamelCase )
if push_to_hub:
# Push model and image processor to hub
print(f'''Pushing converted {model_name} to the hub...''' )
__UpperCAmelCase : List[str] = f'''efficientnet-{model_name}'''
preprocessor.push_to_hub(_UpperCamelCase )
hf_model.push_to_hub(_UpperCamelCase )
if __name__ == "__main__":
UpperCAmelCase : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='b0',
type=str,
help='Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default='hf_model',
type=str,
help='Path to the output PyTorch model directory.',
)
parser.add_argument('--save_model', action='store_true', help='Save model to local')
parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
UpperCAmelCase : Any = parser.parse_args()
convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
| 320 | 0 |
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel
class UpperCAmelCase__ :
"""simple docstring"""
def __init__( self , A_ , A_=13 , A_=7 , A_=True , A_=True , A_=False , A_=True , A_=99 , A_=32 , A_=5 , A_=4 , A_=37 , A_="gelu" , A_=0.1 , A_=0.1 , A_=512 , A_=16 , A_=2 , A_=0.02 , A_=3 , A_=4 , A_=None , ) -> int:
__UpperCamelCase =parent
__UpperCamelCase =batch_size
__UpperCamelCase =seq_length
__UpperCamelCase =is_training
__UpperCamelCase =use_input_mask
__UpperCamelCase =use_token_type_ids
__UpperCamelCase =use_labels
__UpperCamelCase =vocab_size
__UpperCamelCase =hidden_size
__UpperCamelCase =num_hidden_layers
__UpperCamelCase =num_attention_heads
__UpperCamelCase =intermediate_size
__UpperCamelCase =hidden_act
__UpperCamelCase =hidden_dropout_prob
__UpperCamelCase =attention_probs_dropout_prob
__UpperCamelCase =max_position_embeddings
__UpperCamelCase =type_vocab_size
__UpperCamelCase =type_sequence_label_size
__UpperCamelCase =initializer_range
__UpperCamelCase =num_labels
__UpperCamelCase =num_choices
__UpperCamelCase =scope
def _a ( self ) -> Dict:
__UpperCamelCase =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__UpperCamelCase =None
if self.use_input_mask:
__UpperCamelCase =random_attention_mask([self.batch_size, self.seq_length] )
__UpperCamelCase =None
if self.use_token_type_ids:
__UpperCamelCase =ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__UpperCamelCase =None
__UpperCamelCase =None
__UpperCamelCase =None
if self.use_labels:
__UpperCamelCase =ids_tensor([self.batch_size] , self.type_sequence_label_size )
__UpperCamelCase =ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__UpperCamelCase =ids_tensor([self.batch_size] , self.num_choices )
__UpperCamelCase =self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _a ( self ) -> int:
return OpenLlamaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=A_ , initializer_range=self.initializer_range , use_stable_embedding=A_ , )
def _a ( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ ) -> Dict:
__UpperCamelCase =OpenLlamaModel(config=A_ )
model.to(A_ )
model.eval()
__UpperCamelCase =model(A_ , attention_mask=A_ )
__UpperCamelCase =model(A_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _a ( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ , A_ , A_ , ) -> Optional[int]:
__UpperCamelCase =True
__UpperCamelCase =OpenLlamaModel(A_ )
model.to(A_ )
model.eval()
__UpperCamelCase =model(
A_ , attention_mask=A_ , encoder_hidden_states=A_ , encoder_attention_mask=A_ , )
__UpperCamelCase =model(
A_ , attention_mask=A_ , encoder_hidden_states=A_ , )
__UpperCamelCase =model(A_ , attention_mask=A_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _a ( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ , A_ , A_ , ) -> Union[str, Any]:
__UpperCamelCase =OpenLlamaForCausalLM(config=A_ )
model.to(A_ )
model.eval()
__UpperCamelCase =model(A_ , attention_mask=A_ , labels=A_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _a ( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ , A_ , A_ , ) -> List[Any]:
__UpperCamelCase =True
__UpperCamelCase =True
__UpperCamelCase =OpenLlamaForCausalLM(config=A_ )
model.to(A_ )
model.eval()
# first forward pass
__UpperCamelCase =model(
A_ , attention_mask=A_ , encoder_hidden_states=A_ , encoder_attention_mask=A_ , use_cache=A_ , )
__UpperCamelCase =outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
__UpperCamelCase =ids_tensor((self.batch_size, 3) , config.vocab_size )
__UpperCamelCase =ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
__UpperCamelCase =torch.cat([input_ids, next_tokens] , dim=-1 )
__UpperCamelCase =torch.cat([input_mask, next_mask] , dim=-1 )
__UpperCamelCase =model(
A_ , attention_mask=A_ , encoder_hidden_states=A_ , encoder_attention_mask=A_ , output_hidden_states=A_ , )['hidden_states'][0]
__UpperCamelCase =model(
A_ , attention_mask=A_ , encoder_hidden_states=A_ , encoder_attention_mask=A_ , past_key_values=A_ , output_hidden_states=A_ , )['hidden_states'][0]
# select random slice
__UpperCamelCase =ids_tensor((1,) , output_from_past.shape[-1] ).item()
__UpperCamelCase =output_from_no_past[:, -3:, random_slice_idx].detach()
__UpperCamelCase =output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(A_ , A_ , atol=1E-3 ) )
def _a ( self ) -> List[str]:
__UpperCamelCase =self.prepare_config_and_inputs()
(
(
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) ,
) =config_and_inputs
__UpperCamelCase ={'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class UpperCAmelCase__ ( A_ , A_ , A_ , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase__ : Optional[int] = (
(OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
)
UpperCAmelCase__ : List[Any] = (OpenLlamaForCausalLM,) if is_torch_available() else ()
UpperCAmelCase__ : str = (
{
"feature-extraction": OpenLlamaModel,
"text-classification": OpenLlamaForSequenceClassification,
"text-generation": OpenLlamaForCausalLM,
"zero-shot": OpenLlamaForSequenceClassification,
}
if is_torch_available()
else {}
)
UpperCAmelCase__ : Optional[Any] = False
UpperCAmelCase__ : Optional[int] = False
def _a ( self ) -> List[str]:
__UpperCamelCase =OpenLlamaModelTester(self )
__UpperCamelCase =ConfigTester(self , config_class=A_ , hidden_size=37 )
def _a ( self ) -> Tuple:
self.config_tester.run_common_tests()
def _a ( self ) -> Any:
__UpperCamelCase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A_ )
def _a ( self ) -> str:
__UpperCamelCase =self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
__UpperCamelCase =type
self.model_tester.create_and_check_model(*A_ )
def _a ( self ) -> Dict:
__UpperCamelCase , __UpperCamelCase =self.model_tester.prepare_config_and_inputs_for_common()
__UpperCamelCase =3
__UpperCamelCase =input_dict['input_ids']
__UpperCamelCase =input_ids.ne(1 ).to(A_ )
__UpperCamelCase =ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
__UpperCamelCase =OpenLlamaForSequenceClassification(A_ )
model.to(A_ )
model.eval()
__UpperCamelCase =model(A_ , attention_mask=A_ , labels=A_ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def _a ( self ) -> Tuple:
__UpperCamelCase , __UpperCamelCase =self.model_tester.prepare_config_and_inputs_for_common()
__UpperCamelCase =3
__UpperCamelCase ='single_label_classification'
__UpperCamelCase =input_dict['input_ids']
__UpperCamelCase =input_ids.ne(1 ).to(A_ )
__UpperCamelCase =ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
__UpperCamelCase =OpenLlamaForSequenceClassification(A_ )
model.to(A_ )
model.eval()
__UpperCamelCase =model(A_ , attention_mask=A_ , labels=A_ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def _a ( self ) -> Tuple:
__UpperCamelCase , __UpperCamelCase =self.model_tester.prepare_config_and_inputs_for_common()
__UpperCamelCase =3
__UpperCamelCase ='multi_label_classification'
__UpperCamelCase =input_dict['input_ids']
__UpperCamelCase =input_ids.ne(1 ).to(A_ )
__UpperCamelCase =ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
__UpperCamelCase =OpenLlamaForSequenceClassification(A_ )
model.to(A_ )
model.eval()
__UpperCamelCase =model(A_ , attention_mask=A_ , labels=A_ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@unittest.skip('Open-Llama buffers include complex numbers, which breaks this test' )
def _a ( self ) -> List[Any]:
pass
@parameterized.expand([('linear',), ('dynamic',)] )
def _a ( self , A_ ) -> Tuple:
__UpperCamelCase , __UpperCamelCase =self.model_tester.prepare_config_and_inputs_for_common()
__UpperCamelCase =ids_tensor([1, 10] , config.vocab_size )
__UpperCamelCase =ids_tensor([1, int(config.max_position_embeddings * 1.5 )] , config.vocab_size )
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
__UpperCamelCase =OpenLlamaModel(A_ )
original_model.to(A_ )
original_model.eval()
__UpperCamelCase =original_model(A_ ).last_hidden_state
__UpperCamelCase =original_model(A_ ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
__UpperCamelCase ={'type': scaling_type, 'factor': 10.0}
__UpperCamelCase =OpenLlamaModel(A_ )
scaled_model.to(A_ )
scaled_model.eval()
__UpperCamelCase =scaled_model(A_ ).last_hidden_state
__UpperCamelCase =scaled_model(A_ ).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(A_ , A_ , atol=1E-5 ) )
else:
self.assertFalse(torch.allclose(A_ , A_ , atol=1E-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(A_ , A_ , atol=1E-5 ) )
| 62 |
def UpperCAmelCase__ (UpperCamelCase_ = 4_00_00_00 ):
"""simple docstring"""
snake_case = [0, 1]
snake_case = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
break
i += 1
snake_case = 0
for j in range(len(UpperCamelCase_ ) - 1 ):
if fib[j] % 2 == 0:
total += fib[j]
return total
if __name__ == "__main__":
print(f'''{solution() = }''')
| 127 | 0 |
from typing import List
import numpy as np
def lowerCamelCase_ ( lowerCamelCase__ ):
lowerCamelCase_ = {key: len(lowerCamelCase__ ) for key, value in gen_kwargs.items() if isinstance(lowerCamelCase__ , lowerCamelCase__ )}
if len(set(lists_lengths.values() ) ) > 1:
raise RuntimeError(
(
"Sharding is ambiguous for this dataset: "
+ "we found several data sources lists of different lengths, and we don't know over which list we should parallelize:\n"
+ "\n".join(F'\t- key {key} has length {length}' for key, length in lists_lengths.items() )
+ "\nTo fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, "
+ "and use tuples otherwise. In the end there should only be one single list, or several lists with the same length."
) )
lowerCamelCase_ = max(lists_lengths.values() , default=0 )
return max(1 , lowerCamelCase__ )
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = []
for group_idx in range(lowerCamelCase__ ):
lowerCamelCase_ = num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
if num_shards_to_add == 0:
break
lowerCamelCase_ = shards_indices_per_group[-1].stop if shards_indices_per_group else 0
lowerCamelCase_ = range(lowerCamelCase__ , start + num_shards_to_add )
shards_indices_per_group.append(lowerCamelCase__ )
return shards_indices_per_group
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = _number_of_shards_in_gen_kwargs(lowerCamelCase__ )
if num_shards == 1:
return [dict(lowerCamelCase__ )]
else:
lowerCamelCase_ = _distribute_shards(num_shards=lowerCamelCase__ , max_num_jobs=lowerCamelCase__ )
return [
{
key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
if isinstance(lowerCamelCase__ , lowerCamelCase__ )
else value
for key, value in gen_kwargs.items()
}
for group_idx in range(len(lowerCamelCase__ ) )
]
def lowerCamelCase_ ( lowerCamelCase__ ):
return {
key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
if isinstance(gen_kwargs_list[0][key] , lowerCamelCase__ )
else gen_kwargs_list[0][key]
for key in gen_kwargs_list[0]
}
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = {len(lowerCamelCase__ ) for value in gen_kwargs.values() if isinstance(lowerCamelCase__ , lowerCamelCase__ )}
lowerCamelCase_ = {}
for size in list_sizes:
lowerCamelCase_ = list(range(lowerCamelCase__ ) )
rng.shuffle(indices_per_size[size] )
# Now let's copy the gen_kwargs and shuffle the lists based on their sizes
lowerCamelCase_ = dict(lowerCamelCase__ )
for key, value in shuffled_kwargs.items():
if isinstance(lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = [value[i] for i in indices_per_size[len(lowerCamelCase__ )]]
return shuffled_kwargs
| 353 |
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class _SCREAMING_SNAKE_CASE :
def __init__( self , lowercase , lowercase , lowercase ) -> List[Any]:
if dst_width < 0 or dst_height < 0:
raise ValueError("Destination width/height should be > 0" )
lowerCamelCase_ = img
lowerCamelCase_ = img.shape[1]
lowerCamelCase_ = img.shape[0]
lowerCamelCase_ = dst_width
lowerCamelCase_ = dst_height
lowerCamelCase_ = self.src_w / self.dst_w
lowerCamelCase_ = self.src_h / self.dst_h
lowerCamelCase_ = lowerCamelCase_ = (
np.ones((self.dst_h, self.dst_w, 3) , np.uinta ) * 255
)
def SCREAMING_SNAKE_CASE_( self ) -> Optional[int]:
for i in range(self.dst_h ):
for j in range(self.dst_w ):
lowerCamelCase_ = self.img[self.get_y(lowercase )][self.get_x(lowercase )]
def SCREAMING_SNAKE_CASE_( self , lowercase ) -> int:
return int(self.ratio_x * x )
def SCREAMING_SNAKE_CASE_( self , lowercase ) -> int:
return int(self.ratio_y * y )
if __name__ == "__main__":
__A, __A =8_0_0, 6_0_0
__A =imread('''image_data/lena.jpg''', 1)
__A =NearestNeighbour(im, dst_w, dst_h)
n.process()
imshow(
F"""Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}""", n.output
)
waitKey(0)
destroyAllWindows()
| 47 | 0 |
"""simple docstring"""
import math
def _snake_case ( lowercase__ , lowercase__ ):
_lowerCamelCase : List[Any] = len(lowercase__ )
_lowerCamelCase : Optional[Any] = int(math.floor(math.sqrt(lowercase__ ) ) )
_lowerCamelCase : Optional[int] = 0
while arr[min(lowercase__ , lowercase__ ) - 1] < x:
_lowerCamelCase : Tuple = step
step += int(math.floor(math.sqrt(lowercase__ ) ) )
if prev >= n:
return -1
while arr[prev] < x:
_lowerCamelCase : str = prev + 1
if prev == min(lowercase__ , lowercase__ ):
return -1
if arr[prev] == x:
return prev
return -1
if __name__ == "__main__":
lowercase__ = input("""Enter numbers separated by a comma:\n""").strip()
lowercase__ = [int(item) for item in user_input.split(""",""")]
lowercase__ = int(input("""Enter the number to be searched:\n"""))
lowercase__ = jump_search(arr, x)
if res == -1:
print("""Number not found!""")
else:
print(F"Number {x} is at index {res}") | 96 |
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class snake_case__ :
"""simple docstring"""
def __init__( self : Tuple, _snake_case : Any, _snake_case : int=1_3, _snake_case : Optional[int]=3_2, _snake_case : Tuple=2, _snake_case : Any=3, _snake_case : Tuple=1_6, _snake_case : Tuple=[1, 2, 1], _snake_case : Dict=[2, 2, 4], _snake_case : str=2, _snake_case : Union[str, Any]=2.0, _snake_case : Dict=True, _snake_case : Dict=0.0, _snake_case : str=0.0, _snake_case : str=0.1, _snake_case : List[str]="gelu", _snake_case : int=False, _snake_case : Optional[Any]=True, _snake_case : List[Any]=0.0_2, _snake_case : Union[str, Any]=1e-5, _snake_case : Union[str, Any]=True, _snake_case : List[Any]=None, _snake_case : Any=True, _snake_case : List[Any]=1_0, _snake_case : str=8, ) ->Union[str, Any]:
snake_case__ : Any = parent
snake_case__ : Tuple = batch_size
snake_case__ : Tuple = image_size
snake_case__ : Any = patch_size
snake_case__ : Optional[int] = num_channels
snake_case__ : Tuple = embed_dim
snake_case__ : Any = depths
snake_case__ : Any = num_heads
snake_case__ : List[str] = window_size
snake_case__ : Dict = mlp_ratio
snake_case__ : Optional[int] = qkv_bias
snake_case__ : Optional[Any] = hidden_dropout_prob
snake_case__ : List[str] = attention_probs_dropout_prob
snake_case__ : Union[str, Any] = drop_path_rate
snake_case__ : str = hidden_act
snake_case__ : Union[str, Any] = use_absolute_embeddings
snake_case__ : Union[str, Any] = patch_norm
snake_case__ : Any = layer_norm_eps
snake_case__ : Tuple = initializer_range
snake_case__ : Dict = is_training
snake_case__ : Any = scope
snake_case__ : Optional[Any] = use_labels
snake_case__ : str = type_sequence_label_size
snake_case__ : List[Any] = encoder_stride
def lowercase_ ( self : Tuple ) ->str:
snake_case__ : Tuple = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case__ : List[Any] = None
if self.use_labels:
snake_case__ : Optional[Any] = ids_tensor([self.batch_size], self.type_sequence_label_size )
snake_case__ : Any = self.get_config()
return config, pixel_values, labels
def lowercase_ ( self : Optional[int] ) ->Optional[int]:
return SwinvaConfig(
image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, embed_dim=self.embed_dim, depths=self.depths, num_heads=self.num_heads, window_size=self.window_size, mlp_ratio=self.mlp_ratio, qkv_bias=self.qkv_bias, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, drop_path_rate=self.drop_path_rate, hidden_act=self.hidden_act, use_absolute_embeddings=self.use_absolute_embeddings, path_norm=self.patch_norm, layer_norm_eps=self.layer_norm_eps, initializer_range=self.initializer_range, encoder_stride=self.encoder_stride, )
def lowercase_ ( self : Optional[int], _snake_case : str, _snake_case : List[str], _snake_case : int ) ->Dict:
snake_case__ : List[Any] = SwinvaModel(config=_snake_case )
model.to(_snake_case )
model.eval()
snake_case__ : Optional[int] = model(_snake_case )
snake_case__ : List[Any] = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
snake_case__ : List[Any] = int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, expected_seq_len, expected_dim) )
def lowercase_ ( self : Optional[Any], _snake_case : Any, _snake_case : List[str], _snake_case : Dict ) ->List[Any]:
snake_case__ : List[str] = SwinvaForMaskedImageModeling(config=_snake_case )
model.to(_snake_case )
model.eval()
snake_case__ : Union[str, Any] = model(_snake_case )
self.parent.assertEqual(
result.logits.shape, (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
snake_case__ : Optional[Any] = 1
snake_case__ : Optional[int] = SwinvaForMaskedImageModeling(_snake_case )
model.to(_snake_case )
model.eval()
snake_case__ : Tuple = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
snake_case__ : Any = model(_snake_case )
self.parent.assertEqual(result.logits.shape, (self.batch_size, 1, self.image_size, self.image_size) )
def lowercase_ ( self : List[str], _snake_case : int, _snake_case : List[Any], _snake_case : Optional[int] ) ->Any:
snake_case__ : Tuple = self.type_sequence_label_size
snake_case__ : int = SwinvaForImageClassification(_snake_case )
model.to(_snake_case )
model.eval()
snake_case__ : Tuple = model(_snake_case, labels=_snake_case )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size) )
def lowercase_ ( self : Any ) ->Dict:
snake_case__ : str = self.prepare_config_and_inputs()
snake_case__ , snake_case__ , snake_case__ : List[str] = config_and_inputs
snake_case__ : Union[str, Any] = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class snake_case__ ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = (
(SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
)
_SCREAMING_SNAKE_CASE = (
{"""feature-extraction""": SwinvaModel, """image-classification""": SwinvaForImageClassification}
if is_torch_available()
else {}
)
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = False
def lowercase_ ( self : Union[str, Any] ) ->Dict:
snake_case__ : Optional[int] = SwinvaModelTester(self )
snake_case__ : int = ConfigTester(self, config_class=_snake_case, embed_dim=3_7 )
def lowercase_ ( self : Tuple ) ->int:
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 : Any ) ->str:
snake_case__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_snake_case )
@unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.' )
def lowercase_ ( self : Any ) ->Union[str, Any]:
pass
@unittest.skip(reason='Swinv2 does not use inputs_embeds' )
def lowercase_ ( self : str ) ->Union[str, Any]:
pass
def lowercase_ ( self : Optional[Any] ) ->Union[str, Any]:
snake_case__ , snake_case__ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case__ : Union[str, Any] = model_class(_snake_case )
self.assertIsInstance(model.get_input_embeddings(), (nn.Module) )
snake_case__ : Union[str, Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_snake_case, nn.Linear ) )
def lowercase_ ( self : List[str] ) ->Optional[int]:
snake_case__ , snake_case__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case__ : Any = model_class(_snake_case )
snake_case__ : Dict = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case__ : Optional[Any] = [*signature.parameters.keys()]
snake_case__ : List[Any] = ['pixel_values']
self.assertListEqual(arg_names[:1], _snake_case )
def lowercase_ ( self : str ) ->Union[str, Any]:
snake_case__ , snake_case__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
snake_case__ : int = True
for model_class in self.all_model_classes:
snake_case__ : str = True
snake_case__ : Union[str, Any] = False
snake_case__ : Tuple = True
snake_case__ : int = model_class(_snake_case )
model.to(_snake_case )
model.eval()
with torch.no_grad():
snake_case__ : Optional[int] = model(**self._prepare_for_class(_snake_case, _snake_case ) )
snake_case__ : List[str] = outputs.attentions
snake_case__ : List[Any] = len(self.model_tester.depths )
self.assertEqual(len(_snake_case ), _snake_case )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
snake_case__ : str = True
snake_case__ : Tuple = config.window_size**2
snake_case__ : Optional[int] = model_class(_snake_case )
model.to(_snake_case )
model.eval()
with torch.no_grad():
snake_case__ : str = model(**self._prepare_for_class(_snake_case, _snake_case ) )
snake_case__ : Tuple = outputs.attentions
self.assertEqual(len(_snake_case ), _snake_case )
self.assertListEqual(
list(attentions[0].shape[-3:] ), [self.model_tester.num_heads[0], window_size_squared, window_size_squared], )
snake_case__ : Optional[Any] = len(_snake_case )
# Check attention is always last and order is fine
snake_case__ : Optional[int] = True
snake_case__ : Dict = True
snake_case__ : List[Any] = model_class(_snake_case )
model.to(_snake_case )
model.eval()
with torch.no_grad():
snake_case__ : Optional[int] = model(**self._prepare_for_class(_snake_case, _snake_case ) )
if hasattr(self.model_tester, 'num_hidden_states_types' ):
snake_case__ : str = self.model_tester.num_hidden_states_types
else:
# also another +1 for reshaped_hidden_states
snake_case__ : Dict = 2
self.assertEqual(out_len + added_hidden_states, len(_snake_case ) )
snake_case__ : Any = outputs.attentions
self.assertEqual(len(_snake_case ), _snake_case )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ), [self.model_tester.num_heads[0], window_size_squared, window_size_squared], )
def lowercase_ ( self : Dict, _snake_case : Tuple, _snake_case : Any, _snake_case : int, _snake_case : Optional[int] ) ->str:
snake_case__ : Dict = model_class(_snake_case )
model.to(_snake_case )
model.eval()
with torch.no_grad():
snake_case__ : List[Any] = model(**self._prepare_for_class(_snake_case, _snake_case ) )
snake_case__ : Dict = outputs.hidden_states
snake_case__ : int = getattr(
self.model_tester, 'expected_num_hidden_layers', len(self.model_tester.depths ) + 1 )
self.assertEqual(len(_snake_case ), _snake_case )
# Swinv2 has a different seq_length
snake_case__ : int = (
config.patch_size
if isinstance(config.patch_size, collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
snake_case__ : Optional[Any] = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ), [num_patches, self.model_tester.embed_dim], )
snake_case__ : Union[str, Any] = outputs.reshaped_hidden_states
self.assertEqual(len(_snake_case ), _snake_case )
snake_case__ , snake_case__ , snake_case__ , snake_case__ : str = reshaped_hidden_states[0].shape
snake_case__ : Any = (
reshaped_hidden_states[0].view(_snake_case, _snake_case, height * width ).permute(0, 2, 1 )
)
self.assertListEqual(
list(reshaped_hidden_states.shape[-2:] ), [num_patches, self.model_tester.embed_dim], )
def lowercase_ ( self : str ) ->List[Any]:
snake_case__ , snake_case__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
snake_case__ : List[Any] = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size, collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes:
snake_case__ : Optional[int] = True
self.check_hidden_states_output(_snake_case, _snake_case, _snake_case, _snake_case )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case__ : Dict = True
self.check_hidden_states_output(_snake_case, _snake_case, _snake_case, _snake_case )
def lowercase_ ( self : List[str] ) ->str:
snake_case__ , snake_case__ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
snake_case__ : List[str] = 3
snake_case__ : Union[str, Any] = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size, collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
snake_case__ : str = (
config.patch_size
if isinstance(config.patch_size, collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
snake_case__ : Tuple = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
snake_case__ : Optional[Any] = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
snake_case__ : int = True
self.check_hidden_states_output(_snake_case, _snake_case, _snake_case, (padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case__ : List[str] = True
self.check_hidden_states_output(_snake_case, _snake_case, _snake_case, (padded_height, padded_width) )
def lowercase_ ( self : List[str] ) ->Optional[int]:
snake_case__ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*_snake_case )
def lowercase_ ( self : List[Any] ) ->str:
snake_case__ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_snake_case )
@slow
def lowercase_ ( self : str ) ->Union[str, Any]:
for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case__ : Dict = SwinvaModel.from_pretrained(_snake_case )
self.assertIsNotNone(_snake_case )
def lowercase_ ( self : Optional[int] ) ->List[str]:
snake_case__ , snake_case__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
snake_case__ : List[Any] = _config_zero_init(_snake_case )
for model_class in self.all_model_classes:
snake_case__ : List[str] = model_class(config=_snake_case )
for name, param in model.named_parameters():
if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(), [0.0, 1.0], msg=F'''Parameter {name} of model {model_class} seems not properly initialized''', )
@require_vision
@require_torch
class snake_case__ ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def lowercase_ ( self : Union[str, Any] ) ->List[str]:
return (
AutoImageProcessor.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256' )
if is_vision_available()
else None
)
@slow
def lowercase_ ( self : int ) ->List[Any]:
snake_case__ : Any = SwinvaForImageClassification.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256' ).to(
_snake_case )
snake_case__ : int = self.default_image_processor
snake_case__ : Union[str, Any] = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
snake_case__ : Optional[Any] = image_processor(images=_snake_case, return_tensors='pt' ).to(_snake_case )
# forward pass
with torch.no_grad():
snake_case__ : List[str] = model(**_snake_case )
# verify the logits
snake_case__ : int = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape, _snake_case )
snake_case__ : Optional[int] = torch.tensor([-0.3_9_4_7, -0.4_3_0_6, 0.0_0_2_6] ).to(_snake_case )
self.assertTrue(torch.allclose(outputs.logits[0, :3], _snake_case, atol=1e-4 ) )
| 277 | 0 |
'''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, logging
lowerCAmelCase :Tuple = logging.get_logger(__name__)
class _lowerCamelCase ( lowercase__ ):
'''simple docstring'''
A_ : int = ["""pixel_values"""]
def __init__( self : Any , _A : bool = True , _A : Optional[Dict[str, int]] = None , _A : PILImageResampling = PILImageResampling.BILINEAR , _A : bool = True , _A : Dict[str, int] = None , _A : bool = True , _A : Union[int, float] = 1 / 255 , _A : bool = True , _A : Optional[Union[float, List[float]]] = None , _A : Optional[Union[float, List[float]]] = None , **_A : Optional[Any] , ) -> None:
super().__init__(**_A )
__magic_name__ : List[str] = size if size is not None else {'shortest_edge': 256}
__magic_name__ : str = get_size_dict(_A , default_to_square=_A )
__magic_name__ : List[str] = crop_size if crop_size is not None else {'height': 224, 'width': 224}
__magic_name__ : Optional[int] = get_size_dict(_A )
__magic_name__ : Union[str, Any] = do_resize
__magic_name__ : List[Any] = size
__magic_name__ : List[str] = resample
__magic_name__ : Dict = do_center_crop
__magic_name__ : List[str] = crop_size
__magic_name__ : int = do_rescale
__magic_name__ : Tuple = rescale_factor
__magic_name__ : List[str] = do_normalize
__magic_name__ : Union[str, Any] = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
__magic_name__ : Dict = image_std if image_std is not None else IMAGENET_STANDARD_STD
def __lowerCAmelCase ( self : Optional[Any] , _A : np.ndarray , _A : Dict[str, int] , _A : PILImageResampling = PILImageResampling.BICUBIC , _A : Optional[Union[str, ChannelDimension]] = None , **_A : List[str] , ) -> np.ndarray:
__magic_name__ : Optional[Any] = get_size_dict(_A , default_to_square=_A )
if "shortest_edge" not in size:
raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' )
__magic_name__ : Dict = get_resize_output_image_size(_A , size=size['shortest_edge'] , default_to_square=_A )
return resize(_A , size=_A , resample=_A , data_format=_A , **_A )
def __lowerCAmelCase ( self : Dict , _A : np.ndarray , _A : Dict[str, int] , _A : Optional[Union[str, ChannelDimension]] = None , **_A : Optional[int] , ) -> np.ndarray:
__magic_name__ : int = get_size_dict(_A )
return center_crop(_A , size=(size['height'], size['width']) , data_format=_A , **_A )
def __lowerCAmelCase ( self : List[str] , _A : np.ndarray , _A : float , _A : Optional[Union[str, ChannelDimension]] = None , **_A : Tuple ) -> np.ndarray:
return rescale(_A , scale=_A , data_format=_A , **_A )
def __lowerCAmelCase ( self : str , _A : np.ndarray , _A : Union[float, List[float]] , _A : Union[float, List[float]] , _A : Optional[Union[str, ChannelDimension]] = None , **_A : int , ) -> np.ndarray:
return normalize(_A , mean=_A , std=_A , data_format=_A , **_A )
def __lowerCAmelCase ( self : List[str] , _A : ImageInput , _A : Optional[bool] = None , _A : Dict[str, int] = None , _A : PILImageResampling = None , _A : bool = None , _A : Dict[str, int] = None , _A : Optional[bool] = None , _A : Optional[float] = None , _A : Optional[bool] = None , _A : Optional[Union[float, List[float]]] = None , _A : Optional[Union[float, List[float]]] = None , _A : Optional[Union[str, TensorType]] = None , _A : Union[str, ChannelDimension] = ChannelDimension.FIRST , **_A : List[Any] , ) -> List[str]:
__magic_name__ : int = do_resize if do_resize is not None else self.do_resize
__magic_name__ : Tuple = size if size is not None else self.size
__magic_name__ : Optional[Any] = get_size_dict(_A , default_to_square=_A )
__magic_name__ : Dict = resample if resample is not None else self.resample
__magic_name__ : Optional[Any] = do_center_crop if do_center_crop is not None else self.do_center_crop
__magic_name__ : Dict = crop_size if crop_size is not None else self.crop_size
__magic_name__ : List[str] = get_size_dict(_A )
__magic_name__ : Union[str, Any] = do_rescale if do_rescale is not None else self.do_rescale
__magic_name__ : str = rescale_factor if rescale_factor is not None else self.rescale_factor
__magic_name__ : Any = do_normalize if do_normalize is not None else self.do_normalize
__magic_name__ : Tuple = image_mean if image_mean is not None else self.image_mean
__magic_name__ : Union[str, Any] = image_std if image_std is not None else self.image_std
__magic_name__ : int = make_list_of_images(_A )
if not valid_images(_A ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None:
raise ValueError('Size must be specified if do_resize is True.' )
if do_center_crop and crop_size is None:
raise ValueError('Crop size must be specified if do_center_crop is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.' )
# All transformations expect numpy arrays.
__magic_name__ : List[Any] = [to_numpy_array(_A ) for image in images]
if do_resize:
__magic_name__ : Union[str, Any] = [self.resize(image=_A , size=_A , resample=_A ) for image in images]
if do_center_crop:
__magic_name__ : Union[str, Any] = [self.center_crop(image=_A , size=_A ) for image in images]
if do_rescale:
__magic_name__ : List[Any] = [self.rescale(image=_A , scale=_A ) for image in images]
if do_normalize:
__magic_name__ : Optional[Any] = [self.normalize(image=_A , mean=_A , std=_A ) for image in images]
__magic_name__ : Union[str, Any] = [to_channel_dimension_format(_A , _A ) for image in images]
__magic_name__ : List[str] = {'pixel_values': images}
return BatchFeature(data=_A , tensor_type=_A ) | 275 |
'''simple docstring'''
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
lowerCAmelCase :Any = TypeVar('''T''')
def lowerCamelCase ( lowerCAmelCase : int ):
"""simple docstring"""
return (position - 1) // 2
def lowerCamelCase ( lowerCAmelCase : int ):
"""simple docstring"""
return (2 * position) + 1
def lowerCamelCase ( lowerCAmelCase : int ):
"""simple docstring"""
return (2 * position) + 2
class _lowerCamelCase ( Generic[T] ):
'''simple docstring'''
def __init__( self : Union[str, Any] ) -> None:
__magic_name__ : list[tuple[T, int]] = []
__magic_name__ : dict[T, int] = {}
__magic_name__ : int = 0
def __len__( self : Optional[int] ) -> int:
return self.elements
def __repr__( self : Optional[Any] ) -> str:
return str(self.heap )
def __lowerCAmelCase ( self : List[Any] ) -> bool:
# Check if the priority queue is empty
return self.elements == 0
def __lowerCAmelCase ( self : Dict , _A : T , _A : int ) -> None:
# Add an element with given priority to the queue
self.heap.append((elem, weight) )
__magic_name__ : List[str] = self.elements
self.elements += 1
self._bubble_up(_A )
def __lowerCAmelCase ( self : List[Any] ) -> T:
# Remove and return the element with lowest weight (highest priority)
if self.elements > 1:
self._swap_nodes(0 , self.elements - 1 )
__magic_name__ , __magic_name__ : Optional[Any] = self.heap.pop()
del self.position_map[elem]
self.elements -= 1
if self.elements > 0:
__magic_name__ , __magic_name__ : Tuple = self.heap[0]
self._bubble_down(_A )
return elem
def __lowerCAmelCase ( self : Dict , _A : T , _A : int ) -> None:
# Update the weight of the given key
__magic_name__ : Optional[Any] = self.position_map[elem]
__magic_name__ : Any = (elem, weight)
if position > 0:
__magic_name__ : Optional[int] = get_parent_position(_A )
__magic_name__ , __magic_name__ : Any = self.heap[parent_position]
if parent_weight > weight:
self._bubble_up(_A )
else:
self._bubble_down(_A )
else:
self._bubble_down(_A )
def __lowerCAmelCase ( self : Optional[int] , _A : T ) -> None:
# Place a node at the proper position (upward movement) [to be used internally
# only]
__magic_name__ : Union[str, Any] = self.position_map[elem]
if curr_pos == 0:
return None
__magic_name__ : Optional[Any] = get_parent_position(_A )
__magic_name__ , __magic_name__ : Any = self.heap[curr_pos]
__magic_name__ , __magic_name__ : int = self.heap[parent_position]
if parent_weight > weight:
self._swap_nodes(_A , _A )
return self._bubble_up(_A )
return None
def __lowerCAmelCase ( self : int , _A : T ) -> None:
# Place a node at the proper position (downward movement) [to be used
# internally only]
__magic_name__ : Dict = self.position_map[elem]
__magic_name__ , __magic_name__ : List[str] = self.heap[curr_pos]
__magic_name__ : str = get_child_left_position(_A )
__magic_name__ : List[str] = get_child_right_position(_A )
if child_left_position < self.elements and child_right_position < self.elements:
__magic_name__ , __magic_name__ : int = self.heap[child_left_position]
__magic_name__ , __magic_name__ : int = self.heap[child_right_position]
if child_right_weight < child_left_weight and child_right_weight < weight:
self._swap_nodes(_A , _A )
return self._bubble_down(_A )
if child_left_position < self.elements:
__magic_name__ , __magic_name__ : Any = self.heap[child_left_position]
if child_left_weight < weight:
self._swap_nodes(_A , _A )
return self._bubble_down(_A )
else:
return None
if child_right_position < self.elements:
__magic_name__ , __magic_name__ : str = self.heap[child_right_position]
if child_right_weight < weight:
self._swap_nodes(_A , _A )
return self._bubble_down(_A )
return None
def __lowerCAmelCase ( self : int , _A : int , _A : int ) -> None:
# Swap the nodes at the given positions
__magic_name__ : List[Any] = self.heap[nodea_pos][0]
__magic_name__ : Union[str, Any] = self.heap[nodea_pos][0]
__magic_name__ , __magic_name__ : Optional[Any] = (
self.heap[nodea_pos],
self.heap[nodea_pos],
)
__magic_name__ : Optional[Any] = nodea_pos
__magic_name__ : Optional[Any] = nodea_pos
class _lowerCamelCase ( Generic[T] ):
'''simple docstring'''
def __init__( self : Union[str, Any] ) -> None:
__magic_name__ : dict[T, dict[T, int]] = {}
__magic_name__ : int = 0
def __repr__( self : str ) -> str:
return str(self.connections )
def __len__( self : Union[str, Any] ) -> int:
return self.nodes
def __lowerCAmelCase ( self : Dict , _A : T ) -> None:
# Add a node in the graph if it is not in the graph
if node not in self.connections:
__magic_name__ : str = {}
self.nodes += 1
def __lowerCAmelCase ( self : List[str] , _A : T , _A : T , _A : int ) -> None:
# Add an edge between 2 nodes in the graph
self.add_node(_A )
self.add_node(_A )
__magic_name__ : Any = weight
__magic_name__ : Dict = weight
def lowerCamelCase ( lowerCAmelCase : GraphUndirectedWeighted[T] , ):
"""simple docstring"""
__magic_name__ : dict[T, int] = {node: maxsize for node in graph.connections}
__magic_name__ : dict[T, T | None] = {node: None for node in graph.connections}
__magic_name__ : MinPriorityQueue[T] = MinPriorityQueue()
for node, weight in dist.items():
priority_queue.push(lowerCAmelCase , lowerCAmelCase )
if priority_queue.is_empty():
return dist, parent
# initialization
__magic_name__ : Any = priority_queue.extract_min()
__magic_name__ : List[str] = 0
for neighbour in graph.connections[node]:
if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
__magic_name__ : Any = dist[node] + graph.connections[node][neighbour]
priority_queue.update_key(lowerCAmelCase , dist[neighbour] )
__magic_name__ : Tuple = node
# running prim's algorithm
while not priority_queue.is_empty():
__magic_name__ : Tuple = priority_queue.extract_min()
for neighbour in graph.connections[node]:
if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
__magic_name__ : int = dist[node] + graph.connections[node][neighbour]
priority_queue.update_key(lowerCAmelCase , dist[neighbour] )
__magic_name__ : Union[str, Any] = node
return dist, parent | 275 | 1 |
'''simple docstring'''
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def lowercase ( __magic_name__ , __magic_name__ ):
'''simple docstring'''
UpperCAmelCase : Any = k_size // 2
UpperCAmelCase , UpperCAmelCase : List[str] = mgrid[0 - center : k_size - center, 0 - center : k_size - center]
UpperCAmelCase : List[str] = 1 / (2 * pi * sigma) * exp(-(square(__magic_name__ ) + square(__magic_name__ )) / (2 * square(__magic_name__ )) )
return g
def lowercase ( __magic_name__ , __magic_name__ , __magic_name__ ):
'''simple docstring'''
UpperCAmelCase , UpperCAmelCase : Union[str, Any] = image.shape[0], image.shape[1]
# dst image height and width
UpperCAmelCase : Any = height - k_size + 1
UpperCAmelCase : str = width - k_size + 1
# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
UpperCAmelCase : Tuple = zeros((dst_height * dst_width, k_size * k_size) )
UpperCAmelCase : str = 0
for i, j in product(range(__magic_name__ ) , range(__magic_name__ ) ):
UpperCAmelCase : List[Any] = ravel(image[i : i + k_size, j : j + k_size] )
UpperCAmelCase : List[str] = window
row += 1
# turn the kernel into shape(k*k, 1)
UpperCAmelCase : Dict = gen_gaussian_kernel(__magic_name__ , __magic_name__ )
UpperCAmelCase : str = ravel(__magic_name__ )
# reshape and get the dst image
UpperCAmelCase : Any = dot(__magic_name__ , __magic_name__ ).reshape(__magic_name__ , __magic_name__ ).astype(__magic_name__ )
return dst
if __name__ == "__main__":
# read original image
a : Any = imread(R"../image_data/lena.jpg")
# turn image in gray scale value
a : Dict = cvtColor(img, COLOR_BGR2GRAY)
# get values with two different mask size
a : Dict = gaussian_filter(gray, 3, sigma=1)
a : Optional[Any] = gaussian_filter(gray, 5, sigma=0.8)
# show result images
imshow("gaussian filter with 3x3 mask", gaussianaxa)
imshow("gaussian filter with 5x5 mask", gaussianaxa)
waitKey()
| 311 |
'''simple docstring'''
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
MusicgenProcessor,
TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging
logging.set_verbosity_info()
a : List[str] = logging.get_logger(__name__)
a : Optional[Any] = ["model.decoder.embed_positions.weights"]
def lowercase ( __magic_name__ ):
'''simple docstring'''
if "emb" in name:
UpperCAmelCase : str = name.replace("emb" , "model.decoder.embed_tokens" )
if "transformer" in name:
UpperCAmelCase : List[str] = name.replace("transformer" , "model.decoder" )
if "cross_attention" in name:
UpperCAmelCase : int = name.replace("cross_attention" , "encoder_attn" )
if "linear1" in name:
UpperCAmelCase : List[Any] = name.replace("linear1" , "fc1" )
if "linear2" in name:
UpperCAmelCase : int = name.replace("linear2" , "fc2" )
if "norm1" in name:
UpperCAmelCase : Dict = name.replace("norm1" , "self_attn_layer_norm" )
if "norm_cross" in name:
UpperCAmelCase : Any = name.replace("norm_cross" , "encoder_attn_layer_norm" )
if "norm2" in name:
UpperCAmelCase : Union[str, Any] = name.replace("norm2" , "final_layer_norm" )
if "out_norm" in name:
UpperCAmelCase : Dict = name.replace("out_norm" , "model.decoder.layer_norm" )
if "linears" in name:
UpperCAmelCase : List[Any] = name.replace("linears" , "lm_heads" )
if "condition_provider.conditioners.description.output_proj" in name:
UpperCAmelCase : Any = name.replace("condition_provider.conditioners.description.output_proj" , "enc_to_dec_proj" )
return name
def lowercase ( __magic_name__ , __magic_name__ ):
'''simple docstring'''
UpperCAmelCase : Any = list(state_dict.keys() )
UpperCAmelCase : List[Any] = {}
for key in keys:
UpperCAmelCase : Any = state_dict.pop(__magic_name__ )
UpperCAmelCase : str = rename_keys(__magic_name__ )
if "in_proj_weight" in key:
# split fused qkv proj
UpperCAmelCase : Optional[int] = val[:hidden_size, :]
UpperCAmelCase : Optional[Any] = val[hidden_size : 2 * hidden_size, :]
UpperCAmelCase : Optional[Any] = val[-hidden_size:, :]
elif "enc_to_dec_proj" in key:
UpperCAmelCase : str = val
else:
UpperCAmelCase : int = val
return state_dict, enc_dec_proj_state_dict
def lowercase ( __magic_name__ ):
'''simple docstring'''
if checkpoint == "small":
# default config values
UpperCAmelCase : List[Any] = 1024
UpperCAmelCase : Tuple = 24
UpperCAmelCase : Union[str, Any] = 16
elif checkpoint == "medium":
UpperCAmelCase : List[Any] = 1536
UpperCAmelCase : Optional[Any] = 48
UpperCAmelCase : List[str] = 24
elif checkpoint == "large":
UpperCAmelCase : List[Any] = 2048
UpperCAmelCase : str = 48
UpperCAmelCase : Optional[Any] = 32
else:
raise ValueError(F"Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}." )
UpperCAmelCase : Tuple = MusicgenDecoderConfig(
hidden_size=__magic_name__ , ffn_dim=hidden_size * 4 , num_hidden_layers=__magic_name__ , num_attention_heads=__magic_name__ , )
return config
@torch.no_grad()
def lowercase ( __magic_name__ , __magic_name__=None , __magic_name__=None , __magic_name__="cpu" ):
'''simple docstring'''
UpperCAmelCase : Union[str, Any] = MusicGen.get_pretrained(__magic_name__ , device=__magic_name__ )
UpperCAmelCase : List[str] = decoder_config_from_checkpoint(__magic_name__ )
UpperCAmelCase : Dict = fairseq_model.lm.state_dict()
UpperCAmelCase , UpperCAmelCase : List[str] = rename_state_dict(
__magic_name__ , hidden_size=decoder_config.hidden_size )
UpperCAmelCase : Any = TaEncoderModel.from_pretrained("t5-base" )
UpperCAmelCase : Any = EncodecModel.from_pretrained("facebook/encodec_32khz" )
UpperCAmelCase : int = MusicgenForCausalLM(__magic_name__ ).eval()
# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
UpperCAmelCase , UpperCAmelCase : Optional[int] = decoder.load_state_dict(__magic_name__ , strict=__magic_name__ )
for key in missing_keys.copy():
if key.startswith(("text_encoder", "audio_encoder") ) or key in EXPECTED_MISSING_KEYS:
missing_keys.remove(__magic_name__ )
if len(__magic_name__ ) > 0:
raise ValueError(F"Missing key(s) in state_dict: {missing_keys}" )
if len(__magic_name__ ) > 0:
raise ValueError(F"Unexpected key(s) in state_dict: {unexpected_keys}" )
# init the composite model
UpperCAmelCase : List[Any] = MusicgenForConditionalGeneration(text_encoder=__magic_name__ , audio_encoder=__magic_name__ , decoder=__magic_name__ )
# load the pre-trained enc-dec projection (from the decoder state dict)
model.enc_to_dec_proj.load_state_dict(__magic_name__ )
# check we can do a forward pass
UpperCAmelCase : Union[str, Any] = torch.arange(0 , 8 , dtype=torch.long ).reshape(2 , -1 )
UpperCAmelCase : Optional[Any] = input_ids.reshape(2 * 4 , -1 )
with torch.no_grad():
UpperCAmelCase : str = model(input_ids=__magic_name__ , decoder_input_ids=__magic_name__ ).logits
if logits.shape != (8, 1, 2048):
raise ValueError("Incorrect shape for logits" )
# now construct the processor
UpperCAmelCase : Dict = AutoTokenizer.from_pretrained("t5-base" )
UpperCAmelCase : List[str] = AutoFeatureExtractor.from_pretrained("facebook/encodec_32khz" , padding_side="left" )
UpperCAmelCase : Dict = MusicgenProcessor(feature_extractor=__magic_name__ , tokenizer=__magic_name__ )
# set the appropriate bos/pad token ids
UpperCAmelCase : List[Any] = 2048
UpperCAmelCase : Tuple = 2048
# set other default generation config params
UpperCAmelCase : Tuple = int(30 * audio_encoder.config.frame_rate )
UpperCAmelCase : str = True
UpperCAmelCase : Tuple = 3.0
if pytorch_dump_folder is not None:
Path(__magic_name__ ).mkdir(exist_ok=__magic_name__ )
logger.info(F"Saving model {checkpoint} to {pytorch_dump_folder}" )
model.save_pretrained(__magic_name__ )
processor.save_pretrained(__magic_name__ )
if repo_id:
logger.info(F"Pushing model {checkpoint} to {repo_id}" )
model.push_to_hub(__magic_name__ )
processor.push_to_hub(__magic_name__ )
if __name__ == "__main__":
a : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--checkpoint",
default="small",
type=str,
help="Checkpoint size of the MusicGen model you'd like to convert. Can be one of: `['small', 'medium', 'large']`.",
)
parser.add_argument(
"--pytorch_dump_folder",
required=True,
default=None,
type=str,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
)
parser.add_argument(
"--device", default="cpu", type=str, help="Torch device to run the conversion, either cpu or cuda."
)
a : int = parser.parse_args()
convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)
| 311 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFDebertaVaForMaskedLM,
TFDebertaVaForQuestionAnswering,
TFDebertaVaForSequenceClassification,
TFDebertaVaForTokenClassification,
TFDebertaVaModel,
)
class _a :
'''simple docstring'''
def __init__( self, A, A=13, A=7, A=True, A=True, A=True, A=True, A=99, A=32, A=2, A=4, A=37, A="gelu", A=0.1, A=0.1, A=512, A=16, A=2, A=0.02, A=False, A=True, A="None", A=3, A=4, A=None, ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = parent
SCREAMING_SNAKE_CASE : Any = batch_size
SCREAMING_SNAKE_CASE : int = seq_length
SCREAMING_SNAKE_CASE : Dict = is_training
SCREAMING_SNAKE_CASE : Union[str, Any] = use_input_mask
SCREAMING_SNAKE_CASE : List[Any] = use_token_type_ids
SCREAMING_SNAKE_CASE : Optional[Any] = use_labels
SCREAMING_SNAKE_CASE : List[str] = vocab_size
SCREAMING_SNAKE_CASE : List[str] = hidden_size
SCREAMING_SNAKE_CASE : Tuple = num_hidden_layers
SCREAMING_SNAKE_CASE : Tuple = num_attention_heads
SCREAMING_SNAKE_CASE : Optional[Any] = intermediate_size
SCREAMING_SNAKE_CASE : str = hidden_act
SCREAMING_SNAKE_CASE : Optional[int] = hidden_dropout_prob
SCREAMING_SNAKE_CASE : Dict = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Union[str, Any] = max_position_embeddings
SCREAMING_SNAKE_CASE : List[str] = type_vocab_size
SCREAMING_SNAKE_CASE : Optional[int] = type_sequence_label_size
SCREAMING_SNAKE_CASE : Dict = initializer_range
SCREAMING_SNAKE_CASE : Tuple = num_labels
SCREAMING_SNAKE_CASE : Dict = num_choices
SCREAMING_SNAKE_CASE : str = relative_attention
SCREAMING_SNAKE_CASE : List[str] = position_biased_input
SCREAMING_SNAKE_CASE : Optional[Any] = pos_att_type
SCREAMING_SNAKE_CASE : List[str] = scope
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = ids_tensor([self.batch_size, self.seq_length], self.vocab_size )
SCREAMING_SNAKE_CASE : List[str] = None
if self.use_input_mask:
SCREAMING_SNAKE_CASE : Union[str, Any] = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE : Dict = None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE : Dict = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size )
SCREAMING_SNAKE_CASE : Optional[int] = None
SCREAMING_SNAKE_CASE : str = None
SCREAMING_SNAKE_CASE : Any = None
if self.use_labels:
SCREAMING_SNAKE_CASE : str = ids_tensor([self.batch_size], self.type_sequence_label_size )
SCREAMING_SNAKE_CASE : Dict = ids_tensor([self.batch_size, self.seq_length], self.num_labels )
SCREAMING_SNAKE_CASE : int = DebertaVaConfig(
vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, relative_attention=self.relative_attention, position_biased_input=self.position_biased_input, initializer_range=self.initializer_range, return_dict=A, )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCamelCase_ ( self, A, A, A, A, A, A, A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = TFDebertaVaModel(config=A )
SCREAMING_SNAKE_CASE : Dict = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
SCREAMING_SNAKE_CASE : Any = [input_ids, input_mask]
SCREAMING_SNAKE_CASE : Tuple = model(A )
SCREAMING_SNAKE_CASE : int = model(A )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase_ ( self, A, A, A, A, A, A, A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = TFDebertaVaForMaskedLM(config=A )
SCREAMING_SNAKE_CASE : str = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
SCREAMING_SNAKE_CASE : List[Any] = model(A )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase_ ( self, A, A, A, A, A, A, A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = self.num_labels
SCREAMING_SNAKE_CASE : Dict = TFDebertaVaForSequenceClassification(config=A )
SCREAMING_SNAKE_CASE : Dict = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
SCREAMING_SNAKE_CASE : Optional[int] = model(A )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels) )
def UpperCamelCase_ ( self, A, A, A, A, A, A, A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = self.num_labels
SCREAMING_SNAKE_CASE : List[str] = TFDebertaVaForTokenClassification(config=A )
SCREAMING_SNAKE_CASE : Union[str, Any] = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
SCREAMING_SNAKE_CASE : Optional[int] = model(A )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels) )
def UpperCamelCase_ ( self, A, A, A, A, A, A, A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = TFDebertaVaForQuestionAnswering(config=A )
SCREAMING_SNAKE_CASE : List[Any] = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
SCREAMING_SNAKE_CASE : Optional[int] = model(A )
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length) )
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = self.prepare_config_and_inputs()
(
(
SCREAMING_SNAKE_CASE
) , (
SCREAMING_SNAKE_CASE
) , (
SCREAMING_SNAKE_CASE
) , (
SCREAMING_SNAKE_CASE
) , (
SCREAMING_SNAKE_CASE
) , (
SCREAMING_SNAKE_CASE
) , (
SCREAMING_SNAKE_CASE
) ,
) : str = config_and_inputs
SCREAMING_SNAKE_CASE : Dict = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_tf
class _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
A : int = (
(
TFDebertaVaModel,
TFDebertaVaForMaskedLM,
TFDebertaVaForQuestionAnswering,
TFDebertaVaForSequenceClassification,
TFDebertaVaForTokenClassification,
)
if is_tf_available()
else ()
)
A : Optional[Any] = (
{
'''feature-extraction''': TFDebertaVaModel,
'''fill-mask''': TFDebertaVaForMaskedLM,
'''question-answering''': TFDebertaVaForQuestionAnswering,
'''text-classification''': TFDebertaVaForSequenceClassification,
'''token-classification''': TFDebertaVaForTokenClassification,
'''zero-shot''': TFDebertaVaForSequenceClassification,
}
if is_tf_available()
else {}
)
A : Any = False
A : List[Any] = False
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = TFDebertaVaModelTester(self )
SCREAMING_SNAKE_CASE : str = ConfigTester(self, config_class=A, hidden_size=37 )
def UpperCamelCase_ ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*A )
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*A )
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*A )
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*A )
@slow
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge' )
self.assertIsNotNone(A )
@require_tf
class _a ( unittest.TestCase ):
'''simple docstring'''
@unittest.skip(reason='Model not available yet' )
def UpperCamelCase_ ( self ):
'''simple docstring'''
pass
@slow
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge' )
SCREAMING_SNAKE_CASE : Dict = tf.constant([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] )
SCREAMING_SNAKE_CASE : str = tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
SCREAMING_SNAKE_CASE : Any = model(A, attention_mask=A )[0]
SCREAMING_SNAKE_CASE : Union[str, Any] = tf.constant(
[[[0.23_56, 0.19_48, 0.03_69], [-0.10_63, 0.35_86, -0.51_52], [-0.63_99, -0.02_59, -0.25_25]]] )
tf.debugging.assert_near(output[:, 1:4, 1:4], A, atol=1E-4 )
| 246 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_nllb import NllbTokenizer
else:
UpperCamelCase_ = None
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
UpperCamelCase_ = {
"vocab_file": {
"facebook/nllb-200-distilled-600M": (
"https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model"
),
},
"tokenizer_file": {
"facebook/nllb-200-distilled-600M": (
"https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json"
),
},
}
UpperCamelCase_ = {
"facebook/nllb-large-en-ro": 1_0_2_4,
"facebook/nllb-200-distilled-600M": 1_0_2_4,
}
# fmt: off
UpperCamelCase_ = ["ace_Arab", "ace_Latn", "acm_Arab", "acq_Arab", "aeb_Arab", "afr_Latn", "ajp_Arab", "aka_Latn", "amh_Ethi", "apc_Arab", "arb_Arab", "ars_Arab", "ary_Arab", "arz_Arab", "asm_Beng", "ast_Latn", "awa_Deva", "ayr_Latn", "azb_Arab", "azj_Latn", "bak_Cyrl", "bam_Latn", "ban_Latn", "bel_Cyrl", "bem_Latn", "ben_Beng", "bho_Deva", "bjn_Arab", "bjn_Latn", "bod_Tibt", "bos_Latn", "bug_Latn", "bul_Cyrl", "cat_Latn", "ceb_Latn", "ces_Latn", "cjk_Latn", "ckb_Arab", "crh_Latn", "cym_Latn", "dan_Latn", "deu_Latn", "dik_Latn", "dyu_Latn", "dzo_Tibt", "ell_Grek", "eng_Latn", "epo_Latn", "est_Latn", "eus_Latn", "ewe_Latn", "fao_Latn", "pes_Arab", "fij_Latn", "fin_Latn", "fon_Latn", "fra_Latn", "fur_Latn", "fuv_Latn", "gla_Latn", "gle_Latn", "glg_Latn", "grn_Latn", "guj_Gujr", "hat_Latn", "hau_Latn", "heb_Hebr", "hin_Deva", "hne_Deva", "hrv_Latn", "hun_Latn", "hye_Armn", "ibo_Latn", "ilo_Latn", "ind_Latn", "isl_Latn", "ita_Latn", "jav_Latn", "jpn_Jpan", "kab_Latn", "kac_Latn", "kam_Latn", "kan_Knda", "kas_Arab", "kas_Deva", "kat_Geor", "knc_Arab", "knc_Latn", "kaz_Cyrl", "kbp_Latn", "kea_Latn", "khm_Khmr", "kik_Latn", "kin_Latn", "kir_Cyrl", "kmb_Latn", "kon_Latn", "kor_Hang", "kmr_Latn", "lao_Laoo", "lvs_Latn", "lij_Latn", "lim_Latn", "lin_Latn", "lit_Latn", "lmo_Latn", "ltg_Latn", "ltz_Latn", "lua_Latn", "lug_Latn", "luo_Latn", "lus_Latn", "mag_Deva", "mai_Deva", "mal_Mlym", "mar_Deva", "min_Latn", "mkd_Cyrl", "plt_Latn", "mlt_Latn", "mni_Beng", "khk_Cyrl", "mos_Latn", "mri_Latn", "zsm_Latn", "mya_Mymr", "nld_Latn", "nno_Latn", "nob_Latn", "npi_Deva", "nso_Latn", "nus_Latn", "nya_Latn", "oci_Latn", "gaz_Latn", "ory_Orya", "pag_Latn", "pan_Guru", "pap_Latn", "pol_Latn", "por_Latn", "prs_Arab", "pbt_Arab", "quy_Latn", "ron_Latn", "run_Latn", "rus_Cyrl", "sag_Latn", "san_Deva", "sat_Beng", "scn_Latn", "shn_Mymr", "sin_Sinh", "slk_Latn", "slv_Latn", "smo_Latn", "sna_Latn", "snd_Arab", "som_Latn", "sot_Latn", "spa_Latn", "als_Latn", "srd_Latn", "srp_Cyrl", "ssw_Latn", "sun_Latn", "swe_Latn", "swh_Latn", "szl_Latn", "tam_Taml", "tat_Cyrl", "tel_Telu", "tgk_Cyrl", "tgl_Latn", "tha_Thai", "tir_Ethi", "taq_Latn", "taq_Tfng", "tpi_Latn", "tsn_Latn", "tso_Latn", "tuk_Latn", "tum_Latn", "tur_Latn", "twi_Latn", "tzm_Tfng", "uig_Arab", "ukr_Cyrl", "umb_Latn", "urd_Arab", "uzn_Latn", "vec_Latn", "vie_Latn", "war_Latn", "wol_Latn", "xho_Latn", "ydd_Hebr", "yor_Latn", "yue_Hant", "zho_Hans", "zho_Hant", "zul_Latn"]
class _a ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
A : Optional[Any] = VOCAB_FILES_NAMES
A : Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A : Dict = PRETRAINED_VOCAB_FILES_MAP
A : Any = ['''input_ids''', '''attention_mask''']
A : Dict = NllbTokenizer
A : List[int] = []
A : List[int] = []
def __init__( self, A=None, A=None, A="<s>", A="</s>", A="</s>", A="<s>", A="<unk>", A="<pad>", A="<mask>", A=None, A=None, A=None, A=False, **A, ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = AddedToken(A, lstrip=A, rstrip=A ) if isinstance(A, A ) else mask_token
SCREAMING_SNAKE_CASE : Tuple = legacy_behaviour
super().__init__(
vocab_file=A, tokenizer_file=A, bos_token=A, eos_token=A, sep_token=A, cls_token=A, unk_token=A, pad_token=A, mask_token=A, src_lang=A, tgt_lang=A, additional_special_tokens=A, legacy_behaviour=A, **A, )
SCREAMING_SNAKE_CASE : Any = vocab_file
SCREAMING_SNAKE_CASE : Optional[int] = False if not self.vocab_file else True
SCREAMING_SNAKE_CASE : List[str] = FAIRSEQ_LANGUAGE_CODES.copy()
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
_additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in _additional_special_tokens] )
self.add_special_tokens({'additional_special_tokens': _additional_special_tokens} )
SCREAMING_SNAKE_CASE : Optional[Any] = {
lang_code: self.convert_tokens_to_ids(A ) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
SCREAMING_SNAKE_CASE : Dict = src_lang if src_lang is not None else 'eng_Latn'
SCREAMING_SNAKE_CASE : Optional[Any] = self.convert_tokens_to_ids(self._src_lang )
SCREAMING_SNAKE_CASE : List[str] = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def UpperCamelCase_ ( self ):
'''simple docstring'''
return self._src_lang
@src_lang.setter
def UpperCamelCase_ ( self, A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def UpperCamelCase_ ( self, A, A = None ):
'''simple docstring'''
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, A, A = None ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = [self.sep_token_id]
SCREAMING_SNAKE_CASE : Dict = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def UpperCamelCase_ ( self, A, A, A, A, **A ):
'''simple docstring'''
if src_lang is None or tgt_lang is None:
raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model' )
SCREAMING_SNAKE_CASE : Union[str, Any] = src_lang
SCREAMING_SNAKE_CASE : List[Any] = self(A, add_special_tokens=A, return_tensors=A, **A )
SCREAMING_SNAKE_CASE : Any = self.convert_tokens_to_ids(A )
SCREAMING_SNAKE_CASE : int = tgt_lang_id
return inputs
def UpperCamelCase_ ( self, A, A = "eng_Latn", A = None, A = "fra_Latn", **A, ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = src_lang
SCREAMING_SNAKE_CASE : List[Any] = tgt_lang
return super().prepare_seqaseq_batch(A, A, **A )
def UpperCamelCase_ ( self ):
'''simple docstring'''
return self.set_src_lang_special_tokens(self.src_lang )
def UpperCamelCase_ ( self ):
'''simple docstring'''
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def UpperCamelCase_ ( self, A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = self.convert_tokens_to_ids(A )
if self.legacy_behaviour:
SCREAMING_SNAKE_CASE : str = []
SCREAMING_SNAKE_CASE : List[Any] = [self.eos_token_id, self.cur_lang_code]
else:
SCREAMING_SNAKE_CASE : Tuple = [self.cur_lang_code]
SCREAMING_SNAKE_CASE : List[Any] = [self.eos_token_id]
SCREAMING_SNAKE_CASE : Tuple = self.convert_ids_to_tokens(self.prefix_tokens )
SCREAMING_SNAKE_CASE : int = self.convert_ids_to_tokens(self.suffix_tokens )
SCREAMING_SNAKE_CASE : Tuple = processors.TemplateProcessing(
single=prefix_tokens_str + ['$A'] + suffix_tokens_str, pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str, special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str, self.prefix_tokens + self.suffix_tokens ) ), )
def UpperCamelCase_ ( self, A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = self.convert_tokens_to_ids(A )
if self.legacy_behaviour:
SCREAMING_SNAKE_CASE : List[Any] = []
SCREAMING_SNAKE_CASE : List[str] = [self.eos_token_id, self.cur_lang_code]
else:
SCREAMING_SNAKE_CASE : Dict = [self.cur_lang_code]
SCREAMING_SNAKE_CASE : str = [self.eos_token_id]
SCREAMING_SNAKE_CASE : Optional[int] = self.convert_ids_to_tokens(self.prefix_tokens )
SCREAMING_SNAKE_CASE : List[str] = self.convert_ids_to_tokens(self.suffix_tokens )
SCREAMING_SNAKE_CASE : Union[str, Any] = processors.TemplateProcessing(
single=prefix_tokens_str + ['$A'] + suffix_tokens_str, pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str, special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str, self.prefix_tokens + self.suffix_tokens ) ), )
def UpperCamelCase_ ( self, A, A = None ):
'''simple docstring'''
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(A ):
logger.error(F"Vocabulary path ({save_directory}) should be a directory." )
return
SCREAMING_SNAKE_CASE : int = os.path.join(
A, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(A ):
copyfile(self.vocab_file, A )
return (out_vocab_file,)
| 246 | 1 |
"""simple docstring"""
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
_lowerCamelCase : Optional[int] = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
_lowerCamelCase : str = 50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
_lowerCamelCase : Any = 0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))
def lowercase_ ( _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
A_ : Union[str, Any] = len([g for position, g in enumerate(_UpperCAmelCase ) if g == main_target[position]] )
return (item, float(_UpperCAmelCase ))
def lowercase_ ( _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
A_ : str = random.randint(0 , len(_UpperCAmelCase ) - 1 )
A_ : Tuple = parent_a[:random_slice] + parent_a[random_slice:]
A_ : Tuple = parent_a[:random_slice] + parent_a[random_slice:]
return (child_a, child_a)
def lowercase_ ( _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
A_ : Tuple = list(_UpperCAmelCase )
if random.uniform(0 , 1 ) < MUTATION_PROBABILITY:
A_ : Tuple = random.choice(_UpperCAmelCase )
return "".join(_UpperCAmelCase )
def lowercase_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , ):
"""simple docstring"""
A_ : Any = []
# Generate more children proportionally to the fitness score.
A_ : int = int(parent_a[1] * 100 ) + 1
A_ : Tuple = 10 if child_n >= 10 else child_n
for _ in range(_UpperCAmelCase ):
A_ : List[Any] = population_score[random.randint(0 , _UpperCAmelCase )][0]
A_ , A_ : Any = crossover(parent_a[0] , _UpperCAmelCase )
# Append new string to the population list.
pop.append(mutate(_UpperCAmelCase , _UpperCAmelCase ) )
pop.append(mutate(_UpperCAmelCase , _UpperCAmelCase ) )
return pop
def lowercase_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = True ):
"""simple docstring"""
if N_POPULATION < N_SELECTED:
A_ : Union[str, Any] = f"""{N_POPULATION} must be bigger than {N_SELECTED}"""
raise ValueError(_UpperCAmelCase )
# Verify that the target contains no genes besides the ones inside genes variable.
A_ : Tuple = sorted({c for c in target if c not in genes} )
if not_in_genes_list:
A_ : int = f"""{not_in_genes_list} is not in genes list, evolution cannot converge"""
raise ValueError(_UpperCAmelCase )
# Generate random starting population.
A_ : List[Any] = []
for _ in range(_UpperCAmelCase ):
population.append(''''''.join([random.choice(_UpperCAmelCase ) for i in range(len(_UpperCAmelCase ) )] ) )
# Just some logs to know what the algorithms is doing.
A_ , A_ : str = 0, 0
# This loop will end when we find a perfect match for our target.
while True:
generation += 1
total_population += len(_UpperCAmelCase )
# Random population created. Now it's time to evaluate.
# Adding a bit of concurrency can make everything faster,
#
# import concurrent.futures
# population_score: list[tuple[str, float]] = []
# with concurrent.futures.ThreadPoolExecutor(
# max_workers=NUM_WORKERS) as executor:
# futures = {executor.submit(evaluate, item) for item in population}
# concurrent.futures.wait(futures)
# population_score = [item.result() for item in futures]
#
# but with a simple algorithm like this, it will probably be slower.
# We just need to call evaluate for every item inside the population.
A_ : Union[str, Any] = [evaluate(_UpperCAmelCase , _UpperCAmelCase ) for item in population]
# Check if there is a matching evolution.
A_ : List[Any] = sorted(_UpperCAmelCase , key=lambda _UpperCAmelCase : x[1] , reverse=_UpperCAmelCase )
if population_score[0][0] == target:
return (generation, total_population, population_score[0][0])
# Print the best result every 10 generation.
# Just to know that the algorithm is working.
if debug and generation % 10 == 0:
print(
f"""\nGeneration: {generation}"""
f"""\nTotal Population:{total_population}"""
f"""\nBest score: {population_score[0][1]}"""
f"""\nBest string: {population_score[0][0]}""" )
# Flush the old population, keeping some of the best evolutions.
# Keeping this avoid regression of evolution.
A_ : Optional[Any] = population[: int(N_POPULATION / 3 )]
population.clear()
population.extend(_UpperCAmelCase )
# Normalize population score to be between 0 and 1.
A_ : Tuple = [
(item, score / len(_UpperCAmelCase )) for item, score in population_score
]
# This is selection
for i in range(_UpperCAmelCase ):
population.extend(select(population_score[int(_UpperCAmelCase )] , _UpperCAmelCase , _UpperCAmelCase ) )
# Check if the population has already reached the maximum value and if so,
# break the cycle. If this check is disabled, the algorithm will take
# forever to compute large strings, but will also calculate small strings in
# a far fewer generations.
if len(_UpperCAmelCase ) > N_POPULATION:
break
if __name__ == "__main__":
_lowerCamelCase : Optional[Any] = (
'This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!'
)
_lowerCamelCase : List[str] = list(
' ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm'
'nopqrstuvwxyz.,;!?+-*#@^\'èéòà€ù=)(&%$£/\\'
)
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase : List[Any] = basic(target_str, genes_list)
print(
f'\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'
)
| 167 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : Any = {
'configuration_git': ['GIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GitConfig', 'GitVisionConfig'],
'processing_git': ['GitProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Dict = [
'GIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'GitForCausalLM',
'GitModel',
'GitPreTrainedModel',
'GitVisionModel',
]
if TYPE_CHECKING:
from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
from .processing_git import GitProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_git import (
GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GitForCausalLM,
GitModel,
GitPreTrainedModel,
GitVisionModel,
)
else:
import sys
_lowerCamelCase : Union[str, Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 167 | 1 |
"""simple docstring"""
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : Optional[int] = logging.get_logger(__name__)
a__ : Optional[Any] = {
'''facebook/encodec_24khz''': '''https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json''',
'''facebook/encodec_48khz''': '''https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json''',
}
class UpperCamelCase_ ( UpperCamelCase):
"""simple docstring"""
snake_case__ : Optional[Any] = "encodec"
def __init__( self : Optional[int] , UpperCAmelCase__ : str=[1.5, 3.0, 6.0, 12.0, 24.0] , UpperCAmelCase__ : Tuple=2_4_0_0_0 , UpperCAmelCase__ : List[str]=1 , UpperCAmelCase__ : Dict=False , UpperCAmelCase__ : List[Any]=None , UpperCAmelCase__ : Tuple=None , UpperCAmelCase__ : List[str]=1_2_8 , UpperCAmelCase__ : Any=3_2 , UpperCAmelCase__ : Optional[int]=1 , UpperCAmelCase__ : Optional[Any]=[8, 5, 4, 2] , UpperCAmelCase__ : Any="weight_norm" , UpperCAmelCase__ : List[str]=7 , UpperCAmelCase__ : str=7 , UpperCAmelCase__ : int=3 , UpperCAmelCase__ : int=2 , UpperCAmelCase__ : Any=True , UpperCAmelCase__ : Optional[int]="reflect" , UpperCAmelCase__ : Optional[Any]=2 , UpperCAmelCase__ : Dict=2 , UpperCAmelCase__ : Dict=1.0 , UpperCAmelCase__ : Union[str, Any]=1_0_2_4 , UpperCAmelCase__ : Dict=None , UpperCAmelCase__ : Any=True , **UpperCAmelCase__ : str , ) -> Dict:
__SCREAMING_SNAKE_CASE = target_bandwidths
__SCREAMING_SNAKE_CASE = sampling_rate
__SCREAMING_SNAKE_CASE = audio_channels
__SCREAMING_SNAKE_CASE = normalize
__SCREAMING_SNAKE_CASE = chunk_length_s
__SCREAMING_SNAKE_CASE = overlap
__SCREAMING_SNAKE_CASE = hidden_size
__SCREAMING_SNAKE_CASE = num_filters
__SCREAMING_SNAKE_CASE = num_residual_layers
__SCREAMING_SNAKE_CASE = upsampling_ratios
__SCREAMING_SNAKE_CASE = norm_type
__SCREAMING_SNAKE_CASE = kernel_size
__SCREAMING_SNAKE_CASE = last_kernel_size
__SCREAMING_SNAKE_CASE = residual_kernel_size
__SCREAMING_SNAKE_CASE = dilation_growth_rate
__SCREAMING_SNAKE_CASE = use_causal_conv
__SCREAMING_SNAKE_CASE = pad_mode
__SCREAMING_SNAKE_CASE = compress
__SCREAMING_SNAKE_CASE = num_lstm_layers
__SCREAMING_SNAKE_CASE = trim_right_ratio
__SCREAMING_SNAKE_CASE = codebook_size
__SCREAMING_SNAKE_CASE = codebook_dim if codebook_dim is not None else hidden_size
__SCREAMING_SNAKE_CASE = use_conv_shortcut
if self.norm_type not in ["weight_norm", "time_group_norm"]:
raise ValueError(
F"""self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}""" )
super().__init__(**UpperCAmelCase__ )
@property
def UpperCAmelCase_ ( self : Optional[int] ) -> Optional[int]:
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate )
@property
def UpperCAmelCase_ ( self : Tuple ) -> Optional[int]:
if self.chunk_length_s is None or self.overlap is None:
return None
else:
return max(1 , int((1.0 - self.overlap) * self.chunk_length ) )
@property
def UpperCAmelCase_ ( self : Any ) -> int:
__SCREAMING_SNAKE_CASE = np.prod(self.upsampling_ratios )
return math.ceil(self.sampling_rate / hop_length )
@property
def UpperCAmelCase_ ( self : Optional[Any] ) -> int:
return int(1_0_0_0 * self.target_bandwidths[-1] // (self.frame_rate * 1_0) )
| 195 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : int = logging.get_logger(__name__)
a__ : Tuple = {'''ctrl''': '''https://huggingface.co/ctrl/resolve/main/config.json'''}
class UpperCamelCase_ ( UpperCamelCase):
"""simple docstring"""
snake_case__ : Dict = "ctrl"
snake_case__ : int = ["past_key_values"]
snake_case__ : Tuple = {
"max_position_embeddings": "n_positions",
"hidden_size": "n_embd",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self : List[Any] , UpperCAmelCase__ : Any=2_4_6_5_3_4 , UpperCAmelCase__ : List[Any]=2_5_6 , UpperCAmelCase__ : Optional[int]=1_2_8_0 , UpperCAmelCase__ : Optional[Any]=8_1_9_2 , UpperCAmelCase__ : int=4_8 , UpperCAmelCase__ : Optional[int]=1_6 , UpperCAmelCase__ : Tuple=0.1 , UpperCAmelCase__ : Optional[Any]=0.1 , UpperCAmelCase__ : Any=1E-6 , UpperCAmelCase__ : List[str]=0.02 , UpperCAmelCase__ : Union[str, Any]=True , **UpperCAmelCase__ : Tuple , ) -> str:
__SCREAMING_SNAKE_CASE = vocab_size
__SCREAMING_SNAKE_CASE = n_positions
__SCREAMING_SNAKE_CASE = n_embd
__SCREAMING_SNAKE_CASE = n_layer
__SCREAMING_SNAKE_CASE = n_head
__SCREAMING_SNAKE_CASE = dff
__SCREAMING_SNAKE_CASE = resid_pdrop
__SCREAMING_SNAKE_CASE = embd_pdrop
__SCREAMING_SNAKE_CASE = layer_norm_epsilon
__SCREAMING_SNAKE_CASE = initializer_range
__SCREAMING_SNAKE_CASE = use_cache
super().__init__(**UpperCAmelCase__ )
| 195 | 1 |
from __future__ import annotations
from decimal import Decimal
from numpy import array
def lowercase_ ( _lowerCamelCase : list[list[float]]):
lowercase__ : Tuple = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementation only works for 2x2 matrices
if len(_lowerCamelCase) == 2 and len(matrix[0]) == 2 and len(matrix[1]) == 2:
# Calculate the determinant of the matrix
lowercase__ : str = float(
d(matrix[0][0]) * d(matrix[1][1]) - d(matrix[1][0]) * d(matrix[0][1]))
if determinant == 0:
raise ValueError("This matrix has no inverse.")
# Creates a copy of the matrix with swapped positions of the elements
lowercase__ : Dict = [[0.0, 0.0], [0.0, 0.0]]
lowercase__ , lowercase__ : Union[str, Any] = matrix[1][1], matrix[0][0]
lowercase__ , lowercase__ : int = -matrix[1][0], -matrix[0][1]
# Calculate the inverse of the matrix
return [
[(float(d(_lowerCamelCase)) / determinant) or 0.0 for n in row] for row in swapped_matrix
]
elif (
len(_lowerCamelCase) == 3
and len(matrix[0]) == 3
and len(matrix[1]) == 3
and len(matrix[2]) == 3
):
# Calculate the determinant of the matrix using Sarrus rule
lowercase__ : Any = float(
(
(d(matrix[0][0]) * d(matrix[1][1]) * d(matrix[2][2]))
+ (d(matrix[0][1]) * d(matrix[1][2]) * d(matrix[2][0]))
+ (d(matrix[0][2]) * d(matrix[1][0]) * d(matrix[2][1]))
)
- (
(d(matrix[0][2]) * d(matrix[1][1]) * d(matrix[2][0]))
+ (d(matrix[0][1]) * d(matrix[1][0]) * d(matrix[2][2]))
+ (d(matrix[0][0]) * d(matrix[1][2]) * d(matrix[2][1]))
))
if determinant == 0:
raise ValueError("This matrix has no inverse.")
# Creating cofactor matrix
lowercase__ : Optional[Any] = [
[d(0.0), d(0.0), d(0.0)],
[d(0.0), d(0.0), d(0.0)],
[d(0.0), d(0.0), d(0.0)],
]
lowercase__ : Optional[int] = (d(matrix[1][1]) * d(matrix[2][2])) - (
d(matrix[1][2]) * d(matrix[2][1])
)
lowercase__ : List[Any] = -(
(d(matrix[1][0]) * d(matrix[2][2])) - (d(matrix[1][2]) * d(matrix[2][0]))
)
lowercase__ : List[Any] = (d(matrix[1][0]) * d(matrix[2][1])) - (
d(matrix[1][1]) * d(matrix[2][0])
)
lowercase__ : Optional[Any] = -(
(d(matrix[0][1]) * d(matrix[2][2])) - (d(matrix[0][2]) * d(matrix[2][1]))
)
lowercase__ : Optional[int] = (d(matrix[0][0]) * d(matrix[2][2])) - (
d(matrix[0][2]) * d(matrix[2][0])
)
lowercase__ : int = -(
(d(matrix[0][0]) * d(matrix[2][1])) - (d(matrix[0][1]) * d(matrix[2][0]))
)
lowercase__ : int = (d(matrix[0][1]) * d(matrix[1][2])) - (
d(matrix[0][2]) * d(matrix[1][1])
)
lowercase__ : Any = -(
(d(matrix[0][0]) * d(matrix[1][2])) - (d(matrix[0][2]) * d(matrix[1][0]))
)
lowercase__ : List[Any] = (d(matrix[0][0]) * d(matrix[1][1])) - (
d(matrix[0][1]) * d(matrix[1][0])
)
# Transpose the cofactor matrix (Adjoint matrix)
lowercase__ : List[Any] = array(_lowerCamelCase)
for i in range(3):
for j in range(3):
lowercase__ : int = cofactor_matrix[j][i]
# Inverse of the matrix using the formula (1/determinant) * adjoint matrix
lowercase__ : List[Any] = array(_lowerCamelCase)
for i in range(3):
for j in range(3):
inverse_matrix[i][j] /= d(_lowerCamelCase)
# Calculate the inverse of the matrix
return [[float(d(_lowerCamelCase)) or 0.0 for n in row] for row in inverse_matrix]
raise ValueError("Please provide a matrix of size 2x2 or 3x3.")
| 87 |
'''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 = logging.get_logger(__name__)
def a_ ( _lowerCAmelCase ) -> Tuple:
__lowerCamelCase : Optional[int] = DPTConfig()
if "large" in checkpoint_url:
__lowerCamelCase : List[Any] = 1024
__lowerCamelCase : Union[str, Any] = 4096
__lowerCamelCase : Any = 24
__lowerCamelCase : List[str] = 16
__lowerCamelCase : int = [5, 11, 17, 23]
__lowerCamelCase : List[Any] = [256, 512, 1024, 1024]
__lowerCamelCase : Tuple = (1, 384, 384)
if "ade" in checkpoint_url:
__lowerCamelCase : Tuple = True
__lowerCamelCase : Union[str, Any] = 150
__lowerCamelCase : Any = 'huggingface/label-files'
__lowerCamelCase : Dict = 'ade20k-id2label.json'
__lowerCamelCase : Tuple = json.load(open(cached_download(hf_hub_url(_lowerCAmelCase ,_lowerCAmelCase ,repo_type='dataset' ) ) ,'r' ) )
__lowerCamelCase : List[Any] = {int(_lowerCAmelCase ): v for k, v in idalabel.items()}
__lowerCamelCase : List[Any] = idalabel
__lowerCamelCase : List[str] = {v: k for k, v in idalabel.items()}
__lowerCamelCase : Optional[int] = [1, 150, 480, 480]
return config, expected_shape
def a_ ( _lowerCAmelCase ) -> Tuple:
__lowerCamelCase : Optional[int] = ['pretrained.model.head.weight', 'pretrained.model.head.bias']
for k in ignore_keys:
state_dict.pop(_lowerCAmelCase ,_lowerCAmelCase )
def a_ ( _lowerCAmelCase ) -> Dict:
if (
"pretrained.model" in name
and "cls_token" not in name
and "pos_embed" not in name
and "patch_embed" not in name
):
__lowerCamelCase : str = name.replace('pretrained.model' ,'dpt.encoder' )
if "pretrained.model" in name:
__lowerCamelCase : Union[str, Any] = name.replace('pretrained.model' ,'dpt.embeddings' )
if "patch_embed" in name:
__lowerCamelCase : int = name.replace('patch_embed' ,'patch_embeddings' )
if "pos_embed" in name:
__lowerCamelCase : Optional[Any] = name.replace('pos_embed' ,'position_embeddings' )
if "attn.proj" in name:
__lowerCamelCase : Union[str, Any] = name.replace('attn.proj' ,'attention.output.dense' )
if "proj" in name and "project" not in name:
__lowerCamelCase : List[str] = name.replace('proj' ,'projection' )
if "blocks" in name:
__lowerCamelCase : Optional[int] = name.replace('blocks' ,'layer' )
if "mlp.fc1" in name:
__lowerCamelCase : Dict = name.replace('mlp.fc1' ,'intermediate.dense' )
if "mlp.fc2" in name:
__lowerCamelCase : int = name.replace('mlp.fc2' ,'output.dense' )
if "norm1" in name:
__lowerCamelCase : Optional[int] = name.replace('norm1' ,'layernorm_before' )
if "norm2" in name:
__lowerCamelCase : str = name.replace('norm2' ,'layernorm_after' )
if "scratch.output_conv" in name:
__lowerCamelCase : int = name.replace('scratch.output_conv' ,'head' )
if "scratch" in name:
__lowerCamelCase : Any = name.replace('scratch' ,'neck' )
if "layer1_rn" in name:
__lowerCamelCase : List[str] = name.replace('layer1_rn' ,'convs.0' )
if "layer2_rn" in name:
__lowerCamelCase : str = name.replace('layer2_rn' ,'convs.1' )
if "layer3_rn" in name:
__lowerCamelCase : List[Any] = name.replace('layer3_rn' ,'convs.2' )
if "layer4_rn" in name:
__lowerCamelCase : Optional[Any] = name.replace('layer4_rn' ,'convs.3' )
if "refinenet" in name:
__lowerCamelCase : Any = 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
__lowerCamelCase : Tuple = name.replace(F'refinenet{layer_idx}' ,F'fusion_stage.layers.{abs(layer_idx-4 )}' )
if "out_conv" in name:
__lowerCamelCase : Any = name.replace('out_conv' ,'projection' )
if "resConfUnit1" in name:
__lowerCamelCase : Optional[Any] = name.replace('resConfUnit1' ,'residual_layer1' )
if "resConfUnit2" in name:
__lowerCamelCase : List[str] = name.replace('resConfUnit2' ,'residual_layer2' )
if "conv1" in name:
__lowerCamelCase : Any = name.replace('conv1' ,'convolution1' )
if "conv2" in name:
__lowerCamelCase : Optional[Any] = name.replace('conv2' ,'convolution2' )
# readout blocks
if "pretrained.act_postprocess1.0.project.0" in name:
__lowerCamelCase : str = name.replace('pretrained.act_postprocess1.0.project.0' ,'neck.reassemble_stage.readout_projects.0.0' )
if "pretrained.act_postprocess2.0.project.0" in name:
__lowerCamelCase : List[Any] = name.replace('pretrained.act_postprocess2.0.project.0' ,'neck.reassemble_stage.readout_projects.1.0' )
if "pretrained.act_postprocess3.0.project.0" in name:
__lowerCamelCase : Tuple = name.replace('pretrained.act_postprocess3.0.project.0' ,'neck.reassemble_stage.readout_projects.2.0' )
if "pretrained.act_postprocess4.0.project.0" in name:
__lowerCamelCase : List[Any] = name.replace('pretrained.act_postprocess4.0.project.0' ,'neck.reassemble_stage.readout_projects.3.0' )
# resize blocks
if "pretrained.act_postprocess1.3" in name:
__lowerCamelCase : Optional[int] = name.replace('pretrained.act_postprocess1.3' ,'neck.reassemble_stage.layers.0.projection' )
if "pretrained.act_postprocess1.4" in name:
__lowerCamelCase : Dict = name.replace('pretrained.act_postprocess1.4' ,'neck.reassemble_stage.layers.0.resize' )
if "pretrained.act_postprocess2.3" in name:
__lowerCamelCase : Dict = name.replace('pretrained.act_postprocess2.3' ,'neck.reassemble_stage.layers.1.projection' )
if "pretrained.act_postprocess2.4" in name:
__lowerCamelCase : Any = name.replace('pretrained.act_postprocess2.4' ,'neck.reassemble_stage.layers.1.resize' )
if "pretrained.act_postprocess3.3" in name:
__lowerCamelCase : Tuple = name.replace('pretrained.act_postprocess3.3' ,'neck.reassemble_stage.layers.2.projection' )
if "pretrained.act_postprocess4.3" in name:
__lowerCamelCase : List[Any] = name.replace('pretrained.act_postprocess4.3' ,'neck.reassemble_stage.layers.3.projection' )
if "pretrained.act_postprocess4.4" in name:
__lowerCamelCase : Optional[int] = name.replace('pretrained.act_postprocess4.4' ,'neck.reassemble_stage.layers.3.resize' )
if "pretrained" in name:
__lowerCamelCase : Union[str, Any] = name.replace('pretrained' ,'dpt' )
if "bn" in name:
__lowerCamelCase : Union[str, Any] = name.replace('bn' ,'batch_norm' )
if "head" in name:
__lowerCamelCase : Dict = name.replace('head' ,'head.head' )
if "encoder.norm" in name:
__lowerCamelCase : str = name.replace('encoder.norm' ,'layernorm' )
if "auxlayer" in name:
__lowerCamelCase : int = name.replace('auxlayer' ,'auxiliary_head.head' )
return name
def a_ ( _lowerCAmelCase ,_lowerCAmelCase ) -> Optional[Any]:
for i in range(config.num_hidden_layers ):
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
__lowerCamelCase : Union[str, Any] = state_dict.pop(F'dpt.encoder.layer.{i}.attn.qkv.weight' )
__lowerCamelCase : Optional[Any] = state_dict.pop(F'dpt.encoder.layer.{i}.attn.qkv.bias' )
# next, add query, keys and values (in that order) to the state dict
__lowerCamelCase : Union[str, Any] = in_proj_weight[: config.hidden_size, :]
__lowerCamelCase : List[Any] = in_proj_bias[: config.hidden_size]
__lowerCamelCase : Union[str, Any] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
__lowerCamelCase : Any = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
__lowerCamelCase : Any = in_proj_weight[
-config.hidden_size :, :
]
__lowerCamelCase : Optional[Any] = in_proj_bias[-config.hidden_size :]
def a_ ( ) -> Optional[int]:
__lowerCamelCase : int = 'http://images.cocodataset.org/val2017/000000039769.jpg'
__lowerCamelCase : Dict = Image.open(requests.get(_lowerCAmelCase ,stream=_lowerCAmelCase ).raw )
return im
@torch.no_grad()
def a_ ( _lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ) -> List[Any]:
__lowerCamelCase ,__lowerCamelCase : List[Any] = get_dpt_config(_lowerCAmelCase )
# load original state_dict from URL
__lowerCamelCase : str = torch.hub.load_state_dict_from_url(_lowerCAmelCase ,map_location='cpu' )
# remove certain keys
remove_ignore_keys_(_lowerCAmelCase )
# rename keys
for key in state_dict.copy().keys():
__lowerCamelCase : int = state_dict.pop(_lowerCAmelCase )
__lowerCamelCase : List[str] = val
# read in qkv matrices
read_in_q_k_v(_lowerCAmelCase ,_lowerCAmelCase )
# load HuggingFace model
__lowerCamelCase : Tuple = DPTForSemanticSegmentation(_lowerCAmelCase ) if 'ade' in checkpoint_url else DPTForDepthEstimation(_lowerCAmelCase )
model.load_state_dict(_lowerCAmelCase )
model.eval()
# Check outputs on an image
__lowerCamelCase : Dict = 480 if 'ade' in checkpoint_url else 384
__lowerCamelCase : Dict = DPTImageProcessor(size=_lowerCAmelCase )
__lowerCamelCase : Optional[int] = prepare_img()
__lowerCamelCase : Optional[int] = image_processor(_lowerCAmelCase ,return_tensors='pt' )
# forward pass
__lowerCamelCase : List[Any] = model(**_lowerCAmelCase ).logits if 'ade' in checkpoint_url else model(**_lowerCAmelCase ).predicted_depth
# Assert logits
__lowerCamelCase : Optional[int] = torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]] )
if "ade" in checkpoint_url:
__lowerCamelCase : List[str] = torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]] )
assert outputs.shape == torch.Size(_lowerCAmelCase )
assert (
torch.allclose(outputs[0, 0, :3, :3] ,_lowerCAmelCase ,atol=1E-4 )
if "ade" in checkpoint_url
else torch.allclose(outputs[0, :3, :3] ,_lowerCAmelCase )
)
Path(_lowerCAmelCase ).mkdir(exist_ok=_lowerCAmelCase )
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 push_to_hub:
print('Pushing model to hub...' )
model.push_to_hub(
repo_path_or_name=Path(_lowerCAmelCase ,_lowerCAmelCase ) ,organization='nielsr' ,commit_message='Add model' ,use_temp_dir=_lowerCAmelCase ,)
image_processor.push_to_hub(
repo_path_or_name=Path(_lowerCAmelCase ,_lowerCAmelCase ) ,organization='nielsr' ,commit_message='Add image processor' ,use_temp_dir=_lowerCAmelCase ,)
if __name__ == "__main__":
_UpperCamelCase = 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=True,
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.',
)
_UpperCamelCase = parser.parse_args()
convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
| 208 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor
@require_vision
class lowerCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE_ = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE_ = [
'[UNK]',
'[CLS]',
'[SEP]',
'[PAD]',
'[MASK]',
'的',
'价',
'格',
'是',
'15',
'便',
'alex',
'##andra',
'，',
'。',
'-',
't',
'shirt',
]
SCREAMING_SNAKE_CASE_ = 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] ) )
SCREAMING_SNAKE_CASE_ = {
'do_resize': True,
'size': {'height': 224, 'width': 224},
'do_center_crop': True,
'crop_size': {'height': 18, 'width': 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],
'do_convert_rgb': True,
}
SCREAMING_SNAKE_CASE_ = 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 : Optional[Any] , **_lowerCAmelCase : str ):
return BertTokenizer.from_pretrained(self.tmpdirname , **_lowerCAmelCase )
def lowerCAmelCase_ ( self : Dict , **_lowerCAmelCase : Optional[int] ):
return BertTokenizerFast.from_pretrained(self.tmpdirname , **_lowerCAmelCase )
def lowerCAmelCase_ ( self : Optional[int] , **_lowerCAmelCase : List[Any] ):
return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname , **_lowerCAmelCase )
def lowerCAmelCase_ ( self : Optional[Any] ):
shutil.rmtree(self.tmpdirname )
def lowerCAmelCase_ ( self : str ):
SCREAMING_SNAKE_CASE_ = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
SCREAMING_SNAKE_CASE_ = [Image.fromarray(np.moveaxis(_lowerCAmelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def lowerCAmelCase_ ( self : Any ):
SCREAMING_SNAKE_CASE_ = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE_ = self.get_image_processor()
SCREAMING_SNAKE_CASE_ = ChineseCLIPProcessor(tokenizer=_lowerCAmelCase , image_processor=_lowerCAmelCase )
processor_slow.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE_ = ChineseCLIPProcessor.from_pretrained(self.tmpdirname , use_fast=_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = ChineseCLIPProcessor(tokenizer=_lowerCAmelCase , image_processor=_lowerCAmelCase )
processor_fast.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE_ = ChineseCLIPProcessor.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[int] ):
SCREAMING_SNAKE_CASE_ = ChineseCLIPProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE_ = self.get_tokenizer(cls_token='(CLS)' , sep_token='(SEP)' )
SCREAMING_SNAKE_CASE_ = self.get_image_processor(do_normalize=_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = ChineseCLIPProcessor.from_pretrained(
self.tmpdirname , cls_token='(CLS)' , sep_token='(SEP)' , do_normalize=_lowerCAmelCase )
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 : Optional[int] ):
SCREAMING_SNAKE_CASE_ = self.get_image_processor()
SCREAMING_SNAKE_CASE_ = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ = ChineseCLIPProcessor(tokenizer=_lowerCAmelCase , image_processor=_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE_ = image_processor(_lowerCAmelCase , return_tensors='np' )
SCREAMING_SNAKE_CASE_ = 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 : Union[str, Any] ):
SCREAMING_SNAKE_CASE_ = self.get_image_processor()
SCREAMING_SNAKE_CASE_ = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ = ChineseCLIPProcessor(tokenizer=_lowerCAmelCase , image_processor=_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = 'Alexandra，T-shirt的价格是15便士。'
SCREAMING_SNAKE_CASE_ = processor(text=_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = tokenizer(_lowerCAmelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def lowerCAmelCase_ ( self : Tuple ):
SCREAMING_SNAKE_CASE_ = self.get_image_processor()
SCREAMING_SNAKE_CASE_ = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ = ChineseCLIPProcessor(tokenizer=_lowerCAmelCase , image_processor=_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = 'Alexandra，T-shirt的价格是15便士。'
SCREAMING_SNAKE_CASE_ = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE_ = processor(text=_lowerCAmelCase , images=_lowerCAmelCase )
self.assertListEqual(list(inputs.keys() ) , ['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values'] )
# test if it raises when no input is passed
with pytest.raises(_lowerCAmelCase ):
processor()
def lowerCAmelCase_ ( self : Optional[int] ):
SCREAMING_SNAKE_CASE_ = self.get_image_processor()
SCREAMING_SNAKE_CASE_ = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ = ChineseCLIPProcessor(tokenizer=_lowerCAmelCase , image_processor=_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
SCREAMING_SNAKE_CASE_ = processor.batch_decode(_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = tokenizer.batch_decode(_lowerCAmelCase )
self.assertListEqual(_lowerCAmelCase , _lowerCAmelCase )
def lowerCAmelCase_ ( self : str ):
SCREAMING_SNAKE_CASE_ = self.get_image_processor()
SCREAMING_SNAKE_CASE_ = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ = ChineseCLIPProcessor(tokenizer=_lowerCAmelCase , image_processor=_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = 'Alexandra，T-shirt的价格是15便士。'
SCREAMING_SNAKE_CASE_ = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE_ = processor(text=_lowerCAmelCase , images=_lowerCAmelCase )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names ) | 363 |
import cva
import numpy as np
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : Optional[Any] , _lowerCAmelCase : float , _lowerCAmelCase : int ):
if k in (0.04, 0.06):
SCREAMING_SNAKE_CASE_ = k
SCREAMING_SNAKE_CASE_ = window_size
else:
raise ValueError('invalid k value' )
def __str__( self : Tuple ):
return str(self.k )
def lowerCAmelCase_ ( self : int , _lowerCAmelCase : str ):
SCREAMING_SNAKE_CASE_ = cva.imread(_lowerCAmelCase , 0 )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = img.shape
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = img.copy()
SCREAMING_SNAKE_CASE_ = cva.cvtColor(_lowerCAmelCase , cva.COLOR_GRAY2RGB )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = np.gradient(_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = dx**2
SCREAMING_SNAKE_CASE_ = dy**2
SCREAMING_SNAKE_CASE_ = dx * dy
SCREAMING_SNAKE_CASE_ = 0.04
SCREAMING_SNAKE_CASE_ = self.window_size // 2
for y in range(_lowerCAmelCase , h - offset ):
for x in range(_lowerCAmelCase , w - offset ):
SCREAMING_SNAKE_CASE_ = ixx[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
SCREAMING_SNAKE_CASE_ = iyy[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
SCREAMING_SNAKE_CASE_ = ixy[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
SCREAMING_SNAKE_CASE_ = (wxx * wyy) - (wxy**2)
SCREAMING_SNAKE_CASE_ = wxx + wyy
SCREAMING_SNAKE_CASE_ = det - k * (trace**2)
# Can change the value
if r > 0.5:
corner_list.append([x, y, r] )
color_img.itemset((y, x, 0) , 0 )
color_img.itemset((y, x, 1) , 0 )
color_img.itemset((y, x, 2) , 255 )
return color_img, corner_list
if __name__ == "__main__":
lowerCamelCase__ : Optional[int] = HarrisCorner(0.04, 3)
lowerCamelCase__ , lowerCamelCase__ : str = edge_detect.detect('path_to_image')
cva.imwrite('detect.png', color_img) | 210 | 0 |
'''simple docstring'''
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def lowerCamelCase ( __lowerCamelCase : Tuple , __lowerCamelCase : Union[str, Any]=0.999 , __lowerCamelCase : Dict="cosine" , ) ->Tuple:
if alpha_transform_type == "cosine":
def alpha_bar_fn(__lowerCamelCase : int ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(__lowerCamelCase : List[str] ):
return math.exp(t * -12.0 )
else:
raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}' )
_SCREAMING_SNAKE_CASE = []
for i in range(__lowerCamelCase ):
_SCREAMING_SNAKE_CASE = i / num_diffusion_timesteps
_SCREAMING_SNAKE_CASE = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(__lowerCamelCase ) / alpha_bar_fn(__lowerCamelCase ) , __lowerCamelCase ) )
return torch.tensor(__lowerCamelCase , dtype=torch.floataa )
class a_ ( snake_case_ , snake_case_ ):
'''simple docstring'''
UpperCamelCase = [e.name for e in KarrasDiffusionSchedulers]
UpperCamelCase = 2
@register_to_config
def __init__( self , A = 1000 , A = 0.0_0085 , A = 0.012 , A = "linear" , A = None , A = "epsilon" , A = "linspace" , A = 0 , ) -> Dict:
if trained_betas is not None:
_SCREAMING_SNAKE_CASE = torch.tensor(A , dtype=torch.floataa )
elif beta_schedule == "linear":
_SCREAMING_SNAKE_CASE = torch.linspace(A , A , A , dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
_SCREAMING_SNAKE_CASE = (
torch.linspace(beta_start**0.5 , beta_end**0.5 , A , dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
_SCREAMING_SNAKE_CASE = betas_for_alpha_bar(A )
else:
raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}' )
_SCREAMING_SNAKE_CASE = 1.0 - self.betas
_SCREAMING_SNAKE_CASE = torch.cumprod(self.alphas , dim=0 )
# set all values
self.set_timesteps(A , A , A )
def snake_case_( self , A , A=None ) -> Union[str, Any]:
if schedule_timesteps is None:
_SCREAMING_SNAKE_CASE = self.timesteps
_SCREAMING_SNAKE_CASE = (schedule_timesteps == timestep).nonzero()
# The sigma index that is taken for the **very** first `step`
# is always the second index (or the last index if there is only 1)
# This way we can ensure we don't accidentally skip a sigma in
# case we start in the middle of the denoising schedule (e.g. for image-to-image)
if len(self._index_counter ) == 0:
_SCREAMING_SNAKE_CASE = 1 if len(A ) > 1 else 0
else:
_SCREAMING_SNAKE_CASE = timestep.cpu().item() if torch.is_tensor(A ) else timestep
_SCREAMING_SNAKE_CASE = self._index_counter[timestep_int]
return indices[pos].item()
@property
def snake_case_( self ) -> Tuple:
# standard deviation of the initial noise distribution
if self.config.timestep_spacing in ["linspace", "trailing"]:
return self.sigmas.max()
return (self.sigmas.max() ** 2 + 1) ** 0.5
def snake_case_( self , A , A , ) -> torch.FloatTensor:
_SCREAMING_SNAKE_CASE = self.index_for_timestep(A )
if self.state_in_first_order:
_SCREAMING_SNAKE_CASE = self.sigmas[step_index]
else:
_SCREAMING_SNAKE_CASE = self.sigmas_interpol[step_index]
_SCREAMING_SNAKE_CASE = sample / ((sigma**2 + 1) ** 0.5)
return sample
def snake_case_( self , A , A = None , A = None , ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = num_inference_steps
_SCREAMING_SNAKE_CASE = num_train_timesteps or self.config.num_train_timesteps
# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
if self.config.timestep_spacing == "linspace":
_SCREAMING_SNAKE_CASE = np.linspace(0 , num_train_timesteps - 1 , A , dtype=A )[::-1].copy()
elif self.config.timestep_spacing == "leading":
_SCREAMING_SNAKE_CASE = 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
_SCREAMING_SNAKE_CASE = (np.arange(0 , A ) * step_ratio).round()[::-1].copy().astype(A )
timesteps += self.config.steps_offset
elif self.config.timestep_spacing == "trailing":
_SCREAMING_SNAKE_CASE = 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
_SCREAMING_SNAKE_CASE = (np.arange(A , 0 , -step_ratio )).round().copy().astype(A )
timesteps -= 1
else:
raise ValueError(
f'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.' )
_SCREAMING_SNAKE_CASE = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
_SCREAMING_SNAKE_CASE = torch.from_numpy(np.log(A ) ).to(A )
_SCREAMING_SNAKE_CASE = np.interp(A , np.arange(0 , len(A ) ) , A )
_SCREAMING_SNAKE_CASE = np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
_SCREAMING_SNAKE_CASE = torch.from_numpy(A ).to(device=A )
# interpolate sigmas
_SCREAMING_SNAKE_CASE = sigmas.log().lerp(sigmas.roll(1 ).log() , 0.5 ).exp()
_SCREAMING_SNAKE_CASE = torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2 ), sigmas[-1:]] )
_SCREAMING_SNAKE_CASE = torch.cat(
[sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2 ), sigmas_interpol[-1:]] )
if str(A ).startswith("""mps""" ):
# mps does not support float64
_SCREAMING_SNAKE_CASE = torch.from_numpy(A ).to(A , dtype=torch.floataa )
else:
_SCREAMING_SNAKE_CASE = torch.from_numpy(A ).to(A )
# interpolate timesteps
_SCREAMING_SNAKE_CASE = self.sigma_to_t(A ).to(A , dtype=timesteps.dtype )
_SCREAMING_SNAKE_CASE = torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]) , dim=-1 ).flatten()
_SCREAMING_SNAKE_CASE = torch.cat([timesteps[:1], interleaved_timesteps] )
_SCREAMING_SNAKE_CASE = None
# for exp beta schedules, such as the one for `pipeline_shap_e.py`
# we need an index counter
_SCREAMING_SNAKE_CASE = defaultdict(A )
def snake_case_( self , A ) -> Optional[Any]:
# get log sigma
_SCREAMING_SNAKE_CASE = sigma.log()
# get distribution
_SCREAMING_SNAKE_CASE = log_sigma - self.log_sigmas[:, None]
# get sigmas range
_SCREAMING_SNAKE_CASE = dists.ge(0 ).cumsum(dim=0 ).argmax(dim=0 ).clamp(max=self.log_sigmas.shape[0] - 2 )
_SCREAMING_SNAKE_CASE = low_idx + 1
_SCREAMING_SNAKE_CASE = self.log_sigmas[low_idx]
_SCREAMING_SNAKE_CASE = self.log_sigmas[high_idx]
# interpolate sigmas
_SCREAMING_SNAKE_CASE = (low - log_sigma) / (low - high)
_SCREAMING_SNAKE_CASE = w.clamp(0 , 1 )
# transform interpolation to time range
_SCREAMING_SNAKE_CASE = (1 - w) * low_idx + w * high_idx
_SCREAMING_SNAKE_CASE = t.view(sigma.shape )
return t
@property
def snake_case_( self ) -> int:
return self.sample is None
def snake_case_( self , A , A , A , A = True , ) -> Union[SchedulerOutput, Tuple]:
_SCREAMING_SNAKE_CASE = self.index_for_timestep(A )
# advance index counter by 1
_SCREAMING_SNAKE_CASE = timestep.cpu().item() if torch.is_tensor(A ) else timestep
self._index_counter[timestep_int] += 1
if self.state_in_first_order:
_SCREAMING_SNAKE_CASE = self.sigmas[step_index]
_SCREAMING_SNAKE_CASE = self.sigmas_interpol[step_index + 1]
_SCREAMING_SNAKE_CASE = self.sigmas[step_index + 1]
else:
# 2nd order / KDPM2's method
_SCREAMING_SNAKE_CASE = self.sigmas[step_index - 1]
_SCREAMING_SNAKE_CASE = self.sigmas_interpol[step_index]
_SCREAMING_SNAKE_CASE = self.sigmas[step_index]
# currently only gamma=0 is supported. This usually works best anyways.
# We can support gamma in the future but then need to scale the timestep before
# passing it to the model which requires a change in API
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = sigma * (gamma + 1) # Note: sigma_hat == sigma for now
# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
if self.config.prediction_type == "epsilon":
_SCREAMING_SNAKE_CASE = sigma_hat if self.state_in_first_order else sigma_interpol
_SCREAMING_SNAKE_CASE = sample - sigma_input * model_output
elif self.config.prediction_type == "v_prediction":
_SCREAMING_SNAKE_CASE = sigma_hat if self.state_in_first_order else sigma_interpol
_SCREAMING_SNAKE_CASE = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
sample / (sigma_input**2 + 1)
)
elif self.config.prediction_type == "sample":
raise NotImplementedError("""prediction_type not implemented yet: sample""" )
else:
raise ValueError(
f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`' )
if self.state_in_first_order:
# 2. Convert to an ODE derivative for 1st order
_SCREAMING_SNAKE_CASE = (sample - pred_original_sample) / sigma_hat
# 3. delta timestep
_SCREAMING_SNAKE_CASE = sigma_interpol - sigma_hat
# store for 2nd order step
_SCREAMING_SNAKE_CASE = sample
else:
# DPM-Solver-2
# 2. Convert to an ODE derivative for 2nd order
_SCREAMING_SNAKE_CASE = (sample - pred_original_sample) / sigma_interpol
# 3. delta timestep
_SCREAMING_SNAKE_CASE = sigma_next - sigma_hat
_SCREAMING_SNAKE_CASE = self.sample
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = sample + derivative * dt
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=A )
def snake_case_( self , A , A , A , ) -> torch.FloatTensor:
# Make sure sigmas and timesteps have the same device and dtype as original_samples
_SCREAMING_SNAKE_CASE = self.sigmas.to(device=original_samples.device , dtype=original_samples.dtype )
if original_samples.device.type == "mps" and torch.is_floating_point(A ):
# mps does not support float64
_SCREAMING_SNAKE_CASE = self.timesteps.to(original_samples.device , dtype=torch.floataa )
_SCREAMING_SNAKE_CASE = timesteps.to(original_samples.device , dtype=torch.floataa )
else:
_SCREAMING_SNAKE_CASE = self.timesteps.to(original_samples.device )
_SCREAMING_SNAKE_CASE = timesteps.to(original_samples.device )
_SCREAMING_SNAKE_CASE = [self.index_for_timestep(A , A ) for t in timesteps]
_SCREAMING_SNAKE_CASE = sigmas[step_indices].flatten()
while len(sigma.shape ) < len(original_samples.shape ):
_SCREAMING_SNAKE_CASE = sigma.unsqueeze(-1 )
_SCREAMING_SNAKE_CASE = original_samples + noise * sigma
return noisy_samples
def __len__( self ) -> str:
return self.config.num_train_timesteps
| 58 |
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageResampling,
get_image_size,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
__snake_case = logging.get_logger(__name__)
class lowercase__ ( _UpperCAmelCase ):
A__ : List[str] =["""pixel_values"""]
def __init__( self : Dict , UpperCAmelCase_ : bool = True , UpperCAmelCase_ : int = 32 , UpperCAmelCase_ : int=PILImageResampling.BILINEAR , UpperCAmelCase_ : bool = True , **UpperCAmelCase_ : Any , ):
SCREAMING_SNAKE_CASE__ = do_resize
SCREAMING_SNAKE_CASE__ = do_rescale
SCREAMING_SNAKE_CASE__ = size_divisor
SCREAMING_SNAKE_CASE__ = resample
super().__init__(**UpperCAmelCase_ )
def A_ ( self : Optional[int] , UpperCAmelCase_ : np.ndarray , UpperCAmelCase_ : int , UpperCAmelCase_ : Dict , UpperCAmelCase_ : Optional[ChannelDimension] = None , **UpperCAmelCase_ : Dict ):
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = get_image_size(UpperCAmelCase_ )
# Rounds the height and width down to the closest multiple of size_divisor
SCREAMING_SNAKE_CASE__ = height // size_divisor * size_divisor
SCREAMING_SNAKE_CASE__ = width // size_divisor * size_divisor
SCREAMING_SNAKE_CASE__ = resize(UpperCAmelCase_ , (new_h, new_w) , resample=UpperCAmelCase_ , data_format=UpperCAmelCase_ , **UpperCAmelCase_ )
return image
def A_ ( self : Any , UpperCAmelCase_ : np.ndarray , UpperCAmelCase_ : float , UpperCAmelCase_ : Optional[ChannelDimension] = None , **UpperCAmelCase_ : List[str] ):
return rescale(image=UpperCAmelCase_ , scale=UpperCAmelCase_ , data_format=UpperCAmelCase_ , **UpperCAmelCase_ )
def A_ ( self : Dict , UpperCAmelCase_ : Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]] , UpperCAmelCase_ : Optional[bool] = None , UpperCAmelCase_ : Optional[int] = None , UpperCAmelCase_ : List[str]=None , UpperCAmelCase_ : Optional[bool] = None , UpperCAmelCase_ : Optional[Union[TensorType, str]] = None , UpperCAmelCase_ : ChannelDimension = ChannelDimension.FIRST , **UpperCAmelCase_ : List[Any] , ):
SCREAMING_SNAKE_CASE__ = do_resize if do_resize is not None else self.do_resize
SCREAMING_SNAKE_CASE__ = do_rescale if do_rescale is not None else self.do_rescale
SCREAMING_SNAKE_CASE__ = size_divisor if size_divisor is not None else self.size_divisor
SCREAMING_SNAKE_CASE__ = resample if resample is not None else self.resample
if do_resize and size_divisor is None:
raise ValueError('size_divisor is required for resizing' )
SCREAMING_SNAKE_CASE__ = make_list_of_images(UpperCAmelCase_ )
if not valid_images(UpperCAmelCase_ ):
raise ValueError('Invalid image(s)' )
# All transformations expect numpy arrays.
SCREAMING_SNAKE_CASE__ = [to_numpy_array(UpperCAmelCase_ ) for img in images]
if do_resize:
SCREAMING_SNAKE_CASE__ = [self.resize(UpperCAmelCase_ , size_divisor=UpperCAmelCase_ , resample=UpperCAmelCase_ ) for image in images]
if do_rescale:
SCREAMING_SNAKE_CASE__ = [self.rescale(UpperCAmelCase_ , scale=1 / 255 ) for image in images]
SCREAMING_SNAKE_CASE__ = [to_channel_dimension_format(UpperCAmelCase_ , UpperCAmelCase_ ) for image in images]
SCREAMING_SNAKE_CASE__ = {'pixel_values': images}
return BatchFeature(data=UpperCAmelCase_ , tensor_type=UpperCAmelCase_ )
| 176 | 0 |
"""simple docstring"""
def snake_case__ ( __lowerCamelCase : str ):
"""simple docstring"""
lowerCamelCase__ : str =[]
lowerCamelCase__ : Optional[int] =[]
lowerCamelCase__ : Dict ={
'''^''': 3,
'''*''': 2,
'''/''': 2,
'''%''': 2,
'''+''': 1,
'''-''': 1,
} # Priority of each operator
lowerCamelCase__ : List[Any] =len(__A ) if (len(__A ) > 7) else 7
# Print table header for output
print(
'''Symbol'''.center(8 ) , '''Stack'''.center(__A ) , '''Postfix'''.center(__A ) , sep=''' | ''' , )
print('''-''' * (print_width * 3 + 7) )
for x in infix:
if x.isalpha() or x.isdigit():
post_fix.append(__A ) # if x is Alphabet / Digit, add it to Postfix
elif x == "(":
stack.append(__A ) # if x is "(" push to Stack
elif x == ")": # if x is ")" pop stack until "(" is encountered
while stack[-1] != "(":
post_fix.append(stack.pop() ) # Pop stack & add the content to Postfix
stack.pop()
else:
if len(__A ) == 0:
stack.append(__A ) # If stack is empty, push x to stack
else: # while priority of x is not > priority of element in the stack
while len(__A ) > 0 and priority[x] <= priority[stack[-1]]:
post_fix.append(stack.pop() ) # pop stack & add to Postfix
stack.append(__A ) # push x to stack
print(
x.center(8 ) , (''''''.join(__A )).ljust(__A ) , (''''''.join(__A )).ljust(__A ) , sep=''' | ''' , ) # Output in tabular format
while len(__A ) > 0: # while stack is not empty
post_fix.append(stack.pop() ) # pop stack & add to Postfix
print(
''' '''.center(8 ) , (''''''.join(__A )).ljust(__A ) , (''''''.join(__A )).ljust(__A ) , sep=''' | ''' , ) # Output in tabular format
return "".join(__A ) # return Postfix as str
def snake_case__ ( __lowerCamelCase : List[Any] ):
"""simple docstring"""
lowerCamelCase__ : Dict =list(infix[::-1] ) # reverse the infix equation
for i in range(len(__A ) ):
if infix[i] == "(":
lowerCamelCase__ : List[str] =''')''' # change "(" to ")"
elif infix[i] == ")":
lowerCamelCase__ : Optional[Any] ='''(''' # change ")" to "("
return (infix_2_postfix(''''''.join(__A ) ))[
::-1
] # call infix_2_postfix on Infix, return reverse of Postfix
if __name__ == "__main__":
_lowercase : int = input("\nEnter an Infix Equation = ") # Input an Infix equation
_lowercase : str = "".join(Infix.split()) # Remove spaces from the input
print("\n\t", Infix, "(Infix) -> ", infix_2_prefix(Infix), "(Prefix)")
| 357 |
"""simple docstring"""
def snake_case__ ( __lowerCamelCase : int , __lowerCamelCase : int ):
"""simple docstring"""
return int((input_a, input_a).count(0 ) != 0 )
def snake_case__ ( ):
"""simple docstring"""
assert nand_gate(0 , 0 ) == 1
assert nand_gate(0 , 1 ) == 1
assert nand_gate(1 , 0 ) == 1
assert nand_gate(1 , 1 ) == 0
if __name__ == "__main__":
print(nand_gate(0, 0))
print(nand_gate(0, 1))
print(nand_gate(1, 0))
print(nand_gate(1, 1))
| 272 | 0 |
"""simple docstring"""
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging
__UpperCamelCase = logging.get_logger(__name__)
class UpperCamelCase ( __a ):
SCREAMING_SNAKE_CASE_ = ["""pixel_values"""]
def __init__( self, lowerCAmelCase__ = True, lowerCAmelCase__ = 1 / 255, lowerCAmelCase__ = True, lowerCAmelCase__ = 8, **lowerCAmelCase__, ) -> str:
super().__init__(**lowerCAmelCase__)
snake_case_ = do_rescale
snake_case_ = rescale_factor
snake_case_ = do_pad
snake_case_ = pad_size
def a_ ( self, lowerCAmelCase__, lowerCAmelCase__, lowerCAmelCase__ = None, **lowerCAmelCase__) -> Any:
return rescale(lowerCAmelCase__, scale=lowerCAmelCase__, data_format=lowerCAmelCase__, **lowerCAmelCase__)
def a_ ( self, lowerCAmelCase__, lowerCAmelCase__, lowerCAmelCase__ = None) -> Optional[Any]:
snake_case_ , snake_case_ = get_image_size(lowerCAmelCase__)
snake_case_ = (old_height // size + 1) * size - old_height
snake_case_ = (old_width // size + 1) * size - old_width
return pad(lowerCAmelCase__, ((0, pad_height), (0, pad_width)), mode='symmetric', data_format=lowerCAmelCase__)
def a_ ( self, lowerCAmelCase__, lowerCAmelCase__ = None, lowerCAmelCase__ = None, lowerCAmelCase__ = None, lowerCAmelCase__ = None, lowerCAmelCase__ = None, lowerCAmelCase__ = ChannelDimension.FIRST, **lowerCAmelCase__, ) -> List[Any]:
snake_case_ = do_rescale if do_rescale is not None else self.do_rescale
snake_case_ = rescale_factor if rescale_factor is not None else self.rescale_factor
snake_case_ = do_pad if do_pad is not None else self.do_pad
snake_case_ = pad_size if pad_size is not None else self.pad_size
snake_case_ = make_list_of_images(lowerCAmelCase__)
if not valid_images(lowerCAmelCase__):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.')
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.')
# All transformations expect numpy arrays.
snake_case_ = [to_numpy_array(lowerCAmelCase__) for image in images]
if do_rescale:
snake_case_ = [self.rescale(image=lowerCAmelCase__, scale=lowerCAmelCase__) for image in images]
if do_pad:
snake_case_ = [self.pad(lowerCAmelCase__, size=lowerCAmelCase__) for image in images]
snake_case_ = [to_channel_dimension_format(lowerCAmelCase__, lowerCAmelCase__) for image in images]
snake_case_ = {'pixel_values': images}
return BatchFeature(data=lowerCAmelCase__, tensor_type=lowerCAmelCase__)
| 69 |
'''simple docstring'''
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal
A =logging.get_logger(__name__)
A =TypeVar('DatasetType', Dataset, IterableDataset)
def snake_case_ (_a : List[DatasetType] , _a : Optional[List[float]] = None , _a : Optional[int] = None , _a : Optional[DatasetInfo] = None , _a : Optional[NamedSplit] = None , _a : Literal["first_exhausted", "all_exhausted"] = "first_exhausted" , ):
from .arrow_dataset import Dataset
from .iterable_dataset import IterableDataset
if not datasets:
raise ValueError('''Unable to interleave an empty list of datasets.''' )
for i, dataset in enumerate(_a ):
if not isinstance(_a , (Dataset, IterableDataset) ):
if isinstance(_a , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
'''is an empty dataset dictionary.''' )
raise ValueError(
F"Dataset at position {i} has at least one split: {list(_a )}\n"
F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(_a ) )}']" )
raise ValueError(
F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_a ).__name__}." )
if i == 0:
UpperCAmelCase , UpperCAmelCase = (
(Dataset, IterableDataset) if isinstance(_a , _a ) else (IterableDataset, Dataset)
)
elif not isinstance(_a , _a ):
raise ValueError(
F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects." )
if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
raise ValueError(F"{stopping_strategy} is not supported. Please enter a valid stopping_strategy." )
if dataset_type is Dataset:
return _interleave_map_style_datasets(
_a , _a , _a , info=_a , split=_a , stopping_strategy=_a )
else:
return _interleave_iterable_datasets(
_a , _a , _a , info=_a , split=_a , stopping_strategy=_a )
def snake_case_ (_a : List[DatasetType] , _a : Optional[DatasetInfo] = None , _a : Optional[NamedSplit] = None , _a : int = 0 , ):
if not dsets:
raise ValueError('''Unable to concatenate an empty list of datasets.''' )
for i, dataset in enumerate(_a ):
if not isinstance(_a , (Dataset, IterableDataset) ):
if isinstance(_a , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
'''is an empty dataset dictionary.''' )
raise ValueError(
F"Dataset at position {i} has at least one split: {list(_a )}\n"
F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(_a ) )}']" )
raise ValueError(
F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_a ).__name__}." )
if i == 0:
UpperCAmelCase , UpperCAmelCase = (
(Dataset, IterableDataset) if isinstance(_a , _a ) else (IterableDataset, Dataset)
)
elif not isinstance(_a , _a ):
raise ValueError(
F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects." )
if dataset_type is Dataset:
return _concatenate_map_style_datasets(_a , info=_a , split=_a , axis=_a )
else:
return _concatenate_iterable_datasets(_a , info=_a , split=_a , axis=_a )
| 34 | 0 |
"""simple docstring"""
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def lowerCAmelCase__ ( _UpperCamelCase : List[str] ) -> int:
"""simple docstring"""
snake_case = SwinConfig()
snake_case = swin_name.split('_' )
snake_case = name_split[1]
snake_case = int(name_split[4] )
snake_case = int(name_split[3][-1] )
if model_size == "tiny":
snake_case = 9_6
snake_case = (2, 2, 6, 2)
snake_case = (3, 6, 1_2, 2_4)
elif model_size == "small":
snake_case = 9_6
snake_case = (2, 2, 1_8, 2)
snake_case = (3, 6, 1_2, 2_4)
elif model_size == "base":
snake_case = 1_2_8
snake_case = (2, 2, 1_8, 2)
snake_case = (4, 8, 1_6, 3_2)
else:
snake_case = 1_9_2
snake_case = (2, 2, 1_8, 2)
snake_case = (6, 1_2, 2_4, 4_8)
if "in22k" in swin_name:
snake_case = 2_1_8_4_1
else:
snake_case = 1_0_0_0
snake_case = 'huggingface/label-files'
snake_case = 'imagenet-1k-id2label.json'
snake_case = json.load(open(hf_hub_download(_UpperCamelCase , _UpperCamelCase , repo_type='dataset' ) , 'r' ) )
snake_case = {int(_UpperCamelCase ): v for k, v in idalabel.items()}
snake_case = idalabel
snake_case = {v: k for k, v in idalabel.items()}
snake_case = img_size
snake_case = num_classes
snake_case = embed_dim
snake_case = depths
snake_case = num_heads
snake_case = window_size
return config
def lowerCAmelCase__ ( _UpperCamelCase : int ) -> int:
"""simple docstring"""
if "patch_embed.proj" in name:
snake_case = name.replace('patch_embed.proj' , 'embeddings.patch_embeddings.projection' )
if "patch_embed.norm" in name:
snake_case = name.replace('patch_embed.norm' , 'embeddings.norm' )
if "layers" in name:
snake_case = 'encoder.' + name
if "attn.proj" in name:
snake_case = name.replace('attn.proj' , 'attention.output.dense' )
if "attn" in name:
snake_case = name.replace('attn' , 'attention.self' )
if "norm1" in name:
snake_case = name.replace('norm1' , 'layernorm_before' )
if "norm2" in name:
snake_case = name.replace('norm2' , 'layernorm_after' )
if "mlp.fc1" in name:
snake_case = name.replace('mlp.fc1' , 'intermediate.dense' )
if "mlp.fc2" in name:
snake_case = name.replace('mlp.fc2' , 'output.dense' )
if name == "norm.weight":
snake_case = 'layernorm.weight'
if name == "norm.bias":
snake_case = 'layernorm.bias'
if "head" in name:
snake_case = name.replace('head' , 'classifier' )
else:
snake_case = 'swin.' + name
return name
def lowerCAmelCase__ ( _UpperCamelCase : Union[str, Any] , _UpperCamelCase : Dict ) -> str:
"""simple docstring"""
for key in orig_state_dict.copy().keys():
snake_case = orig_state_dict.pop(_UpperCamelCase )
if "mask" in key:
continue
elif "qkv" in key:
snake_case = key.split('.' )
snake_case = int(key_split[1] )
snake_case = int(key_split[3] )
snake_case = model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
snake_case = val[:dim, :]
snake_case = val[
dim : dim * 2, :
]
snake_case = val[-dim:, :]
else:
snake_case = val[
:dim
]
snake_case = val[
dim : dim * 2
]
snake_case = val[
-dim:
]
else:
snake_case = val
return orig_state_dict
def lowerCAmelCase__ ( _UpperCamelCase : int , _UpperCamelCase : Optional[int] ) -> List[str]:
"""simple docstring"""
snake_case = timm.create_model(_UpperCamelCase , pretrained=_UpperCamelCase )
timm_model.eval()
snake_case = get_swin_config(_UpperCamelCase )
snake_case = SwinForImageClassification(_UpperCamelCase )
model.eval()
snake_case = convert_state_dict(timm_model.state_dict() , _UpperCamelCase )
model.load_state_dict(_UpperCamelCase )
snake_case = 'http://images.cocodataset.org/val2017/000000039769.jpg'
snake_case = AutoImageProcessor.from_pretrained('microsoft/{}'.format(swin_name.replace('_' , '-' ) ) )
snake_case = Image.open(requests.get(_UpperCamelCase , stream=_UpperCamelCase ).raw )
snake_case = image_processor(images=_UpperCamelCase , return_tensors='pt' )
snake_case = timm_model(inputs['pixel_values'] )
snake_case = model(**_UpperCamelCase ).logits
assert torch.allclose(_UpperCamelCase , _UpperCamelCase , atol=1e-3 )
print(f"""Saving model {swin_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 __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--swin_name",
default="swin_tiny_patch4_window7_224",
type=str,
help="Name of the Swin 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."
)
SCREAMING_SNAKE_CASE__ = parser.parse_args()
convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)
| 366 | """simple docstring"""
import os
def lowerCAmelCase__ ( _UpperCamelCase : str = "matrix.txt" ) -> int:
"""simple docstring"""
with open(os.path.join(os.path.dirname(_UpperCamelCase ) , _UpperCamelCase ) ) as in_file:
snake_case = in_file.read()
snake_case = [[int(_UpperCamelCase ) for cell in row.split(',' )] for row in data.strip().splitlines()]
snake_case = [[0 for cell in row] for row in grid]
snake_case = len(grid[0] )
snake_case = [[0 for i in range(_UpperCamelCase )] for j in range(_UpperCamelCase )]
snake_case = grid[0][0]
for i in range(1 , _UpperCamelCase ):
snake_case = grid[0][i] + dp[0][i - 1]
for i in range(1 , _UpperCamelCase ):
snake_case = grid[i][0] + dp[i - 1][0]
for i in range(1 , _UpperCamelCase ):
for j in range(1 , _UpperCamelCase ):
snake_case = grid[i][j] + min(dp[i - 1][j] , dp[i][j - 1] )
return dp[-1][-1]
if __name__ == "__main__":
print(f"""{solution() = }""")
| 149 | 0 |
'''simple docstring'''
import qiskit
def lowercase__ ( __lowercase : int , __lowercase : int ) -> qiskit.result.counts.Counts:
"""simple docstring"""
__UpperCamelCase = qiskit.Aer.get_backend('aer_simulator' )
# Create a Quantum Circuit acting on the q register
__UpperCamelCase = qiskit.QuantumCircuit(__lowercase , __lowercase )
# Apply X (NOT) Gate to Qubits 0 & 1
circuit.x(0 )
circuit.x(1 )
# Map the quantum measurement to the classical bits
circuit.measure([0, 1] , [0, 1] )
# Execute the circuit on the qasm simulator
__UpperCamelCase = qiskit.execute(__lowercase , __lowercase , shots=1000 )
# Return the histogram data of the results of the experiment.
return job.result().get_counts(__lowercase )
if __name__ == "__main__":
a__ : Tuple =single_qubit_measure(2, 2)
print(f'Total count for various states are: {counts}')
| 53 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : List[Any] =logging.get_logger(__name__)
a__ : List[Any] ={
'''BAAI/AltCLIP''': '''https://huggingface.co/BAAI/AltCLIP/resolve/main/config.json''',
# See all AltCLIP models at https://huggingface.co/models?filter=altclip
}
class snake_case ( __lowerCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple ="altclip_text_model"
def __init__( self : str , __A : List[Any]=2_5_0_0_0_2 , __A : Any=1_0_2_4 , __A : int=2_4 , __A : Dict=1_6 , __A : Optional[Any]=4_0_9_6 , __A : Union[str, Any]="gelu" , __A : Dict=0.1 , __A : Dict=0.1 , __A : List[str]=5_1_4 , __A : Optional[int]=1 , __A : int=0.02 , __A : Optional[Any]=0.02 , __A : Optional[Any]=1e-05 , __A : Dict=1 , __A : List[Any]=0 , __A : int=2 , __A : Tuple="absolute" , __A : Optional[Any]=True , __A : Optional[int]=7_6_8 , **__A : List[str] , ):
super().__init__(pad_token_id=__A , bos_token_id=__A , eos_token_id=__A , **__A )
__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 = initializer_factor
__UpperCamelCase = layer_norm_eps
__UpperCamelCase = position_embedding_type
__UpperCamelCase = use_cache
__UpperCamelCase = project_dim
class snake_case ( __lowerCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple ="altclip_vision_model"
def __init__( self : List[Any] , __A : Union[str, Any]=7_6_8 , __A : Optional[int]=3_0_7_2 , __A : Optional[Any]=5_1_2 , __A : Tuple=1_2 , __A : Union[str, Any]=1_2 , __A : Optional[int]=3 , __A : Dict=2_2_4 , __A : Tuple=3_2 , __A : str="quick_gelu" , __A : Dict=1e-5 , __A : Optional[int]=0.0 , __A : List[Any]=0.02 , __A : int=1.0 , **__A : Optional[int] , ):
super().__init__(**__A )
__UpperCamelCase = hidden_size
__UpperCamelCase = intermediate_size
__UpperCamelCase = projection_dim
__UpperCamelCase = num_hidden_layers
__UpperCamelCase = num_attention_heads
__UpperCamelCase = num_channels
__UpperCamelCase = patch_size
__UpperCamelCase = image_size
__UpperCamelCase = initializer_range
__UpperCamelCase = initializer_factor
__UpperCamelCase = attention_dropout
__UpperCamelCase = layer_norm_eps
__UpperCamelCase = hidden_act
@classmethod
def _lowerCamelCase ( cls : Optional[Any] , __A : Union[str, os.PathLike] , **__A : Optional[Any] ):
cls._set_token_in_kwargs(__A )
__UpperCamelCase , __UpperCamelCase = cls.get_config_dict(__A , **__A )
# get the vision config dict if we are loading from AltCLIPConfig
if config_dict.get('model_type' ) == "altclip":
__UpperCamelCase = config_dict['vision_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(__A , **__A )
class snake_case ( __lowerCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] ="altclip"
SCREAMING_SNAKE_CASE_ : Optional[int] =True
def __init__( self : Any , __A : List[str]=None , __A : List[Any]=None , __A : List[str]=7_6_8 , __A : List[str]=2.6592 , **__A : Dict ):
# If `_config_dict` exist, we use them for the backward compatibility.
# We pop out these 2 attributes before calling `super().__init__` to avoid them being saved (which causes a lot
# of confusion!).
__UpperCamelCase = kwargs.pop('text_config_dict' , __A )
__UpperCamelCase = kwargs.pop('vision_config_dict' , __A )
super().__init__(**__A )
# Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in
# `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most
# cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`.
if text_config_dict is not None:
if text_config is None:
__UpperCamelCase = {}
# This is the complete result when using `text_config_dict`.
__UpperCamelCase = AltCLIPTextConfig(**__A ).to_dict()
# Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
for key, value in _text_config_dict.items():
if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
# If specified in `text_config_dict`
if key in text_config_dict:
__UpperCamelCase = (
f'''`{key}` is found in both `text_config_dict` and `text_config` but with different values. '''
f'''The value `text_config_dict["{key}"]` will be used instead.'''
)
# If inferred from default argument values (just to be super careful)
else:
__UpperCamelCase = (
f'''`text_config_dict` is provided which will be used to initialize `AltCLIPTextConfig`. The '''
f'''value `text_config["{key}"]` will be overriden.'''
)
logger.warning(__A )
# Update all values in `text_config` with the ones in `_text_config_dict`.
text_config.update(_text_config_dict )
if vision_config_dict is not None:
if vision_config is None:
__UpperCamelCase = {}
# This is the complete result when using `vision_config_dict`.
__UpperCamelCase = AltCLIPVisionConfig(**__A ).to_dict()
# convert keys to string instead of integer
if "id2label" in _vision_config_dict:
__UpperCamelCase = {
str(__A ): value for key, value in _vision_config_dict['id2label'].items()
}
# Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different.
for key, value in _vision_config_dict.items():
if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]:
# If specified in `vision_config_dict`
if key in vision_config_dict:
__UpperCamelCase = (
f'''`{key}` is found in both `vision_config_dict` and `vision_config` but with different '''
f'''values. The value `vision_config_dict["{key}"]` will be used instead.'''
)
# If inferred from default argument values (just to be super careful)
else:
__UpperCamelCase = (
f'''`vision_config_dict` is provided which will be used to initialize `AltCLIPVisionConfig`. '''
f'''The value `vision_config["{key}"]` will be overriden.'''
)
logger.warning(__A )
# Update all values in `vision_config` with the ones in `_vision_config_dict`.
vision_config.update(_vision_config_dict )
if text_config is None:
__UpperCamelCase = {}
logger.info('`text_config` is `None`. Initializing the `AltCLIPTextConfig` with default values.' )
if vision_config is None:
__UpperCamelCase = {}
logger.info('`vision_config` is `None`. initializing the `AltCLIPVisionConfig` with default values.' )
__UpperCamelCase = AltCLIPTextConfig(**__A )
__UpperCamelCase = AltCLIPVisionConfig(**__A )
__UpperCamelCase = projection_dim
__UpperCamelCase = logit_scale_init_value
__UpperCamelCase = 1.0
@classmethod
def _lowerCamelCase ( cls : Union[str, Any] , __A : AltCLIPTextConfig , __A : AltCLIPVisionConfig , **__A : Optional[Any] ):
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **__A )
def _lowerCamelCase ( self : List[Any] ):
__UpperCamelCase = copy.deepcopy(self.__dict__ )
__UpperCamelCase = self.text_config.to_dict()
__UpperCamelCase = self.vision_config.to_dict()
__UpperCamelCase = self.__class__.model_type
return output
| 53 | 1 |
'''simple docstring'''
import math
def lowerCamelCase__ ( _A ):
a : Union[str, Any] = [True] * n
a : int = False
a : List[str] = False
a : List[Any] = True
for i in range(3 , int(n**0.5 + 1 ) , 2 ):
a : Any = i * 2
while index < n:
a : Any = False
a : Optional[Any] = index + i
a : str = [2]
for i in range(3 , _A , 2 ):
if is_prime[i]:
primes.append(_A )
return primes
def lowerCamelCase__ ( _A = 9999_6666_3333 ):
a : Optional[int] = math.floor(math.sqrt(_A ) ) + 100
a : Dict = prime_sieve(_A )
a : List[Any] = 0
a : Tuple = 0
a : int = primes[prime_index]
while (last_prime**2) <= limit:
a : Tuple = primes[prime_index + 1]
a : Union[str, Any] = last_prime**2
a : Optional[int] = next_prime**2
# Get numbers divisible by lps(current)
a : List[str] = lower_bound + last_prime
while upper_bound > current <= limit:
matches_sum += current
current += last_prime
# Reset the upper_bound
while (upper_bound - next_prime) > limit:
upper_bound -= next_prime
# Add the numbers divisible by ups(current)
a : Optional[Any] = upper_bound - next_prime
while current > lower_bound:
matches_sum += current
current -= next_prime
# Remove the numbers divisible by both ups and lps
a : str = 0
while upper_bound > current <= limit:
if current <= lower_bound:
# Increment the current number
current += last_prime * next_prime
continue
if current > limit:
break
# Remove twice since it was added by both ups and lps
matches_sum -= current * 2
# Increment the current number
current += last_prime * next_prime
# Setup for next pair
a : List[str] = next_prime
prime_index += 1
return matches_sum
if __name__ == "__main__":
print(solution()) | 96 |
'''simple docstring'''
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_torch_available():
import torch
from transformers import (
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraModel,
)
from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST
class a__:
def __init__( self : Union[str, Any] , __snake_case : Optional[int] , __snake_case : Optional[int]=2 , __snake_case : Union[str, Any]=8 , __snake_case : List[str]=True , __snake_case : Dict=True , __snake_case : Optional[Any]=True , __snake_case : List[str]=True , __snake_case : Tuple=99 , __snake_case : int=16 , __snake_case : Optional[int]=5 , __snake_case : int=2 , __snake_case : Tuple=36 , __snake_case : Optional[Any]="gelu" , __snake_case : str=0.0 , __snake_case : Optional[int]=0.0 , __snake_case : Tuple=5_12 , __snake_case : str=16 , __snake_case : str=2 , __snake_case : int=0.02 , __snake_case : Optional[int]=3 , __snake_case : List[Any]=4 , __snake_case : Any=None , ):
a : int = parent
a : Any = batch_size
a : Optional[int] = seq_length
a : List[str] = is_training
a : Dict = use_input_mask
a : Union[str, Any] = use_token_type_ids
a : Tuple = use_labels
a : Dict = vocab_size
a : Optional[int] = hidden_size
a : List[Any] = num_hidden_layers
a : Optional[Any] = num_attention_heads
a : str = intermediate_size
a : Dict = hidden_act
a : str = hidden_dropout_prob
a : Tuple = attention_probs_dropout_prob
a : Optional[Any] = max_position_embeddings
a : Tuple = type_vocab_size
a : int = type_sequence_label_size
a : List[Any] = initializer_range
a : List[str] = num_labels
a : List[str] = num_choices
a : Optional[Any] = scope
def lowercase_ ( self : Union[str, Any] ):
a : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a : Optional[Any] = None
if self.use_input_mask:
a : List[Any] = random_attention_mask([self.batch_size, self.seq_length] )
a : Tuple = None
if self.use_token_type_ids:
a : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
a : str = None
a : int = None
a : Any = None
if self.use_labels:
a : int = ids_tensor([self.batch_size] , self.type_sequence_label_size )
a : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
a : List[str] = ids_tensor([self.batch_size] , self.num_choices )
a : Dict = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowercase_ ( self : Union[str, Any] ):
return MraConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__snake_case , initializer_range=self.initializer_range , )
def lowercase_ ( self : List[str] ):
a : List[Any] = self.get_config()
a : Optional[Any] = 3_00
return config
def lowercase_ ( self : Union[str, Any] ):
(
(
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) ,
) : Optional[Any] = self.prepare_config_and_inputs()
a : Union[str, Any] = True
a : Tuple = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
a : Dict = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def lowercase_ ( self : int , __snake_case : int , __snake_case : List[Any] , __snake_case : Union[str, Any] , __snake_case : List[Any] , __snake_case : Union[str, Any] , __snake_case : Dict , __snake_case : Any ):
a : Dict = MraModel(config=__snake_case )
model.to(__snake_case )
model.eval()
a : str = model(__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case )
a : List[str] = model(__snake_case , token_type_ids=__snake_case )
a : Union[str, Any] = model(__snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowercase_ ( self : List[str] , __snake_case : Tuple , __snake_case : List[str] , __snake_case : str , __snake_case : Optional[Any] , __snake_case : Union[str, Any] , __snake_case : Any , __snake_case : Optional[Any] , __snake_case : Optional[int] , __snake_case : List[Any] , ):
a : Optional[Any] = True
a : Optional[int] = MraModel(__snake_case )
model.to(__snake_case )
model.eval()
a : List[Any] = model(
__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , encoder_hidden_states=__snake_case , encoder_attention_mask=__snake_case , )
a : Any = model(
__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , encoder_hidden_states=__snake_case , )
a : Optional[int] = model(__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowercase_ ( self : Optional[Any] , __snake_case : int , __snake_case : List[Any] , __snake_case : Optional[int] , __snake_case : Optional[Any] , __snake_case : List[Any] , __snake_case : Dict , __snake_case : Optional[Any] ):
a : Union[str, Any] = MraForMaskedLM(config=__snake_case )
model.to(__snake_case )
model.eval()
a : List[str] = model(__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , labels=__snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowercase_ ( self : Tuple , __snake_case : Union[str, Any] , __snake_case : str , __snake_case : Tuple , __snake_case : str , __snake_case : Tuple , __snake_case : Optional[int] , __snake_case : int ):
a : Optional[int] = MraForQuestionAnswering(config=__snake_case )
model.to(__snake_case )
model.eval()
a : Optional[int] = model(
__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , start_positions=__snake_case , end_positions=__snake_case , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def lowercase_ ( self : Dict , __snake_case : Tuple , __snake_case : List[Any] , __snake_case : List[Any] , __snake_case : Optional[Any] , __snake_case : Any , __snake_case : List[str] , __snake_case : str ):
a : Tuple = self.num_labels
a : Dict = MraForSequenceClassification(__snake_case )
model.to(__snake_case )
model.eval()
a : Any = model(__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , labels=__snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowercase_ ( self : str , __snake_case : Dict , __snake_case : Optional[Any] , __snake_case : int , __snake_case : List[Any] , __snake_case : Any , __snake_case : List[Any] , __snake_case : int ):
a : Tuple = self.num_labels
a : Tuple = MraForTokenClassification(config=__snake_case )
model.to(__snake_case )
model.eval()
a : List[Any] = model(__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , labels=__snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowercase_ ( self : Any , __snake_case : Any , __snake_case : str , __snake_case : Dict , __snake_case : List[Any] , __snake_case : Any , __snake_case : Any , __snake_case : str ):
a : Optional[int] = self.num_choices
a : int = MraForMultipleChoice(config=__snake_case )
model.to(__snake_case )
model.eval()
a : Tuple = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a : Optional[int] = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a : Optional[Any] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a : int = model(
__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , labels=__snake_case , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowercase_ ( self : Optional[Any] ):
a : Union[str, Any] = self.prepare_config_and_inputs()
(
(
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) , (
a
) ,
) : Union[str, Any] = config_and_inputs
a : List[str] = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class a__( lowerCamelCase__ , unittest.TestCase ):
lowercase__ = (
(
MraModel,
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
)
if is_torch_available()
else ()
)
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = ()
def lowercase_ ( self : Any ):
a : Tuple = MraModelTester(self )
a : str = ConfigTester(self , config_class=__snake_case , hidden_size=37 )
def lowercase_ ( self : List[str] ):
self.config_tester.run_common_tests()
def lowercase_ ( self : List[str] ):
a : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__snake_case )
def lowercase_ ( self : Any ):
a : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
a : Dict = type
self.model_tester.create_and_check_model(*__snake_case )
def lowercase_ ( self : List[Any] ):
a : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__snake_case )
def lowercase_ ( self : Optional[Any] ):
a : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*__snake_case )
def lowercase_ ( self : List[Any] ):
a : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__snake_case )
def lowercase_ ( self : Tuple ):
a : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__snake_case )
def lowercase_ ( self : Optional[Any] ):
a : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__snake_case )
@slow
def lowercase_ ( self : int ):
for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a : Dict = MraModel.from_pretrained(__snake_case )
self.assertIsNotNone(__snake_case )
@unittest.skip(reason='MRA does not output attentions' )
def lowercase_ ( self : Union[str, Any] ):
return
@require_torch
class a__( unittest.TestCase ):
@slow
def lowercase_ ( self : Union[str, Any] ):
a : Union[str, Any] = MraModel.from_pretrained('uw-madison/mra-base-512-4' )
a : List[str] = torch.arange(2_56 ).unsqueeze(0 )
with torch.no_grad():
a : Optional[int] = model(__snake_case )[0]
a : Any = torch.Size((1, 2_56, 7_68) )
self.assertEqual(output.shape , __snake_case )
a : str = torch.tensor(
[[[-0.0140, 0.0830, -0.0381], [0.1546, 0.1402, 0.0220], [0.1162, 0.0851, 0.0165]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __snake_case , atol=1e-4 ) )
@slow
def lowercase_ ( self : Optional[int] ):
a : Dict = MraForMaskedLM.from_pretrained('uw-madison/mra-base-512-4' )
a : Optional[int] = torch.arange(2_56 ).unsqueeze(0 )
with torch.no_grad():
a : Dict = model(__snake_case )[0]
a : Union[str, Any] = 5_02_65
a : Dict = torch.Size((1, 2_56, vocab_size) )
self.assertEqual(output.shape , __snake_case )
a : Dict = torch.tensor(
[[[9.2595, -3.6038, 11.8819], [9.3869, -3.2693, 11.0956], [11.8524, -3.4938, 13.1210]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __snake_case , atol=1e-4 ) )
@slow
def lowercase_ ( self : Any ):
a : Dict = MraForMaskedLM.from_pretrained('uw-madison/mra-base-4096-8-d3' )
a : Optional[int] = torch.arange(40_96 ).unsqueeze(0 )
with torch.no_grad():
a : Tuple = model(__snake_case )[0]
a : List[Any] = 5_02_65
a : str = torch.Size((1, 40_96, vocab_size) )
self.assertEqual(output.shape , __snake_case )
a : int = torch.tensor(
[[[5.4789, -2.3564, 7.5064], [7.9067, -1.3369, 9.9668], [9.0712, -1.8106, 7.0380]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , __snake_case , atol=1e-4 ) ) | 96 | 1 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool
if TYPE_CHECKING:
from PIL import Image
class __magic_name__ ( _UpperCamelCase ):
lowerCAmelCase : Tuple = 'Salesforce/blip-image-captioning-base'
lowerCAmelCase : Tuple = (
'This is a tool that generates a description of an image. It takes an input named `image` which should be the '
'image to caption, and returns a text that contains the description in English.'
)
lowerCAmelCase : Optional[int] = 'image_captioner'
lowerCAmelCase : List[str] = AutoModelForVisionaSeq
lowerCAmelCase : Tuple = ['image']
lowerCAmelCase : Optional[Any] = ['text']
def __init__( self : Dict ,*_UpperCAmelCase : List[Any] ,**_UpperCAmelCase : str ):
requires_backends(self ,['vision'] )
super().__init__(*_UpperCAmelCase ,**_UpperCAmelCase )
def __lowercase ( self : Optional[int] ,_UpperCAmelCase : "Image" ):
return self.pre_processor(images=_UpperCAmelCase ,return_tensors='pt' )
def __lowercase ( self : List[str] ,_UpperCAmelCase : int ):
return self.model.generate(**_UpperCAmelCase )
def __lowercase ( self : int ,_UpperCAmelCase : Dict ):
return self.pre_processor.batch_decode(_UpperCAmelCase ,skip_special_tokens=_UpperCAmelCase )[0].strip()
| 89 |
'''simple docstring'''
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import BeitImageProcessor
class snake_case__ ( unittest.TestCase):
def __init__( self : int , _A : List[str] , _A : Dict=7 , _A : List[str]=3 , _A : List[str]=18 , _A : Dict=30 , _A : Union[str, Any]=4_00 , _A : List[str]=True , _A : List[str]=None , _A : int=True , _A : Tuple=None , _A : Union[str, Any]=True , _A : Tuple=[0.5, 0.5, 0.5] , _A : Union[str, Any]=[0.5, 0.5, 0.5] , _A : Tuple=False , ) -> List[Any]:
UpperCAmelCase_ : Union[str, Any] = size if size is not None else {'''height''': 20, '''width''': 20}
UpperCAmelCase_ : List[Any] = crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
UpperCAmelCase_ : Tuple = parent
UpperCAmelCase_ : Optional[int] = batch_size
UpperCAmelCase_ : Any = num_channels
UpperCAmelCase_ : Optional[Any] = image_size
UpperCAmelCase_ : Tuple = min_resolution
UpperCAmelCase_ : Tuple = max_resolution
UpperCAmelCase_ : Optional[int] = do_resize
UpperCAmelCase_ : Tuple = size
UpperCAmelCase_ : Optional[Any] = do_center_crop
UpperCAmelCase_ : Optional[int] = crop_size
UpperCAmelCase_ : Tuple = do_normalize
UpperCAmelCase_ : Optional[Any] = image_mean
UpperCAmelCase_ : int = image_std
UpperCAmelCase_ : List[Any] = do_reduce_labels
def A ( self : Union[str, Any] ) -> str:
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_reduce_labels": self.do_reduce_labels,
}
def __UpperCAmelCase ( ) -> Optional[Any]:
UpperCAmelCase_ : Union[str, Any] = load_dataset('''hf-internal-testing/fixtures_ade20k''' , split='''test''' )
UpperCAmelCase_ : Optional[Any] = Image.open(dataset[0]['''file'''] )
UpperCAmelCase_ : str = Image.open(dataset[1]['''file'''] )
return image, map
def __UpperCAmelCase ( ) -> Any:
UpperCAmelCase_ : int = load_dataset('''hf-internal-testing/fixtures_ade20k''' , split='''test''' )
UpperCAmelCase_ : int = Image.open(ds[0]['''file'''] )
UpperCAmelCase_ : Optional[Any] = Image.open(ds[1]['''file'''] )
UpperCAmelCase_ : Dict = Image.open(ds[2]['''file'''] )
UpperCAmelCase_ : List[str] = Image.open(ds[3]['''file'''] )
return [imagea, imagea], [mapa, mapa]
@require_torch
@require_vision
class snake_case__ ( UpperCamelCase , unittest.TestCase):
a_ = BeitImageProcessor if is_vision_available() else None
def A ( self : Optional[Any] ) -> Union[str, Any]:
UpperCAmelCase_ : Tuple = BeitImageProcessingTester(self )
@property
def A ( self : List[Any] ) -> Tuple:
return self.image_processor_tester.prepare_image_processor_dict()
def A ( self : List[Any] ) -> Optional[Any]:
UpperCAmelCase_ : Dict = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_A , '''do_resize''' ) )
self.assertTrue(hasattr(_A , '''size''' ) )
self.assertTrue(hasattr(_A , '''do_center_crop''' ) )
self.assertTrue(hasattr(_A , '''center_crop''' ) )
self.assertTrue(hasattr(_A , '''do_normalize''' ) )
self.assertTrue(hasattr(_A , '''image_mean''' ) )
self.assertTrue(hasattr(_A , '''image_std''' ) )
def A ( self : List[str] ) -> Optional[int]:
UpperCAmelCase_ : List[str] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''height''': 20, '''width''': 20} )
self.assertEqual(image_processor.crop_size , {'''height''': 18, '''width''': 18} )
self.assertEqual(image_processor.do_reduce_labels , _A )
UpperCAmelCase_ : Union[str, Any] = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , crop_size=84 , reduce_labels=_A )
self.assertEqual(image_processor.size , {'''height''': 42, '''width''': 42} )
self.assertEqual(image_processor.crop_size , {'''height''': 84, '''width''': 84} )
self.assertEqual(image_processor.do_reduce_labels , _A )
def A ( self : Optional[Any] ) -> Any:
pass
def A ( self : List[str] ) -> Optional[int]:
# Initialize image_processing
UpperCAmelCase_ : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCAmelCase_ : Tuple = prepare_image_inputs(self.image_processor_tester , equal_resolution=_A )
for image in image_inputs:
self.assertIsInstance(_A , Image.Image )
# Test not batched input
UpperCAmelCase_ : Tuple = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
UpperCAmelCase_ : Any = image_processing(_A , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def A ( self : Union[str, Any] ) -> Union[str, Any]:
# Initialize image_processing
UpperCAmelCase_ : Any = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCAmelCase_ : Optional[int] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_A , numpify=_A )
for image in image_inputs:
self.assertIsInstance(_A , np.ndarray )
# Test not batched input
UpperCAmelCase_ : List[Any] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
UpperCAmelCase_ : int = image_processing(_A , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def A ( self : Optional[int] ) -> str:
# Initialize image_processing
UpperCAmelCase_ : List[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCAmelCase_ : Optional[int] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_A , torchify=_A )
for image in image_inputs:
self.assertIsInstance(_A , torch.Tensor )
# Test not batched input
UpperCAmelCase_ : Any = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
UpperCAmelCase_ : int = image_processing(_A , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def A ( self : Any ) -> Optional[Any]:
# Initialize image_processing
UpperCAmelCase_ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCAmelCase_ : Dict = prepare_image_inputs(self.image_processor_tester , equal_resolution=_A , torchify=_A )
UpperCAmelCase_ : Union[str, Any] = []
for image in image_inputs:
self.assertIsInstance(_A , torch.Tensor )
maps.append(torch.zeros(image.shape[-2:] ).long() )
# Test not batched input
UpperCAmelCase_ : str = image_processing(image_inputs[0] , maps[0] , return_tensors='''pt''' )
self.assertEqual(
encoding['''pixel_values'''].shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(
encoding['''labels'''].shape , (
1,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(encoding['''labels'''].dtype , torch.long )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 2_55 )
# Test batched
UpperCAmelCase_ : List[Any] = image_processing(_A , _A , return_tensors='''pt''' )
self.assertEqual(
encoding['''pixel_values'''].shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(
encoding['''labels'''].shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(encoding['''labels'''].dtype , torch.long )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 2_55 )
# Test not batched input (PIL images)
UpperCAmelCase_ , UpperCAmelCase_ : Any = prepare_semantic_single_inputs()
UpperCAmelCase_ : List[str] = image_processing(_A , _A , return_tensors='''pt''' )
self.assertEqual(
encoding['''pixel_values'''].shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(
encoding['''labels'''].shape , (
1,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(encoding['''labels'''].dtype , torch.long )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 2_55 )
# Test batched input (PIL images)
UpperCAmelCase_ , UpperCAmelCase_ : List[str] = prepare_semantic_batch_inputs()
UpperCAmelCase_ : int = image_processing(_A , _A , return_tensors='''pt''' )
self.assertEqual(
encoding['''pixel_values'''].shape , (
2,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(
encoding['''labels'''].shape , (
2,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
self.assertEqual(encoding['''labels'''].dtype , torch.long )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 2_55 )
def A ( self : List[Any] ) -> Union[str, Any]:
# Initialize image_processing
UpperCAmelCase_ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
UpperCAmelCase_ , UpperCAmelCase_ : Any = prepare_semantic_single_inputs()
UpperCAmelCase_ : Dict = image_processing(_A , _A , return_tensors='''pt''' )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 1_50 )
UpperCAmelCase_ : int = True
UpperCAmelCase_ : Dict = image_processing(_A , _A , return_tensors='''pt''' )
self.assertTrue(encoding['''labels'''].min().item() >= 0 )
self.assertTrue(encoding['''labels'''].max().item() <= 2_55 )
| 304 | 0 |
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
lowercase__ :List[Any] = {
"susnato/ernie-m-base_pytorch": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json",
"susnato/ernie-m-large_pytorch": "https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json",
}
class lowercase ( SCREAMING_SNAKE_CASE__ ):
lowercase_ : List[Any] ='''ernie_m'''
lowercase_ : Dict[str, str] ={"dropout": "classifier_dropout", "num_classes": "num_labels"}
def __init__( self ,A__ = 2_5_0_0_0_2 ,A__ = 7_6_8 ,A__ = 1_2 ,A__ = 1_2 ,A__ = 3_0_7_2 ,A__ = "gelu" ,A__ = 0.1 ,A__ = 0.1 ,A__ = 5_1_4 ,A__ = 0.02 ,A__ = 1 ,A__ = 1E-05 ,A__=None ,A__=False ,A__=0.0 ,**A__ ,):
super().__init__(pad_token_id=A__ ,**A__)
lowercase = vocab_size
lowercase = hidden_size
lowercase = num_hidden_layers
lowercase = num_attention_heads
lowercase = intermediate_size
lowercase = hidden_act
lowercase = hidden_dropout_prob
lowercase = attention_probs_dropout_prob
lowercase = max_position_embeddings
lowercase = initializer_range
lowercase = layer_norm_eps
lowercase = classifier_dropout
lowercase = is_decoder
lowercase = act_dropout
| 97 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFXLMRobertaModel
@require_tf
@require_sentencepiece
@require_tokenizers
class lowercase ( unittest.TestCase ):
@slow
def A__ ( self):
lowercase = TFXLMRobertaModel.from_pretrained('''jplu/tf-xlm-roberta-base''')
lowercase = {
'''input_ids''': tf.convert_to_tensor([[0, 2_6_4_6, 1_0_2_6_9, 8_3, 9_9_9_4_2, 2]] ,dtype=tf.intaa), # "My dog is cute"
'''attention_mask''': tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]] ,dtype=tf.intaa),
}
lowercase = model(A__)['''last_hidden_state''']
lowercase = tf.TensorShape((1, 6, 7_6_8))
self.assertEqual(output.shape ,A__)
# compare the actual values for a slice.
lowercase = tf.convert_to_tensor(
[
[
[0.0681762, 0.10894451, 0.06772504],
[-0.06423668, 0.02366615, 0.04329344],
[-0.06057295, 0.09974135, -0.00070584],
]
] ,dtype=tf.floataa ,)
self.assertTrue(np.allclose(output[:, :3, :3].numpy() ,expected_slice.numpy() ,atol=1E-4))
| 97 | 1 |
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
lowercase : int = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow('', '|', '|'),
datarow=DataRow('', '|', '|'),
padding=1,
with_header_hide=None,
)
lowercase : Optional[int] = []
lowercase : int = []
lowercase : Any = {'type': 'section', 'text': {'type': 'plain_text', 'text': 'No failed tests! 🤗', 'emoji': True}}
lowercase : List[str] = [
{
'type': 'header',
'text': {
'type': 'plain_text',
'text': f"🤗 Accelerate nightly {os.environ.get('TEST_TYPE', '')} test results",
'emoji': True,
},
}
]
lowercase : Union[str, Any] = 0
for log in Path().glob('*.log'):
lowercase : Optional[int] = 0
with open(log, 'r') as f:
for line in f:
lowercase : Dict = json.loads(line)
if line.get('nodeid', '') != "":
lowercase : List[str] = line['nodeid']
if line.get('duration', None) is not None:
lowercase : List[str] = f"{line['duration']:.4f}"
if line.get('outcome', '') == "failed":
section_num_failed += 1
failed.append([test, duration, log.name.split('_')[0]])
total_num_failed += 1
group_info.append([str(log), section_num_failed, failed])
lowercase : Union[str, Any] = []
log.unlink()
lowercase : Optional[Any] = ''
lowercase : Any = []
if total_num_failed > 0:
for name, num_failed, failed_tests in group_info:
if num_failed > 0:
if num_failed == 1:
message += f"*{name[1:]}: {num_failed} failed test*\n"
else:
message += f"*{name[1:]}: {num_failed} failed tests*\n"
lowercase : List[str] = []
lowercase : Optional[int] = {}
for test in failed_tests:
lowercase : Any = test[0].split('::')
lowercase : List[str] = data[0].split('/')[-1]
if data[0] not in filesafailed:
lowercase : Optional[Any] = [data[1:]]
else:
filesafailed[data[0]] += [data[1:]]
failed_table.append(data)
lowercase : Tuple = [test[0] for test in failed_table]
lowercase : Any = list(set(files))
# Count number of instances in failed_tests
lowercase : Union[str, Any] = []
for file in individual_files:
table.append([file, len(filesafailed[file])])
lowercase : List[str] = tabulate(
table,
headers=['Test Location', 'Num Failed'],
tablefmt=hf_table_format,
stralign='right',
)
message += f"\n```\n{failed_table}\n```"
all_filesafailed.append(filesafailed)
if len(message) > 3000:
lowercase : Optional[Any] = 'Too many failed tests, please see the full report in the Action results.'
lowercase : List[str] = len(err) + 10
lowercase : Dict = message[: 3000 - offset] + f"\n...\n```\n{err}"
print(f"### {message}")
else:
lowercase : Union[str, Any] = 'No failed tests! 🤗'
print(f"## {message}")
payload.append(no_error_payload)
if os.environ.get('TEST_TYPE', '') != "":
from slack_sdk import WebClient
lowercase : Union[str, Any] = WebClient(token=os.environ['SLACK_API_TOKEN'])
if message != "No failed tests! 🤗":
lowercase : Any = {
'type': 'section',
'text': {
'type': 'mrkdwn',
'text': message,
},
}
payload.append(md_report)
lowercase : List[Any] = {
'type': 'section',
'text': {
'type': 'mrkdwn',
'text': '*For more details:*',
},
'accessory': {
'type': 'button',
'text': {
'type': 'plain_text',
'text': 'Check Action results',
'emoji': True,
},
'url': f"https://github.com/{os.environ['GITHUB_REPOSITORY']}/actions/runs/{os.environ['GITHUB_RUN_ID']}",
},
}
payload.append(action_button)
lowercase : Union[str, Any] = {
'type': 'context',
'elements': [
{
'type': 'plain_text',
'text': f"Nightly {os.environ.get('TEST_TYPE')} test results for {date.today()}",
}
],
}
payload.append(date_report)
lowercase : int = client.chat_postMessage(channel='#accelerate-ci-daily', text=message, blocks=payload)
lowercase : Dict = response.data['ts']
for failed_file in all_filesafailed:
for test_location, test_failures in failed_file.items():
# Keep only the first instance of the test name
lowercase : Tuple = ''
for i, row in enumerate(test_failures):
if row[0] != test_class:
lowercase : Dict = row[0]
else:
lowercase : Dict = ''
lowercase : Dict = {
'type': 'section',
'text': {
'type': 'mrkdwn',
'text': f"Test location: {test_location}\n```\n{tabulate(test_failures, headers=['Class', 'Test'], tablefmt=hf_table_format, stralign='right')}\n```",
},
}
client.chat_postMessage(
channel='#accelerate-ci-daily',
thread_ts=ts,
blocks=[payload],
) | 232 |
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def lowercase ( SCREAMING_SNAKE_CASE__ : Optional[int] ) -> int:
return getitem, k
def lowercase ( SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Optional[int] ) -> str:
return setitem, k, v
def lowercase ( SCREAMING_SNAKE_CASE__ : Tuple ) -> Optional[Any]:
return delitem, k
def lowercase ( SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : str , *SCREAMING_SNAKE_CASE__ : int ) -> Optional[int]:
try:
return fun(SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ ), None
except Exception as e:
return None, e
a__ = (
_set("""key_a""", """val_a"""),
_set("""key_b""", """val_b"""),
)
a__ = [
_set("""key_a""", """val_a"""),
_set("""key_a""", """val_b"""),
]
a__ = [
_set("""key_a""", """val_a"""),
_set("""key_b""", """val_b"""),
_del("""key_a"""),
_del("""key_b"""),
_set("""key_a""", """val_a"""),
_del("""key_a"""),
]
a__ = [
_get("""key_a"""),
_del("""key_a"""),
_set("""key_a""", """val_a"""),
_del("""key_a"""),
_del("""key_a"""),
_get("""key_a"""),
]
a__ = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
]
a__ = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
*[_del(x) for x in range(5)],
_set("""key_a""", """val_b"""),
]
@pytest.mark.parametrize(
"""operations""" , (
pytest.param(_add_items , id="""add items""" ),
pytest.param(_overwrite_items , id="""overwrite items""" ),
pytest.param(_delete_items , id="""delete items""" ),
pytest.param(_access_absent_items , id="""access absent items""" ),
pytest.param(_add_with_resize_up , id="""add with resize up""" ),
pytest.param(_add_with_resize_down , id="""add with resize down""" ),
) , )
def lowercase ( SCREAMING_SNAKE_CASE__ : str ) -> Tuple:
_snake_case : List[Any] = HashMap(initial_block_size=4 )
_snake_case : int = {}
for _, (fun, *args) in enumerate(SCREAMING_SNAKE_CASE__ ):
_snake_case , _snake_case : Tuple = _run_operation(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ )
_snake_case , _snake_case : int = _run_operation(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ )
assert my_res == py_res
assert str(SCREAMING_SNAKE_CASE__ ) == str(SCREAMING_SNAKE_CASE__ )
assert set(SCREAMING_SNAKE_CASE__ ) == set(SCREAMING_SNAKE_CASE__ )
assert len(SCREAMING_SNAKE_CASE__ ) == len(SCREAMING_SNAKE_CASE__ )
assert set(my.items() ) == set(py.items() )
def lowercase ( ) -> Optional[int]:
def is_public(SCREAMING_SNAKE_CASE__ : str ) -> bool:
return not name.startswith("""_""" )
_snake_case : Tuple = {name for name in dir({} ) if is_public(SCREAMING_SNAKE_CASE__ )}
_snake_case : Optional[Any] = {name for name in dir(HashMap() ) if is_public(SCREAMING_SNAKE_CASE__ )}
assert dict_public_names > hash_public_names
| 317 | 0 |
class __lowercase :
"""simple docstring"""
def __init__( self , A ) -> None:
snake_case : List[Any] = set_counts
snake_case : Tuple = max(A )
snake_case : Union[str, Any] = len(A )
snake_case : int = [1] * num_sets
snake_case : int = list(range(A ) )
def UpperCAmelCase ( self , A , A ) -> bool:
snake_case : List[str] = self.get_parent(A )
snake_case : List[Any] = self.get_parent(A )
if src_parent == dst_parent:
return False
if self.ranks[dst_parent] >= self.ranks[src_parent]:
self.set_counts[dst_parent] += self.set_counts[src_parent]
snake_case : List[Any] = 0
snake_case : Union[str, Any] = dst_parent
if self.ranks[dst_parent] == self.ranks[src_parent]:
self.ranks[dst_parent] += 1
snake_case : Dict = self.set_counts[dst_parent]
else:
self.set_counts[src_parent] += self.set_counts[dst_parent]
snake_case : str = 0
snake_case : str = src_parent
snake_case : Any = self.set_counts[src_parent]
snake_case : Tuple = max(self.max_set , A )
return True
def UpperCAmelCase ( self , A ) -> int:
if self.parents[disj_set] == disj_set:
return disj_set
snake_case : int = self.get_parent(self.parents[disj_set] )
return self.parents[disj_set]
| 176 |
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging
logging.set_verbosity_info()
lowerCamelCase : List[Any] = logging.get_logger(__name__)
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> int:
snake_case : Dict = SwinConfig.from_pretrained(
"""microsoft/swin-tiny-patch4-window7-224""" ,out_features=["""stage1""", """stage2""", """stage3""", """stage4"""] )
snake_case : List[str] = MaskFormerConfig(backbone_config=lowercase )
snake_case : List[Any] = """huggingface/label-files"""
if "ade20k-full" in model_name:
# this should be ok
snake_case : Dict = 847
snake_case : List[str] = """maskformer-ade20k-full-id2label.json"""
elif "ade" in model_name:
# this should be ok
snake_case : Union[str, Any] = 150
snake_case : List[Any] = """ade20k-id2label.json"""
elif "coco-stuff" in model_name:
# this should be ok
snake_case : Union[str, Any] = 171
snake_case : int = """maskformer-coco-stuff-id2label.json"""
elif "coco" in model_name:
# TODO
snake_case : Optional[Any] = 133
snake_case : Optional[Any] = """coco-panoptic-id2label.json"""
elif "cityscapes" in model_name:
# this should be ok
snake_case : Tuple = 19
snake_case : int = """cityscapes-id2label.json"""
elif "vistas" in model_name:
# this should be ok
snake_case : int = 65
snake_case : Any = """mapillary-vistas-id2label.json"""
snake_case : Optional[Any] = json.load(open(hf_hub_download(lowercase ,lowercase ,repo_type="""dataset""" ) ,"""r""" ) )
snake_case : List[Any] = {int(lowercase ): v for k, v in idalabel.items()}
return config
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> List[str]:
snake_case : Union[str, Any] = []
# stem
# fmt: off
rename_keys.append(("""backbone.patch_embed.proj.weight""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight""") )
rename_keys.append(("""backbone.patch_embed.proj.bias""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias""") )
rename_keys.append(("""backbone.patch_embed.norm.weight""", """model.pixel_level_module.encoder.model.embeddings.norm.weight""") )
rename_keys.append(("""backbone.patch_embed.norm.bias""", """model.pixel_level_module.encoder.model.embeddings.norm.bias""") )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm1.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""") )
rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm1.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""") )
rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""") )
rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.relative_position_index""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""") )
rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.proj.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""") )
rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.proj.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""") )
rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm2.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""") )
rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm2.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""") )
rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc1.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""") )
rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc1.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""") )
rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc2.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""") )
rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc2.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""") )
if i < 3:
rename_keys.append((f"""backbone.layers.{i}.downsample.reduction.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight""") )
rename_keys.append((f"""backbone.layers.{i}.downsample.norm.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight""") )
rename_keys.append((f"""backbone.layers.{i}.downsample.norm.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias""") )
rename_keys.append((f"""backbone.norm{i}.weight""", f"""model.pixel_level_module.encoder.hidden_states_norms.{i}.weight""") )
rename_keys.append((f"""backbone.norm{i}.bias""", f"""model.pixel_level_module.encoder.hidden_states_norms.{i}.bias""") )
# FPN
rename_keys.append(("""sem_seg_head.layer_4.weight""", """model.pixel_level_module.decoder.fpn.stem.0.weight""") )
rename_keys.append(("""sem_seg_head.layer_4.norm.weight""", """model.pixel_level_module.decoder.fpn.stem.1.weight""") )
rename_keys.append(("""sem_seg_head.layer_4.norm.bias""", """model.pixel_level_module.decoder.fpn.stem.1.bias""") )
for source_index, target_index in zip(range(3 ,0 ,-1 ) ,range(0 ,3 ) ):
rename_keys.append((f"""sem_seg_head.adapter_{source_index}.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight""") )
rename_keys.append((f"""sem_seg_head.adapter_{source_index}.norm.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight""") )
rename_keys.append((f"""sem_seg_head.adapter_{source_index}.norm.bias""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias""") )
rename_keys.append((f"""sem_seg_head.layer_{source_index}.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight""") )
rename_keys.append((f"""sem_seg_head.layer_{source_index}.norm.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight""") )
rename_keys.append((f"""sem_seg_head.layer_{source_index}.norm.bias""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias""") )
rename_keys.append(("""sem_seg_head.mask_features.weight""", """model.pixel_level_module.decoder.mask_projection.weight""") )
rename_keys.append(("""sem_seg_head.mask_features.bias""", """model.pixel_level_module.decoder.mask_projection.bias""") )
# Transformer decoder
for idx in range(config.decoder_config.decoder_layers ):
# self-attention out projection
rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight""", f"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight""") )
rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias""", f"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias""") )
# cross-attention out projection
rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight""") )
rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias""") )
# MLP 1
rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight""", f"""model.transformer_module.decoder.layers.{idx}.fc1.weight""") )
rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias""", f"""model.transformer_module.decoder.layers.{idx}.fc1.bias""") )
# MLP 2
rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight""", f"""model.transformer_module.decoder.layers.{idx}.fc2.weight""") )
rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias""", f"""model.transformer_module.decoder.layers.{idx}.fc2.bias""") )
# layernorm 1 (self-attention layernorm)
rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight""", f"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight""") )
rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias""", f"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias""") )
# layernorm 2 (cross-attention layernorm)
rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight""") )
rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias""") )
# layernorm 3 (final layernorm)
rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight""", f"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight""") )
rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias""", f"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias""") )
rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.weight""", """model.transformer_module.decoder.layernorm.weight""") )
rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.bias""", """model.transformer_module.decoder.layernorm.bias""") )
# heads on top
rename_keys.append(("""sem_seg_head.predictor.query_embed.weight""", """model.transformer_module.queries_embedder.weight""") )
rename_keys.append(("""sem_seg_head.predictor.input_proj.weight""", """model.transformer_module.input_projection.weight""") )
rename_keys.append(("""sem_seg_head.predictor.input_proj.bias""", """model.transformer_module.input_projection.bias""") )
rename_keys.append(("""sem_seg_head.predictor.class_embed.weight""", """class_predictor.weight""") )
rename_keys.append(("""sem_seg_head.predictor.class_embed.bias""", """class_predictor.bias""") )
for i in range(3 ):
rename_keys.append((f"""sem_seg_head.predictor.mask_embed.layers.{i}.weight""", f"""mask_embedder.{i}.0.weight""") )
rename_keys.append((f"""sem_seg_head.predictor.mask_embed.layers.{i}.bias""", f"""mask_embedder.{i}.0.bias""") )
# fmt: on
return rename_keys
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ,lowercase ) -> str:
snake_case : Tuple = dct.pop(lowercase )
snake_case : int = val
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ) -> int:
snake_case : List[str] = [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
for i in range(len(backbone_config.depths ) ):
snake_case : Optional[Any] = num_features[i]
for j in range(backbone_config.depths[i] ):
# fmt: off
# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
snake_case : Tuple = state_dict.pop(f"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight""" )
snake_case : Optional[Any] = state_dict.pop(f"""backbone.layers.{i}.blocks.{j}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
snake_case : Optional[Any] = in_proj_weight[:dim, :]
snake_case : Optional[int] = in_proj_bias[: dim]
snake_case : Union[str, Any] = in_proj_weight[
dim : dim * 2, :
]
snake_case : Tuple = in_proj_bias[
dim : dim * 2
]
snake_case : List[Any] = in_proj_weight[
-dim :, :
]
snake_case : Any = in_proj_bias[-dim :]
# fmt: on
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ) -> Any:
# fmt: off
snake_case : int = config.decoder_config.hidden_size
for idx in range(config.decoder_config.decoder_layers ):
# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
snake_case : Any = state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight""" )
snake_case : Tuple = state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) to the state dict
snake_case : Optional[int] = in_proj_weight[: hidden_size, :]
snake_case : Any = in_proj_bias[:config.hidden_size]
snake_case : Any = in_proj_weight[hidden_size : hidden_size * 2, :]
snake_case : int = in_proj_bias[hidden_size : hidden_size * 2]
snake_case : Any = in_proj_weight[-hidden_size :, :]
snake_case : Union[str, Any] = in_proj_bias[-hidden_size :]
# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
snake_case : Dict = state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight""" )
snake_case : str = state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) to the state dict
snake_case : Dict = in_proj_weight[: hidden_size, :]
snake_case : Dict = in_proj_bias[:config.hidden_size]
snake_case : List[Any] = in_proj_weight[hidden_size : hidden_size * 2, :]
snake_case : Optional[Any] = in_proj_bias[hidden_size : hidden_size * 2]
snake_case : Tuple = in_proj_weight[-hidden_size :, :]
snake_case : str = in_proj_bias[-hidden_size :]
# fmt: on
def SCREAMING_SNAKE_CASE__ ( ) -> torch.Tensor:
snake_case : Any = """http://images.cocodataset.org/val2017/000000039769.jpg"""
snake_case : Any = Image.open(requests.get(lowercase ,stream=lowercase ).raw )
return im
@torch.no_grad()
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ,lowercase ,lowercase = False ) -> Dict:
snake_case : List[str] = get_maskformer_config(lowercase )
# load original state_dict
with open(lowercase ,"""rb""" ) as f:
snake_case : Optional[Any] = pickle.load(lowercase )
snake_case : Optional[Any] = data["""model"""]
# for name, param in state_dict.items():
# print(name, param.shape)
# rename keys
snake_case : str = create_rename_keys(lowercase )
for src, dest in rename_keys:
rename_key(lowercase ,lowercase ,lowercase )
read_in_swin_q_k_v(lowercase ,config.backbone_config )
read_in_decoder_q_k_v(lowercase ,lowercase )
# update to torch tensors
for key, value in state_dict.items():
snake_case : List[Any] = torch.from_numpy(lowercase )
# load 🤗 model
snake_case : int = MaskFormerForInstanceSegmentation(lowercase )
model.eval()
for name, param in model.named_parameters():
print(lowercase ,param.shape )
snake_case , snake_case : Optional[int] = model.load_state_dict(lowercase ,strict=lowercase )
assert missing_keys == [
"model.pixel_level_module.encoder.model.layernorm.weight",
"model.pixel_level_module.encoder.model.layernorm.bias",
]
assert len(lowercase ) == 0, f"""Unexpected keys: {unexpected_keys}"""
# verify results
snake_case : List[str] = prepare_img()
if "vistas" in model_name:
snake_case : Optional[int] = 65
elif "cityscapes" in model_name:
snake_case : int = 65535
else:
snake_case : List[str] = 255
snake_case : List[Any] = True if """ade""" in model_name else False
snake_case : Optional[int] = MaskFormerImageProcessor(ignore_index=lowercase ,reduce_labels=lowercase )
snake_case : Tuple = image_processor(lowercase ,return_tensors="""pt""" )
snake_case : str = model(**lowercase )
print("""Logits:""" ,outputs.class_queries_logits[0, :3, :3] )
if model_name == "maskformer-swin-tiny-ade":
snake_case : Union[str, Any] = torch.tensor(
[[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]] )
assert torch.allclose(outputs.class_queries_logits[0, :3, :3] ,lowercase ,atol=1E-4 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
print(f"""Saving model and image processor to {pytorch_dump_folder_path}""" )
Path(lowercase ).mkdir(exist_ok=lowercase )
model.save_pretrained(lowercase )
image_processor.save_pretrained(lowercase )
if push_to_hub:
print("""Pushing model and image processor to the hub...""" )
model.push_to_hub(f"""nielsr/{model_name}""" )
image_processor.push_to_hub(f"""nielsr/{model_name}""" )
if __name__ == "__main__":
lowerCamelCase : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='maskformer-swin-tiny-ade',
type=str,
help=('Name of the MaskFormer model you\'d like to convert',),
)
parser.add_argument(
'--checkpoint_path',
default='/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl',
type=str,
help='Path to the original state dict (.pth file).',
)
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 or not to push the converted model to the 🤗 hub.'
)
lowerCamelCase : List[str] = parser.parse_args()
convert_maskformer_checkpoint(
args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 176 | 1 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
a_ = logging.get_logger(__name__)
a_ = 'https://openaipublic.azureedge.net/jukebox/models/'
a_ = {
'jukebox-1b-lyrics': [
'5b/vqvae.pth.tar',
'5b/prior_level_0.pth.tar',
'5b/prior_level_1.pth.tar',
'1b_lyrics/prior_level_2.pth.tar',
],
'jukebox-5b-lyrics': [
'5b/vqvae.pth.tar',
'5b/prior_level_0.pth.tar',
'5b/prior_level_1.pth.tar',
'5b_lyrics/prior_level_2.pth.tar',
],
}
def lowerCamelCase__ ( _a):
if key.endswith(".model.1.bias") and len(key.split(".")) > 10:
SCREAMING_SNAKE_CASE : Any = key.replace(".model.1.bias" , ".conv1d_1.bias")
elif key.endswith(".model.1.weight") and len(key.split(".")) > 10:
SCREAMING_SNAKE_CASE : Optional[Any] = key.replace(".model.1.weight" , ".conv1d_1.weight")
elif key.endswith(".model.3.bias") and len(key.split(".")) > 10:
SCREAMING_SNAKE_CASE : Union[str, Any] = key.replace(".model.3.bias" , ".conv1d_2.bias")
elif key.endswith(".model.3.weight") and len(key.split(".")) > 10:
SCREAMING_SNAKE_CASE : int = key.replace(".model.3.weight" , ".conv1d_2.weight")
if "conditioner_blocks.0." in key:
SCREAMING_SNAKE_CASE : List[str] = key.replace("conditioner_blocks.0" , "conditioner_blocks")
if "prime_prior" in key:
SCREAMING_SNAKE_CASE : Optional[Any] = key.replace("prime_prior" , "encoder")
if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
SCREAMING_SNAKE_CASE : Union[str, Any] = key.replace(".emb." , ".")
if key.endswith("k"): # replace vqvae.X.k with vqvae.X.codebook
return key.replace(".k" , ".codebook")
if "y_emb." in key:
return key.replace("y_emb." , "metadata_embedding.")
if "x_emb.emb." in key:
SCREAMING_SNAKE_CASE : Dict = key.replace("0.x_emb.emb" , "embed_tokens")
if "prime_state_ln" in key:
return key.replace("prime_state_ln" , "encoder.final_layer_norm")
if ".ln" in key:
return key.replace(".ln" , ".layer_norm")
if "_ln" in key:
return key.replace("_ln" , "_layer_norm")
if "prime_state_proj" in key:
return key.replace("prime_state_proj" , "encoder.proj_in")
if "prime_x_out" in key:
return key.replace("prime_x_out" , "encoder.lm_head")
if "prior.x_out" in key:
return key.replace("x_out" , "fc_proj_out")
if "x_emb" in key:
return key.replace("x_emb" , "embed_tokens")
return key
def lowerCamelCase__ ( _a , _a , _a , _a):
SCREAMING_SNAKE_CASE : Tuple = {}
import re
SCREAMING_SNAKE_CASE : Optional[int] = re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)")
SCREAMING_SNAKE_CASE : Any = re.compile(
r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)")
SCREAMING_SNAKE_CASE : List[str] = re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)")
SCREAMING_SNAKE_CASE : int = re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)")
SCREAMING_SNAKE_CASE : Dict = re.compile(
r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)")
SCREAMING_SNAKE_CASE : Optional[int] = re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)")
SCREAMING_SNAKE_CASE : Dict = re.compile(r"conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)")
SCREAMING_SNAKE_CASE : List[Any] = re.compile(
r"conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)")
SCREAMING_SNAKE_CASE : Optional[Any] = re.compile(r"conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)")
for original_key, value in state_dict.items():
# rename vqvae.encoder keys
if re_encoder_block_conv_in.fullmatch(_a):
SCREAMING_SNAKE_CASE : str = re_encoder_block_conv_in.match(_a)
SCREAMING_SNAKE_CASE : List[Any] = regex_match.groups()
SCREAMING_SNAKE_CASE : List[str] = int(groups[2]) * 2 + int(groups[3])
SCREAMING_SNAKE_CASE : Any = f"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"
SCREAMING_SNAKE_CASE : int = re_encoder_block_conv_in.sub(_a , _a)
elif re_encoder_block_resnet.fullmatch(_a):
SCREAMING_SNAKE_CASE : Optional[int] = re_encoder_block_resnet.match(_a)
SCREAMING_SNAKE_CASE : Dict = regex_match.groups()
SCREAMING_SNAKE_CASE : List[Any] = int(groups[2]) * 2 + int(groups[3])
SCREAMING_SNAKE_CASE : Optional[Any] = {"1": 1, "3": 2}[groups[-2]]
SCREAMING_SNAKE_CASE : List[Any] = f"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}."
SCREAMING_SNAKE_CASE : Optional[Any] = f"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
SCREAMING_SNAKE_CASE : Dict = prefix + resnet_block
SCREAMING_SNAKE_CASE : Tuple = re_encoder_block_resnet.sub(_a , _a)
elif re_encoder_block_proj_out.fullmatch(_a):
SCREAMING_SNAKE_CASE : Optional[Any] = re_encoder_block_proj_out.match(_a)
SCREAMING_SNAKE_CASE : List[str] = regex_match.groups()
SCREAMING_SNAKE_CASE : Union[str, Any] = f"encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"
SCREAMING_SNAKE_CASE : Optional[int] = re_encoder_block_proj_out.sub(_a , _a)
# rename vqvae.decoder keys
elif re_decoder_block_conv_out.fullmatch(_a):
SCREAMING_SNAKE_CASE : Optional[Any] = re_decoder_block_conv_out.match(_a)
SCREAMING_SNAKE_CASE : Any = regex_match.groups()
SCREAMING_SNAKE_CASE : List[Any] = int(groups[2]) * 2 + int(groups[3]) - 2
SCREAMING_SNAKE_CASE : str = f"decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}"
SCREAMING_SNAKE_CASE : int = re_decoder_block_conv_out.sub(_a , _a)
elif re_decoder_block_resnet.fullmatch(_a):
SCREAMING_SNAKE_CASE : int = re_decoder_block_resnet.match(_a)
SCREAMING_SNAKE_CASE : str = regex_match.groups()
SCREAMING_SNAKE_CASE : List[str] = int(groups[2]) * 2 + int(groups[3]) - 2
SCREAMING_SNAKE_CASE : List[Any] = {"1": 1, "3": 2}[groups[-2]]
SCREAMING_SNAKE_CASE : Tuple = f"decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."
SCREAMING_SNAKE_CASE : str = f"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
SCREAMING_SNAKE_CASE : Union[str, Any] = prefix + resnet_block
SCREAMING_SNAKE_CASE : Optional[Any] = re_decoder_block_resnet.sub(_a , _a)
elif re_decoder_block_proj_in.fullmatch(_a):
SCREAMING_SNAKE_CASE : List[str] = re_decoder_block_proj_in.match(_a)
SCREAMING_SNAKE_CASE : Any = regex_match.groups()
SCREAMING_SNAKE_CASE : List[str] = f"decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"
SCREAMING_SNAKE_CASE : Union[str, Any] = re_decoder_block_proj_in.sub(_a , _a)
# rename prior cond.model to upsampler.upsample_block and resnet
elif re_prior_cond_conv_out.fullmatch(_a):
SCREAMING_SNAKE_CASE : Optional[int] = re_prior_cond_conv_out.match(_a)
SCREAMING_SNAKE_CASE : int = regex_match.groups()
SCREAMING_SNAKE_CASE : Optional[int] = int(groups[1]) * 2 + int(groups[2]) - 2
SCREAMING_SNAKE_CASE : Optional[Any] = f"conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"
SCREAMING_SNAKE_CASE : Any = re_prior_cond_conv_out.sub(_a , _a)
elif re_prior_cond_resnet.fullmatch(_a):
SCREAMING_SNAKE_CASE : List[Any] = re_prior_cond_resnet.match(_a)
SCREAMING_SNAKE_CASE : int = regex_match.groups()
SCREAMING_SNAKE_CASE : Tuple = int(groups[1]) * 2 + int(groups[2]) - 2
SCREAMING_SNAKE_CASE : Dict = {"1": 1, "3": 2}[groups[-2]]
SCREAMING_SNAKE_CASE : Tuple = f"conditioner_blocks.upsampler.upsample_block.{block_index}."
SCREAMING_SNAKE_CASE : Any = f"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
SCREAMING_SNAKE_CASE : Optional[Any] = prefix + resnet_block
SCREAMING_SNAKE_CASE : List[Any] = re_prior_cond_resnet.sub(_a , _a)
elif re_prior_cond_proj_in.fullmatch(_a):
SCREAMING_SNAKE_CASE : Optional[int] = re_prior_cond_proj_in.match(_a)
SCREAMING_SNAKE_CASE : Optional[int] = regex_match.groups()
SCREAMING_SNAKE_CASE : Any = f"conditioner_blocks.upsampler.proj_in.{groups[-1]}"
SCREAMING_SNAKE_CASE : Dict = re_prior_cond_proj_in.sub(_a , _a)
# keep original key
else:
SCREAMING_SNAKE_CASE : List[Any] = original_key
SCREAMING_SNAKE_CASE : Optional[int] = replace_key(_a)
if f"{key_prefix}.{key}" not in model_state_dict or key is None:
print(f"failed converting {original_key} to {key}, does not match")
# handle missmatched shape
elif value.shape != model_state_dict[f"{key_prefix}.{key}"].shape:
SCREAMING_SNAKE_CASE : Tuple = model_state_dict[f"{key_prefix}.{key}"]
print(f"{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match")
SCREAMING_SNAKE_CASE : List[Any] = original_key
SCREAMING_SNAKE_CASE : str = original_key
SCREAMING_SNAKE_CASE : List[str] = value
return new_dict
@torch.no_grad()
def lowerCamelCase__ ( _a=None , _a=None):
for file in MODEL_MAPPING[model_name]:
if not os.path.isfile(f"{pytorch_dump_folder_path}/{file.split('/')[-1]}"):
SCREAMING_SNAKE_CASE : List[Any] = requests.get(f"{PREFIX}{file}" , allow_redirects=_a)
os.makedirs(f"{pytorch_dump_folder_path}/" , exist_ok=_a)
open(f"{pytorch_dump_folder_path}/{file.split('/')[-1]}" , "wb").write(r.content)
SCREAMING_SNAKE_CASE : Tuple = MODEL_MAPPING[model_name.split("/")[-1]]
SCREAMING_SNAKE_CASE : Union[str, Any] = JukeboxConfig.from_pretrained(_a)
SCREAMING_SNAKE_CASE : Any = JukeboxModel(_a)
SCREAMING_SNAKE_CASE : Tuple = []
SCREAMING_SNAKE_CASE : Tuple = {}
for i, dict_name in enumerate(_a):
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.load(f"{pytorch_dump_folder_path}/{dict_name.split('/')[-1]}")["model"]
SCREAMING_SNAKE_CASE : str = {}
for k in old_dic.keys():
if k.endswith(".b"):
SCREAMING_SNAKE_CASE : List[str] = old_dic[k]
elif k.endswith(".w"):
SCREAMING_SNAKE_CASE : Optional[Any] = old_dic[k]
elif "level_2" not in dict_name and "cond.model." in k:
SCREAMING_SNAKE_CASE : List[Any] = old_dic[k]
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = old_dic[k]
SCREAMING_SNAKE_CASE : Tuple = "vqvae" if i == 0 else f"priors.{3 - i}"
SCREAMING_SNAKE_CASE : int = fix_jukebox_keys(_a , model.state_dict() , _a , _a)
weight_dict.append(_a)
SCREAMING_SNAKE_CASE : Tuple = weight_dict.pop(0)
model.vqvae.load_state_dict(_a)
for i in range(len(_a)):
model.priors[i].load_state_dict(weight_dict[2 - i])
Path(_a).mkdir(exist_ok=_a)
with open(f"{pytorch_dump_folder_path}/mapping.json" , "w") as txtfile:
json.dump(_a , _a)
print(f"Saving model {model_name} to {pytorch_dump_folder_path}")
model.save_pretrained(_a)
return weight_dict
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='jukebox-5b-lyrics',
type=str,
help='Name of the model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default='jukebox-5b-lyrics-converted',
type=str,
help='Path to the output PyTorch model directory.',
)
a_ = parser.parse_args()
convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path) | 76 |
import math
class _UpperCamelCase :
def __init__( self :Union[str, Any] , lowerCamelCase :Union[str, Any]=0 ) -> Tuple: # a graph with Node 0,1,...,N-1
UpperCAmelCase__ = n
UpperCAmelCase__ = [
[math.inf for j in range(0 , lowerCamelCase )] for i in range(0 , lowerCamelCase )
] # adjacency matrix for weight
UpperCAmelCase__ = [
[math.inf for j in range(0 , lowerCamelCase )] for i in range(0 , lowerCamelCase )
] # dp[i][j] stores minimum distance from i to j
def UpperCAmelCase_ ( self :Tuple , lowerCamelCase :List[Any] , lowerCamelCase :Optional[Any] , lowerCamelCase :int ) -> List[Any]:
UpperCAmelCase__ = w
def UpperCAmelCase_ ( self :Optional[int] ) -> Optional[Any]:
for k in range(0 , self.n ):
for i in range(0 , self.n ):
for j in range(0 , self.n ):
UpperCAmelCase__ = min(self.dp[i][j] , self.dp[i][k] + self.dp[k][j] )
def UpperCAmelCase_ ( self :int , lowerCamelCase :List[Any] , lowerCamelCase :Dict ) -> List[str]:
return self.dp[u][v]
if __name__ == "__main__":
_lowerCAmelCase : Optional[int] = Graph(5)
graph.add_edge(0, 2, 9)
graph.add_edge(0, 4, 1_0)
graph.add_edge(1, 3, 5)
graph.add_edge(2, 3, 7)
graph.add_edge(3, 0, 1_0)
graph.add_edge(3, 1, 2)
graph.add_edge(3, 2, 1)
graph.add_edge(3, 4, 6)
graph.add_edge(4, 1, 3)
graph.add_edge(4, 2, 4)
graph.add_edge(4, 3, 9)
graph.floyd_warshall()
graph.show_min(1, 4)
graph.show_min(0, 3)
| 169 | 0 |
"""simple docstring"""
from random import randint
from tempfile import TemporaryFile
import numpy as np
def snake_case (A_ :str , A_ :List[str] , A_ :List[Any] ):
'''simple docstring'''
a : Tuple = 0
if start < end:
a : Optional[Any] = randint(A_ , A_ )
a : Optional[Any] = a[end]
a : Union[str, Any] = a[pivot]
a : str = temp
a, a : str = _in_place_partition(A_ , A_ , A_ )
count += _in_place_quick_sort(A_ , A_ , p - 1 )
count += _in_place_quick_sort(A_ , p + 1 , A_ )
return count
def snake_case (A_ :List[str] , A_ :Optional[int] , A_ :List[str] ):
'''simple docstring'''
a : Union[str, Any] = 0
a : Optional[Any] = randint(A_ , A_ )
a : Optional[int] = a[end]
a : Dict = a[pivot]
a : Dict = temp
a : Dict = start - 1
for index in range(A_ , A_ ):
count += 1
if a[index] < a[end]: # check if current val is less than pivot value
a : Optional[Any] = new_pivot_index + 1
a : int = a[new_pivot_index]
a : List[str] = a[index]
a : Union[str, Any] = temp
a : Optional[int] = a[new_pivot_index + 1]
a : int = a[end]
a : Optional[Any] = temp
return new_pivot_index + 1, count
_UpperCamelCase : Any = TemporaryFile()
_UpperCamelCase : Optional[int] = 100 # 1000 elements are to be sorted
_UpperCamelCase , _UpperCamelCase : int = 0, 1 # mean and standard deviation
_UpperCamelCase : str = np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('The array is')
print(X)
outfile.seek(0) # using the same array
_UpperCamelCase : Dict = np.load(outfile)
_UpperCamelCase : str = len(M) - 1
_UpperCamelCase : List[str] = _in_place_quick_sort(M, 0, r)
print(
'No of Comparisons for 100 elements selected from a standard normal distribution'
'is :'
)
print(z)
| 186 |
"""simple docstring"""
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_UpperCamelCase : List[str] = get_tests_dir('fixtures/test_sentencepiece.model')
@require_sentencepiece
@require_tokenizers
class snake_case ( UpperCAmelCase , unittest.TestCase ):
__magic_name__ = XLNetTokenizer
__magic_name__ = XLNetTokenizerFast
__magic_name__ = True
__magic_name__ = True
def lowerCamelCase__ ( self : List[Any] ):
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
a : Tuple = XLNetTokenizer(A , keep_accents=A )
tokenizer.sanitize_special_tokens()
tokenizer.save_pretrained(self.tmpdirname )
def lowerCamelCase__ ( self : Optional[int] ):
'''simple docstring'''
a : Optional[int] = '<s>'
a : int = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(A ) , A )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(A ) , A )
def lowerCamelCase__ ( self : Tuple ):
'''simple docstring'''
a : str = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '<unk>' )
self.assertEqual(vocab_keys[1] , '<s>' )
self.assertEqual(vocab_keys[-1] , '<eod>' )
self.assertEqual(len(A ) , 1_0_0_6 )
def lowerCamelCase__ ( self : Any ):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 1_0_0_0 )
def lowerCamelCase__ ( self : Optional[int] ):
'''simple docstring'''
a : Optional[Any] = XLNetTokenizer(A , keep_accents=A )
a : List[str] = tokenizer.tokenize('This is a test' )
self.assertListEqual(A , ['▁This', '▁is', '▁a', '▁t', 'est'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(A ) , [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2] )
a : List[str] = tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
A , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'9',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'é',
'.',
] , )
a : Union[str, Any] = tokenizer.convert_tokens_to_ids(A )
self.assertListEqual(A , [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4] )
a : Tuple = tokenizer.convert_ids_to_tokens(A )
self.assertListEqual(
A , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'<unk>',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'<unk>',
'.',
] , )
def lowerCamelCase__ ( self : Union[str, Any] ):
'''simple docstring'''
a : Optional[int] = XLNetTokenizer(A , do_lower_case=A )
a : Any = tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
A , [
SPIECE_UNDERLINE + '',
'i',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'9',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
'se',
'.',
] , )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['▁he', 'll', 'o'] )
def lowerCamelCase__ ( self : int ):
'''simple docstring'''
a : str = XLNetTokenizer(A , do_lower_case=A )
a : List[Any] = tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
A , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'9',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
'se',
'.',
] , )
@slow
def lowerCamelCase__ ( self : Optional[int] ):
'''simple docstring'''
a : Dict = XLNetTokenizer.from_pretrained('xlnet-base-cased' )
a : int = tokenizer.encode('sequence builders' , add_special_tokens=A )
a : List[Any] = tokenizer.encode('multi-sequence build' , add_special_tokens=A )
a : Optional[int] = tokenizer.build_inputs_with_special_tokens(A )
a : Optional[Any] = tokenizer.build_inputs_with_special_tokens(A , A )
assert encoded_sentence == text + [4, 3]
assert encoded_pair == text + [4] + text_a + [4, 3]
@slow
def lowerCamelCase__ ( self : Any ):
'''simple docstring'''
a : Union[str, Any] = {'input_ids': [[1_7, 2_1_4_4_2, 2_7_0, 1_7, 1_0, 1_4_6_4_5, 3_1_8, 3_4, 1_7, 4_5_4_6, 3_1_4_5, 7_8_7, 1_3, 7_7_5_2, 2_2_0_1_8, 2_3, 2_1, 1_7, 4_5_4_6, 3_1_4_5, 7_8_7, 1_3, 3_3_5_2, 1_4_4_3_1, 1_3, 5_5_0_0, 1_1, 1_1_7_6, 5_8_0, 1_3, 1_6_8_1_9, 4_7_9_7, 2_3, 1_7, 1_0, 1_7_1_3_5, 6_5_8, 1_9, 4_5_7, 7_9_3_2, 1_3, 1_8_4, 1_9, 3_1_5_4, 1_7_1_3_5, 6_4_6_8, 1_9, 1_4_0_4, 1_2_2_6_9, 1_9, 4_2_2_9, 5_3_5_6, 1_6_2_6_4, 4_6, 1_9, 1_7, 2_0_5_4_5, 1_0_3_9_5, 9, 9, 9, 1_1, 2_8, 6_4_2_1, 9_5_3_1, 2_0_7_2_9, 1_7, 1_0, 3_5_3, 1_7_0_2_2, 1_1, 2_1, 6_4_2_1, 9_5_3_1, 1_6_9_4_9, 1_7, 1_0, 1_1_5_0_9, 7_5_3, 1_1, 3_3, 9_5, 2_4_2_1, 7_3_8_5, 9_5_6, 1_4_4_3_1, 2_6_2_6, 2_5, 8_4_2, 7_3_8_5, 4_8_3_6, 2_1, 1_4_2_9, 2_2_7_2, 9_8_5_5, 3_1_2_0, 1_6_1, 2_4_7_3_8, 1_9, 1_3_2_0_3, 6_5_8, 2_1_8, 7_8_7, 2_1, 4_3_0, 1_8_4_8_2, 8_4_7, 2_6_3_7, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3_2_2, 2_2_1_7_8, 2_7, 1_0_6_4, 2_2, 9_5_6, 1_3, 1_1_1_0_1, 1_4_2_9, 5_8_5_4, 2_4_3_1_3, 1_8_9_5_3, 4_0, 4_2_2, 2_4_3_6_6, 6_8, 1_7_5_8, 3_7, 1_0_4_8_3, 1_4_2_5_7, 3_1, 2_0_7, 2_6_3, 2_1, 2_0_3, 3_7_7_3, 2_5, 7_1, 9_7_3_5, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3_2, 2_0_4_9, 3_4_4_2, 1_7, 1_3_8_9_4, 3_3_8_0, 2_3, 9_5, 1_8, 1_7_6_3_4, 2_2_8_8, 9, 4, 3]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=A , model_name='xlnet-base-cased' , revision='c841166438c31ec7ca9a106dee7bb312b73ae511' , )
| 186 | 1 |
'''simple docstring'''
from math import ceil, sqrt
def lowerCAmelCase_ ( _lowerCamelCase: int = 1_00_00_00 ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
__SCREAMING_SNAKE_CASE : Union[str, Any] = max(ceil(sqrt(outer_width**2 - limit ) ) , 1 )
else:
__SCREAMING_SNAKE_CASE : Union[str, Any] = 1
if (outer_width - hole_width_lower_bound) % 2:
hole_width_lower_bound += 1
answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1
return answer
if __name__ == "__main__":
print(f"{solution() = }") | 112 |
'''simple docstring'''
from collections.abc import Iterable
from typing import Any
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Optional[int] , lowerCAmelCase__ : int | None = None ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Union[str, Any] = value
__SCREAMING_SNAKE_CASE : Node | None = None # Added in order to delete a node easier
__SCREAMING_SNAKE_CASE : Node | None = None
__SCREAMING_SNAKE_CASE : Node | None = None
def __repr__( self : Optional[Any] ):
"""simple docstring"""
from pprint import pformat
if self.left is None and self.right is None:
return str(self.value )
return pformat({F"{self.value}": (self.left, self.right)} , indent=1 )
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Optional[int] , lowerCAmelCase__ : Node | None = None ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : str = root
def __str__( self : Union[str, Any] ):
"""simple docstring"""
return str(self.root )
def UpperCamelCase__ ( self : Dict , lowerCAmelCase__ : Node , lowerCAmelCase__ : Node | None ):
"""simple docstring"""
if new_children is not None: # reset its kids
__SCREAMING_SNAKE_CASE : List[str] = node.parent
if node.parent is not None: # reset its parent
if self.is_right(lowerCAmelCase__ ): # If it is the right children
__SCREAMING_SNAKE_CASE : Any = new_children
else:
__SCREAMING_SNAKE_CASE : int = new_children
else:
__SCREAMING_SNAKE_CASE : int = new_children
def UpperCamelCase__ ( self : str , lowerCAmelCase__ : Node ):
"""simple docstring"""
if node.parent and node.parent.right:
return node == node.parent.right
return False
def UpperCamelCase__ ( self : List[Any] ):
"""simple docstring"""
return self.root is None
def UpperCamelCase__ ( self : Optional[Any] , lowerCAmelCase__ : str ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Union[str, Any] = Node(lowerCAmelCase__ ) # create a new Node
if self.empty(): # if Tree is empty
__SCREAMING_SNAKE_CASE : Optional[int] = new_node # set its root
else: # Tree is not empty
__SCREAMING_SNAKE_CASE : Optional[int] = self.root # from root
if parent_node is None:
return
while True: # While we don't get to a leaf
if value < parent_node.value: # We go left
if parent_node.left is None:
__SCREAMING_SNAKE_CASE : List[str] = new_node # We insert the new node in a leaf
break
else:
__SCREAMING_SNAKE_CASE : Any = parent_node.left
else:
if parent_node.right is None:
__SCREAMING_SNAKE_CASE : Tuple = new_node
break
else:
__SCREAMING_SNAKE_CASE : List[str] = parent_node.right
__SCREAMING_SNAKE_CASE : Tuple = parent_node
def UpperCamelCase__ ( self : str , *lowerCAmelCase__ : List[Any] ):
"""simple docstring"""
for value in values:
self.__insert(lowerCAmelCase__ )
def UpperCamelCase__ ( self : List[str] , lowerCAmelCase__ : Union[str, Any] ):
"""simple docstring"""
if self.empty():
raise IndexError("""Warning: Tree is empty! please use another.""" )
else:
__SCREAMING_SNAKE_CASE : List[Any] = self.root
# use lazy evaluation here to avoid NoneType Attribute error
while node is not None and node.value is not value:
__SCREAMING_SNAKE_CASE : Any = node.left if value < node.value else node.right
return node
def UpperCamelCase__ ( self : List[Any] , lowerCAmelCase__ : Node | None = None ):
"""simple docstring"""
if node is None:
if self.root is None:
return None
__SCREAMING_SNAKE_CASE : Optional[Any] = self.root
if not self.empty():
while node.right is not None:
__SCREAMING_SNAKE_CASE : Tuple = node.right
return node
def UpperCamelCase__ ( self : Dict , lowerCAmelCase__ : Node | None = None ):
"""simple docstring"""
if node is None:
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.root
if self.root is None:
return None
if not self.empty():
__SCREAMING_SNAKE_CASE : Optional[Any] = self.root
while node.left is not None:
__SCREAMING_SNAKE_CASE : Any = node.left
return node
def UpperCamelCase__ ( self : str , lowerCAmelCase__ : int ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[str] = self.search(lowerCAmelCase__ ) # Look for the node with that label
if node is not None:
if node.left is None and node.right is None: # If it has no children
self.__reassign_nodes(lowerCAmelCase__ , lowerCAmelCase__ )
elif node.left is None: # Has only right children
self.__reassign_nodes(lowerCAmelCase__ , node.right )
elif node.right is None: # Has only left children
self.__reassign_nodes(lowerCAmelCase__ , node.left )
else:
__SCREAMING_SNAKE_CASE : Tuple = self.get_max(
node.left ) # Gets the max value of the left branch
self.remove(tmp_node.value ) # type: ignore
__SCREAMING_SNAKE_CASE : Optional[Any] = (
tmp_node.value # type: ignore
) # Assigns the value to the node to delete and keep tree structure
def UpperCamelCase__ ( self : Tuple , lowerCAmelCase__ : Node | None ):
"""simple docstring"""
if node is not None:
yield node # Preorder Traversal
yield from self.preorder_traverse(node.left )
yield from self.preorder_traverse(node.right )
def UpperCamelCase__ ( self : Optional[int] , lowerCAmelCase__ : Optional[Any]=None ):
"""simple docstring"""
if traversal_function is None:
return self.preorder_traverse(self.root )
else:
return traversal_function(self.root )
def UpperCamelCase__ ( self : str , lowerCAmelCase__ : list , lowerCAmelCase__ : Node | None ):
"""simple docstring"""
if node:
self.inorder(lowerCAmelCase__ , node.left )
arr.append(node.value )
self.inorder(lowerCAmelCase__ , node.right )
def UpperCamelCase__ ( self : int , lowerCAmelCase__ : int , lowerCAmelCase__ : Node ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : list[int] = []
self.inorder(lowerCAmelCase__ , lowerCAmelCase__ ) # append all values to list using inorder traversal
return arr[k - 1]
def lowerCAmelCase_ ( _lowerCamelCase: Node | None ):
__SCREAMING_SNAKE_CASE : Optional[Any] = []
if curr_node is not None:
__SCREAMING_SNAKE_CASE : Optional[int] = postorder(curr_node.left ) + postorder(curr_node.right ) + [curr_node]
return node_list
def lowerCAmelCase_ ( ):
__SCREAMING_SNAKE_CASE : str = (8, 3, 6, 1, 10, 14, 13, 4, 7)
__SCREAMING_SNAKE_CASE : Dict = BinarySearchTree()
for i in testlist:
t.insert(_lowerCamelCase )
# Prints all the elements of the list in order traversal
print(_lowerCamelCase )
if t.search(6 ) is not None:
print("""The value 6 exists""" )
else:
print("""The value 6 doesn't exist""" )
if t.search(-1 ) is not None:
print("""The value -1 exists""" )
else:
print("""The value -1 doesn't exist""" )
if not t.empty():
print("""Max Value: """ , t.get_max().value ) # type: ignore
print("""Min Value: """ , t.get_min().value ) # type: ignore
for i in testlist:
t.remove(_lowerCamelCase )
print(_lowerCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) | 112 | 1 |
'''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_lowercase : Dict = get_tests_dir("""fixtures/test_sentencepiece.model""")
@require_sentencepiece
@require_tokenizers
class UpperCamelCase__( lowerCAmelCase , unittest.TestCase ):
__magic_name__ : Tuple = XLNetTokenizer
__magic_name__ : Any = XLNetTokenizerFast
__magic_name__ : Optional[int] = True
__magic_name__ : Union[str, Any] = True
def a__( self : List[Any] )-> List[Any]:
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
UpperCAmelCase = XLNetTokenizer(lowerCAmelCase , keep_accents=lowerCAmelCase )
tokenizer.sanitize_special_tokens()
tokenizer.save_pretrained(self.tmpdirname )
def a__( self : List[str] )-> Optional[Any]:
"""simple docstring"""
UpperCAmelCase = '''<s>'''
UpperCAmelCase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase ) , lowerCAmelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase ) , lowerCAmelCase )
def a__( self : Optional[int] )-> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<unk>''' )
self.assertEqual(vocab_keys[1] , '''<s>''' )
self.assertEqual(vocab_keys[-1] , '''<eod>''' )
self.assertEqual(len(lowerCAmelCase ) , 1006 )
def a__( self : int )-> str:
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 1000 )
def a__( self : int )-> Tuple:
"""simple docstring"""
UpperCAmelCase = XLNetTokenizer(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 ) , [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 , [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] )
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 a__( self : Optional[Any] )-> Optional[int]:
"""simple docstring"""
UpperCAmelCase = XLNetTokenizer(lowerCAmelCase , do_lower_case=lowerCAmelCase )
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''',
'''se''',
'''.''',
] , )
self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' ) , ['''▁he''', '''ll''', '''o'''] )
def a__( self : Any )-> Dict:
"""simple docstring"""
UpperCAmelCase = XLNetTokenizer(lowerCAmelCase , do_lower_case=lowerCAmelCase )
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''',
'''se''',
'''.''',
] , )
@slow
def a__( self : Optional[Any] )-> str:
"""simple docstring"""
UpperCAmelCase = XLNetTokenizer.from_pretrained('''xlnet-base-cased''' )
UpperCAmelCase = tokenizer.encode('''sequence builders''' , add_special_tokens=lowerCAmelCase )
UpperCAmelCase = tokenizer.encode('''multi-sequence build''' , add_special_tokens=lowerCAmelCase )
UpperCAmelCase = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase )
UpperCAmelCase = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase , lowerCAmelCase )
assert encoded_sentence == text + [4, 3]
assert encoded_pair == text + [4] + text_a + [4, 3]
@slow
def a__( self : List[str] )-> Tuple:
"""simple docstring"""
UpperCAmelCase = {'''input_ids''': [[17, 21442, 270, 17, 10, 14645, 318, 34, 17, 4546, 3145, 787, 13, 7752, 22018, 23, 21, 17, 4546, 3145, 787, 13, 3352, 14431, 13, 5500, 11, 1176, 580, 13, 16819, 4797, 23, 17, 10, 17135, 658, 19, 457, 7932, 13, 184, 19, 3154, 17135, 6468, 19, 1404, 12269, 19, 4229, 5356, 16264, 46, 19, 17, 20545, 10395, 9, 9, 9, 11, 28, 6421, 9531, 20729, 17, 10, 353, 17022, 11, 21, 6421, 9531, 16949, 17, 10, 11509, 753, 11, 33, 95, 2421, 7385, 956, 14431, 2626, 25, 842, 7385, 4836, 21, 1429, 2272, 9855, 3120, 161, 24738, 19, 13203, 658, 218, 787, 21, 430, 18482, 847, 2637, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 322, 22178, 27, 1064, 22, 956, 13, 11101, 1429, 5854, 24313, 18953, 40, 422, 24366, 68, 1758, 37, 10483, 14257, 31, 207, 263, 21, 203, 3773, 25, 71, 9735, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 32, 2049, 3442, 17, 13894, 3380, 23, 95, 18, 17634, 2288, 9, 4, 3]], '''token_type_ids''': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowerCAmelCase , model_name='''xlnet-base-cased''' , revision='''c841166438c31ec7ca9a106dee7bb312b73ae511''' , )
| 364 |
'''simple docstring'''
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def lowerCamelCase__ ( A : int , A : int , A : int , A : int , A : int , A : int ):
'''simple docstring'''
if (ksize % 2) == 0:
UpperCAmelCase = ksize + 1
UpperCAmelCase = np.zeros((ksize, ksize) , dtype=np.floataa )
# each value
for y in range(A ):
for x in range(A ):
# distance from center
UpperCAmelCase = x - ksize // 2
UpperCAmelCase = y - ksize // 2
# degree to radiant
UpperCAmelCase = theta / 1_80 * np.pi
UpperCAmelCase = np.cos(_theta )
UpperCAmelCase = np.sin(_theta )
# get kernel x
UpperCAmelCase = cos_theta * px + sin_theta * py
# get kernel y
UpperCAmelCase = -sin_theta * px + cos_theta * py
# fill kernel
UpperCAmelCase = np.exp(
-(_x**2 + gamma**2 * _y**2) / (2 * sigma**2) ) * np.cos(2 * np.pi * _x / lambd + psi )
return gabor
if __name__ == "__main__":
import doctest
doctest.testmod()
# read original image
_lowercase : Tuple = imread("""../image_data/lena.jpg""")
# turn image in gray scale value
_lowercase : int = cvtColor(img, COLOR_BGR2GRAY)
# Apply multiple Kernel to detect edges
_lowercase : List[str] = np.zeros(gray.shape[:2])
for theta in [0, 30, 60, 90, 120, 150]:
_lowercase : List[Any] = gabor_filter_kernel(10, 8, theta, 10, 0, 0)
out += filteraD(gray, CV_8UC3, kernel_aa)
_lowercase : Optional[int] = out / out.max() * 255
_lowercase : Optional[int] = out.astype(np.uinta)
imshow("""Original""", gray)
imshow("""Gabor filter with 20x20 mask and 6 directions""", out)
waitKey(0)
| 91 | 0 |
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ConditionalDetrImageProcessor
class lowerCamelCase (unittest.TestCase ):
def __init__( self : str , __UpperCAmelCase : Any , __UpperCAmelCase : str=7 , __UpperCAmelCase : Optional[int]=3 , __UpperCAmelCase : List[Any]=3_0 , __UpperCAmelCase : Tuple=4_0_0 , __UpperCAmelCase : Tuple=True , __UpperCAmelCase : List[str]=None , __UpperCAmelCase : Any=True , __UpperCAmelCase : Optional[Any]=[0.5, 0.5, 0.5] , __UpperCAmelCase : Dict=[0.5, 0.5, 0.5] , __UpperCAmelCase : Union[str, Any]=True , __UpperCAmelCase : Dict=1 / 2_5_5 , __UpperCAmelCase : int=True , ) -> Optional[Any]:
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
SCREAMING_SNAKE_CASE__ = size if size is not None else {"""shortest_edge""": 1_8, """longest_edge""": 1_3_3_3}
SCREAMING_SNAKE_CASE__ = parent
SCREAMING_SNAKE_CASE__ = batch_size
SCREAMING_SNAKE_CASE__ = num_channels
SCREAMING_SNAKE_CASE__ = min_resolution
SCREAMING_SNAKE_CASE__ = max_resolution
SCREAMING_SNAKE_CASE__ = do_resize
SCREAMING_SNAKE_CASE__ = size
SCREAMING_SNAKE_CASE__ = do_normalize
SCREAMING_SNAKE_CASE__ = image_mean
SCREAMING_SNAKE_CASE__ = image_std
SCREAMING_SNAKE_CASE__ = do_rescale
SCREAMING_SNAKE_CASE__ = rescale_factor
SCREAMING_SNAKE_CASE__ = do_pad
def SCREAMING_SNAKE_CASE ( self : Tuple ) -> str:
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def SCREAMING_SNAKE_CASE ( self : Tuple , __UpperCAmelCase : Tuple , __UpperCAmelCase : Dict=False ) -> Dict:
if not batched:
SCREAMING_SNAKE_CASE__ = image_inputs[0]
if isinstance(__UpperCAmelCase , Image.Image ):
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = image.size
else:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = image.shape[1], image.shape[2]
if w < h:
SCREAMING_SNAKE_CASE__ = int(self.size["""shortest_edge"""] * h / w )
SCREAMING_SNAKE_CASE__ = self.size["""shortest_edge"""]
elif w > h:
SCREAMING_SNAKE_CASE__ = self.size["""shortest_edge"""]
SCREAMING_SNAKE_CASE__ = int(self.size["""shortest_edge"""] * w / h )
else:
SCREAMING_SNAKE_CASE__ = self.size["""shortest_edge"""]
SCREAMING_SNAKE_CASE__ = self.size["""shortest_edge"""]
else:
SCREAMING_SNAKE_CASE__ = []
for image in image_inputs:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
SCREAMING_SNAKE_CASE__ = max(__UpperCAmelCase , key=lambda __UpperCAmelCase : item[0] )[0]
SCREAMING_SNAKE_CASE__ = max(__UpperCAmelCase , key=lambda __UpperCAmelCase : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class lowerCamelCase (A__ ,unittest.TestCase ):
lowerCamelCase__ : Optional[Any] = ConditionalDetrImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE ( self : Dict ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ = ConditionalDetrImageProcessingTester(self )
@property
def SCREAMING_SNAKE_CASE ( self : List[Any] ) -> Tuple:
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE ( self : str ) -> Dict:
SCREAMING_SNAKE_CASE__ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__UpperCAmelCase , """image_mean""" ) )
self.assertTrue(hasattr(__UpperCAmelCase , """image_std""" ) )
self.assertTrue(hasattr(__UpperCAmelCase , """do_normalize""" ) )
self.assertTrue(hasattr(__UpperCAmelCase , """do_resize""" ) )
self.assertTrue(hasattr(__UpperCAmelCase , """size""" ) )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> Dict:
SCREAMING_SNAKE_CASE__ = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"""shortest_edge""": 1_8, """longest_edge""": 1_3_3_3} )
self.assertEqual(image_processor.do_pad , __UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = self.image_processing_class.from_dict(
self.image_processor_dict , size=4_2 , max_size=8_4 , pad_and_return_pixel_mask=__UpperCAmelCase )
self.assertEqual(image_processor.size , {"""shortest_edge""": 4_2, """longest_edge""": 8_4} )
self.assertEqual(image_processor.do_pad , __UpperCAmelCase )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> Tuple:
pass
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> str:
# Initialize image_processing
SCREAMING_SNAKE_CASE__ = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
SCREAMING_SNAKE_CASE__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=__UpperCAmelCase )
for image in image_inputs:
self.assertIsInstance(__UpperCAmelCase , Image.Image )
# Test not batched input
SCREAMING_SNAKE_CASE__ = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self.image_processor_tester.get_expected_values(__UpperCAmelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self.image_processor_tester.get_expected_values(__UpperCAmelCase , batched=__UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = image_processing(__UpperCAmelCase , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE ( self : List[Any] ) -> int:
# Initialize image_processing
SCREAMING_SNAKE_CASE__ = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
SCREAMING_SNAKE_CASE__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=__UpperCAmelCase , numpify=__UpperCAmelCase )
for image in image_inputs:
self.assertIsInstance(__UpperCAmelCase , np.ndarray )
# Test not batched input
SCREAMING_SNAKE_CASE__ = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self.image_processor_tester.get_expected_values(__UpperCAmelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
SCREAMING_SNAKE_CASE__ = image_processing(__UpperCAmelCase , return_tensors="""pt""" ).pixel_values
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self.image_processor_tester.get_expected_values(__UpperCAmelCase , batched=__UpperCAmelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE ( self : str ) -> List[Any]:
# Initialize image_processing
SCREAMING_SNAKE_CASE__ = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
SCREAMING_SNAKE_CASE__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=__UpperCAmelCase , torchify=__UpperCAmelCase )
for image in image_inputs:
self.assertIsInstance(__UpperCAmelCase , torch.Tensor )
# Test not batched input
SCREAMING_SNAKE_CASE__ = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self.image_processor_tester.get_expected_values(__UpperCAmelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
SCREAMING_SNAKE_CASE__ = image_processing(__UpperCAmelCase , return_tensors="""pt""" ).pixel_values
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self.image_processor_tester.get_expected_values(__UpperCAmelCase , batched=__UpperCAmelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def SCREAMING_SNAKE_CASE ( self : List[str] ) -> Union[str, Any]:
# prepare image and target
SCREAMING_SNAKE_CASE__ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt""" , """r""" ) as f:
SCREAMING_SNAKE_CASE__ = json.loads(f.read() )
SCREAMING_SNAKE_CASE__ = {"""image_id""": 3_9_7_6_9, """annotations""": target}
# encode them
SCREAMING_SNAKE_CASE__ = ConditionalDetrImageProcessor.from_pretrained("""microsoft/conditional-detr-resnet-50""" )
SCREAMING_SNAKE_CASE__ = image_processing(images=__UpperCAmelCase , annotations=__UpperCAmelCase , return_tensors="""pt""" )
# verify pixel values
SCREAMING_SNAKE_CASE__ = torch.Size([1, 3, 8_0_0, 1_0_6_6] )
self.assertEqual(encoding["""pixel_values"""].shape , __UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = torch.tensor([0.2_796, 0.3_138, 0.3_481] )
self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] , __UpperCAmelCase , atol=1e-4 ) )
# verify area
SCREAMING_SNAKE_CASE__ = torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""] , __UpperCAmelCase ) )
# verify boxes
SCREAMING_SNAKE_CASE__ = torch.Size([6, 4] )
self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape , __UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0] , __UpperCAmelCase , atol=1e-3 ) )
# verify image_id
SCREAMING_SNAKE_CASE__ = torch.tensor([3_9_7_6_9] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""] , __UpperCAmelCase ) )
# verify is_crowd
SCREAMING_SNAKE_CASE__ = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""] , __UpperCAmelCase ) )
# verify class_labels
SCREAMING_SNAKE_CASE__ = torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""] , __UpperCAmelCase ) )
# verify orig_size
SCREAMING_SNAKE_CASE__ = torch.tensor([4_8_0, 6_4_0] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""] , __UpperCAmelCase ) )
# verify size
SCREAMING_SNAKE_CASE__ = torch.tensor([8_0_0, 1_0_6_6] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""] , __UpperCAmelCase ) )
@slow
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> Any:
# prepare image, target and masks_path
SCREAMING_SNAKE_CASE__ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt""" , """r""" ) as f:
SCREAMING_SNAKE_CASE__ = json.loads(f.read() )
SCREAMING_SNAKE_CASE__ = {"""file_name""": """000000039769.png""", """image_id""": 3_9_7_6_9, """segments_info""": target}
SCREAMING_SNAKE_CASE__ = pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic""" )
# encode them
SCREAMING_SNAKE_CASE__ = ConditionalDetrImageProcessor(format="""coco_panoptic""" )
SCREAMING_SNAKE_CASE__ = image_processing(images=__UpperCAmelCase , annotations=__UpperCAmelCase , masks_path=__UpperCAmelCase , return_tensors="""pt""" )
# verify pixel values
SCREAMING_SNAKE_CASE__ = torch.Size([1, 3, 8_0_0, 1_0_6_6] )
self.assertEqual(encoding["""pixel_values"""].shape , __UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = torch.tensor([0.2_796, 0.3_138, 0.3_481] )
self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] , __UpperCAmelCase , atol=1e-4 ) )
# verify area
SCREAMING_SNAKE_CASE__ = torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""] , __UpperCAmelCase ) )
# verify boxes
SCREAMING_SNAKE_CASE__ = torch.Size([6, 4] )
self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape , __UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0] , __UpperCAmelCase , atol=1e-3 ) )
# verify image_id
SCREAMING_SNAKE_CASE__ = torch.tensor([3_9_7_6_9] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""] , __UpperCAmelCase ) )
# verify is_crowd
SCREAMING_SNAKE_CASE__ = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""] , __UpperCAmelCase ) )
# verify class_labels
SCREAMING_SNAKE_CASE__ = torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""] , __UpperCAmelCase ) )
# verify masks
SCREAMING_SNAKE_CASE__ = 8_2_2_8_7_3
self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item() , __UpperCAmelCase )
# verify orig_size
SCREAMING_SNAKE_CASE__ = torch.tensor([4_8_0, 6_4_0] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""] , __UpperCAmelCase ) )
# verify size
SCREAMING_SNAKE_CASE__ = torch.tensor([8_0_0, 1_0_6_6] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""] , __UpperCAmelCase ) )
| 165 |
"""simple docstring"""
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def A ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ ):
'''simple docstring'''
with open(snake_case__ ) as metadata_file:
SCREAMING_SNAKE_CASE__ = json.load(snake_case__ )
SCREAMING_SNAKE_CASE__ = LukeConfig(use_entity_aware_attention=snake_case__ , **metadata["""model_config"""] )
# Load in the weights from the checkpoint_path
SCREAMING_SNAKE_CASE__ = torch.load(snake_case__ , map_location="""cpu""" )
# Load the entity vocab file
SCREAMING_SNAKE_CASE__ = load_entity_vocab(snake_case__ )
SCREAMING_SNAKE_CASE__ = RobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""] )
# Add special tokens to the token vocabulary for downstream tasks
SCREAMING_SNAKE_CASE__ = AddedToken("""<ent>""" , lstrip=snake_case__ , rstrip=snake_case__ )
SCREAMING_SNAKE_CASE__ = AddedToken("""<ent2>""" , lstrip=snake_case__ , rstrip=snake_case__ )
tokenizer.add_special_tokens({"""additional_special_tokens""": [entity_token_a, entity_token_a]} )
config.vocab_size += 2
print(f"""Saving tokenizer to {pytorch_dump_folder_path}""" )
tokenizer.save_pretrained(snake_case__ )
with open(os.path.join(snake_case__ , LukeTokenizer.vocab_files_names["""entity_vocab_file"""] ) , """w""" ) as f:
json.dump(snake_case__ , snake_case__ )
SCREAMING_SNAKE_CASE__ = LukeTokenizer.from_pretrained(snake_case__ )
# Initialize the embeddings of the special tokens
SCREAMING_SNAKE_CASE__ = state_dict["""embeddings.word_embeddings.weight"""]
SCREAMING_SNAKE_CASE__ = word_emb[tokenizer.convert_tokens_to_ids(["""@"""] )[0]].unsqueeze(0 )
SCREAMING_SNAKE_CASE__ = word_emb[tokenizer.convert_tokens_to_ids(["""#"""] )[0]].unsqueeze(0 )
SCREAMING_SNAKE_CASE__ = torch.cat([word_emb, ent_emb, enta_emb] )
# Initialize the query layers of the entity-aware self-attention mechanism
for layer_index in range(config.num_hidden_layers ):
for matrix_name in ["query.weight", "query.bias"]:
SCREAMING_SNAKE_CASE__ = f"""encoder.layer.{layer_index}.attention.self."""
SCREAMING_SNAKE_CASE__ = state_dict[prefix + matrix_name]
SCREAMING_SNAKE_CASE__ = state_dict[prefix + matrix_name]
SCREAMING_SNAKE_CASE__ = state_dict[prefix + matrix_name]
# Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
SCREAMING_SNAKE_CASE__ = state_dict["""entity_embeddings.entity_embeddings.weight"""]
SCREAMING_SNAKE_CASE__ = entity_emb[entity_vocab["""[MASK]"""]]
SCREAMING_SNAKE_CASE__ = LukeModel(config=snake_case__ ).eval()
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = model.load_state_dict(snake_case__ , strict=snake_case__ )
if not (len(snake_case__ ) == 1 and missing_keys[0] == "embeddings.position_ids"):
raise ValueError(f"""Missing keys {", ".join(snake_case__ )}. Expected only missing embeddings.position_ids""" )
if not (all(key.startswith("""entity_predictions""" ) or key.startswith("""lm_head""" ) for key in unexpected_keys )):
raise ValueError(
"""Unexpected keys"""
f""" {", ".join([key for key in unexpected_keys if not (key.startswith("entity_predictions" ) or key.startswith("lm_head" ))] )}""" )
# Check outputs
SCREAMING_SNAKE_CASE__ = LukeTokenizer.from_pretrained(snake_case__ , task="""entity_classification""" )
SCREAMING_SNAKE_CASE__ = (
"""Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the"""
""" new world number one avoid a humiliating second- round exit at Wimbledon ."""
)
SCREAMING_SNAKE_CASE__ = (39, 42)
SCREAMING_SNAKE_CASE__ = tokenizer(snake_case__ , entity_spans=[span] , add_prefix_space=snake_case__ , return_tensors="""pt""" )
SCREAMING_SNAKE_CASE__ = model(**snake_case__ )
# Verify word hidden states
if model_size == "large":
SCREAMING_SNAKE_CASE__ = torch.Size((1, 42, 10_24) )
SCREAMING_SNAKE_CASE__ = torch.tensor(
[[0.01_33, 0.08_65, 0.00_95], [0.30_93, -0.25_76, -0.74_18], [-0.17_20, -0.21_17, -0.28_69]] )
else: # base
SCREAMING_SNAKE_CASE__ = torch.Size((1, 42, 7_68) )
SCREAMING_SNAKE_CASE__ = torch.tensor([[0.00_37, 0.13_68, -0.00_91], [0.10_99, 0.33_29, -0.10_95], [0.07_65, 0.53_35, 0.11_79]] )
if not (outputs.last_hidden_state.shape == expected_shape):
raise ValueError(
f"""Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}""" )
if not torch.allclose(outputs.last_hidden_state[0, :3, :3] , snake_case__ , atol=1e-4 ):
raise ValueError
# Verify entity hidden states
if model_size == "large":
SCREAMING_SNAKE_CASE__ = torch.Size((1, 1, 10_24) )
SCREAMING_SNAKE_CASE__ = torch.tensor([[0.04_66, -0.01_06, -0.01_79]] )
else: # base
SCREAMING_SNAKE_CASE__ = torch.Size((1, 1, 7_68) )
SCREAMING_SNAKE_CASE__ = torch.tensor([[0.14_57, 0.10_44, 0.01_74]] )
if not (outputs.entity_last_hidden_state.shape != expected_shape):
raise ValueError(
f"""Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is"""
f""" {expected_shape}""" )
if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3] , snake_case__ , atol=1e-4 ):
raise ValueError
# Finally, save our PyTorch model and tokenizer
print("""Saving PyTorch model to {}""".format(snake_case__ ) )
model.save_pretrained(snake_case__ )
def A ( snake_case__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = {}
with open(snake_case__ , """r""" , encoding="""utf-8""" ) as f:
for index, line in enumerate(snake_case__ ):
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = line.rstrip().split("""\t""" )
SCREAMING_SNAKE_CASE__ = index
return entity_vocab
if __name__ == "__main__":
A_ : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--checkpoint_path", type=str, help="Path to a pytorch_model.bin file.")
parser.add_argument(
"--metadata_path", default=None, type=str, help="Path to a metadata.json file, defining the configuration."
)
parser.add_argument(
"--entity_vocab_path",
default=None,
type=str,
help="Path to an entity_vocab.tsv file, containing the entity vocabulary.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to where to dump the output PyTorch model."
)
parser.add_argument(
"--model_size", default="base", type=str, choices=["base", "large"], help="Size of the model to be converted."
)
A_ : Optional[Any] = parser.parse_args()
convert_luke_checkpoint(
args.checkpoint_path,
args.metadata_path,
args.entity_vocab_path,
args.pytorch_dump_folder_path,
args.model_size,
)
| 165 | 1 |
"""simple docstring"""
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed
__UpperCamelCase = '''true'''
def lowercase (SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Tuple=82 , SCREAMING_SNAKE_CASE_ : List[Any]=16 ) -> Union[str, Any]:
set_seed(42 )
SCREAMING_SNAKE_CASE = RegressionModel()
SCREAMING_SNAKE_CASE = deepcopy(SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE = RegressionDataset(length=SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE = DataLoader(SCREAMING_SNAKE_CASE_ , batch_size=SCREAMING_SNAKE_CASE_ )
model.to(accelerator.device )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = accelerator.prepare(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
return model, ddp_model, dataloader
def lowercase (SCREAMING_SNAKE_CASE_ : Accelerator , SCREAMING_SNAKE_CASE_ : Union[str, Any]=False ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased' )
SCREAMING_SNAKE_CASE = load_dataset('glue' , 'mrpc' , split='validation' )
def tokenize_function(SCREAMING_SNAKE_CASE_ : List[str] ):
SCREAMING_SNAKE_CASE = tokenizer(examples['sentence1'] , examples['sentence2'] , truncation=SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ )
return outputs
with accelerator.main_process_first():
SCREAMING_SNAKE_CASE = dataset.map(
SCREAMING_SNAKE_CASE_ , batched=SCREAMING_SNAKE_CASE_ , remove_columns=['idx', 'sentence1', 'sentence2'] , )
SCREAMING_SNAKE_CASE = tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(SCREAMING_SNAKE_CASE_ : Optional[int] ):
if use_longest:
return tokenizer.pad(SCREAMING_SNAKE_CASE_ , padding='longest' , return_tensors='pt' )
return tokenizer.pad(SCREAMING_SNAKE_CASE_ , padding='max_length' , max_length=1_28 , return_tensors='pt' )
return DataLoader(SCREAMING_SNAKE_CASE_ , shuffle=SCREAMING_SNAKE_CASE_ , collate_fn=SCREAMING_SNAKE_CASE_ , batch_size=16 )
def lowercase (SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : Any ) -> Dict:
SCREAMING_SNAKE_CASE = Accelerator(dispatch_batches=SCREAMING_SNAKE_CASE_ , split_batches=SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE = get_dataloader(SCREAMING_SNAKE_CASE_ , not dispatch_batches )
SCREAMING_SNAKE_CASE = AutoModelForSequenceClassification.from_pretrained(
'hf-internal-testing/mrpc-bert-base-cased' , return_dict=SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = accelerator.prepare(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def lowercase (SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : Dict ) -> Dict:
SCREAMING_SNAKE_CASE = []
for batch in dataloader:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = batch.values()
with torch.no_grad():
SCREAMING_SNAKE_CASE = model(SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = accelerator.gather_for_metrics((logit, target) )
logits_and_targets.append((logit, target) )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = [], []
for logit, targ in logits_and_targets:
logits.append(SCREAMING_SNAKE_CASE_ )
targs.append(SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = torch.cat(SCREAMING_SNAKE_CASE_ ), torch.cat(SCREAMING_SNAKE_CASE_ )
return logits, targs
def lowercase (SCREAMING_SNAKE_CASE_ : Accelerator , SCREAMING_SNAKE_CASE_ : Optional[Any]=82 , SCREAMING_SNAKE_CASE_ : int=False , SCREAMING_SNAKE_CASE_ : List[Any]=False , SCREAMING_SNAKE_CASE_ : Union[str, Any]=16 ) -> List[str]:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = get_basic_setup(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = generate_predictions(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
assert (
len(SCREAMING_SNAKE_CASE_ ) == num_samples
), F'Unexpected number of inputs:\n Expected: {num_samples}\n Actual: {len(SCREAMING_SNAKE_CASE_ )}'
def lowercase (SCREAMING_SNAKE_CASE_ : bool = False , SCREAMING_SNAKE_CASE_ : bool = False ) -> Optional[int]:
SCREAMING_SNAKE_CASE = evaluate.load('glue' , 'mrpc' )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = get_mrpc_setup(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# First do baseline
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = setup['no']
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
for batch in dataloader:
batch.to(SCREAMING_SNAKE_CASE_ )
with torch.inference_mode():
SCREAMING_SNAKE_CASE = model(**SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE = outputs.logits.argmax(dim=-1 )
metric.add_batch(predictions=SCREAMING_SNAKE_CASE_ , references=batch['labels'] )
SCREAMING_SNAKE_CASE = metric.compute()
# Then do distributed
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = setup['ddp']
model.eval()
for batch in dataloader:
with torch.inference_mode():
SCREAMING_SNAKE_CASE = model(**SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE = outputs.logits.argmax(dim=-1 )
SCREAMING_SNAKE_CASE = batch['labels']
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = accelerator.gather_for_metrics((preds, references) )
metric.add_batch(predictions=SCREAMING_SNAKE_CASE_ , references=SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE = metric.compute()
for key in "accuracy f1".split():
assert math.isclose(
baseline[key] , distributed[key] ), F'Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'
def lowercase () -> Dict:
SCREAMING_SNAKE_CASE = Accelerator(split_batches=SCREAMING_SNAKE_CASE_ , dispatch_batches=SCREAMING_SNAKE_CASE_ )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# These are a bit slower so they should only be ran on the GPU or TPU
if torch.cuda.is_available() or is_tpu_available():
if accelerator.is_local_main_process:
print('**Testing gather_for_metrics**' )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
if accelerator.is_local_main_process:
print(F'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`' )
test_mrpc(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print('**Test torch metrics**' )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
SCREAMING_SNAKE_CASE = Accelerator(split_batches=SCREAMING_SNAKE_CASE_ , dispatch_batches=SCREAMING_SNAKE_CASE_ )
if accelerator.is_local_main_process:
print(F'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99' )
test_torch_metrics(SCREAMING_SNAKE_CASE_ , 99 )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print('**Test last batch is not dropped when perfectly divisible**' )
SCREAMING_SNAKE_CASE = Accelerator()
test_torch_metrics(SCREAMING_SNAKE_CASE_ , 5_12 )
accelerator.state._reset_state()
def lowercase (SCREAMING_SNAKE_CASE_ : int ) -> Union[str, Any]:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 38 |
"""simple docstring"""
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCamelCase = logging.get_logger(__name__)
def lowercase (SCREAMING_SNAKE_CASE_ : List[str] ) -> Optional[Any]:
SCREAMING_SNAKE_CASE = torch.load(SCREAMING_SNAKE_CASE_ , map_location='cpu' )
if "model" in sd.keys():
SCREAMING_SNAKE_CASE = torch.load(SCREAMING_SNAKE_CASE_ , map_location='cpu' )['model']
# pop unnecessary weights
SCREAMING_SNAKE_CASE = [
'decoder.version',
'decoder.output_projection.weight',
]
for key in keys_to_delete:
if key in sd:
sd.pop(SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE = {
'decoder.project_in_dim.weight': 'decoder.project_in.weight',
'decoder.project_out_dim.weight': 'decoder.project_out.weight',
'decoder.layer_norm.weight': 'decoder.final_layer_norm.weight',
'decoder.layer_norm.bias': 'decoder.final_layer_norm.bias',
}
for old_key, new_key in keys_to_rename.items():
if old_key in sd:
SCREAMING_SNAKE_CASE = sd.pop(SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE = list(sd.keys() )
for key in keys:
if ".qkv_proj." in key:
SCREAMING_SNAKE_CASE = sd[key]
# We split QKV in separate Q,K,V
SCREAMING_SNAKE_CASE = key.replace('.qkv_proj.' , '.q_proj.' )
SCREAMING_SNAKE_CASE = key.replace('.qkv_proj.' , '.k_proj.' )
SCREAMING_SNAKE_CASE = key.replace('.qkv_proj.' , '.v_proj.' )
SCREAMING_SNAKE_CASE = value.shape[0]
assert depth % 3 == 0
# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = torch.split(SCREAMING_SNAKE_CASE_ , depth // 3 , dim=0 )
SCREAMING_SNAKE_CASE = q
SCREAMING_SNAKE_CASE = k
SCREAMING_SNAKE_CASE = v
del sd[key]
return sd
@torch.no_grad()
def lowercase (SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Optional[int]=None ) -> List[Any]:
SCREAMING_SNAKE_CASE = load_checkpoint(SCREAMING_SNAKE_CASE_ )
if config is not None:
SCREAMING_SNAKE_CASE = OPTConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
else:
SCREAMING_SNAKE_CASE = OPTConfig()
SCREAMING_SNAKE_CASE = OPTModel(SCREAMING_SNAKE_CASE_ ).half().eval()
model.load_state_dict(SCREAMING_SNAKE_CASE_ )
# Check results
Path(SCREAMING_SNAKE_CASE_ ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_ )
model.save_pretrained(SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
__UpperCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--fairseq_path''',
type=str,
help=(
'''path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'''
''' https://huggingface.co/models?other=opt_metasq'''
),
)
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--hf_config''', default=None, type=str, help='''Define HF config.''')
__UpperCamelCase = parser.parse_args()
convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)
| 38 | 1 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''CarlCochet/trajectory-transformer-halfcheetah-medium-v2''': (
'''https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json'''
),
# See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}
class lowercase ( A__ ):
"""simple docstring"""
_a = 'trajectory_transformer'
_a = ['past_key_values']
_a = {
'hidden_size': 'n_embd',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__( self , UpperCamelCase_=100 , UpperCamelCase_=5 , UpperCamelCase_=1 , UpperCamelCase_=1 , UpperCamelCase_=249 , UpperCamelCase_=6 , UpperCamelCase_=17 , UpperCamelCase_=25 , UpperCamelCase_=4 , UpperCamelCase_=4 , UpperCamelCase_=128 , UpperCamelCase_=0.1 , UpperCamelCase_=0.1 , UpperCamelCase_=0.1 , UpperCamelCase_=0.0006 , UpperCamelCase_=512 , UpperCamelCase_=0.02 , UpperCamelCase_=1e-12 , UpperCamelCase_=1 , UpperCamelCase_=True , UpperCamelCase_=1 , UpperCamelCase_=50256 , UpperCamelCase_=50256 , **UpperCamelCase_ , ):
'''simple docstring'''
UpperCamelCase__ :int = vocab_size
UpperCamelCase__ :str = action_weight
UpperCamelCase__ :Dict = reward_weight
UpperCamelCase__ :Optional[int] = value_weight
UpperCamelCase__ :List[Any] = max_position_embeddings
UpperCamelCase__ :int = block_size
UpperCamelCase__ :Optional[Any] = action_dim
UpperCamelCase__ :Union[str, Any] = observation_dim
UpperCamelCase__ :int = transition_dim
UpperCamelCase__ :int = learning_rate
UpperCamelCase__ :int = n_layer
UpperCamelCase__ :int = n_head
UpperCamelCase__ :List[Any] = n_embd
UpperCamelCase__ :Optional[int] = embd_pdrop
UpperCamelCase__ :Dict = attn_pdrop
UpperCamelCase__ :List[str] = resid_pdrop
UpperCamelCase__ :Optional[Any] = initializer_range
UpperCamelCase__ :Any = layer_norm_eps
UpperCamelCase__ :Optional[int] = kaiming_initializer_range
UpperCamelCase__ :Dict = use_cache
super().__init__(pad_token_id=UpperCamelCase_ , bos_token_id=UpperCamelCase_ , eos_token_id=UpperCamelCase_ , **UpperCamelCase_ ) | 97 |
'''simple docstring'''
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : int = logging.get_logger(__name__)
lowercase : Union[str, Any] = {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/config.json",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/config.json",
}
class __UpperCAmelCase ( _lowerCamelCase ):
__lowercase = """xlnet"""
__lowercase = ["""mems"""]
__lowercase = {
"""n_token""": """vocab_size""", # Backward compatibility
"""hidden_size""": """d_model""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self , lowerCAmelCase_=3_20_00 , lowerCAmelCase_=10_24 , lowerCAmelCase_=24 , lowerCAmelCase_=16 , lowerCAmelCase_=40_96 , lowerCAmelCase_="gelu" , lowerCAmelCase_=True , lowerCAmelCase_="bi" , lowerCAmelCase_=0.02 , lowerCAmelCase_=1E-12 , lowerCAmelCase_=0.1 , lowerCAmelCase_=5_12 , lowerCAmelCase_=None , lowerCAmelCase_=True , lowerCAmelCase_=False , lowerCAmelCase_=False , lowerCAmelCase_=-1 , lowerCAmelCase_=False , lowerCAmelCase_="last" , lowerCAmelCase_=True , lowerCAmelCase_="tanh" , lowerCAmelCase_=0.1 , lowerCAmelCase_=5 , lowerCAmelCase_=5 , lowerCAmelCase_=5 , lowerCAmelCase_=1 , lowerCAmelCase_=2 , **lowerCAmelCase_ , ):
"""simple docstring"""
_snake_case = vocab_size
_snake_case = d_model
_snake_case = n_layer
_snake_case = n_head
if d_model % n_head != 0:
raise ValueError(F'\'d_model % n_head\' ({d_model % n_head}) should be equal to 0' )
if "d_head" in kwargs:
if kwargs["d_head"] != d_model // n_head:
raise ValueError(
F'`d_head` ({kwargs["d_head"]}) should be equal to `d_model // n_head` ({d_model // n_head})' )
_snake_case = d_model // n_head
_snake_case = ff_activation
_snake_case = d_inner
_snake_case = untie_r
_snake_case = attn_type
_snake_case = initializer_range
_snake_case = layer_norm_eps
_snake_case = dropout
_snake_case = mem_len
_snake_case = reuse_len
_snake_case = bi_data
_snake_case = clamp_len
_snake_case = same_length
_snake_case = summary_type
_snake_case = summary_use_proj
_snake_case = summary_activation
_snake_case = summary_last_dropout
_snake_case = start_n_top
_snake_case = end_n_top
_snake_case = bos_token_id
_snake_case = pad_token_id
_snake_case = eos_token_id
if "use_cache" in kwargs:
warnings.warn(
'The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`'
' instead.' , lowerCAmelCase_ , )
_snake_case = kwargs['use_cache']
_snake_case = use_mems_eval
_snake_case = use_mems_train
super().__init__(pad_token_id=lowerCAmelCase_ , bos_token_id=lowerCAmelCase_ , eos_token_id=lowerCAmelCase_ , **lowerCAmelCase_ )
@property
def lowerCamelCase ( self ):
"""simple docstring"""
logger.info(F'The model {self.model_type} is one of the few models that has no sequence length limit.' )
return -1
@max_position_embeddings.setter
def lowerCamelCase ( self , lowerCAmelCase_ ):
"""simple docstring"""
raise NotImplementedError(
F'The model {self.model_type} is one of the few models that has no sequence length limit.' )
| 42 | 0 |
"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__UpperCamelCase : Optional[Any] = abspath(join(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 __SCREAMING_SNAKE_CASE ( A_ ):
config.addinivalue_line(
'''markers''' , '''is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested''' )
config.addinivalue_line(
'''markers''' , '''is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested''' )
config.addinivalue_line('''markers''' , '''is_pipeline_test: mark test to run only when pipelines are tested''' )
config.addinivalue_line('''markers''' , '''is_staging_test: mark test to run only in the staging environment''' )
config.addinivalue_line('''markers''' , '''accelerate_tests: mark test that require accelerate''' )
config.addinivalue_line('''markers''' , '''tool_tests: mark the tool tests that are run on their specific schedule''' )
def __SCREAMING_SNAKE_CASE ( A_ ):
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(A_ )
def __SCREAMING_SNAKE_CASE ( A_ ):
from transformers.testing_utils import pytest_terminal_summary_main
lowerCAmelCase__ : Optional[int] = terminalreporter.config.getoption('''--make-reports''' )
if make_reports:
pytest_terminal_summary_main(A_ , id=A_ )
def __SCREAMING_SNAKE_CASE ( A_ , A_ ):
# If no tests are collected, pytest exists with code 5, which makes the CI fail.
if exitstatus == 5:
lowerCAmelCase__ : List[Any] = 0
# Doctest custom flag to ignore output.
__UpperCamelCase : Any = doctest.register_optionflag('''IGNORE_RESULT''')
__UpperCamelCase : List[Any] = doctest.OutputChecker
class SCREAMING_SNAKE_CASE ( a_ ):
"""simple docstring"""
def __lowerCAmelCase ( self : Dict ,lowercase_ : List[Any] ,lowercase_ : Dict ,lowercase_ : str ):
if IGNORE_RESULT & optionflags:
return True
return OutputChecker.check_output(self ,lowercase_ ,lowercase_ ,lowercase_ )
__UpperCamelCase : int = CustomOutputChecker
__UpperCamelCase : Union[str, Any] = HfDoctestModule
__UpperCamelCase : int = HfDocTestParser
| 74 |
"""simple docstring"""
from __future__ import annotations
import math
__UpperCamelCase : Dict = '''2020.9.26'''
__UpperCamelCase : Tuple = '''xcodz-dot, cclaus, dhruvmanila'''
def __SCREAMING_SNAKE_CASE ( A_ , A_ , A_ , A_ , A_ ):
if not all(isinstance(A_ , (float, int) ) for val in locals().values() ):
lowerCAmelCase__ : Optional[Any] = f'Input values must either be float or int: {list(locals().values() )}'
raise TypeError(A_ )
lowerCAmelCase__ : Optional[Any] = ((x * distance) / (z + distance)) * scale
lowerCAmelCase__ : Optional[int] = ((y * distance) / (z + distance)) * scale
return projected_x, projected_y
def __SCREAMING_SNAKE_CASE ( A_ , A_ , A_ , A_ , A_ ):
if not isinstance(A_ , A_ ):
raise TypeError('''Axis must be a str''' )
lowerCAmelCase__ : str = locals()
del input_variables["axis"]
if not all(isinstance(A_ , (float, int) ) for val in input_variables.values() ):
lowerCAmelCase__ : int = (
'''Input values except axis must either be float or int: '''
f'{list(input_variables.values() )}'
)
raise TypeError(A_ )
lowerCAmelCase__ : Any = (angle % 3_60) / 4_50 * 1_80 / math.pi
if axis == "z":
lowerCAmelCase__ : Tuple = x * math.cos(A_ ) - y * math.sin(A_ )
lowerCAmelCase__ : List[str] = y * math.cos(A_ ) + x * math.sin(A_ )
lowerCAmelCase__ : Optional[Any] = z
elif axis == "x":
lowerCAmelCase__ : List[str] = y * math.cos(A_ ) - z * math.sin(A_ )
lowerCAmelCase__ : str = z * math.cos(A_ ) + y * math.sin(A_ )
lowerCAmelCase__ : Union[str, Any] = x
elif axis == "y":
lowerCAmelCase__ : Optional[int] = x * math.cos(A_ ) - z * math.sin(A_ )
lowerCAmelCase__ : Tuple = z * math.cos(A_ ) + x * math.sin(A_ )
lowerCAmelCase__ : Optional[int] = y
else:
raise ValueError('''not a valid axis, choose one of \'x\', \'y\', \'z\'''' )
return new_x, new_y, new_z
if __name__ == "__main__":
import doctest
doctest.testmod()
print(F'''{convert_to_ad(1.0, 2.0, 3.0, 1_0.0, 1_0.0) = }''')
print(F'''{rotate(1.0, 2.0, 3.0, 'y', 9_0.0) = }''')
| 74 | 1 |
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class lowercase__ :
def __init__( self : str , UpperCAmelCase_ : Tuple , UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
if dst_width < 0 or dst_height < 0:
raise ValueError('Destination width/height should be > 0' )
SCREAMING_SNAKE_CASE__ = img
SCREAMING_SNAKE_CASE__ = img.shape[1]
SCREAMING_SNAKE_CASE__ = img.shape[0]
SCREAMING_SNAKE_CASE__ = dst_width
SCREAMING_SNAKE_CASE__ = dst_height
SCREAMING_SNAKE_CASE__ = self.src_w / self.dst_w
SCREAMING_SNAKE_CASE__ = self.src_h / self.dst_h
SCREAMING_SNAKE_CASE__ = SCREAMING_SNAKE_CASE__ = (
np.ones((self.dst_h, self.dst_w, 3) , np.uinta ) * 255
)
def A_ ( self : int ):
for i in range(self.dst_h ):
for j in range(self.dst_w ):
SCREAMING_SNAKE_CASE__ = self.img[self.get_y(UpperCAmelCase_ )][self.get_x(UpperCAmelCase_ )]
def A_ ( self : Tuple , UpperCAmelCase_ : int ):
return int(self.ratio_x * x )
def A_ ( self : List[str] , UpperCAmelCase_ : int ):
return int(self.ratio_y * y )
if __name__ == "__main__":
__snake_case ,__snake_case = 8_00, 6_00
__snake_case = imread("""image_data/lena.jpg""", 1)
__snake_case = NearestNeighbour(im, dst_w, dst_h)
n.process()
imshow(
F"""Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}""", n.output
)
waitKey(0)
destroyAllWindows()
| 176 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer
__snake_case = logging.get_logger(__name__)
__snake_case = {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}
# See all BART models at https://huggingface.co/models?filter=bart
__snake_case = {
"""vocab_file""": {
"""facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/vocab.json""",
"""facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/vocab.json""",
"""facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json""",
"""facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json""",
"""facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json""",
"""yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json""",
},
"""merges_file""": {
"""facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/merges.txt""",
"""facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/merges.txt""",
"""facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt""",
"""facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt""",
"""facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt""",
"""yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt""",
},
"""tokenizer_file""": {
"""facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json""",
"""facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json""",
"""facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json""",
"""facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json""",
"""facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json""",
"""yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json""",
},
}
__snake_case = {
"""facebook/bart-base""": 10_24,
"""facebook/bart-large""": 10_24,
"""facebook/bart-large-mnli""": 10_24,
"""facebook/bart-large-cnn""": 10_24,
"""facebook/bart-large-xsum""": 10_24,
"""yjernite/bart_eli5""": 10_24,
}
class lowercase__ ( _UpperCAmelCase ):
A__ : Tuple =VOCAB_FILES_NAMES
A__ : Any =PRETRAINED_VOCAB_FILES_MAP
A__ : str =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A__ : Tuple =["""input_ids""", """attention_mask"""]
A__ : Optional[int] =BartTokenizer
def __init__( self : str , UpperCAmelCase_ : Optional[Any]=None , UpperCAmelCase_ : int=None , UpperCAmelCase_ : str=None , UpperCAmelCase_ : Optional[Any]="replace" , UpperCAmelCase_ : int="<s>" , UpperCAmelCase_ : List[Any]="</s>" , UpperCAmelCase_ : int="</s>" , UpperCAmelCase_ : Tuple="<s>" , UpperCAmelCase_ : Any="<unk>" , UpperCAmelCase_ : Any="<pad>" , UpperCAmelCase_ : Optional[Any]="<mask>" , UpperCAmelCase_ : Optional[int]=False , UpperCAmelCase_ : List[str]=True , **UpperCAmelCase_ : List[Any] , ):
super().__init__(
UpperCAmelCase_ , UpperCAmelCase_ , tokenizer_file=UpperCAmelCase_ , errors=UpperCAmelCase_ , bos_token=UpperCAmelCase_ , eos_token=UpperCAmelCase_ , sep_token=UpperCAmelCase_ , cls_token=UpperCAmelCase_ , unk_token=UpperCAmelCase_ , pad_token=UpperCAmelCase_ , mask_token=UpperCAmelCase_ , add_prefix_space=UpperCAmelCase_ , trim_offsets=UpperCAmelCase_ , **UpperCAmelCase_ , )
SCREAMING_SNAKE_CASE__ = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('add_prefix_space' , UpperCAmelCase_ ) != add_prefix_space:
SCREAMING_SNAKE_CASE__ = getattr(UpperCAmelCase_ , pre_tok_state.pop('type' ) )
SCREAMING_SNAKE_CASE__ = add_prefix_space
SCREAMING_SNAKE_CASE__ = pre_tok_class(**UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
SCREAMING_SNAKE_CASE__ = 'post_processor'
SCREAMING_SNAKE_CASE__ = getattr(self.backend_tokenizer , UpperCAmelCase_ , UpperCAmelCase_ )
if tokenizer_component_instance:
SCREAMING_SNAKE_CASE__ = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
SCREAMING_SNAKE_CASE__ = tuple(state['sep'] )
if "cls" in state:
SCREAMING_SNAKE_CASE__ = tuple(state['cls'] )
SCREAMING_SNAKE_CASE__ = False
if state.get('add_prefix_space' , UpperCAmelCase_ ) != add_prefix_space:
SCREAMING_SNAKE_CASE__ = add_prefix_space
SCREAMING_SNAKE_CASE__ = True
if state.get('trim_offsets' , UpperCAmelCase_ ) != trim_offsets:
SCREAMING_SNAKE_CASE__ = trim_offsets
SCREAMING_SNAKE_CASE__ = True
if changes_to_apply:
SCREAMING_SNAKE_CASE__ = getattr(UpperCAmelCase_ , state.pop('type' ) )
SCREAMING_SNAKE_CASE__ = component_class(**UpperCAmelCase_ )
setattr(self.backend_tokenizer , UpperCAmelCase_ , UpperCAmelCase_ )
@property
def A_ ( self : Tuple ):
if self._mask_token is None:
if self.verbose:
logger.error('Using mask_token, but it is not set yet.' )
return None
return str(self._mask_token )
@mask_token.setter
def A_ ( self : Any , UpperCAmelCase_ : List[Any] ):
SCREAMING_SNAKE_CASE__ = AddedToken(UpperCAmelCase_ , lstrip=UpperCAmelCase_ , rstrip=UpperCAmelCase_ ) if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) else value
SCREAMING_SNAKE_CASE__ = value
def A_ ( self : List[str] , *UpperCAmelCase_ : Tuple , **UpperCAmelCase_ : str ):
SCREAMING_SNAKE_CASE__ = kwargs.get('is_split_into_words' , UpperCAmelCase_ )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
'to use it with pretokenized inputs.' )
return super()._batch_encode_plus(*UpperCAmelCase_ , **UpperCAmelCase_ )
def A_ ( self : List[str] , *UpperCAmelCase_ : Dict , **UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE__ = kwargs.get('is_split_into_words' , UpperCAmelCase_ )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
'to use it with pretokenized inputs.' )
return super()._encode_plus(*UpperCAmelCase_ , **UpperCAmelCase_ )
def A_ ( self : List[str] , UpperCAmelCase_ : str , UpperCAmelCase_ : Optional[str] = None ):
SCREAMING_SNAKE_CASE__ = self._tokenizer.model.save(UpperCAmelCase_ , name=UpperCAmelCase_ )
return tuple(UpperCAmelCase_ )
def A_ ( self : Optional[Any] , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : Union[str, Any]=None ):
SCREAMING_SNAKE_CASE__ = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def A_ ( self : str , UpperCAmelCase_ : List[int] , UpperCAmelCase_ : Optional[List[int]] = None ):
SCREAMING_SNAKE_CASE__ = [self.sep_token_id]
SCREAMING_SNAKE_CASE__ = [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]
| 176 | 1 |
"""simple docstring"""
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy
UpperCamelCase_ = logging.getLogger(__name__)
UpperCamelCase_ = 'pytorch_model.bin'
@dataclasses.dataclass
class snake_case :
a_ : str = dataclasses.field(
metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models."""} )
a_ : Optional[str] = dataclasses.field(
default=SCREAMING_SNAKE_CASE_ , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co."""} , )
@dataclasses.dataclass
class snake_case :
a_ : str = dataclasses.field(metadata={"""help""": """A csv or a json file containing the training data."""} )
a_ : str = dataclasses.field(metadata={"""help""": """A csv or a json file containing the data to predict on."""} )
a_ : Optional[str] = dataclasses.field(
default=SCREAMING_SNAKE_CASE_ , metadata={"""help""": """A csv or a json file containing the validation data."""} )
a_ : Optional[str] = dataclasses.field(
default=SCREAMING_SNAKE_CASE_ , metadata={"""help""": """The name of the task to train on."""} , )
a_ : Optional[List[str]] = dataclasses.field(
default=SCREAMING_SNAKE_CASE_ , metadata={"""help""": """The list of labels for the task."""} )
@dataclasses.dataclass
class snake_case :
a_ : str = dataclasses.field(
metadata={"""help""": """The output directory where the model predictions and checkpoints will be written."""} )
a_ : Optional[str] = dataclasses.field(
default="""accuracy""" , metadata={"""help""": """The evaluation metric used for the task."""} )
a_ : Optional[str] = dataclasses.field(
default="""no""" , metadata={
"""help""": """The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]"""
} , )
a_ : Optional[int] = dataclasses.field(
default=10 , metadata={"""help""": """Number of evaluation calls with no improvement after which training will be stopped."""} , )
a_ : Optional[float] = dataclasses.field(
default=0.0 , metadata={
"""help""": """How much the specified evaluation metric must improve to satisfy early stopping conditions."""
} , )
a_ : Optional[bool] = dataclasses.field(
default=SCREAMING_SNAKE_CASE_ , metadata={"""help""": """Whether to filter the pseudo-labeled data based on the confidence score."""} , )
a_ : Optional[bool] = dataclasses.field(
default=SCREAMING_SNAKE_CASE_ , metadata={"""help""": """Whether to filter the pseudo-labeled data based on the validation performance."""} , )
a_ : Optional[bool] = dataclasses.field(
default=SCREAMING_SNAKE_CASE_ , metadata={"""help""": """Whether to fine-tune on labeled data after pseudo training."""} , )
a_ : Optional[float] = dataclasses.field(
default=0.0 , metadata={"""help""": """Confidence threshold for pseudo-labeled data filtering."""} , )
a_ : Optional[int] = dataclasses.field(
default=100 , metadata={"""help""": """Number of evaluation calls with no improvement after which training will be stopped."""} , )
a_ : Optional[int] = dataclasses.field(
default=SCREAMING_SNAKE_CASE_ , metadata={"""help""": """Random seed for initialization."""} , )
def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) ->Dict:
"""simple docstring"""
a_ = datasets.concatenate_datasets([infer_input, infer_output] , axis=1 )
if args.do_filter_by_confidence:
a_ = dataset.filter(lambda UpperCAmelCase : example["probability"] > args.confidence_threshold )
if args.do_filter_by_val_performance:
assert eval_result >= 0.0 and eval_result <= 1.0
a_ = int(eval_result * len(UpperCAmelCase ) )
print(UpperCAmelCase )
a_ = dataset.sort("probability" , reverse=UpperCAmelCase )
a_ = dataset.select(range(UpperCAmelCase ) )
a_ = dataset.remove_columns(["label", "probability"] )
a_ = dataset.rename_column("prediction" , "label" )
a_ = dataset.map(lambda UpperCAmelCase : {"label": idalabel[example["label"]]} )
a_ = dataset.shuffle(seed=args.seed )
a_ = os.path.join(UpperCAmelCase , F'''train_pseudo.{args.data_file_extension}''' )
if args.data_file_extension == "csv":
dataset.to_csv(UpperCAmelCase , index=UpperCAmelCase )
else:
dataset.to_json(UpperCAmelCase )
def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase ) ->List[Any]:
"""simple docstring"""
a_ = Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , level=logging.INFO , )
logger.info(accelerator.state )
# Setup logging, we only want one process per machine to log things on the
# screen. accelerator.is_local_main_process is only True for one process per
# machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
a_ = STModelArguments(model_name_or_path=UpperCAmelCase )
a_ = STDataArguments(train_file=UpperCAmelCase , infer_file=UpperCAmelCase )
a_ = STTrainingArguments(output_dir=UpperCAmelCase )
a_ = argparse.Namespace()
for arg_class in (model_args, data_args, training_args):
for key, value in vars(UpperCAmelCase ).items():
setattr(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
for key, value in kwargs.items():
if hasattr(UpperCAmelCase , UpperCAmelCase ):
setattr(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
# Sanity checks
a_ = {}
a_ = None
# You need to provide the training data and the data to predict on
assert args.train_file is not None
assert args.infer_file is not None
a_ = args.train_file
a_ = args.infer_file
if args.evaluation_strategy != IntervalStrategy.NO.value:
assert args.eval_file is not None
a_ = args.eval_file
for key in data_files:
a_ = data_files[key].split("." )[-1]
assert extension in ["csv", "json"], F'''`{key}_file` should be a csv or a json file.'''
if args.data_file_extension is None:
a_ = extension
else:
assert extension == args.data_file_extension, F'''`{key}_file` should be a {args.data_file_extension} file`.'''
assert (
args.eval_metric in datasets.list_metrics()
), F'''{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}.'''
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed )
logger.info("Creating the initial data directory for self-training..." )
a_ = F'''{args.output_dir}/self-train_iter-{{}}'''.format
a_ = data_dir_format(0 )
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir , exist_ok=UpperCAmelCase )
os.makedirs(UpperCAmelCase , exist_ok=UpperCAmelCase )
accelerator.wait_for_everyone()
a_ = None
a_ = None
a_ = 0
a_ = False
# Show the progress bar
a_ = tqdm(range(args.max_selftrain_iterations ) , disable=not accelerator.is_local_main_process )
# Self-train
for iteration in range(0 , int(args.max_selftrain_iterations ) ):
a_ = data_dir_format(UpperCAmelCase )
assert os.path.exists(UpperCAmelCase )
# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
# iteration > 0
a_ = os.path.join(UpperCAmelCase , "stage-1" )
a_ = {
"accelerator": accelerator,
"model_name_or_path": args.model_name_or_path,
"cache_dir": args.cache_dir,
"do_train": True,
"train_file": data_files["train"] if iteration == 0 else data_files["train_pseudo"],
"do_eval": True if args.eval_file is not None else False,
"eval_file": data_files["eval"],
"do_predict": True,
"infer_file": data_files["infer"],
"task_name": args.task_name,
"label_list": args.label_list,
"output_dir": current_output_dir,
"eval_metric": args.eval_metric,
"evaluation_strategy": args.evaluation_strategy,
"early_stopping_patience": args.early_stopping_patience,
"early_stopping_threshold": args.early_stopping_threshold,
"seed": args.seed,
}
# Add additional training arguments
for key, value in kwargs.items():
if key not in arguments_dict and not hasattr(UpperCAmelCase , UpperCAmelCase ):
arguments_dict.update({key: value} )
a_ = os.path.join(UpperCAmelCase , "best-checkpoint" , UpperCAmelCase )
if os.path.exists(UpperCAmelCase ):
logger.info(
"Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1." , UpperCAmelCase , UpperCAmelCase , )
else:
logger.info("***** Running self-training: iteration: %d, stage: 1 *****" , UpperCAmelCase )
finetune(**UpperCAmelCase )
accelerator.wait_for_everyone()
assert os.path.exists(UpperCAmelCase )
logger.info("Self-training job completed: iteration: %d, stage: 1." , UpperCAmelCase )
if iteration > 0 and args.finetune_on_labeled_data:
# Stage 2 (optional): fine-tuning on the original labeled data
a_ = os.path.join(UpperCAmelCase , "best-checkpoint" )
a_ = os.path.join(UpperCAmelCase , "stage-2" )
# Update arguments_dict
a_ = model_path
a_ = data_files["train"]
a_ = current_output_dir
a_ = os.path.join(UpperCAmelCase , "best-checkpoint" , UpperCAmelCase )
if os.path.exists(UpperCAmelCase ):
logger.info(
"Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2." , UpperCAmelCase , UpperCAmelCase , )
else:
logger.info("***** Running self-training: iteration: %d, stage: 2 *****" , UpperCAmelCase )
finetune(**UpperCAmelCase )
accelerator.wait_for_everyone()
assert os.path.exists(UpperCAmelCase )
logger.info("Self-training job completed: iteration: %d, stage: 2." , UpperCAmelCase )
a_ = iteration
a_ = data_dir_format(iteration + 1 )
a_ = AutoConfig.from_pretrained(os.path.join(UpperCAmelCase , "best-checkpoint" ) )
a_ = config.idalabel
a_ = os.path.join(UpperCAmelCase , "eval_results_best-checkpoint.json" )
a_ = os.path.join(UpperCAmelCase , "test_results_best-checkpoint.json" )
assert os.path.exists(UpperCAmelCase )
with open(UpperCAmelCase , "r" ) as f:
a_ = float(json.load(UpperCAmelCase )[args.eval_metric] )
a_ = os.path.join(UpperCAmelCase , "infer_output_best-checkpoint.csv" )
assert os.path.exists(UpperCAmelCase )
# Loading the dataset from local csv or json files.
a_ = load_dataset(args.data_file_extension , data_files={"data": data_files["infer"]} )["data"]
a_ = load_dataset("csv" , data_files={"data": infer_output_file} )["data"]
if accelerator.is_main_process:
os.makedirs(UpperCAmelCase , exist_ok=UpperCAmelCase )
shutil.copy(UpperCAmelCase , os.path.join(UpperCAmelCase , F'''eval_results_iter-{iteration}.json''' ) )
if os.path.exists(UpperCAmelCase ):
shutil.copy(UpperCAmelCase , os.path.join(UpperCAmelCase , F'''test_results_iter-{iteration}.json''' ) )
create_pseudo_labeled_data(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
accelerator.wait_for_everyone()
a_ = os.path.join(UpperCAmelCase , F'''train_pseudo.{args.data_file_extension}''' )
if args.evaluation_strategy != IntervalStrategy.NO.value:
a_ = eval_result
if best_iteration is None:
a_ = new_iteration
a_ = new_eval_result
else:
if new_eval_result - best_eval_result > args.early_stopping_threshold:
a_ = new_iteration
a_ = new_eval_result
a_ = 0
else:
if new_eval_result == best_eval_result:
a_ = new_iteration
a_ = new_eval_result
early_stopping_patience_counter += 1
if early_stopping_patience_counter >= args.early_stopping_patience:
a_ = True
progress_bar.update(1 )
if should_training_stop:
break
if best_iteration is not None:
# Save the best iteration
logger.info("Best iteration: %d" , UpperCAmelCase )
logger.info("Best evaluation result: %s = %f" , args.eval_metric , UpperCAmelCase )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(UpperCAmelCase , F'''eval_results_iter-{iteration}.json''' ) , os.path.join(UpperCAmelCase , "eval_results_best-iteration.json" ) , )
else:
# Assume that the last iteration is the best
logger.info("Best iteration: %d" , args.max_selftrain_iterations - 1 )
logger.info("Best evaluation result: %s = %f" , args.eval_metric , UpperCAmelCase )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(UpperCAmelCase , F'''eval_results_iter-{args.max_selftrain_iterations - 1}.json''' ) , os.path.join(UpperCAmelCase , "eval_results_best-iteration.json" ) , ) | 303 |
"""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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.set_verbosity_info()
UpperCamelCase_ = logging.get_logger(__name__)
def UpperCamelCase ( UpperCAmelCase ) ->Union[str, Any]:
"""simple docstring"""
if "resnet-50" in model_name:
a_ = ResNetConfig.from_pretrained("microsoft/resnet-50" )
elif "resnet-101" in model_name:
a_ = ResNetConfig.from_pretrained("microsoft/resnet-101" )
else:
raise ValueError("Model name should include either resnet50 or resnet101" )
a_ = DetrConfig(use_timm_backbone=UpperCAmelCase , backbone_config=UpperCAmelCase )
# set label attributes
a_ = "panoptic" in model_name
if is_panoptic:
a_ = 250
else:
a_ = 91
a_ = "huggingface/label-files"
a_ = "coco-detection-id2label.json"
a_ = json.load(open(hf_hub_download(UpperCAmelCase , UpperCAmelCase , repo_type="dataset" ) , "r" ) )
a_ = {int(UpperCAmelCase ): v for k, v in idalabel.items()}
a_ = idalabel
a_ = {v: k for k, v in idalabel.items()}
return config, is_panoptic
def UpperCamelCase ( UpperCAmelCase ) ->List[str]:
"""simple docstring"""
a_ = []
# stem
# fmt: off
rename_keys.append(("backbone.0.body.conv1.weight", "backbone.conv_encoder.model.embedder.embedder.convolution.weight") )
rename_keys.append(("backbone.0.body.bn1.weight", "backbone.conv_encoder.model.embedder.embedder.normalization.weight") )
rename_keys.append(("backbone.0.body.bn1.bias", "backbone.conv_encoder.model.embedder.embedder.normalization.bias") )
rename_keys.append(("backbone.0.body.bn1.running_mean", "backbone.conv_encoder.model.embedder.embedder.normalization.running_mean") )
rename_keys.append(("backbone.0.body.bn1.running_var", "backbone.conv_encoder.model.embedder.embedder.normalization.running_var") )
# stages
for stage_idx in range(len(config.backbone_config.depths ) ):
for layer_idx in range(config.backbone_config.depths[stage_idx] ):
# shortcut
if layer_idx == 0:
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var''',
) )
# 3 convs
for i in range(3 ):
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean''',
) )
rename_keys.append(
(
F'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var''',
F'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var''',
) )
# fmt: on
for i in range(config.encoder_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(
F'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''',
F'''encoder.layers.{i}.self_attn.out_proj.weight''',
) )
rename_keys.append(
(F'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', F'''encoder.layers.{i}.self_attn.out_proj.bias''') )
rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''encoder.layers.{i}.fc1.weight''') )
rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''encoder.layers.{i}.fc1.bias''') )
rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''encoder.layers.{i}.fc2.weight''') )
rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''encoder.layers.{i}.fc2.bias''') )
rename_keys.append(
(F'''transformer.encoder.layers.{i}.norm1.weight''', F'''encoder.layers.{i}.self_attn_layer_norm.weight''') )
rename_keys.append(
(F'''transformer.encoder.layers.{i}.norm1.bias''', F'''encoder.layers.{i}.self_attn_layer_norm.bias''') )
rename_keys.append(
(F'''transformer.encoder.layers.{i}.norm2.weight''', F'''encoder.layers.{i}.final_layer_norm.weight''') )
rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''encoder.layers.{i}.final_layer_norm.bias''') )
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(
F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''',
F'''decoder.layers.{i}.self_attn.out_proj.weight''',
) )
rename_keys.append(
(F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''decoder.layers.{i}.self_attn.out_proj.bias''') )
rename_keys.append(
(
F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.weight''',
F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
) )
rename_keys.append(
(
F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.bias''',
F'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
) )
rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''decoder.layers.{i}.fc1.weight''') )
rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''decoder.layers.{i}.fc1.bias''') )
rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''decoder.layers.{i}.fc2.weight''') )
rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''decoder.layers.{i}.fc2.bias''') )
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm1.weight''', F'''decoder.layers.{i}.self_attn_layer_norm.weight''') )
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm1.bias''', F'''decoder.layers.{i}.self_attn_layer_norm.bias''') )
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm2.weight''', F'''decoder.layers.{i}.encoder_attn_layer_norm.weight''') )
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm2.bias''', F'''decoder.layers.{i}.encoder_attn_layer_norm.bias''') )
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm3.weight''', F'''decoder.layers.{i}.final_layer_norm.weight''') )
rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''decoder.layers.{i}.final_layer_norm.bias''') )
# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
rename_keys.extend(
[
("input_proj.weight", "input_projection.weight"),
("input_proj.bias", "input_projection.bias"),
("query_embed.weight", "query_position_embeddings.weight"),
("transformer.decoder.norm.weight", "decoder.layernorm.weight"),
("transformer.decoder.norm.bias", "decoder.layernorm.bias"),
("class_embed.weight", "class_labels_classifier.weight"),
("class_embed.bias", "class_labels_classifier.bias"),
("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"),
("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"),
("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"),
("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"),
("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"),
("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"),
] )
return rename_keys
def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) ->Union[str, Any]:
"""simple docstring"""
a_ = state_dict.pop(UpperCAmelCase )
a_ = val
def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase=False ) ->Optional[Any]:
"""simple docstring"""
a_ = ""
if is_panoptic:
a_ = "detr."
# first: transformer encoder
for i in range(6 ):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
a_ = state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
a_ = state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
a_ = in_proj_weight[:256, :]
a_ = in_proj_bias[:256]
a_ = in_proj_weight[256:512, :]
a_ = in_proj_bias[256:512]
a_ = in_proj_weight[-256:, :]
a_ = in_proj_bias[-256:]
# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
for i in range(6 ):
# read in weights + bias of input projection layer of self-attention
a_ = state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight''' )
a_ = state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
a_ = in_proj_weight[:256, :]
a_ = in_proj_bias[:256]
a_ = in_proj_weight[256:512, :]
a_ = in_proj_bias[256:512]
a_ = in_proj_weight[-256:, :]
a_ = in_proj_bias[-256:]
# read in weights + bias of input projection layer of cross-attention
a_ = state_dict.pop(
F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight''' )
a_ = state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) of cross-attention to the state dict
a_ = in_proj_weight_cross_attn[:256, :]
a_ = in_proj_bias_cross_attn[:256]
a_ = in_proj_weight_cross_attn[256:512, :]
a_ = in_proj_bias_cross_attn[256:512]
a_ = in_proj_weight_cross_attn[-256:, :]
a_ = in_proj_bias_cross_attn[-256:]
def UpperCamelCase ( ) ->Dict:
"""simple docstring"""
a_ = "http://images.cocodataset.org/val2017/000000039769.jpg"
a_ = Image.open(requests.get(UpperCAmelCase , stream=UpperCAmelCase ).raw )
return im
@torch.no_grad()
def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase=None , UpperCAmelCase=False ) ->List[str]:
"""simple docstring"""
a_ , a_ = get_detr_config(UpperCAmelCase )
# load original model from torch hub
a_ = {
"detr-resnet-50": "detr_resnet50",
"detr-resnet-101": "detr_resnet101",
}
logger.info(F'''Converting model {model_name}...''' )
a_ = torch.hub.load("facebookresearch/detr" , model_name_to_original_name[model_name] , pretrained=UpperCAmelCase ).eval()
a_ = detr.state_dict()
# rename keys
for src, dest in create_rename_keys(UpperCAmelCase ):
if is_panoptic:
a_ = "detr." + src
rename_key(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
# query, key and value matrices need special treatment
read_in_q_k_v(UpperCAmelCase , is_panoptic=UpperCAmelCase )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
a_ = "detr.model." if is_panoptic else "model."
for key in state_dict.copy().keys():
if is_panoptic:
if (
key.startswith("detr" )
and not key.startswith("class_labels_classifier" )
and not key.startswith("bbox_predictor" )
):
a_ = state_dict.pop(UpperCAmelCase )
a_ = val
elif "class_labels_classifier" in key or "bbox_predictor" in key:
a_ = state_dict.pop(UpperCAmelCase )
a_ = val
elif key.startswith("bbox_attention" ) or key.startswith("mask_head" ):
continue
else:
a_ = state_dict.pop(UpperCAmelCase )
a_ = val
else:
if not key.startswith("class_labels_classifier" ) and not key.startswith("bbox_predictor" ):
a_ = state_dict.pop(UpperCAmelCase )
a_ = val
# finally, create HuggingFace model and load state dict
a_ = DetrForSegmentation(UpperCAmelCase ) if is_panoptic else DetrForObjectDetection(UpperCAmelCase )
model.load_state_dict(UpperCAmelCase )
model.eval()
# verify our conversion on an image
a_ = "coco_panoptic" if is_panoptic else "coco_detection"
a_ = DetrImageProcessor(format=UpperCAmelCase )
a_ = processor(images=prepare_img() , return_tensors="pt" )
a_ = encoding["pixel_values"]
a_ = detr(UpperCAmelCase )
a_ = model(UpperCAmelCase )
assert torch.allclose(outputs.logits , original_outputs["pred_logits"] , atol=1E-3 )
assert torch.allclose(outputs.pred_boxes , original_outputs["pred_boxes"] , atol=1E-3 )
if is_panoptic:
assert torch.allclose(outputs.pred_masks , original_outputs["pred_masks"] , atol=1E-4 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
# Save model and image processor
logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''' )
Path(UpperCAmelCase ).mkdir(exist_ok=UpperCAmelCase )
model.save_pretrained(UpperCAmelCase )
processor.save_pretrained(UpperCAmelCase )
if push_to_hub:
# Upload model and image processor to the hub
logger.info("Uploading PyTorch model and image processor to the hub..." )
model.push_to_hub(F'''nielsr/{model_name}''' )
processor.push_to_hub(F'''nielsr/{model_name}''' )
if __name__ == "__main__":
UpperCamelCase_ = argparse.ArgumentParser()
parser.add_argument(
'--model_name',
default='detr-resnet-50',
type=str,
choices=['detr-resnet-50', 'detr-resnet-101'],
help='Name of the DETR model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
)
parser.add_argument('--push_to_hub', action='store_true', help='Whether to push the model to the hub or not.')
UpperCamelCase_ = parser.parse_args()
convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub) | 303 | 1 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class _A( snake_case__ , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase : Tuple = '''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''
def UpperCAmelCase_ ( self , _A=0 ):
__A : Dict = floats_tensor((1, 3, 128, 128) , rng=random.Random(_A ) )
__A : List[Any] = np.random.RandomState(_A )
__A : str = {
'prompt': 'A painting of a squirrel eating a burger',
'image': image,
'generator': generator,
'num_inference_steps': 3,
'strength': 0.7_5,
'guidance_scale': 7.5,
'output_type': 'numpy',
}
return inputs
def UpperCAmelCase_ ( self ):
__A : Dict = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
pipe.set_progress_bar_config(disable=_A )
__A : int = self.get_dummy_inputs()
__A : List[Any] = pipe(**_A ).images
__A : Optional[Any] = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 128, 128, 3)
__A : Dict = np.array([0.6_9_6_4_3, 0.5_8_4_8_4, 0.5_0_3_1_4, 0.5_8_7_6_0, 0.5_5_3_6_8, 0.5_9_6_4_3, 0.5_1_5_2_9, 0.4_1_2_1_7, 0.4_9_0_8_7] )
assert np.abs(image_slice - expected_slice ).max() < 1e-1
def UpperCAmelCase_ ( self ):
__A : int = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
__A : List[str] = PNDMScheduler.from_config(pipe.scheduler.config , skip_prk_steps=_A )
pipe.set_progress_bar_config(disable=_A )
__A : List[str] = self.get_dummy_inputs()
__A : Optional[Any] = pipe(**_A ).images
__A : Union[str, Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__A : Union[str, Any] = np.array([0.6_1_7_3_7, 0.5_4_6_4_2, 0.5_3_1_8_3, 0.5_4_4_6_5, 0.5_2_7_4_2, 0.6_0_5_2_5, 0.4_9_9_6_9, 0.4_0_6_5_5, 0.4_8_1_5_4] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def UpperCAmelCase_ ( self ):
__A : Any = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
__A : Optional[Any] = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_A )
# warmup pass to apply optimizations
__A : Tuple = pipe(**self.get_dummy_inputs() )
__A : Any = self.get_dummy_inputs()
__A : Dict = pipe(**_A ).images
__A : List[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__A : int = np.array([0.5_2_7_6_1, 0.5_9_9_7_7, 0.4_9_0_3_3, 0.4_9_6_1_9, 0.5_4_2_8_2, 0.5_0_3_1_1, 0.4_7_6_0_0, 0.4_0_9_1_8, 0.4_5_2_0_3] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def UpperCAmelCase_ ( self ):
__A : Optional[int] = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
__A : Any = EulerDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_A )
__A : Optional[Any] = self.get_dummy_inputs()
__A : List[Any] = pipe(**_A ).images
__A : Union[str, Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__A : Optional[Any] = np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def UpperCAmelCase_ ( self ):
__A : Any = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
__A : Optional[int] = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_A )
__A : List[Any] = self.get_dummy_inputs()
__A : Tuple = pipe(**_A ).images
__A : Dict = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__A : Any = np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def UpperCAmelCase_ ( self ):
__A : Any = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='CPUExecutionProvider' )
__A : str = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_A )
__A : int = self.get_dummy_inputs()
__A : Dict = pipe(**_A ).images
__A : Dict = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__A : List[str] = np.array([0.6_5_3_3_1, 0.5_8_2_7_7, 0.4_8_2_0_4, 0.5_6_0_5_9, 0.5_3_6_6_5, 0.5_6_2_3_5, 0.5_0_9_6_9, 0.4_0_0_0_9, 0.4_6_5_5_2] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
@nightly
@require_onnxruntime
@require_torch_gpu
class _A( unittest.TestCase ):
"""simple docstring"""
@property
def UpperCAmelCase_ ( self ):
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def UpperCAmelCase_ ( self ):
__A : List[str] = ort.SessionOptions()
__A : List[str] = False
return options
def UpperCAmelCase_ ( self ):
__A : Optional[int] = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/img2img/sketch-mountains-input.jpg' )
__A : Any = init_image.resize((768, 512) )
# using the PNDM scheduler by default
__A : Any = OnnxStableDiffusionImgaImgPipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4' , revision='onnx' , safety_checker=_A , feature_extractor=_A , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=_A )
__A : Optional[int] = 'A fantasy landscape, trending on artstation'
__A : Any = np.random.RandomState(0 )
__A : Optional[Any] = pipe(
prompt=_A , image=_A , strength=0.7_5 , guidance_scale=7.5 , num_inference_steps=10 , generator=_A , output_type='np' , )
__A : Optional[int] = output.images
__A : Any = images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 768, 3)
__A : Tuple = np.array([0.4_9_0_9, 0.5_0_5_9, 0.5_3_7_2, 0.4_6_2_3, 0.4_8_7_6, 0.5_0_4_9, 0.4_8_2_0, 0.4_9_5_6, 0.5_0_1_9] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
def UpperCAmelCase_ ( self ):
__A : Optional[Any] = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/img2img/sketch-mountains-input.jpg' )
__A : List[str] = init_image.resize((768, 512) )
__A : Union[str, Any] = LMSDiscreteScheduler.from_pretrained(
'runwayml/stable-diffusion-v1-5' , subfolder='scheduler' , revision='onnx' )
__A : int = OnnxStableDiffusionImgaImgPipeline.from_pretrained(
'runwayml/stable-diffusion-v1-5' , revision='onnx' , scheduler=_A , safety_checker=_A , feature_extractor=_A , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=_A )
__A : List[Any] = 'A fantasy landscape, trending on artstation'
__A : Optional[Any] = np.random.RandomState(0 )
__A : List[Any] = pipe(
prompt=_A , image=_A , strength=0.7_5 , guidance_scale=7.5 , num_inference_steps=20 , generator=_A , output_type='np' , )
__A : str = output.images
__A : Union[str, Any] = images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 768, 3)
__A : str = np.array([0.8_0_4_3, 0.9_2_6, 0.9_5_8_1, 0.8_1_1_9, 0.8_9_5_4, 0.9_1_3, 0.7_2_0_9, 0.7_4_6_3, 0.7_4_3_1] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
| 280 |
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn
from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertSelfAttention,
BertSelfOutput,
)
from transformers.utils import logging
if version.parse(nlp.__version__) != version.parse('''0.8.3'''):
raise Exception('''requires gluonnlp == 0.8.3''')
if version.parse(mx.__version__) != version.parse('''1.5.0'''):
raise Exception('''requires mxnet == 1.5.0''')
logging.set_verbosity_info()
UpperCAmelCase : List[Any] = logging.get_logger(__name__)
UpperCAmelCase : Optional[Any] = '''The Nymphenburg Palace is a beautiful palace in Munich!'''
def _SCREAMING_SNAKE_CASE ( a , a ) -> Optional[Any]:
__A : Any = {
'attention_cell': 'multi_head',
'num_layers': 4,
'units': 10_24,
'hidden_size': 7_68,
'max_length': 5_12,
'num_heads': 8,
'scaled': True,
'dropout': 0.1,
'use_residual': True,
'embed_size': 10_24,
'embed_dropout': 0.1,
'word_embed': None,
'layer_norm_eps': 1e-5,
'token_type_vocab_size': 2,
}
__A : str = bort_4_8_768_1024_hparams
# Let's construct the original Bort model here
# Taken from official BERT implementation, see:
# https://github.com/alexa/bort/blob/master/bort/bort.py
__A : Optional[int] = BERTEncoder(
attention_cell=predefined_args['attention_cell'] , num_layers=predefined_args['num_layers'] , units=predefined_args['units'] , hidden_size=predefined_args['hidden_size'] , max_length=predefined_args['max_length'] , num_heads=predefined_args['num_heads'] , scaled=predefined_args['scaled'] , dropout=predefined_args['dropout'] , output_attention=a , output_all_encodings=a , use_residual=predefined_args['use_residual'] , activation=predefined_args.get('activation' , 'gelu' ) , layer_norm_eps=predefined_args.get('layer_norm_eps' , a ) , )
# Vocab information needs to be fetched first
# It's the same as RoBERTa, so RobertaTokenizer can be used later
__A : Union[str, Any] = 'openwebtext_ccnews_stories_books_cased'
# Specify download folder to Gluonnlp's vocab
__A : Any = os.path.join(get_home_dir() , 'models' )
__A : List[Any] = _load_vocab(a , a , a , cls=a )
__A : Dict = nlp.model.BERTModel(
a , len(a ) , units=predefined_args['units'] , embed_size=predefined_args['embed_size'] , embed_dropout=predefined_args['embed_dropout'] , word_embed=predefined_args['word_embed'] , use_pooler=a , use_token_type_embed=a , token_type_vocab_size=predefined_args['token_type_vocab_size'] , use_classifier=a , use_decoder=a , )
original_bort.load_parameters(a , cast_dtype=a , ignore_extra=a )
__A : Union[str, Any] = original_bort._collect_params_with_prefix()
# Build our config 🤗
__A : Any = {
'architectures': ['BertForMaskedLM'],
'attention_probs_dropout_prob': predefined_args['dropout'],
'hidden_act': 'gelu',
'hidden_dropout_prob': predefined_args['dropout'],
'hidden_size': predefined_args['embed_size'],
'initializer_range': 0.02,
'intermediate_size': predefined_args['hidden_size'],
'layer_norm_eps': predefined_args['layer_norm_eps'],
'max_position_embeddings': predefined_args['max_length'],
'model_type': 'bort',
'num_attention_heads': predefined_args['num_heads'],
'num_hidden_layers': predefined_args['num_layers'],
'pad_token_id': 1, # 2 = BERT, 1 = RoBERTa
'type_vocab_size': 1, # 2 = BERT, 1 = RoBERTa
'vocab_size': len(a ),
}
__A : int = BertConfig.from_dict(a )
__A : Union[str, Any] = BertForMaskedLM(a )
hf_bort_model.eval()
# Parameter mapping table (Gluonnlp to Transformers)
# * denotes layer index
#
# | Gluon Parameter | Transformers Parameter
# | -------------------------------------------------------------- | ----------------------
# | `encoder.layer_norm.beta` | `bert.embeddings.LayerNorm.bias`
# | `encoder.layer_norm.gamma` | `bert.embeddings.LayerNorm.weight`
# | `encoder.position_weight` | `bert.embeddings.position_embeddings.weight`
# | `word_embed.0.weight` | `bert.embeddings.word_embeddings.weight`
# | `encoder.transformer_cells.*.attention_cell.proj_key.bias` | `bert.encoder.layer.*.attention.self.key.bias`
# | `encoder.transformer_cells.*.attention_cell.proj_key.weight` | `bert.encoder.layer.*.attention.self.key.weight`
# | `encoder.transformer_cells.*.attention_cell.proj_query.bias` | `bert.encoder.layer.*.attention.self.query.bias`
# | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
# | `encoder.transformer_cells.*.attention_cell.proj_value.bias` | `bert.encoder.layer.*.attention.self.value.bias`
# | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
# | `encoder.transformer_cells.*.ffn.ffn_2.bias` | `bert.encoder.layer.*.attention.output.dense.bias`
# | `encoder.transformer_cells.*.ffn.ffn_2.weight` | `bert.encoder.layer.*.attention.output.dense.weight`
# | `encoder.transformer_cells.*.layer_norm.beta` | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
# | `encoder.transformer_cells.*.layer_norm.gamma` | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
# | `encoder.transformer_cells.*.ffn.ffn_1.bias` | `bert.encoder.layer.*.intermediate.dense.bias`
# | `encoder.transformer_cells.*.ffn.ffn_1.weight` | `bert.encoder.layer.*.intermediate.dense.weight`
# | `encoder.transformer_cells.*.ffn.layer_norm.beta` | `bert.encoder.layer.*.output.LayerNorm.bias`
# | `encoder.transformer_cells.*.ffn.layer_norm.gamma` | `bert.encoder.layer.*.output.LayerNorm.weight`
# | `encoder.transformer_cells.*.proj.bias` | `bert.encoder.layer.*.output.dense.bias`
# | `encoder.transformer_cells.*.proj.weight` | `bert.encoder.layer.*.output.dense.weight`
# Helper function to convert MXNET Arrays to PyTorch
def to_torch(a ) -> nn.Parameter:
return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy() ) )
# Check param shapes and map new HF param back
def check_and_map_params(a , a ):
__A : Tuple = hf_param.shape
__A : str = to_torch(params[gluon_param] )
__A : Union[str, Any] = gluon_param.shape
assert (
shape_hf == shape_gluon
), F"""The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers"""
return gluon_param
__A : str = check_and_map_params(
hf_bort_model.bert.embeddings.word_embeddings.weight , 'word_embed.0.weight' )
__A : Tuple = check_and_map_params(
hf_bort_model.bert.embeddings.position_embeddings.weight , 'encoder.position_weight' )
__A : List[str] = check_and_map_params(
hf_bort_model.bert.embeddings.LayerNorm.bias , 'encoder.layer_norm.beta' )
__A : Tuple = check_and_map_params(
hf_bort_model.bert.embeddings.LayerNorm.weight , 'encoder.layer_norm.gamma' )
# Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
__A : Tuple = torch.zeros_like(
hf_bort_model.bert.embeddings.token_type_embeddings.weight.data )
for i in range(hf_bort_config.num_hidden_layers ):
__A : BertLayer = hf_bort_model.bert.encoder.layer[i]
# self attention
__A : BertSelfAttention = layer.attention.self
__A : Optional[Any] = check_and_map_params(
self_attn.key.bias.data , F"""encoder.transformer_cells.{i}.attention_cell.proj_key.bias""" )
__A : Optional[int] = check_and_map_params(
self_attn.key.weight.data , F"""encoder.transformer_cells.{i}.attention_cell.proj_key.weight""" )
__A : Union[str, Any] = check_and_map_params(
self_attn.query.bias.data , F"""encoder.transformer_cells.{i}.attention_cell.proj_query.bias""" )
__A : Optional[Any] = check_and_map_params(
self_attn.query.weight.data , F"""encoder.transformer_cells.{i}.attention_cell.proj_query.weight""" )
__A : Union[str, Any] = check_and_map_params(
self_attn.value.bias.data , F"""encoder.transformer_cells.{i}.attention_cell.proj_value.bias""" )
__A : Optional[int] = check_and_map_params(
self_attn.value.weight.data , F"""encoder.transformer_cells.{i}.attention_cell.proj_value.weight""" )
# self attention output
__A : BertSelfOutput = layer.attention.output
__A : Tuple = check_and_map_params(
self_output.dense.bias , F"""encoder.transformer_cells.{i}.proj.bias""" )
__A : int = check_and_map_params(
self_output.dense.weight , F"""encoder.transformer_cells.{i}.proj.weight""" )
__A : List[Any] = check_and_map_params(
self_output.LayerNorm.bias , F"""encoder.transformer_cells.{i}.layer_norm.beta""" )
__A : str = check_and_map_params(
self_output.LayerNorm.weight , F"""encoder.transformer_cells.{i}.layer_norm.gamma""" )
# intermediate
__A : BertIntermediate = layer.intermediate
__A : int = check_and_map_params(
intermediate.dense.bias , F"""encoder.transformer_cells.{i}.ffn.ffn_1.bias""" )
__A : List[Any] = check_and_map_params(
intermediate.dense.weight , F"""encoder.transformer_cells.{i}.ffn.ffn_1.weight""" )
# output
__A : BertOutput = layer.output
__A : List[Any] = check_and_map_params(
bert_output.dense.bias , F"""encoder.transformer_cells.{i}.ffn.ffn_2.bias""" )
__A : Dict = check_and_map_params(
bert_output.dense.weight , F"""encoder.transformer_cells.{i}.ffn.ffn_2.weight""" )
__A : Optional[int] = check_and_map_params(
bert_output.LayerNorm.bias , F"""encoder.transformer_cells.{i}.ffn.layer_norm.beta""" )
__A : Dict = check_and_map_params(
bert_output.LayerNorm.weight , F"""encoder.transformer_cells.{i}.ffn.layer_norm.gamma""" )
# Save space and energy 🎄
hf_bort_model.half()
# Compare output of both models
__A : Any = RobertaTokenizer.from_pretrained('roberta-base' )
__A : List[str] = tokenizer.encode_plus(a )['input_ids']
# Get gluon output
__A : List[str] = mx.nd.array([input_ids] )
__A : Union[str, Any] = original_bort(inputs=a , token_types=[] )
# Get Transformer output (save and reload model again)
hf_bort_model.save_pretrained(a )
__A : Optional[Any] = BertModel.from_pretrained(a )
hf_bort_model.eval()
__A : Tuple = tokenizer.encode_plus(a , return_tensors='pt' )
__A : Any = hf_bort_model(**a )[0]
__A : Union[str, Any] = output_gluon[0].asnumpy()
__A : Tuple = output_hf[0].detach().numpy()
__A : int = np.max(np.abs(hf_layer - gluon_layer ) ).item()
__A : int = np.allclose(a , a , atol=1e-3 )
if success:
print('✔️ Both model do output the same tensors' )
else:
print('❌ Both model do **NOT** output the same tensors' )
print('Absolute difference is:' , a )
if __name__ == "__main__":
UpperCAmelCase : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--bort_checkpoint_path''', default=None, type=str, required=True, help='''Path the official Bort params file.'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
UpperCAmelCase : Dict = parser.parse_args()
convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)
| 280 | 1 |
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[Any] = 256
# Modulus to hash a string
__SCREAMING_SNAKE_CASE : Optional[int] = 1_000_003
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> bool:
snake_case_ = len(_SCREAMING_SNAKE_CASE )
snake_case_ = len(_SCREAMING_SNAKE_CASE )
if p_len > t_len:
return False
snake_case_ = 0
snake_case_ = 0
snake_case_ = 1
# Calculating the hash of pattern and substring of text
for i in range(_SCREAMING_SNAKE_CASE ):
snake_case_ = (ord(pattern[i] ) + p_hash * alphabet_size) % modulus
snake_case_ = (ord(text[i] ) + text_hash * alphabet_size) % modulus
if i == p_len - 1:
continue
snake_case_ = (modulus_power * alphabet_size) % modulus
for i in range(0 , t_len - p_len + 1 ):
if text_hash == p_hash and text[i : i + p_len] == pattern:
return True
if i == t_len - p_len:
continue
# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
snake_case_ = (
(text_hash - ord(text[i] ) * modulus_power) * alphabet_size
+ ord(text[i + p_len] )
) % modulus
return False
def _a ( ) -> None:
snake_case_ = """abc1abc12"""
snake_case_ = """alskfjaldsabc1abc1abc12k23adsfabcabc"""
snake_case_ = """alskfjaldsk23adsfabcabc"""
assert rabin_karp(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) and not rabin_karp(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Test 2)
snake_case_ = """ABABX"""
snake_case_ = """ABABZABABYABABX"""
assert rabin_karp(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Test 3)
snake_case_ = """AAAB"""
snake_case_ = """ABAAAAAB"""
assert rabin_karp(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Test 4)
snake_case_ = """abcdabcy"""
snake_case_ = """abcxabcdabxabcdabcdabcy"""
assert rabin_karp(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Test 5)
snake_case_ = """Lü"""
snake_case_ = """Lüsai"""
assert rabin_karp(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
snake_case_ = """Lue"""
assert not rabin_karp(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
print("""Success.""" )
if __name__ == "__main__":
test_rabin_karp()
| 369 |
"""simple docstring"""
def _a ( _SCREAMING_SNAKE_CASE ) -> list:
if len(_SCREAMING_SNAKE_CASE ) <= 1:
return [tuple(_SCREAMING_SNAKE_CASE )]
snake_case_ = []
def generate(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
if k == 1:
res.append(tuple(arr[:] ) )
return
generate(k - 1 , _SCREAMING_SNAKE_CASE )
for i in range(k - 1 ):
if k % 2 == 0: # k is even
snake_case_ , snake_case_ = arr[k - 1], arr[i]
else: # k is odd
snake_case_ , snake_case_ = arr[k - 1], arr[0]
generate(k - 1 , _SCREAMING_SNAKE_CASE )
generate(len(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
return res
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : List[Any] = input('Enter numbers separated by a comma:\n').strip()
__SCREAMING_SNAKE_CASE : str = [int(item) for item in user_input.split(',')]
print(heaps(arr))
| 233 | 0 |
"""simple docstring"""
def lowercase (snake_case__ : list[int] ) -> int:
'''simple docstring'''
if not numbers:
return 0
if not isinstance(snake_case__ , (list, tuple) ) or not all(
isinstance(snake_case__ , snake_case__ ) for number in numbers ):
raise ValueError("""numbers must be an iterable of integers""" )
lowerCAmelCase = lowerCAmelCase = lowerCAmelCase = numbers[0]
for i in range(1 , len(snake_case__ ) ):
# update the maximum and minimum subarray products
lowerCAmelCase = numbers[i]
if number < 0:
lowerCAmelCase , lowerCAmelCase = min_till_now, max_till_now
lowerCAmelCase = max(snake_case__ , max_till_now * number )
lowerCAmelCase = min(snake_case__ , min_till_now * number )
# update the maximum product found till now
lowerCAmelCase = max(snake_case__ , snake_case__ )
return max_prod
| 155 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
a = 'https://www.indeed.co.in/jobs?q=mobile+app+development&l='
def lowercase (snake_case__ : str = "mumbai" ) -> Generator[tuple[str, str], None, None]:
'''simple docstring'''
lowerCAmelCase = 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"""} ):
lowerCAmelCase = job.find("""a""" , attrs={"""data-tn-element""": """jobTitle"""} ).text.strip()
lowerCAmelCase = 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]}""")
| 155 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
__snake_case = logging.get_logger(__name__)
__snake_case = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
__snake_case = {
'''vocab_file''': {
'''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt''',
'''distilbert-base-uncased-distilled-squad''': (
'''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt'''
),
'''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt''',
'''distilbert-base-cased-distilled-squad''': (
'''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt'''
),
'''distilbert-base-german-cased''': '''https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt''',
'''distilbert-base-multilingual-cased''': (
'''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt'''
),
},
'''tokenizer_file''': {
'''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json''',
'''distilbert-base-uncased-distilled-squad''': (
'''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json'''
),
'''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json''',
'''distilbert-base-cased-distilled-squad''': (
'''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json'''
),
'''distilbert-base-german-cased''': (
'''https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json'''
),
'''distilbert-base-multilingual-cased''': (
'''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json'''
),
},
}
__snake_case = {
'''distilbert-base-uncased''': 512,
'''distilbert-base-uncased-distilled-squad''': 512,
'''distilbert-base-cased''': 512,
'''distilbert-base-cased-distilled-squad''': 512,
'''distilbert-base-german-cased''': 512,
'''distilbert-base-multilingual-cased''': 512,
}
__snake_case = {
'''distilbert-base-uncased''': {'''do_lower_case''': True},
'''distilbert-base-uncased-distilled-squad''': {'''do_lower_case''': True},
'''distilbert-base-cased''': {'''do_lower_case''': False},
'''distilbert-base-cased-distilled-squad''': {'''do_lower_case''': False},
'''distilbert-base-german-cased''': {'''do_lower_case''': False},
'''distilbert-base-multilingual-cased''': {'''do_lower_case''': False},
}
class lowercase ( A__ ):
"""simple docstring"""
_a = VOCAB_FILES_NAMES
_a = PRETRAINED_VOCAB_FILES_MAP
_a = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_a = PRETRAINED_INIT_CONFIGURATION
_a = ['input_ids', 'attention_mask']
_a = DistilBertTokenizer
def __init__( self , UpperCamelCase_=None , UpperCamelCase_=None , UpperCamelCase_=True , UpperCamelCase_="[UNK]" , UpperCamelCase_="[SEP]" , UpperCamelCase_="[PAD]" , UpperCamelCase_="[CLS]" , UpperCamelCase_="[MASK]" , UpperCamelCase_=True , UpperCamelCase_=None , **UpperCamelCase_ , ):
'''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_ , )
UpperCamelCase__ :int = 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
):
UpperCamelCase__ :int = getattr(UpperCamelCase_ , normalizer_state.pop('''type''' ) )
UpperCamelCase__ :Optional[Any] = do_lower_case
UpperCamelCase__ :Optional[Any] = strip_accents
UpperCamelCase__ :List[Any] = tokenize_chinese_chars
UpperCamelCase__ :Any = normalizer_class(**UpperCamelCase_ )
UpperCamelCase__ :int = do_lower_case
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_=None ):
'''simple docstring'''
UpperCamelCase__ :Tuple = [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 , UpperCamelCase_ , UpperCamelCase_ = None ):
'''simple docstring'''
UpperCamelCase__ :List[str] = [self.sep_token_id]
UpperCamelCase__ :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 , UpperCamelCase_ , UpperCamelCase_ = None ):
'''simple docstring'''
UpperCamelCase__ :str = self._tokenizer.model.save(UpperCamelCase_ , name=UpperCamelCase_ )
return tuple(UpperCamelCase_ ) | 219 |
'''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
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 a ( __a=None ) -> List[str]:
'''simple docstring'''
UpperCamelCase__ :Optional[Any] = argparse.ArgumentParser(add_help=__a , allow_abbrev=__a )
# The main config parser
UpperCamelCase__ :str = config_command_parser(__a )
# The subparser to add commands to
UpperCamelCase__ :Union[str, Any] = config_parser.add_subparsers(title='''subcommands''' , dest='''subcommand''' )
# Then add other parsers with the parent parser
default_command_parser(__a , parents=[parent_parser] )
update_command_parser(__a , parents=[parent_parser] )
return config_parser
def a ( ) -> Any:
'''simple docstring'''
UpperCamelCase__ :int = get_config_parser()
UpperCamelCase__ :List[Any] = config_parser.parse_args()
if not hasattr(__a , '''func''' ):
config_parser.print_help()
exit(1 )
# Run
args.func(__a )
if __name__ == "__main__":
main() | 219 | 1 |
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class SCREAMING_SNAKE_CASE__ :
A_ : int
A_ : TreeNode | None = None
A_ : TreeNode | None = None
snake_case_ = namedtuple('CoinsDistribResult', 'moves excess')
def lowerCamelCase__ ( snake_case_ : TreeNode | None ) -> int:
if root is None:
return 0
# Validation
def count_nodes(snake_case_ : TreeNode | None ) -> int:
if node is None:
return 0
return count_nodes(node.left ) + count_nodes(node.right ) + 1
def count_coins(snake_case_ : TreeNode | None ) -> int:
if node is None:
return 0
return count_coins(node.left ) + count_coins(node.right ) + node.data
if count_nodes(snake_case_ ) != count_coins(snake_case_ ):
raise ValueError('''The nodes number should be same as the number of coins''' )
# Main calculation
def get_distrib(snake_case_ : TreeNode | None ) -> CoinsDistribResult:
if node is None:
return CoinsDistribResult(0 , 1 )
__snake_case , __snake_case = get_distrib(node.left )
__snake_case , __snake_case = get_distrib(node.right )
__snake_case = 1 - left_distrib_excess
__snake_case = 1 - right_distrib_excess
__snake_case = (
left_distrib_moves
+ right_distrib_moves
+ abs(snake_case_ )
+ abs(snake_case_ )
)
__snake_case = node.data - coins_to_left - coins_to_right
return CoinsDistribResult(snake_case_ , snake_case_ )
return get_distrib(snake_case_ )[0]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 24 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( __A = 1_000_000 ) -> int:
_snake_case = limit + 1
_snake_case = [0] * limit
for first_term in range(1 , __A ):
for n in range(__A , __A , __A ):
_snake_case = first_term + n / first_term
if common_difference % 4: # d must be divisble by 4
continue
else:
common_difference /= 4
if (
first_term > common_difference
and first_term < 4 * common_difference
): # since x,y,z are positive integers
frequency[n] += 1 # so z>0 and a>d ,also 4d<a
_snake_case = sum(1 for x in frequency[1:limit] if x == 10 )
return count
if __name__ == "__main__":
print(F'''{solution() = }''')
| 42 | 0 |
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def lowercase__( __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : str ):
lowercase_ : List[str] = list(__SCREAMING_SNAKE_CASE )
lowercase_ : Optional[Any] = list(__SCREAMING_SNAKE_CASE )
lowercase_ : Dict = 0
for i in range(len(__SCREAMING_SNAKE_CASE ) ):
if lista[i] != lista[i]:
count += 1
lowercase_ : List[Any] = '_'
if count > 1:
return False
else:
return "".join(__SCREAMING_SNAKE_CASE )
def lowercase__( __SCREAMING_SNAKE_CASE : list[str] ):
lowercase_ : List[Any] = []
while True:
lowercase_ : Optional[Any] = ['$'] * len(__SCREAMING_SNAKE_CASE )
lowercase_ : int = []
for i in range(len(__SCREAMING_SNAKE_CASE ) ):
for j in range(i + 1 , len(__SCREAMING_SNAKE_CASE ) ):
lowercase_ : Tuple = compare_string(binary[i] , binary[j] )
if k is False:
lowercase_ : List[str] = '*'
lowercase_ : Optional[Any] = '*'
temp.append('X' )
for i in range(len(__SCREAMING_SNAKE_CASE ) ):
if checka[i] == "$":
pi.append(binary[i] )
if len(__SCREAMING_SNAKE_CASE ) == 0:
return pi
lowercase_ : Optional[Any] = list(set(__SCREAMING_SNAKE_CASE ) )
def lowercase__( __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : Sequence[float] ):
lowercase_ : Any = []
for minterm in minterms:
lowercase_ : Union[str, Any] = ''
for _ in range(__SCREAMING_SNAKE_CASE ):
lowercase_ : Dict = str(minterm % 2 ) + string
minterm //= 2
temp.append(__SCREAMING_SNAKE_CASE )
return temp
def lowercase__( __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : int ):
lowercase_ : Dict = list(__SCREAMING_SNAKE_CASE )
lowercase_ : Optional[Any] = list(__SCREAMING_SNAKE_CASE )
lowercase_ : str = 0
for i in range(len(__SCREAMING_SNAKE_CASE ) ):
if lista[i] != lista[i]:
count_n += 1
return count_n == count
def lowercase__( __SCREAMING_SNAKE_CASE : list[list[int]] , __SCREAMING_SNAKE_CASE : list[str] ):
lowercase_ : Tuple = []
lowercase_ : Optional[Any] = [0] * len(__SCREAMING_SNAKE_CASE )
for i in range(len(chart[0] ) ):
lowercase_ : int = 0
lowercase_ : Dict = -1
for j in range(len(__SCREAMING_SNAKE_CASE ) ):
if chart[j][i] == 1:
count += 1
lowercase_ : Union[str, Any] = j
if count == 1:
lowercase_ : Dict = 1
for i in range(len(__SCREAMING_SNAKE_CASE ) ):
if select[i] == 1:
for j in range(len(chart[0] ) ):
if chart[i][j] == 1:
for k in range(len(__SCREAMING_SNAKE_CASE ) ):
lowercase_ : List[str] = 0
temp.append(prime_implicants[i] )
while True:
lowercase_ : Optional[Any] = 0
lowercase_ : str = -1
lowercase_ : Any = 0
for i in range(len(__SCREAMING_SNAKE_CASE ) ):
lowercase_ : Dict = chart[i].count(1 )
if count_n > max_n:
lowercase_ : List[str] = count_n
lowercase_ : str = i
if max_n == 0:
return temp
temp.append(prime_implicants[rem] )
for i in range(len(chart[0] ) ):
if chart[rem][i] == 1:
for j in range(len(__SCREAMING_SNAKE_CASE ) ):
lowercase_ : List[Any] = 0
def lowercase__( __SCREAMING_SNAKE_CASE : list[str] , __SCREAMING_SNAKE_CASE : list[str] ):
lowercase_ : int = [[0 for x in range(len(__SCREAMING_SNAKE_CASE ) )] for x in range(len(__SCREAMING_SNAKE_CASE ) )]
for i in range(len(__SCREAMING_SNAKE_CASE ) ):
lowercase_ : str = prime_implicants[i].count('_' )
for j in range(len(__SCREAMING_SNAKE_CASE ) ):
if is_for_table(prime_implicants[i] , binary[j] , __SCREAMING_SNAKE_CASE ):
lowercase_ : Union[str, Any] = 1
return chart
def lowercase__( ):
lowercase_ : List[Any] = int(input('Enter the no. of variables\n' ) )
lowercase_ : Optional[Any] = [
float(__SCREAMING_SNAKE_CASE )
for x in input(
'Enter the decimal representation of Minterms \'Spaces Separated\'\n' ).split()
]
lowercase_ : Optional[int] = decimal_to_binary(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
lowercase_ : Union[str, Any] = check(__SCREAMING_SNAKE_CASE )
print('Prime Implicants are:' )
print(__SCREAMING_SNAKE_CASE )
lowercase_ : Optional[int] = prime_implicant_chart(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
lowercase_ : Optional[Any] = selection(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
print('Essential Prime Implicants are:' )
print(__SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 362 | """simple docstring"""
class UpperCamelCase :
def __init__( self ,__UpperCamelCase ,__UpperCamelCase ) -> int:
'''simple docstring'''
lowercase_ : List[Any] = name
lowercase_ : int = val
def __str__( self ) -> Tuple:
'''simple docstring'''
return f'''{self.__class__.__name__}({self.name}, {self.val})'''
def __lt__( self ,__UpperCamelCase ) -> Tuple:
'''simple docstring'''
return self.val < other.val
class UpperCamelCase :
def __init__( self ,__UpperCamelCase ) -> Dict:
'''simple docstring'''
lowercase_ : Optional[int] = {}
lowercase_ : Tuple = {}
lowercase_ : Union[str, Any] = self.build_heap(__UpperCamelCase )
def __getitem__( self ,__UpperCamelCase ) -> int:
'''simple docstring'''
return self.get_value(__UpperCamelCase )
def _UpperCAmelCase ( self ,__UpperCamelCase ) -> List[str]:
'''simple docstring'''
return (idx - 1) // 2
def _UpperCAmelCase ( self ,__UpperCamelCase ) -> Optional[Any]:
'''simple docstring'''
return idx * 2 + 1
def _UpperCAmelCase ( self ,__UpperCamelCase ) -> Tuple:
'''simple docstring'''
return idx * 2 + 2
def _UpperCAmelCase ( self ,__UpperCamelCase ) -> List[Any]:
'''simple docstring'''
return self.heap_dict[key]
def _UpperCAmelCase ( self ,__UpperCamelCase ) -> List[str]:
'''simple docstring'''
lowercase_ : Optional[int] = len(__UpperCamelCase ) - 1
lowercase_ : Optional[int] = self.get_parent_idx(__UpperCamelCase )
for idx, i in enumerate(__UpperCamelCase ):
lowercase_ : Any = idx
lowercase_ : str = i.val
for i in range(__UpperCamelCase ,-1 ,-1 ):
self.sift_down(__UpperCamelCase ,__UpperCamelCase )
return array
def _UpperCAmelCase ( self ,__UpperCamelCase ,__UpperCamelCase ) -> Tuple:
'''simple docstring'''
while True:
lowercase_ : List[str] = self.get_left_child_idx(__UpperCamelCase ) # noqa: E741
lowercase_ : List[str] = self.get_right_child_idx(__UpperCamelCase )
lowercase_ : List[str] = idx
if l < len(__UpperCamelCase ) and array[l] < array[idx]:
lowercase_ : List[str] = l
if r < len(__UpperCamelCase ) and array[r] < array[smallest]:
lowercase_ : Dict = r
if smallest != idx:
lowercase_ , lowercase_ : Union[str, Any] = array[smallest], array[idx]
(
(
lowercase_
) , (
lowercase_
) ,
) : str = (
self.idx_of_element[array[smallest]],
self.idx_of_element[array[idx]],
)
lowercase_ : Any = smallest
else:
break
def _UpperCAmelCase ( self ,__UpperCamelCase ) -> int:
'''simple docstring'''
lowercase_ : Dict = self.get_parent_idx(__UpperCamelCase )
while p >= 0 and self.heap[p] > self.heap[idx]:
lowercase_ , lowercase_ : Any = self.heap[idx], self.heap[p]
lowercase_ , lowercase_ : Tuple = (
self.idx_of_element[self.heap[idx]],
self.idx_of_element[self.heap[p]],
)
lowercase_ : int = p
lowercase_ : str = self.get_parent_idx(__UpperCamelCase )
def _UpperCAmelCase ( self ) -> str:
'''simple docstring'''
return self.heap[0]
def _UpperCAmelCase ( self ) -> Optional[int]:
'''simple docstring'''
lowercase_ , lowercase_ : Optional[Any] = self.heap[-1], self.heap[0]
lowercase_ , lowercase_ : Tuple = (
self.idx_of_element[self.heap[-1]],
self.idx_of_element[self.heap[0]],
)
lowercase_ : Tuple = self.heap.pop()
del self.idx_of_element[x]
self.sift_down(0 ,self.heap )
return x
def _UpperCAmelCase ( self ,__UpperCamelCase ) -> Dict:
'''simple docstring'''
self.heap.append(__UpperCamelCase )
lowercase_ : Tuple = len(self.heap ) - 1
lowercase_ : Optional[int] = node.val
self.sift_up(len(self.heap ) - 1 )
def _UpperCAmelCase ( self ) -> Optional[Any]:
'''simple docstring'''
return len(self.heap ) == 0
def _UpperCAmelCase ( self ,__UpperCamelCase ,__UpperCamelCase ) -> List[Any]:
'''simple docstring'''
assert (
self.heap[self.idx_of_element[node]].val > new_value
), "newValue must be less that current value"
lowercase_ : Any = new_value
lowercase_ : List[str] = new_value
self.sift_up(self.idx_of_element[node] )
__SCREAMING_SNAKE_CASE =Node("R", -1)
__SCREAMING_SNAKE_CASE =Node("B", 6)
__SCREAMING_SNAKE_CASE =Node("A", 3)
__SCREAMING_SNAKE_CASE =Node("X", 1)
__SCREAMING_SNAKE_CASE =Node("E", 4)
# Use one of these two ways to generate Min-Heap
# Generating Min-Heap from array
__SCREAMING_SNAKE_CASE =MinHeap([r, b, a, x, e])
# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)
# Before
print("Min Heap - before decrease key")
for i in my_min_heap.heap:
print(i)
print("Min Heap - After decrease key of node [B -> -17]")
my_min_heap.decrease_key(b, -17)
# After
for i in my_min_heap.heap:
print(i)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 321 | 0 |
'''simple docstring'''
import math
import sys
def __lowerCAmelCase ( UpperCamelCase__ ) -> str:
__lowerCamelCase = ''''''
try:
with open(UpperCamelCase__ , '''rb''' ) as binary_file:
__lowerCamelCase = binary_file.read()
for dat in data:
__lowerCamelCase = f"""{dat:08b}"""
result += curr_byte
return result
except OSError:
print('''File not accessible''' )
sys.exit()
def __lowerCAmelCase ( UpperCamelCase__ ) -> str:
__lowerCamelCase = {'''0''': '''0''', '''1''': '''1'''}
__lowerCamelCase , __lowerCamelCase = '''''', ''''''
__lowerCamelCase = len(UpperCamelCase__ )
for i in range(len(UpperCamelCase__ ) ):
curr_string += data_bits[i]
if curr_string not in lexicon:
continue
__lowerCamelCase = lexicon[curr_string]
result += last_match_id
__lowerCamelCase = last_match_id + '''0'''
if math.loga(UpperCamelCase__ ).is_integer():
__lowerCamelCase = {}
for curr_key in list(UpperCamelCase__ ):
__lowerCamelCase = lexicon.pop(UpperCamelCase__ )
__lowerCamelCase = new_lex
__lowerCamelCase = last_match_id + '''1'''
index += 1
__lowerCamelCase = ''''''
return result
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ ) -> None:
__lowerCamelCase = 8
try:
with open(UpperCamelCase__ , '''wb''' ) as opened_file:
__lowerCamelCase = [
to_write[i : i + byte_length]
for i in range(0 , len(UpperCamelCase__ ) , UpperCamelCase__ )
]
if len(result_byte_array[-1] ) % byte_length == 0:
result_byte_array.append('''10000000''' )
else:
result_byte_array[-1] += "1" + "0" * (
byte_length - len(result_byte_array[-1] ) - 1
)
for elem in result_byte_array[:-1]:
opened_file.write(int(UpperCamelCase__ , 2 ).to_bytes(1 , byteorder='''big''' ) )
except OSError:
print('''File not accessible''' )
sys.exit()
def __lowerCAmelCase ( UpperCamelCase__ ) -> str:
__lowerCamelCase = 0
for letter in data_bits:
if letter == "1":
break
counter += 1
__lowerCamelCase = data_bits[counter:]
__lowerCamelCase = data_bits[counter + 1 :]
return data_bits
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ ) -> None:
__lowerCamelCase = read_file_binary(UpperCamelCase__ )
__lowerCamelCase = remove_prefix(UpperCamelCase__ )
__lowerCamelCase = decompress_data(UpperCamelCase__ )
write_file_binary(UpperCamelCase__ , UpperCamelCase__ )
if __name__ == "__main__":
compress(sys.argv[1], sys.argv[2])
| 67 |
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
_UpperCAmelCase : Optional[int] = logging.get_logger(__name__)
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase ) -> Dict:
lowerCamelCase__ : str = set()
lowerCamelCase__ : Any = []
def parse_line(_UpperCAmelCase ):
for line in fp:
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
lowerCamelCase__ : Any = line.decode('UTF-8' )
if "warnings summary (final)" in line:
continue
# This means we are outside the body of a warning
elif not line.startswith(' ' ):
# process a single warning and move it to `selected_warnings`.
if len(_UpperCAmelCase ) > 0:
lowerCamelCase__ : str = '\n'.join(_UpperCAmelCase )
# Only keep the warnings specified in `targets`
if any(F""": {x}: """ in warning for x in targets ):
selected_warnings.add(_UpperCAmelCase )
buffer.clear()
continue
else:
lowerCamelCase__ : List[str] = line.strip()
buffer.append(_UpperCAmelCase )
if from_gh:
for filename in os.listdir(_UpperCAmelCase ):
lowerCamelCase__ : Dict = os.path.join(_UpperCAmelCase , _UpperCAmelCase )
if not os.path.isdir(_UpperCAmelCase ):
# read the file
if filename != "warnings.txt":
continue
with open(_UpperCAmelCase ) as fp:
parse_line(_UpperCAmelCase )
else:
try:
with zipfile.ZipFile(_UpperCAmelCase ) as z:
for filename in z.namelist():
if not os.path.isdir(_UpperCAmelCase ):
# read the file
if filename != "warnings.txt":
continue
with z.open(_UpperCAmelCase ) as fp:
parse_line(_UpperCAmelCase )
except Exception:
logger.warning(
F"""{artifact_path} is either an invalid zip file or something else wrong. This file is skipped.""" )
return selected_warnings
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase ) -> Union[str, Any]:
lowerCamelCase__ : Tuple = set()
lowerCamelCase__ : Optional[int] = [os.path.join(_UpperCAmelCase , _UpperCAmelCase ) for p in os.listdir(_UpperCAmelCase ) if (p.endswith('.zip' ) or from_gh)]
for p in paths:
selected_warnings.update(extract_warnings_from_single_artifact(_UpperCAmelCase , _UpperCAmelCase ) )
return selected_warnings
if __name__ == "__main__":
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> Tuple:
return values.split(',' )
_UpperCAmelCase : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument("""--workflow_run_id""", type=str, required=True, help="""A GitHub Actions workflow run id.""")
parser.add_argument(
"""--output_dir""",
type=str,
required=True,
help="""Where to store the downloaded artifacts and other result files.""",
)
parser.add_argument("""--token""", default=None, type=str, help="""A token that has actions:read permission.""")
# optional parameters
parser.add_argument(
"""--targets""",
default="""DeprecationWarning,UserWarning,FutureWarning""",
type=list_str,
help="""Comma-separated list of target warning(s) which we want to extract.""",
)
parser.add_argument(
"""--from_gh""",
action="""store_true""",
help="""If running from a GitHub action workflow and collecting warnings from its artifacts.""",
)
_UpperCAmelCase : Union[str, Any] = parser.parse_args()
_UpperCAmelCase : Dict = args.from_gh
if from_gh:
# The artifacts have to be downloaded using `actions/download-artifact@v3`
pass
else:
os.makedirs(args.output_dir, exist_ok=True)
# get download links
_UpperCAmelCase : Union[str, Any] = get_artifacts_links(args.workflow_run_id, token=args.token)
with open(os.path.join(args.output_dir, """artifacts.json"""), """w""", encoding="""UTF-8""") as fp:
json.dump(artifacts, fp, ensure_ascii=False, indent=4)
# download artifacts
for idx, (name, url) in enumerate(artifacts.items()):
print(name)
print(url)
print("""=""" * 80)
download_artifact(name, url, args.output_dir, args.token)
# Be gentle to GitHub
time.sleep(1)
# extract warnings from artifacts
_UpperCAmelCase : Dict = extract_warnings(args.output_dir, args.targets)
_UpperCAmelCase : Optional[Any] = sorted(selected_warnings)
with open(os.path.join(args.output_dir, """selected_warnings.json"""), """w""", encoding="""UTF-8""") as fp:
json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)
| 50 | 0 |
'''simple docstring'''
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase = {
'facebook/mask2former-swin-small-coco-instance': (
'https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json'
)
# See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}
UpperCAmelCase = logging.get_logger(__name__)
class __snake_case( _lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : Dict = "mask2former"
UpperCAmelCase : Union[str, Any] = ["swin"]
UpperCAmelCase : Optional[Any] = {"hidden_size": "hidden_dim"}
def __init__( self , A_ = None , A_ = 256 , A_ = 256 , A_ = 256 , A_ = 1024 , A_ = "relu" , A_ = 6 , A_ = 10 , A_ = 8 , A_ = 0.0 , A_ = 2048 , A_ = False , A_ = False , A_ = 4 , A_ = 255 , A_ = 100 , A_ = 0.1 , A_ = 2.0 , A_ = 5.0 , A_ = 5.0 , A_ = 1_2544 , A_ = 3.0 , A_ = 0.7_5 , A_ = 0.0_2 , A_ = 1.0 , A_ = True , A_ = [4, 8, 16, 32] , A_ = None , **A_ , ) -> List[Any]:
if backbone_config is None:
logger.info("""`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.""" )
lowerCAmelCase = CONFIG_MAPPING["""swin"""](
image_size=224 , in_channels=3 , patch_size=4 , embed_dim=96 , depths=[2, 2, 18, 2] , num_heads=[3, 6, 12, 24] , window_size=7 , drop_path_rate=0.3 , use_absolute_embeddings=A_ , out_features=["""stage1""", """stage2""", """stage3""", """stage4"""] , )
if isinstance(A_ , A_ ):
lowerCAmelCase = backbone_config.pop("""model_type""" )
lowerCAmelCase = CONFIG_MAPPING[backbone_model_type]
lowerCAmelCase = config_class.from_dict(A_ )
# verify that the backbone is supported
if backbone_config.model_type not in self.backbones_supported:
logger.warning_once(
f'Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. '
f'Supported model types: {",".join(self.backbones_supported )}' )
lowerCAmelCase = backbone_config
lowerCAmelCase = feature_size
lowerCAmelCase = mask_feature_size
lowerCAmelCase = hidden_dim
lowerCAmelCase = encoder_feedforward_dim
lowerCAmelCase = activation_function
lowerCAmelCase = encoder_layers
lowerCAmelCase = decoder_layers
lowerCAmelCase = num_attention_heads
lowerCAmelCase = dropout
lowerCAmelCase = dim_feedforward
lowerCAmelCase = pre_norm
lowerCAmelCase = enforce_input_projection
lowerCAmelCase = common_stride
lowerCAmelCase = ignore_value
lowerCAmelCase = num_queries
lowerCAmelCase = no_object_weight
lowerCAmelCase = class_weight
lowerCAmelCase = mask_weight
lowerCAmelCase = dice_weight
lowerCAmelCase = train_num_points
lowerCAmelCase = oversample_ratio
lowerCAmelCase = importance_sample_ratio
lowerCAmelCase = init_std
lowerCAmelCase = init_xavier_std
lowerCAmelCase = use_auxiliary_loss
lowerCAmelCase = feature_strides
lowerCAmelCase = output_auxiliary_logits
lowerCAmelCase = decoder_layers
super().__init__(**A_ )
@classmethod
def __snake_case ( cls , A_ , **A_ ) -> Tuple:
return cls(
backbone_config=A_ , **A_ , )
def __snake_case ( self ) -> Dict[str, any]:
lowerCAmelCase = copy.deepcopy(self.__dict__ )
lowerCAmelCase = self.backbone_config.to_dict()
lowerCAmelCase = self.__class__.model_type
return output | 187 |
'''simple docstring'''
from __future__ import annotations
import pandas as pd
def _snake_case ( _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING_SNAKE_CASE : int ) -> list[int]:
"""simple docstring"""
lowerCAmelCase = [0] * no_of_processes
lowerCAmelCase = [0] * no_of_processes
# Copy the burst time into remaining_time[]
for i in range(_SCREAMING_SNAKE_CASE ):
lowerCAmelCase = burst_time[i]
lowerCAmelCase = 0
lowerCAmelCase = 0
lowerCAmelCase = 999_999_999
lowerCAmelCase = 0
lowerCAmelCase = False
# Process until all processes are completed
while complete != no_of_processes:
for j in range(_SCREAMING_SNAKE_CASE ):
if arrival_time[j] <= increment_time and remaining_time[j] > 0:
if remaining_time[j] < minm:
lowerCAmelCase = remaining_time[j]
lowerCAmelCase = j
lowerCAmelCase = True
if not check:
increment_time += 1
continue
remaining_time[short] -= 1
lowerCAmelCase = remaining_time[short]
if minm == 0:
lowerCAmelCase = 999_999_999
if remaining_time[short] == 0:
complete += 1
lowerCAmelCase = False
# Find finish time of current process
lowerCAmelCase = increment_time + 1
# Calculate waiting time
lowerCAmelCase = finish_time - arrival_time[short]
lowerCAmelCase = finar - burst_time[short]
if waiting_time[short] < 0:
lowerCAmelCase = 0
# Increment time
increment_time += 1
return waiting_time
def _snake_case ( _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : list[int] ) -> list[int]:
"""simple docstring"""
lowerCAmelCase = [0] * no_of_processes
for i in range(_SCREAMING_SNAKE_CASE ):
lowerCAmelCase = burst_time[i] + waiting_time[i]
return turn_around_time
def _snake_case ( _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING_SNAKE_CASE : int ) -> None:
"""simple docstring"""
lowerCAmelCase = 0
lowerCAmelCase = 0
for i in range(_SCREAMING_SNAKE_CASE ):
lowerCAmelCase = total_waiting_time + waiting_time[i]
lowerCAmelCase = total_turn_around_time + turn_around_time[i]
print(f'Average waiting time = {total_waiting_time / no_of_processes:.5f}' )
print("""Average turn around time =""" , total_turn_around_time / no_of_processes )
if __name__ == "__main__":
print('Enter how many process you want to analyze')
UpperCAmelCase = int(input())
UpperCAmelCase = [0] * no_of_processes
UpperCAmelCase = [0] * no_of_processes
UpperCAmelCase = list(range(1, no_of_processes + 1))
for i in range(no_of_processes):
print('Enter the arrival time and burst time for process:--' + str(i + 1))
UpperCAmelCase , UpperCAmelCase = map(int, input().split())
UpperCAmelCase = calculate_waitingtime(arrival_time, burst_time, no_of_processes)
UpperCAmelCase = burst_time
UpperCAmelCase = no_of_processes
UpperCAmelCase = waiting_time
UpperCAmelCase = calculate_turnaroundtime(bt, n, wt)
calculate_average_times(waiting_time, turn_around_time, no_of_processes)
UpperCAmelCase = pd.DataFrame(
list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
columns=[
'Process',
'BurstTime',
'ArrivalTime',
'WaitingTime',
'TurnAroundTime',
],
)
# Printing the dataFrame
pd.set_option('display.max_rows', fcfs.shape[0] + 1)
print(fcfs) | 187 | 1 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCamelCase : Optional[int] = {"""configuration_mmbt""": ["""MMBTConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : List[Any] = ["""MMBTForClassification""", """MMBTModel""", """ModalEmbeddings"""]
if TYPE_CHECKING:
from .configuration_mmbt import MMBTConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings
else:
import sys
__lowerCamelCase : int = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 52 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class A ( _UpperCAmelCase ):
"""simple docstring"""
lowerCamelCase = (DPMSolverSinglestepScheduler,)
lowerCamelCase = (('num_inference_steps', 25),)
def snake_case__ ( self : Tuple,**lowercase_ : Dict )-> Optional[int]:
'''simple docstring'''
A__ = {
'num_train_timesteps': 1_0_0_0,
'beta_start': 0.0_001,
'beta_end': 0.02,
'beta_schedule': 'linear',
'solver_order': 2,
'prediction_type': 'epsilon',
'thresholding': False,
'sample_max_value': 1.0,
'algorithm_type': 'dpmsolver++',
'solver_type': 'midpoint',
'lambda_min_clipped': -float('inf' ),
'variance_type': None,
}
config.update(**lowercase_ )
return config
def snake_case__ ( self : str,lowercase_ : Optional[Any]=0,**lowercase_ : Any )-> List[Any]:
'''simple docstring'''
A__ = dict(self.forward_default_kwargs )
A__ = kwargs.pop('num_inference_steps',lowercase_ )
A__ = self.dummy_sample
A__ = 0.1 * sample
A__ = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
A__ = self.get_scheduler_config(**lowercase_ )
A__ = scheduler_class(**lowercase_ )
scheduler.set_timesteps(lowercase_ )
# copy over dummy past residuals
A__ = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(lowercase_ )
A__ = scheduler_class.from_pretrained(lowercase_ )
new_scheduler.set_timesteps(lowercase_ )
# copy over dummy past residuals
A__ = dummy_past_residuals[: new_scheduler.config.solver_order]
A__ , A__ = sample, sample
for t in range(lowercase_,time_step + scheduler.config.solver_order + 1 ):
A__ = scheduler.step(lowercase_,lowercase_,lowercase_,**lowercase_ ).prev_sample
A__ = new_scheduler.step(lowercase_,lowercase_,lowercase_,**lowercase_ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def snake_case__ ( self : List[str] )-> List[Any]:
'''simple docstring'''
pass
def snake_case__ ( self : Tuple,lowercase_ : Union[str, Any]=0,**lowercase_ : Union[str, Any] )-> Union[str, Any]:
'''simple docstring'''
A__ = dict(self.forward_default_kwargs )
A__ = kwargs.pop('num_inference_steps',lowercase_ )
A__ = self.dummy_sample
A__ = 0.1 * sample
A__ = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
A__ = self.get_scheduler_config()
A__ = scheduler_class(**lowercase_ )
scheduler.set_timesteps(lowercase_ )
# copy over dummy past residuals (must be after setting timesteps)
A__ = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(lowercase_ )
A__ = scheduler_class.from_pretrained(lowercase_ )
# copy over dummy past residuals
new_scheduler.set_timesteps(lowercase_ )
# copy over dummy past residual (must be after setting timesteps)
A__ = dummy_past_residuals[: new_scheduler.config.solver_order]
A__ = scheduler.step(lowercase_,lowercase_,lowercase_,**lowercase_ ).prev_sample
A__ = new_scheduler.step(lowercase_,lowercase_,lowercase_,**lowercase_ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def snake_case__ ( self : Optional[Any],lowercase_ : Optional[int]=None,**lowercase_ : int )-> int:
'''simple docstring'''
if scheduler is None:
A__ = self.scheduler_classes[0]
A__ = self.get_scheduler_config(**lowercase_ )
A__ = scheduler_class(**lowercase_ )
A__ = self.scheduler_classes[0]
A__ = self.get_scheduler_config(**lowercase_ )
A__ = scheduler_class(**lowercase_ )
A__ = 1_0
A__ = self.dummy_model()
A__ = self.dummy_sample_deter
scheduler.set_timesteps(lowercase_ )
for i, t in enumerate(scheduler.timesteps ):
A__ = model(lowercase_,lowercase_ )
A__ = scheduler.step(lowercase_,lowercase_,lowercase_ ).prev_sample
return sample
def snake_case__ ( self : Any )-> str:
'''simple docstring'''
A__ = DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
A__ = 5_0
A__ = self.dummy_model()
A__ = self.dummy_sample_deter
scheduler.set_timesteps(lowercase_ )
# make sure that the first t is uneven
for i, t in enumerate(scheduler.timesteps[3:] ):
A__ = model(lowercase_,lowercase_ )
A__ = scheduler.step(lowercase_,lowercase_,lowercase_ ).prev_sample
A__ = torch.mean(torch.abs(lowercase_ ) )
assert abs(result_mean.item() - 0.2_574 ) < 1E-3
def snake_case__ ( self : Optional[Any] )-> List[Any]:
'''simple docstring'''
for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=lowercase_ )
def snake_case__ ( self : int )-> Optional[Any]:
'''simple docstring'''
A__ = DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
A__ = self.full_loop(scheduler=lowercase_ )
A__ = torch.mean(torch.abs(lowercase_ ) )
assert abs(result_mean.item() - 0.2_791 ) < 1E-3
A__ = DEISMultistepScheduler.from_config(scheduler.config )
A__ = DPMSolverMultistepScheduler.from_config(scheduler.config )
A__ = UniPCMultistepScheduler.from_config(scheduler.config )
A__ = DPMSolverSinglestepScheduler.from_config(scheduler.config )
A__ = self.full_loop(scheduler=lowercase_ )
A__ = torch.mean(torch.abs(lowercase_ ) )
assert abs(result_mean.item() - 0.2_791 ) < 1E-3
def snake_case__ ( self : Tuple )-> Any:
'''simple docstring'''
self.check_over_configs(thresholding=lowercase_ )
for order in [1, 2, 3]:
for solver_type in ["midpoint", "heun"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=lowercase_,prediction_type=lowercase_,sample_max_value=lowercase_,algorithm_type='dpmsolver++',solver_order=lowercase_,solver_type=lowercase_,)
def snake_case__ ( self : List[Any] )-> int:
'''simple docstring'''
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=lowercase_ )
def snake_case__ ( self : Dict )-> List[Any]:
'''simple docstring'''
for algorithm_type in ["dpmsolver", "dpmsolver++"]:
for solver_type in ["midpoint", "heun"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=lowercase_,solver_type=lowercase_,prediction_type=lowercase_,algorithm_type=lowercase_,)
A__ = self.full_loop(
solver_order=lowercase_,solver_type=lowercase_,prediction_type=lowercase_,algorithm_type=lowercase_,)
assert not torch.isnan(lowercase_ ).any(), "Samples have nan numbers"
def snake_case__ ( self : Optional[int] )-> Tuple:
'''simple docstring'''
self.check_over_configs(lower_order_final=lowercase_ )
self.check_over_configs(lower_order_final=lowercase_ )
def snake_case__ ( self : Tuple )-> Optional[int]:
'''simple docstring'''
self.check_over_configs(lambda_min_clipped=-float('inf' ) )
self.check_over_configs(lambda_min_clipped=-5.1 )
def snake_case__ ( self : Optional[Any] )-> Tuple:
'''simple docstring'''
self.check_over_configs(variance_type=lowercase_ )
self.check_over_configs(variance_type='learned_range' )
def snake_case__ ( self : str )-> Any:
'''simple docstring'''
for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
self.check_over_forward(num_inference_steps=lowercase_,time_step=0 )
def snake_case__ ( self : Tuple )-> Tuple:
'''simple docstring'''
A__ = self.full_loop()
A__ = torch.mean(torch.abs(lowercase_ ) )
assert abs(result_mean.item() - 0.2_791 ) < 1E-3
def snake_case__ ( self : Any )-> Union[str, Any]:
'''simple docstring'''
A__ = self.full_loop(use_karras_sigmas=lowercase_ )
A__ = torch.mean(torch.abs(lowercase_ ) )
assert abs(result_mean.item() - 0.2_248 ) < 1E-3
def snake_case__ ( self : Union[str, Any] )-> Tuple:
'''simple docstring'''
A__ = self.full_loop(prediction_type='v_prediction' )
A__ = torch.mean(torch.abs(lowercase_ ) )
assert abs(result_mean.item() - 0.1_453 ) < 1E-3
def snake_case__ ( self : Tuple )-> int:
'''simple docstring'''
A__ = self.full_loop(prediction_type='v_prediction',use_karras_sigmas=lowercase_ )
A__ = torch.mean(torch.abs(lowercase_ ) )
assert abs(result_mean.item() - 0.0_649 ) < 1E-3
def snake_case__ ( self : List[Any] )-> int:
'''simple docstring'''
A__ = self.scheduler_classes[0]
A__ = self.get_scheduler_config(thresholding=lowercase_,dynamic_thresholding_ratio=0 )
A__ = scheduler_class(**lowercase_ )
A__ = 1_0
A__ = self.dummy_model()
A__ = self.dummy_sample_deter.half()
scheduler.set_timesteps(lowercase_ )
for i, t in enumerate(scheduler.timesteps ):
A__ = model(lowercase_,lowercase_ )
A__ = scheduler.step(lowercase_,lowercase_,lowercase_ ).prev_sample
assert sample.dtype == torch.floataa
| 7 | 0 |
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
lowerCamelCase_ = '''src/transformers'''
# This is to make sure the transformers module imported is the one in the repo.
lowerCamelCase_ = importlib.util.spec_from_file_location(
'''transformers''',
os.path.join(PATH_TO_TRANSFORMERS, '''__init__.py'''),
submodule_search_locations=[PATH_TO_TRANSFORMERS],
)
lowerCamelCase_ = spec.loader.load_module()
lowerCamelCase_ = transformers.models.auto.configuration_auto.CONFIG_MAPPING
# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
lowerCamelCase_ = re.compile('''\[(.+?)\]\((https://huggingface\.co/.+?)\)''')
lowerCamelCase_ = {
'''CLIPConfigMixin''',
'''DecisionTransformerConfigMixin''',
'''EncoderDecoderConfigMixin''',
'''RagConfigMixin''',
'''SpeechEncoderDecoderConfigMixin''',
'''VisionEncoderDecoderConfigMixin''',
'''VisionTextDualEncoderConfigMixin''',
}
def __magic_name__ ( ):
'''simple docstring'''
UpperCamelCase__ = []
for config_class in list(CONFIG_MAPPING.values() ):
UpperCamelCase__ = False
# source code of `config_class`
UpperCamelCase__ = inspect.getsource(__a )
UpperCamelCase__ = _re_checkpoint.findall(__a )
for checkpoint in checkpoints:
# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
UpperCamelCase__ , UpperCamelCase__ = checkpoint
# verify the checkpoint name corresponds to the checkpoint link
UpperCamelCase__ = f"https://huggingface.co/{ckpt_name}"
if ckpt_link == ckpt_link_from_name:
UpperCamelCase__ = True
break
UpperCamelCase__ = config_class.__name__
if not checkpoint_found and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
configs_without_checkpoint.append(__a )
if len(__a ) > 0:
UpperCamelCase__ = """\n""".join(sorted(__a ) )
raise ValueError(f"The following configurations don't contain any valid checkpoint:\n{message}" )
if __name__ == "__main__":
check_config_docstrings_have_checkpoints()
| 358 |
import argparse
from collections import defaultdict
import yaml
lowerCamelCase_ = '''docs/source/en/_toctree.yml'''
def __magic_name__ ( __a : Union[str, Any] ):
'''simple docstring'''
UpperCamelCase__ = defaultdict(__a )
for doc in model_doc:
counts[doc["local"]] += 1
UpperCamelCase__ = [key for key, value in counts.items() if value > 1]
UpperCamelCase__ = []
for duplicate_key in duplicates:
UpperCamelCase__ = list({doc["""title"""] for doc in model_doc if doc["""local"""] == duplicate_key} )
if len(__a ) > 1:
raise ValueError(
f"{duplicate_key} is present several times in the documentation table of content at "
"""`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the """
"""others.""" )
# Only add this once
new_doc.append({"""local""": duplicate_key, """title""": titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in model_doc if counts[doc["""local"""]] == 1] )
# Sort
return sorted(__a , key=lambda __a : s["title"].lower() )
def __magic_name__ ( __a : int=False ):
'''simple docstring'''
with open(__a , encoding="""utf-8""" ) as f:
UpperCamelCase__ = yaml.safe_load(f.read() )
# Get to the API doc
UpperCamelCase__ = 0
while content[api_idx]["title"] != "API":
api_idx += 1
UpperCamelCase__ = content[api_idx]["""sections"""]
# Then to the model doc
UpperCamelCase__ = 0
while api_doc[model_idx]["title"] != "Models":
model_idx += 1
UpperCamelCase__ = api_doc[model_idx]["""sections"""]
UpperCamelCase__ = [(idx, section) for idx, section in enumerate(__a ) if """sections""" in section]
UpperCamelCase__ = False
for idx, modality_doc in modalities_docs:
UpperCamelCase__ = modality_doc["""sections"""]
UpperCamelCase__ = clean_model_doc_toc(__a )
if old_modality_doc != new_modality_doc:
UpperCamelCase__ = True
if overwrite:
UpperCamelCase__ = new_modality_doc
if diff:
if overwrite:
UpperCamelCase__ = model_doc
UpperCamelCase__ = api_doc
with open(__a , """w""" , encoding="""utf-8""" ) as f:
f.write(yaml.dump(__a , allow_unicode=__a ) )
else:
raise ValueError(
"""The model doc part of the table of content is not properly sorted, run `make style` to fix this.""" )
if __name__ == "__main__":
lowerCamelCase_ = argparse.ArgumentParser()
parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
lowerCamelCase_ = parser.parse_args()
check_model_doc(args.fix_and_overwrite)
| 178 | 0 |
'''simple docstring'''
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : Union[str, Any] = len(SCREAMING_SNAKE_CASE__ )
for i in range(SCREAMING_SNAKE_CASE__ ):
for j in range(i + 1 , SCREAMING_SNAKE_CASE__ ):
if numbers[j] < numbers[i]:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : List[str] = numbers[j], numbers[i]
return numbers
if __name__ == "__main__":
UpperCAmelCase_ : Any = input('Enter numbers separated by a comma:\n').strip()
UpperCAmelCase_ : Dict = [int(item) for item in user_input.split(',')]
print(exchange_sort(unsorted))
| 200 |
'''simple docstring'''
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def __init__( self , __snake_case , __snake_case=3 , __snake_case=32 , __snake_case=3 , __snake_case=10 , __snake_case=[10, 20, 30, 40] , __snake_case=[1, 1, 2, 1] , __snake_case=True , __snake_case=True , __snake_case="relu" , __snake_case=3 , __snake_case=None , ):
_SCREAMING_SNAKE_CASE : Union[str, Any] = parent
_SCREAMING_SNAKE_CASE : Dict = batch_size
_SCREAMING_SNAKE_CASE : int = image_size
_SCREAMING_SNAKE_CASE : Any = num_channels
_SCREAMING_SNAKE_CASE : Optional[int] = embeddings_size
_SCREAMING_SNAKE_CASE : Tuple = hidden_sizes
_SCREAMING_SNAKE_CASE : str = depths
_SCREAMING_SNAKE_CASE : str = is_training
_SCREAMING_SNAKE_CASE : Any = use_labels
_SCREAMING_SNAKE_CASE : Dict = hidden_act
_SCREAMING_SNAKE_CASE : int = num_labels
_SCREAMING_SNAKE_CASE : str = scope
_SCREAMING_SNAKE_CASE : Any = len(__snake_case )
def UpperCAmelCase_ ( self ):
_SCREAMING_SNAKE_CASE : List[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_SCREAMING_SNAKE_CASE : Any = self.get_config()
return config, pixel_values
def UpperCAmelCase_ ( self ):
return RegNetConfig(
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 UpperCAmelCase_ ( self , __snake_case , __snake_case ):
_SCREAMING_SNAKE_CASE : List[str] = FlaxRegNetModel(config=__snake_case )
_SCREAMING_SNAKE_CASE : Optional[int] = model(__snake_case )
# Output shape (b, c, h, w)
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def UpperCAmelCase_ ( self , __snake_case , __snake_case ):
_SCREAMING_SNAKE_CASE : Tuple = self.num_labels
_SCREAMING_SNAKE_CASE : List[Any] = FlaxRegNetForImageClassification(config=__snake_case )
_SCREAMING_SNAKE_CASE : Tuple = model(__snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCAmelCase_ ( self ):
_SCREAMING_SNAKE_CASE : List[Any] = self.prepare_config_and_inputs()
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : List[Any] = config_and_inputs
_SCREAMING_SNAKE_CASE : Dict = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_flax
class lowercase__ ( _snake_case , unittest.TestCase ):
'''simple docstring'''
A_ : Dict = (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()
A_ : Union[str, Any] = False
A_ : List[str] = False
A_ : str = False
def UpperCAmelCase_ ( self ):
_SCREAMING_SNAKE_CASE : str = FlaxRegNetModelTester(self )
_SCREAMING_SNAKE_CASE : Any = ConfigTester(self , config_class=__snake_case , has_text_modality=__snake_case )
def UpperCAmelCase_ ( self ):
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 UpperCAmelCase_ ( self ):
return
def UpperCAmelCase_ ( self ):
_SCREAMING_SNAKE_CASE : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__snake_case )
def UpperCAmelCase_ ( self ):
_SCREAMING_SNAKE_CASE : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__snake_case )
@unittest.skip(reason="""RegNet does not use inputs_embeds""" )
def UpperCAmelCase_ ( self ):
pass
@unittest.skip(reason="""RegNet does not support input and output embeddings""" )
def UpperCAmelCase_ ( self ):
pass
def UpperCAmelCase_ ( self ):
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_SCREAMING_SNAKE_CASE : List[Any] = model_class(__snake_case )
_SCREAMING_SNAKE_CASE : Union[str, Any] = inspect.signature(model.__call__ )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_SCREAMING_SNAKE_CASE : int = [*signature.parameters.keys()]
_SCREAMING_SNAKE_CASE : Tuple = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , __snake_case )
def UpperCAmelCase_ ( self ):
def check_hidden_states_output(__snake_case , __snake_case , __snake_case ):
_SCREAMING_SNAKE_CASE : Any = model_class(__snake_case )
_SCREAMING_SNAKE_CASE : int = model(**self._prepare_for_class(__snake_case , __snake_case ) )
_SCREAMING_SNAKE_CASE : Dict = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
_SCREAMING_SNAKE_CASE : Optional[Any] = self.model_tester.num_stages
self.assertEqual(len(__snake_case ) , expected_num_stages + 1 )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_SCREAMING_SNAKE_CASE : Optional[int] = True
check_hidden_states_output(__snake_case , __snake_case , __snake_case )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_SCREAMING_SNAKE_CASE : Union[str, Any] = True
check_hidden_states_output(__snake_case , __snake_case , __snake_case )
def UpperCAmelCase_ ( self ):
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
_SCREAMING_SNAKE_CASE : List[Any] = self._prepare_for_class(__snake_case , __snake_case )
_SCREAMING_SNAKE_CASE : str = model_class(__snake_case )
@jax.jit
def model_jitted(__snake_case , **__snake_case ):
return model(pixel_values=__snake_case , **__snake_case )
with self.subTest("""JIT Enabled""" ):
_SCREAMING_SNAKE_CASE : Any = model_jitted(**__snake_case ).to_tuple()
with self.subTest("""JIT Disabled""" ):
with jax.disable_jit():
_SCREAMING_SNAKE_CASE : Optional[int] = model_jitted(**__snake_case ).to_tuple()
self.assertEqual(len(__snake_case ) , len(__snake_case ) )
for jitted_output, output in zip(__snake_case , __snake_case ):
self.assertEqual(jitted_output.shape , output.shape )
def snake_case_ ( ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : List[str] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_flax
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def UpperCAmelCase_ ( self ):
return AutoImageProcessor.from_pretrained("""facebook/regnet-y-040""" ) if is_vision_available() else None
@slow
def UpperCAmelCase_ ( self ):
_SCREAMING_SNAKE_CASE : Any = FlaxRegNetForImageClassification.from_pretrained("""facebook/regnet-y-040""" )
_SCREAMING_SNAKE_CASE : List[Any] = self.default_image_processor
_SCREAMING_SNAKE_CASE : Optional[Any] = prepare_img()
_SCREAMING_SNAKE_CASE : Tuple = image_processor(images=__snake_case , return_tensors="""np""" )
_SCREAMING_SNAKE_CASE : str = model(**__snake_case )
# verify the logits
_SCREAMING_SNAKE_CASE : Tuple = (1, 1000)
self.assertEqual(outputs.logits.shape , __snake_case )
_SCREAMING_SNAKE_CASE : List[Any] = jnp.array([-0.4180, -1.5051, -3.4836] )
self.assertTrue(jnp.allclose(outputs.logits[0, :3] , __snake_case , atol=1e-4 ) )
| 200 | 1 |
'''simple docstring'''
import colorsys
from PIL import Image # type: ignore
def a ( __a , __a , __a ) -> float:
'''simple docstring'''
UpperCamelCase__ :Dict = x
UpperCamelCase__ :str = y
for step in range(__a ): # noqa: B007
UpperCamelCase__ :List[Any] = a * a - b * b + x
UpperCamelCase__ :Any = 2 * a * b + y
UpperCamelCase__ :Optional[Any] = a_new
# divergence happens for all complex number with an absolute value
# greater than 4
if a * a + b * b > 4:
break
return step / (max_step - 1)
def a ( __a ) -> tuple:
'''simple docstring'''
if distance == 1:
return (0, 0, 0)
else:
return (255, 255, 255)
def a ( __a ) -> tuple:
'''simple docstring'''
if distance == 1:
return (0, 0, 0)
else:
return tuple(round(i * 255 ) for i in colorsys.hsv_to_rgb(__a , 1 , 1 ) )
def a ( __a = 800 , __a = 600 , __a = -0.6 , __a = 0 , __a = 3.2 , __a = 50 , __a = True , ) -> Image.Image:
'''simple docstring'''
UpperCamelCase__ :List[Any] = Image.new('''RGB''' , (image_width, image_height) )
UpperCamelCase__ :str = img.load()
# loop through the image-coordinates
for image_x in range(__a ):
for image_y in range(__a ):
# determine the figure-coordinates based on the image-coordinates
UpperCamelCase__ :Union[str, Any] = figure_width / image_width * image_height
UpperCamelCase__ :Any = figure_center_x + (image_x / image_width - 0.5) * figure_width
UpperCamelCase__ :int = figure_center_y + (image_y / image_height - 0.5) * figure_height
UpperCamelCase__ :Dict = get_distance(__a , __a , __a )
# color the corresponding pixel based on the selected coloring-function
if use_distance_color_coding:
UpperCamelCase__ :Dict = get_color_coded_rgb(__a )
else:
UpperCamelCase__ :Dict = get_black_and_white_rgb(__a )
return img
if __name__ == "__main__":
import doctest
doctest.testmod()
# colored version, full figure
__snake_case = get_image()
# uncomment for colored version, different section, zoomed in
# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
# figure_width = 0.8)
# uncomment for black and white version, full figure
# img = get_image(use_distance_color_coding = False)
# uncomment to save the image
# img.save("mandelbrot.png")
img.show() | 360 |
'''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.
__snake_case = 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 a ( __a ) -> Optional[int]:
'''simple docstring'''
from diffusers.utils.testing_utils import pytest_addoption_shared
pytest_addoption_shared(__a )
def a ( __a ) -> str:
'''simple docstring'''
from diffusers.utils.testing_utils import pytest_terminal_summary_main
UpperCamelCase__ :Union[str, Any] = terminalreporter.config.getoption('''--make-reports''' )
if make_reports:
pytest_terminal_summary_main(__a , id=__a ) | 219 | 0 |
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